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CROSS INDUSTRY
Next-Best-Action™ Marketing
Increasing profitability through more customer-centric communications
Introduction

    Despite all the hype surrounding customer centricity, many organizations still rely on product-oriented
    customer interaction. For example, traditional direct marketing in which each campaign is set up to
    promote a single product. While this approach aligns well with current thinking about budgeting, staff
    compensation, and organizational structures, it is at odds with customer centricity as well as the ultimate
    corporate objective—profitability.

    This paper discusses a new approach to customer communications called Next-Best-Action marketing
    (N-B-A) that can yield significantly more effective marketing and results. N-B-A is part of the wider
    next-best-action approach to many types of customer interactions such as risk mitigation, remedial
    actionsfor churn and fraud, service provisioning, data collection, arrears, surveys and so forth. When
    used formarketing, N-B-A has direct and immediate effect as sales and retention will improve with a
    corresponding boost in revenues. At the same time, marketing costs will decrease while the possibilities
    for brand expression and control grow. Add to these benefits such desirable ‘side-effects’ as improved
    customer satisfaction and happier customer-facing staff, and everyone will wonder why this new approach
    was not adopted as the standard long ago.

    Adoption of N-B-A marketing has been slow for two reasons: 1) It is a multi-channel proposition, and
    only recently has real-time and customer analytics technology advanced to a level that can support the
    complexity of marketing across channels; 2) Organizational structures, corporate experience, and comfort
    with traditional marketing make such transformational change appear difficult and potentially risky. In
    addition to the discussion of the benefits to be achieved with the N-B-A approach, this paper will suggest
    ways for companies to overcome corporate inertia and transform the way they define, monitor and control
    the customer experience.




2
First Things First: Profitability

To demonstrate how the N-B-A approach actually delivers, it makes sense to look
at the benefits O2, an early adopter of both the methodology and the technology,
has achieved. O2 was one of the first to embrace N-B-A marketing for their 5,000
call center agents, 2 million online visitors per month and 470 retail outlets.

Now the United Kingdom’s leading mobile telecom provider, O2 was only the third
largest when the company began its N-B-A journey in 2004. Recently, Telefonica          “N-B-A has direct and
acquired O2 to create the second largest provider in the world, but because O2 has        immediate effect as
always zealously promoted and protected its brand, its brand is perceived to be a          sales and retention
key asset that is likely to be adopted internationally.                                    will improve with a
                                                                                         corresponding boost
At the 2005 Gartner CRM Summit, O2 reported a bill-value increase of 9%, a
                                                                                                 in revenues.”
response rate of ~75%1, a significant increase in customer satisfaction, and
reduced costs for retaining customers, all in the first month after a customer had
been exposed to an N-B-A guided customer experience.

These benefits were achieved with no appreciable change in average customer
handling time. The results are all the more significant because customer
interaction was not exclusively focused on sales, with equal attention given to
branding, as well as customer care and satisfaction. And although the use of the
N-B-A application was not mandatory at the time in the call centers, O2 achieved
75% adoption among representatives.

The essence of how such numbers were achieved at O2 and elsewhere is in the
sophistication of the interaction and the corresponding quality of the customer
experience. With N-B-A, it is now possible to build a mini-business case (in
real-time when necessary) to determine the best action to take. Once the action
is taken and the customer’s response recorded, the business case is immediately
recalculated and the next best action is recommended. O2 and others are using
sophisticated arbitration metrics to balance insight into customer interests, risks,
loyalty, etc. with corporate priorities, such as revenues, costs, and branding.

O2 has recognized that while the company’s overall goal is profitability, this does
not mean that every interaction must be focused on selling. A better strategy is
to continuously maximize the relationship throughout the customer lifecycle. For
a very few customers, that may mean reaping as much as you can now as there
will be little future in the relationship, but for most customers it means that every
interaction, conducted through any channel, is used not necessarily to sell, but to
nurture the relationship. The end result, as evidenced by the O2 case, is a stable
increase in profitability and customer satisfaction.




                                                                                                           1
The Next-Best-Action Model

    To demonstrate how the N-B-A approach actually delivers, it makes sense
    to look at the benefits O2, an early adopter of both the methodology and the
    technology, has achieved. O2 was one of t The N-B-A model provides a corporate
    “Decisioning Hub” that guides each inbound and outbound customer action
    and communication for every channel and line of business. This centralized
    decisioning capability ensures the following benefits:
                                                                                           “With N-B-A, it is now
       ``Consistency across channels                                                           possible to build a
       ``Collision avoidance                                                                 mini-business case
       ``Cost alignment                                                                       (in real-time when
       ``Optimization of interaction time                                               necessary) to determine
                                                                                         the best action to take.”
       ``Natural conversations
       ``Effective monitoring
       ``Total control

    Consistency and Collision Avoidance across Channels
    Absolute consistency, across channels and over time, is achieved because the
    Decision Hub executes a holistic strategy for each customer that recommends
    the best action. Any customer response or relevant event will have a bearing on
    what the next best action will be.

    For example, a customer who logs into a company’s web site or calls into the call
    center or is contacted through an outbound communication will be subjected to
    the same (best) action, assuming that the channel is not a relevant factor in the
    decision. The action may be to first address the customer’s question about an
    outstanding balance and then to offer her the Premium Platinum card, or 30 free
    minutes of international calling, but regardless of which channel is accessed for
    communication, the same messages are delivered to the customer.

    There is a second advantage to this approach—avoiding campaign collisions.
    In traditional campaign management, it is a challenge to avoid targeting the
    customer with two campaigns simultaneously. She may be selected for the
    Premium Platinum card as well as the higher interest Ultimate Savings card.
    Even if the two products are not mutually exclusive, they will compete for her
    attention.




2
A traditional solution is to run a separate optimization after one campaign
is executed to detect that the customer has received the Premium Platinum
campaign and remove her from the Ultimate Savings campaign. With potentially
hundreds of live campaigns, this is a cumbersome process.

Alternatively, once the Premium Platinum proposition is executed, the customer
may (temporarily) be on the suppression list for other campaigns. The question
then becomes one of sequencing: what’s the best sequence of campaigns to
maximize the customer’s contribution to the bottom line? The fact of the matter
is that both of these solutions are trying to fix a problem that should not exist in
the first place.




   Mortgage              Mortgage               Mortgage
                                                                                       Figure 1:
   Propensity            Eligibility            Strategy                               N-B-A selling strategy
                                                                                       example.




     Loans                  Loans                 Loans               Retention
                                                                        Best
   Propensity             Eligibility            Strategy              Matrix
                                                                        Offer




     Cards                  Cards                 Cards
   Propensity             Eligibility            Strategy




                                                                                                                3
The N-B-A model is an entirely different approach, representing a single,
    continuous campaign that selects the optimal treatment, one step, or decision, at
    a time. Arbitration between alternative treatments can be just-in-time, avoiding
    the time consuming post-processing required to assign the customer to a single
    campaign. With N-B-A marketing, the customer will be considered for a specific
    proposition or treat¬ment during real-time interactions or when generating the
    output file for batch processed outbound communications.

    Consider an N-B-A model as illustrated in Figure 1, a simple selling strategy that
    will be extended later in this paper. When fed to the Decision Hub, this strategy
    (or Decision Logic) will make a decision concerning the best offer to make. The
    decision is made in real-time, the moment the customer enters the website
    or contacts the call center. In batch, the decision is made when the output file
    is generated for outbound communication. In both cases the decision is made
    just-in-time and will select only one (the best) proposition out of the three that
    are possible, depending on the customer’s propensity to buy any of the three
    products, her eligibility to do so, the desirability of making the offer now, and the
    economics of the purchase, such as margin, contribution to branding, effect on
    market share, and so forth.

    The N-B-A strategy avoids collisions because under the organization’s arbitration
    scheme there can be only one best proposition. Assuming the Decision Hub is
    fast enough, it would be a waste of time, effort and data storage to set up three
    campaigns for mortgages, loans and cards and then apply the same arbitration
    afterwards. As will be shown later, a single Decision Logic strategy simplifies
    campaign management, reduces costs and IT effort, and, importantly, does not
    differentiate between inbound or outbound use. How to design and implement
    such an overall strategy in a multi-product organization will be discussed later in
    this paper.

    Cost Alignment
    Resources are scarce in every enterprise – both in terms of time spent in
    communicating and money spent in negotiating terms or incentives to make
    customers agree to a proposition. The ‘art’ in marketing is still choosing where
    to spend scarce resources. N-B-A strategies and their underlying predictive
    models2 enable an organization to be discerning in its use of resources.

    Given the prediction of the customer’s level of interest, some time can be justified
    in offering a proposition to those very likely to purchase if they are simply told
    about it. More time and an incentive can be justified for those less likely to
    be interested because they need to be persuaded. For those with even lower
    interest, time can be spent not on selling the proposition but on explaining it
    and developing the need or desire. Those with a minimal interest may fall below
    a threshold that prevents the proposition from being offered. This hierarchy of
    strategies for a proposition optimizes the use of scarce resources and aligns the
    cost of the effort to the likely value of the customer.


4
The style of the relationship and the costs involved to both parties can be
similarly managed and optimized over time. For example, during the initial
phase of a relationship, the emphasis in the conversation may be on building a
mutual understanding and level of trust. Early propositions can be structured
to be particularly simple to understand and compelling to the customer, if not
particularly profitable for the enterprise. When the value of the proposition is
proven, the customer’s level of trust in what the enterprise offers increases, and
later propositions can be more complex, requiring an element of faith on the part
of the customer. Further in the relationship, when most needs have been met, the
customer can be offered a trade-off in the style of relationship – lower fees or a
discount in exchange for using a lower touch and lower cost channel. The N-B-A
approach enables any and every aspect of the relationship to be explored and
optimized.

Optimization of Interaction Time
The value of an N-B-A Decision Hub within the organization is that it can be used
to intervene at any time and make recommendations concerning the best action.

For example, it is estimated that the total customer interaction time is about
3 hours and 22 minutes per year for a multichannel bank (Figure 2).3 For the
customer, much of this time is consumed by waiting, and it is during these
waiting periods that the Decision Hub can step in and suggest the action that
will most likely be meaningful to the customer. For the bank, this means that
interaction time is used effectively.



                         AVERAGE
    CHANNEL            INTERACTIONS
                        PER MONTH
                                                                                     Figure 2:
                                                                                     Yearly customer interaction
                                                                                     time in banking.
      Branch                  1.7
                                                       Average 3hr 22min
                                                       interaction per year

       Web                    1.8




        Call                  1.5




       ATM                    8.4



                                                                                                              5
It is estimated that marketing intervention during the 3 hours and 22 minutes can
    provide value equivalent to a 40-fold increase in the marketing budget.4 It also
    means that campaigns rarely need scheduling in the traditional sense of creating
    a fixed timeline for contacting the customer in predefined ways. Rather, a single,
    fluid, real-time campaign can be deployed that grabs available time for marketing
    during inbound contacts.

    In addition, the N-B-A Decision Hub can be used to decide on the best action
    for outbound communications, starting with the decision on whether or not
    to engage in outbound communication in the first place. If this decision for
    a particular customer is positive, the outbound proposition can be entirely
    consistent with the inbound proposition should the customer happen to engage in
    an inbound contact first. As a result, outbound campaign costs decrease for two
    reasons:


        ``Outbound communication becomes more effective by avoiding collisions;
        ``Inbound communication is used to make propositions that would
          otherwise be made in the outbound channels.

    Research has shown that customers are more receptive to offers made in the
    inbound channel. One reason for this is that customers choose their moments
    to engage with the company and are not contacted while relaxing at home or
    otherwise engaged. The second reason is that humans are more susceptible to
    offers made during a natural conversation, especially when the N-B-A Decision
    Hub guarantees a dynamic give-and-take process. Industry analyst firm Gartner
    estimates a factor-of-10 improvement in sales with a real-time, customer-
    initiated, relationship-driven conversation over traditional outbound mailings
    (Figure 3). All three features – real-time, customer-initiated, relationship-driven
    – illustrated in Figure 3 can be supported by the N-B-A Decision Hub.

              ENTERPRISE                                    CUSTOMER

                                   CAMPAIGN-DRIVEN
                Loans
           Enterprise-Initiated,                              Loans
                                                            “Intrusive”
                                                             1% to 5%
            Propensity
            Marketing-Driven                                Strategy
                                                              Response
                                                                                          Figure 3:
                                                                                          Effectiveness of inbound
                                                                                          versus outbound offers.
                                     EVENT-DRIVEN


                Loans
           Customer-Triggered                                Loans
                                                           “Convenient”
                                                            5% to 25%
            Propensity
           Product as Service                               Strategy
                                                             Response

                                    Leveraging Inbound
                                   Requests in Real Time


               Loans
           Customer-Initiated                                 Loans
                                                           “Appropriate”
                                                            10% to 50%
            Propensity
           Relationship-Driven                              Strategy
                                                             Response




6
Natural Conversations
For both the customer and the enterprise, achieving a natural conversation yields
greater satisfaction, better use of time, and ultimately greater profits. It is also
more conducive to having customers agree to a proposition because humans
are accustomed to a give-and-take style of dialog. It is important that both the
“giving” and the “taking” match customer interests and corporate objectives.              “Fundamental to the
                                                                                        N-B-A paradigm is that
For example, achieving a natural conversation in a call center means recognizing
                                                                                       there is no such thing as
the customer’s context for the call and aligning the way the agent responds
with the way the customer is thinking. When the customer asks about a specific                one-size-fits-all.”
product, the agent responds with a description of that product and others that
are worthy of comparison. When the customer asks about a particular type of
product, the agent responds with the products in that group. When the customer
expresses a need to do something, the agent offers the relevant products from
several product groups. And finally, when the customer describes some event he
or she is planning or experiencing, the agent again offers the relevant products.
This is more that just a simple prioritization of propositions—it is dynamically
focusing the conversation on the way the customer is thinking.

For many customers, a natural conversation would not stem from a “here’s what
we’ve got” offer of various products. Yet that is typically what happens in call
centers and particularly on websites. Given the context of the call or website
visit, the N-B-A Decision Hub enables the best propositions to be offered and
accepted or declined at an increasing level of specificity by initially offering the
most relevant product groups and then the most relevant products within the
selected group. Even at the product level, the recommended propositions can
be compared, allowing the customer to dismiss some of them based on their
features, while an individual proposition can be illustrated so that the customer
can comprehend its terms and implications and can tune the proposition to best
meet his or her needs. There can even be an element of negotiation, evolving
towards a deal that is a win-win for both parties.




                                                                                                              7
Fundamental to the N-B-A paradigm is that there is no such thing as one-size-
    fits-all. This extends to the way the relationship is conducted. Some customers
    prefer to be presented with the relevant alternatives and to reach a decision
    for themselves. Some prefer a consultative relationship in which they explain
    their circumstances and needs so that the organization can recommend the
    appropriate solutions. Some desire a mutually managed relationship in which
    they negotiate everything – the deal, the products it contains, the terms and the
    pricing. A natural conversation will mean something different to each group of
    customers. Now that these kind of complex interactions can be expressed using
    Decision Logic, consumer psychology can play an increasingly important role in
    designing the optimal customer experience.

    By continuously suggesting ‘what to do next’ during the dialog, the N-B-A
    Decision Hub supports iterative and interactive forms of communication that
    customers recognize as natural. As a result, they know how to conduct their part
    of the communication and focus their attention on what is being offered rather
    than being distracted, confused, or annoyed by the process itself.

    Effective Monitoring
    When all customer-facing decisions are made by the N-B-A Decision Hub, the
    enterprise is in a good position to automatically capture every decision and
    the basis on which it was made. In fact, this is affordable only with a central
    decisioning capability, as the impractical alternative is to trace all legacy rules
    and other logic that is embedded in all the different customer-facing applications
    and processes. When the effect of the decision becomes evident (perhaps
    immediately when the customer clicks a button; perhaps a year later when the
    customer repays the loan), it is then possible to judge the performance of the
    Decision Logic strategies fed to the Decision Hub.

    Such a comprehensive monitoring environment plays three important roles as
    follows:


       ``It operates in real-time, giving management an up-to-date view on
         propositions made and accepted or rejected. This is a key capability as
         management (product, channel, CRM) can exercise total and immediate
         control over all aspects—or at least all marketing aspects—of customer
         interactions.
       ``It allows marketers and others involved in designing the customer
         experience to discover which elements of their strategies work and
         which do not. This becomes even more important when the Decision Hub
         is used–as it should be–for experimentation and trying out alternative
         strategies in a champion/challenger fashion. Such feedback can be at
         the level of whole strategies or individual business rules and predictive




8
models that make up Decision Logic. Figure 4 illustrates the use of
   experimentation. In this case two retention strategies are randomly tested
   once the decision has been made to try to retain a customer. This decision,
   in turn, is based on the risk of defection and the expected customer value,
   the combination of which determines the retention budget, if any.
``Comprehensive monitoring stores each decision as well as the basis
  for that decision, allowing internal and external auditors to replay the
  interaction and assess the underlying policies and assumptions. For some
  areas of decisioning, this is becoming a legal requirement, such as the
  Basel II framework that governs capital requirements in banking.




                                          Retention                              Figure 4:
                                          Strategy 1                             Experimenting with retention
                                                                                 strategies via the decision hub.




  Value             Retention                                 Champion/
Prediction           Matrix                                   Challenger




 Exit Risk                                Retention
Prediction                                Strategy 2




                                                                                                            9
Total Control
     As is the case at O2 and other companies, strategies fed to the N-B-A Decision
     Hub are developed and maintained by line of business management rather
     than IT. The advantage of this approach is that the company can adapt its
     strategies (and, by implication, its profitability and aspects of branding) on
     the fly. It is important to note that this ability does not mean strategies should
     be implemented casually. They will need to be tested before they go into
     production as in any normal business practice. However, with the Decision Hub
     as the central location for making all customer-facing decisions, changing the
     instructions changes the way the company does business. For example, O2 is
     making changes to their extensive customer experience strategies a few times
     each week for a variety of reasons such as:


         ``Experiments (Figure 4) have shown that a particular challenger strategy
           should be promoted to the default strategy.
         ``Agents in the call center complain about a conversational flow (script) that
           is not producing the desired effect and requires modification.
         ``The strategy needs to be changed in reaction to an action by a competitor
           and requires a same day response.

     O2 can do all of these things because line-of-business management, including
     CRM and marketing managers, have total control over the customer strategies.

     While many changes require redeployment and retesting, some tactical changes
     need to take immediate effect. Suppose a company is running out of stock for a
     particular incentive. Operational management needs to be able to change the
     Silver Pen incentive to the Leather Wallet instantly. Similarly, if a flu epidemic
     strikes the call center staff, strategies may need to be adjusted to route inbound
     contacts to other channels or reduce call handling time by shifting to faster-to-
     complete propositions. With an N-B-A Decision Hub, such tactical changes can
     be made by adjusting certain control parameters that are part of the Decision
     Logic itself. Thus, rather than changing the structure of the strategy to any
     degree, parameters used within the strategy are modified. The Decision Logic
     does not have to be redeployed, as the extent of adaptation has been predefined
     and pre-approved, and the effects are merely tactical.10

     Similar to the modern Fly-by-Wire aviation approach where a pilot moves the
     joystick to steer the plane without interfering with the mechanics, the company
     becomes an agile and adaptive Fly-by-Wire enterprise with all checks and
     balances firmly in place.




10
N-B-A Marketing Realized — Shifting
Operational Paradigms
The benefits of the N-B-A paradigm as discussed above are extremely valuable
to any customer-focused organization. And as the approach has been proven
by some of the world’s most successful brands, implementation is clearly
feasible, and will be discussed later in this paper. However, as with all market-
driven paradigm shifts, there must be similar shifts in operational paradigms
within your company to fully realize the potential benefits. Both technical and
organizational in nature, these shifts must be examined in some detail as many
large companies are organized in a way that resists this type of innovation and
change. Certain areas of the business will be impacted the most and may need to
modify their processes, and often, their ways of thinking. These areas include:


    ``Marketing & Organizational Structure
    ``Outbound Marketing
    ``Call Center
    ``Enterprise IT

Marketing — A Collaborative, Customer-centric Approach
Marketing faces perhaps the most important paradigm shift of all. Marketing
strategists have to abandon their natural product focus and adopt customer-
centric thinking. This has been the CRM mantra for some time, but the truth
is that existing CRM technology has never allowed a truly customer-centric
approach. N-B-A marketing, in contrast, forces everyone in the organization to
put the customer first.

All staff involved in thinking about customer interaction strategies, including
branding, product marketing, risk management and CRM, will need to answer
this Critical Question: What is the approach we will take to maximize the
relationship with each customer when contact occurs? The answer triggers
further questions and the full strategy unfolds. From the start, this rigorously
top-down strategy design is at odds with the traditional product-first approach
when setting up campaigns and selecting customers for the propositions.

The answer to the Critical Question immediately requires collaboration between
departments that may hardly interact. This lack of collaboration comes at a
great cost, confusing the brand and bypassing potential revenues. Without this
collaboration, the first decision made will be owned by the business function that
happens to own the interaction. Customer-centric interactions demand that a
company agree on the answer to the Critical Question.

The first step to answering the Critical Question is for the company to agree on
its priorities for the business. Is risk more important than selling? Is selling more
important than retention? Consider the simple N-B-A logic example in Figure
5 that could be executed in any channel to answer the Critical Question and the



                                                                                        11
Is there a credit risk,
            and, if so,            Credit                    Credit
                                    Risk                    Strategy
        how to respond?




                                                            Retention
                                                            Strategy 1


        Is there an exit risk?
      If so, should we invest
                                   Value      Retention                  Champion/             NEXT BEST
         in retention? If so,    Prediction    Matrix                    Challenger
            how to retain?                                                                      ACTION


                                  Exit Risk                 Retention
                                 Prediction                 Strategy 2




                                 Mortgage     Mortgage      Mortgage
                                 Propensity   Eligibility   Strategy



      If there are no risks,
     what proposition should       Loans        Loans        Loans       Retention
                                                                           Best
        we make and how          Propensity   Eligibility   Strategy      Matrix
                                                                           Offer
       should we make it?


                                   Cards        Cards        Cards
                                 Propensity   Eligibility   Strategy



                                                                                      Figure 5:
                                                                                      Building customer centricity
                                                                                      into interactions.




12
subsequent questions that follow that first answer.
Figure 5 illustrates how to fit a single campaign to the customer, not fit the
customer to the campaign as with a traditional approach, where prospects are
assigned to predefined marketing segments. Segmentation, even when based on
a thorough customer analysis and not on ill-founded assumptions, is the ultimate
bottom-up approach to customer interactions. It is not uncommon, for instance,
to see organizations define hundreds, even thousands of customer segments,
while their options to differentiate propositions represent only a fraction of
that. Instead, if companies addressed the Critical Question first, segmentation
would naturally follow from the level of customer differentiation that is actually
operationally supported, reducing costs and enhancing transparency.

With the Critical Question as its starting point, the N-B-A Decision Hub applies
its strategies repetitively to the same customer. As the customer’s state is likely
to be affected by the recommended action, the next-best-action will be different
with each application.

To maximize bottom-line benefits, the Decision Hub considers operational
constraints as well as customer inputs to sometimes defer potential propositions.
At any time, the next-best-action may be determined to be no action. Perhaps
the Decision Hub decides it is best to wait until a later contact moment or not to
burden the customer with a proposition that is likely to be perceived as annoying.

Inside a company, strategy teams from multiple disciplines must come together
to help determine the answer to the Critical Question. Priorities should be based
on expected profitability, operational constraints, branding considerations,
relevant regulations, and company policies.

Having agreed on the Critical Question, the representatives of the different
departments then work on their internal Next-Best-Action sub-strategies, such
as for arrears management or sales. Within the department, this process should
begin with the runner-up question. For example, if the answer to the Critical
Question is to sell, which offer should be made first? Each following question is
answered until the sub-strategy is developed to the degree possible.

Not all sub-strategies need to be determined up-front. In fact, even the perfect
strategy would only be perfect for a limited period of time, as the market is a
fluid environment. Detail to basic assumptions can be added later, as the N-B-A
paradigm is perfectly suited to address a single business issue. For example,
the organization can start with N-B-A for fraud detection with the first decision
during an interaction about the likelihood of fraud. If the risk is deemed to
be satisfactorily minimal, traditional customer processes take over. N-B-A
strategies or sub-strategies for retention, arrears management or selling may
be added later, one at a time. O2 has been in production with N-B-A logic since
early 2004 and has achieved impressive results from the start with proposition-




                                                                                      13
to-acceptance conversion rates in the 50 percent range. Even so, O2 continuously
     improves the logic to address more customer issues in greater detail. Results
     continue to improve with conversion rates now in the 70 percent range.

     Once the virtual strategy team signs off on the sub-strategies, they are
     assembled into a corporate strategy. Technology helps to make this potentially
     hazardous task safe and quick.5 The strategy can then be deployed and go
     live. (See Implications for Enterprise IT Solutions section.) The monitoring
     environment allows different departments to assess and calibrate the
     performance of their respective strategies. (See Effective Monitoring section.)
     The team discusses results and decides on strategy corrections and extensions,
     priority changes and responses to feedback from monitoring or user comment.
     New versions of the strategy, which are typically subtle changes, can be deployed
     any time.

     This is all possible because sophisticated N-B-A Decision Logic can be safely
     created and maintained by non-technical users as it allows them to intuitively
     combine predictive models that were once the realm of statisticians with a
     completely new breed of user-oriented business rules that no longer require IT
     ownership.

     Traditional marketing campaigns are hardly portable because they start with
     the detail and are very product specific. In contrast, N-B-A campaigns start with
     a holistic view of the customer and add the detail last. As a consequence, the
     skeleton logic does not vary much from one telecom company to another, or
     from one bank to another bank. The implication is that best practice blueprints
     can successfully be used to fast track the implementation of N-B-A marketing
     solutions. Indeed, implementation time for early adopters of 8 months to
     recuperate costs and 18 months for a full multichannel rollout, has shrunk to
     about half that because of the availability of N-B-A blueprints.

     In addition to the enterprise strategy team, switching to an N-B-A marketing
     paradigm will require a high-throughput customer analytics department. High
     throughput is necessary to drive successful prediction-based strategies. There
     will be a large demand for high quality predictive models to help make decisions
     about likely customer interests (in buying products, channel preference, likely
     amount spent, etc.), risks (late or no payments, exit risk, fraud, etc.) and values.
     With recent advances in predictive technologies, it is now less important that




14
the analysts have a PhD in statistics than that they fully understand the business
purpose of the analytics. Using these business-friendly tools, companies can
easily integrate the customer analytics department with the marketing or CRM
department.

A company like O2 easily requires a hundred different predictive models, each of
which would take weeks to develop using traditional tools, while they take only
hours using the latest technology, which is more business oriented and offers a
model factory rather than a model laboratory. The demand for predictive models
can be particularly great in sectors like financial services and telecom where
products are increasingly defined just-in-time as a dynamic combination of
features. Working together, the business analysts and strategy team will form
this model factory that can replace or augment the current modeling capability
relied on by many large organizations.

Fueled by the predictive model factory, an N-B-A marketing strategy will
enable companies to market to consumers based on anticipated behavior.
Traditional attribute-driven segmentation can only tell marketers how to sell to
a homogeneous group, it cannot describe exactly who to sell a specific product
to. With behavioral segmentation, an individual can become a “Segment of
One” in which non-linear interplay between individual dimensions can be used
to select exactly the right action or offer for exactly the right individual. For
example, someone making $100,000 per year in Oklahoma will probably have
a different spending pattern than someone with the same salary in Manhattan.
From a marketing perspective, salary cannot be linearly interpreted in order to
understand a particular person’s behavior. Similarly, a set of parents of a certain
age may buy a rubber duck for their child. The same couple may purchase a
rubber duck again for their grandchild. Segmenting this couple into a single
“rubber duck–buying” age category eliminates the possibility of selling to the
grandparents.

To create a successful N-B-A strategy, marketers will have to change their
thinking, from traditional attribute-based segments to multi-dimensional
behavioral segments, that are actionable, and exactly what’s needed to drive
N-B-A strategies.




                                                                                      15
The Role of Outbound Marketing
     In traditional marketing organizations, outbound marketing is an independent
     marketing silo tasked with increasing sales by getting product propositions in
     front of prospects. In an N-B-A-centric marketing organization, this is considered
     one (important) element of the total customer experience. The dialog with the
     customer will touch many channels, and the outbound channel is one of them.
     But rather than just being a direct sales channel, its job should be to encourage
     customers to engage with the more effective inbound channels.

     The real-time inbound channels, as was discussed earlier, are more suitable for
     natural conversation and to exploit the fact that the customer, not the company,
     has chosen the moment to interact. Because of this, inbound is more effective for
     conducting the real business.

     In general, the customer experience is the result of continuous interaction across
     channels and over time. Directed by the Decision Hub all channels will ‘know’
     what role to play and how to carry on with the conversation. For instance, an
     inbound interaction may conclude with the customer’s agreement to be contacted
     later. Outbound communication is required to keep that promise towards the
     customers and ensure from a company perspective that leads are properly
     managed and not wasted. As another example, certain lifecycle events like a
     birthday may trigger an outbound action by the Decision Hub.

     Some enterprises even choose to consolidate these kinds of triggers along with
     most of their outbound campaigns into a holistic outbound customer contact
     strategy. Rather than having a January mailing for product 1, a February mailing
     for product 2, etc., all campaigns are competing for a customer (to be decided in
     true N-B-A fashion) in a single monthly outbound campaign.

     Next-Best-Action in the Call Center
     McKinsey6 states that in a bank that realizes a ‘core product conversion rate’
     of 4%, adding 5 extra agents to the call centre adds additional sales power
     equivalent to that of a mature bank branch. A sales force to behold. The call
     center is only one of the channels used by large organizations, but the effects of
     N-B-A marketing can be felt perhaps most directly in this area. Where previously
     call center agents could be dedicated to a task (service, selling or retention), the
     N-B-A paradigm mandates that the customer take center stage. This means that
     the agent will need to follow the lead of either the customer or the pro-active
     recommendations from the company’s N-B-A Decision Hub.

     The first action an N-B-A Decision Hub could take is to intercept the call while
     it is being handled by the voice response system and route the call to the




16
appropriate queue or agent.7 Or, the Decision Hub can be implemented to get
involved by guiding the agent only once the agent has taken the call. Once in
place, the N-B-A Decision Hub becomes the agent’s partner8 in making each
customer experience as effective as it can be.

A good N-B-A strategy represents the call center’s best practices, which
empowers agents, especially those who are less experienced. With the high
turnover in call center staff, this is an important feature in its own right. In
addition, all agents like recommendations that meet the customer’s needs most
precisely as well as helping those with a sales incentive meet their targets. The
Decision Hub can alert less experienced agents of promising propositions they do
not know about, while reminding highly experienced agents of propositions that
are forgotten, easily overlooked or only rarely relevant. As a result, customers
and agents experience much higher levels of satisfaction.

By receiving continuous recommendations for the next best action from the
N-B-A Decision Hub, each interaction becomes a meaningful conversation that
fills gaps that normally occur when the agent is waiting for data from the back-
office. Because of this, average call handling time is not significantly affected,
even when a whole series of recommendations is made to the agent during the
call.

Another aspect outside the scope of this paper but worth mentioning is that
current technology allows for a call center application that is almost 100% driven
by Decision Logic, including presentation elements. This means that agents can
not only give input on the recommendation logic and expect a quick turnaround
(because of the independence of IT), but also on the look and feel of their
desktop.

Implications for Enterprise IT
The Next Best Action paradigm stands on the shoulders of three important IT
breakthroughs: 1) the adoption of enterprise-wide application servers like IBM’s
WebSphere™ and BEA’s WebLogic™, and the Service-Oriented Architecture (SOA);
2) the speed of even modest hardware today being sufficient to meet real-time
demands; and 3) the coming of age of artificial intelligence techniques, allowing
for the aforementioned model factory to help achieve better alignment between
IT and business.

Implementing the N-B-A model has many implications for IT, most of them quite
technical and out of scope for this paper. For the skeptical reader, it helps to
realize that several large companies have already implemented the concept to its
fullest extent. For the sake of brevity, the IT implications discussed here will be




                                                                                      17
limited to two—opening up operational processes for business intervention and
     the data logistics necessary to support an N-B-A driven enterprise.

     Effect on operational processes. Perhaps the most significant change to IT after
     implementing an N-B-A Decision Hub is that business can take ownership of
     operational processes. Or more accurately, intervene in operational processes
     for marketing purposes.9 Although this intervention is done in a carefully
     controlled manner, it still encroaches on IT realms that previously required hard
     programming or business rule development (assuming the operational CRM
     systems allowed significant customization in the first place).

     In an N-B-A-based organization, IT is able to give business the means to take
     control of the dynamic, decision-driven part of the process while they continue
     to own the process flow itself (and the operational system interfaces defined
     by IT.) If IT implements a Decision Hub and a trusted way of deploying Decision
     Logic to this Hub, business can fulfill its requirements and adapt the Hub when
     appropriate. However, business may need to do so more often than IT might
     appreciate because market dynamics are more volatile than the corporate IT
     infrastructure can afford to be. In fact, such business agility is a key contributor
     to the success of the company. To maintain appropriate checks and balances, IT
     must define Decision Logic criteria in advance and establish a process that forces
     business to adhere to them. In brief, this problem is solved using meta-data, such
     as advanced data dictionaries, to force business staff to design Decision Logic
     that will be safe to deploy from a data logistics point of view.

     Data logistics required for N-B-A. The other major implication from an IT
     perspective is in data logistics. As the N-B-A model puts great value on exploiting
     interaction opportunities in real-time channels, the biggest challenge is to find
     a cost-effective way to manage real-time data. While the details are not in scope
     for this paper, a few key observations can be made:


        ``For real-time decision support, not all data needs to be available in
          real-time. More precisely, the data needs to be delivered in real-time but
          does not always need to reflect the up-to-the-second customer situation.
          A distinction between current real-time, near real-time and non
          real-time data requirements is important. A pragmatic approach extends
          the scope for real-time over time and starts with just the minimally
          required data elements. Even this modest scope produces many of the
          benefits discussed earlier. This is because customers are usually most
          sensitive to the company’s response to the most recent information they
          have given. For example, try repeating an offer if the customer just said
          ‘no’. Fortunately, the in-session data created during the interaction itself




18
is easier to come by than some of the data that needs to be retrieved
       externally, such as the current account balance. In summary, the reason
       is that the customer touch point itself collects the intra-dialog data and
       can send it to the Decision Hub (at the same time as making a decision
       support request for example.) Successive drops of the project will see a
       migration of static customer data from near real-time to current
       real-time, but only where the advantages of doing so are previously known
       in order to weigh the costs.10 Data logistics roadmaps are available that
       reflect best practices in this area.
   ``Contrary to expectations, making decisions, supporting calculations
     and predictions just in time (realtime), is likely to make data logistics
     easier, not more complex. If this seems paradoxical, consider dining
     in a restaurant. The traditional prepare-in-advance approach (or ‘data
     warehousing’ in IT terms) would be comparable to cooking all possible
     courses in advance, ready for when a customer chooses that particular
     dish. If this sounds far fetched and unappetizing, consider that product
     propensities are usually calculated in advance and then added to the
     customer data warehouse. The N-B-A alternative is to calculate the same
     propensity to buy in real-time. This not only avoids the need to store all
     propensities in a huge database, but also means that the calculation
     will be made only for that customer for whom the propensity is relevant.
     Moreover, it is possible to make the calculation using the very latest
     information provided by the customer—a negative response to the
     previous offer, the call reason or answers to questions.

The reason decisions, or more basic output like predictions and scores,
are usually calculated in advance with only the data retrieval as a real-time
process, is that not so long ago there was no other choice from a performance
perspective. Today, decision engine computations are of the same order as
database access in terms of speed. In other words, if data needs to be retrieved
from a database to arrive at a decision, the actual making of the decision does
not add significant overhead.

In summary, it appears the N-B-A way of thinking about customer interactions
has significant technical implications, but in the end will make life easier for
IT. They will become less of a bottleneck for marketing where agility is so very
necessary and the lack of it a significant source of frustration and a drain on
revenues.




                                                                                    19
Conclusion

     Next-Best-Action solutions for marketing and other business areas represent
     a necessary paradigm shift for companies that hope to increase profitability
     while maintaining high levels of customer satisfaction. Technological advances
     in predictive analytics and interface presentation have enabled new levels of
     customer-centricity within an organization. As recent projects in this area have
     been completed well within time and budget constraints, the biggest remaining
     hurdle for companies in implementing such a radical shift in business focus
     might be the new ways of thinking and new levels of intra-company collaboration
     that are required.

     In spite of these potential challenges, the quantitative, tangible business benefits
     described in this paper are of such magnitude, that quick success will inevitably
     engender even more enthusiastic and profitable collaborations between
     departments. In fact, the first implementation of an N-B-A-centric marketing
     organization achieved significant returns on investment within eight months. It
     is too early to tell, but this paradigm shift may finally dissolve the artificial walls
     that exist around departments and move customer experience to the forefront of
     corporate awareness for entire industries.

     Finally, Next-Best-Action thinking coincides nicely with the general trend of using
     inbound channels to extend and partly replace outbound channels. Customers
     are more likely than ever to choose their own moment and their own channel to
     contact the company, then select, negotiate, and configure the product or service
     of their choice. Analyst firm Gartner is not only anticipating the growing use
     of inbound channels, but also an explosive demand for solutions that leverage
     interactions through these channels to create an intelligent inbound channel
     (Figure 6). As a Next-Best-Action solution adds value to every customer decision,
     its bottom line contribution goes up with both the number of interactions and
     their complexity. It appears this revolutionary approach will land on fertile
     grounds. Implementation should have a high priority for organizations that want
     to carefully control the customer experience and fulfill their brand promise as
     well as for companies that are looking to make the most of customer interaction
     time to increase revenues and optimize the use of resources.

     Footer Notes
     1. This high number can in part be attributed to the type of propositions made, such as selling a
     relatively inexpensive texting (SMS) subscription. Perhaps more telling is the change in conversion
     ratio compared to O2’s traditional approach using the same propositions: a factor-of-15!

     2. Predictive models are essentially mathematical expressions that relate known customer data, such
     as age, income, gender, and so forth, to expected behavior, such as the customer will buy product x.
     Such models are usually developed by statistical or data mining software.

     3. Banco Espirito Santo, Presentation at the annual Dutch Dialogue Marketing Association conference,
     September 2001.




20
Figure 6:
                                           TRADITIONAL INBOUND                                               Growth in inbound channel
                                                                                                             interactions.
                                                                                       INTELLIGENT INBOUND

NUMBER
OF CUSTOMER
INTERACTIONS


                                               EVENT-DRIVEN




                                             CAMPAIGN-DRIVEN




                                                              TIME


4. By making extensive use of data dictionaries and other meta-data to make sure the sub-strategies
can be safely blended. The technical aspects of this are outside the scope of this paper.

5. This could go as far as having the Decision Hub predict likely call reasons to optimize the voice
response menu structure in real-time and minimize customer navigation time. This use of real-time
N-B-A Decisioning is outside the scope of this paper.

6. The McKinsey Quarterly, May 2006: Using call centers to boost revenues.

7. This could go as far as having the Decision Hub predict likely call reasons to optimize the voice
response menu structure in real-time and minimize customer navigation time. This use of real-time
N-B-A Decisioning is outside the scope of this paper.

8. AOL, after a first implementation of this new paradigm in Germany, calls this form of advanced
decision support the ‘call center buddy’ (American Marketing Association webcast, June 8th, 2006).

9. This paper emphasizes marketing aspects. However, an N-B-A Decision Hub can be and is used for
different purposes like intelligent lending, collections, fraud, etc.

10. This cost/benefit analysis is greatly facilitated by the fact that all customer experience logic has
been consolidated. Simulating the business benefits of using up-to-date customer data in making
N-B-A recommendations would otherwise be next to impossible.
About Pegasystems

Pegasystems, the leader in business process management and software for customer centricity,
helps organizations enhance customer loyalty, generate new business, and improve productivity. Our
patented Build for Change® technology speeds the delivery of critical business solutions by directly
capturing business objectives and eliminating manual programming. Pegasystems flexible on-
premise and cloud-based solutions enable clients to quickly adapt to changing business conditions
in order to outperform the competition. For more information, please visit us at www.pega.com.




Copyright © 2010 Pegasystems Inc. All rights reserved. PegaRules, Process Commander, Pega BPM and the
Pegasystems logo are trademarks or registered trademarks of Pegasystems Inc. All other product names,
logos and symbols may be registered trademarks of their respective owners.

                                                                                              2012-4

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Pega Next-Best-Action Marketing White Paper

  • 1. CROSS INDUSTRY Next-Best-Action™ Marketing Increasing profitability through more customer-centric communications
  • 2. Introduction Despite all the hype surrounding customer centricity, many organizations still rely on product-oriented customer interaction. For example, traditional direct marketing in which each campaign is set up to promote a single product. While this approach aligns well with current thinking about budgeting, staff compensation, and organizational structures, it is at odds with customer centricity as well as the ultimate corporate objective—profitability. This paper discusses a new approach to customer communications called Next-Best-Action marketing (N-B-A) that can yield significantly more effective marketing and results. N-B-A is part of the wider next-best-action approach to many types of customer interactions such as risk mitigation, remedial actionsfor churn and fraud, service provisioning, data collection, arrears, surveys and so forth. When used formarketing, N-B-A has direct and immediate effect as sales and retention will improve with a corresponding boost in revenues. At the same time, marketing costs will decrease while the possibilities for brand expression and control grow. Add to these benefits such desirable ‘side-effects’ as improved customer satisfaction and happier customer-facing staff, and everyone will wonder why this new approach was not adopted as the standard long ago. Adoption of N-B-A marketing has been slow for two reasons: 1) It is a multi-channel proposition, and only recently has real-time and customer analytics technology advanced to a level that can support the complexity of marketing across channels; 2) Organizational structures, corporate experience, and comfort with traditional marketing make such transformational change appear difficult and potentially risky. In addition to the discussion of the benefits to be achieved with the N-B-A approach, this paper will suggest ways for companies to overcome corporate inertia and transform the way they define, monitor and control the customer experience. 2
  • 3. First Things First: Profitability To demonstrate how the N-B-A approach actually delivers, it makes sense to look at the benefits O2, an early adopter of both the methodology and the technology, has achieved. O2 was one of the first to embrace N-B-A marketing for their 5,000 call center agents, 2 million online visitors per month and 470 retail outlets. Now the United Kingdom’s leading mobile telecom provider, O2 was only the third largest when the company began its N-B-A journey in 2004. Recently, Telefonica “N-B-A has direct and acquired O2 to create the second largest provider in the world, but because O2 has immediate effect as always zealously promoted and protected its brand, its brand is perceived to be a sales and retention key asset that is likely to be adopted internationally. will improve with a corresponding boost At the 2005 Gartner CRM Summit, O2 reported a bill-value increase of 9%, a in revenues.” response rate of ~75%1, a significant increase in customer satisfaction, and reduced costs for retaining customers, all in the first month after a customer had been exposed to an N-B-A guided customer experience. These benefits were achieved with no appreciable change in average customer handling time. The results are all the more significant because customer interaction was not exclusively focused on sales, with equal attention given to branding, as well as customer care and satisfaction. And although the use of the N-B-A application was not mandatory at the time in the call centers, O2 achieved 75% adoption among representatives. The essence of how such numbers were achieved at O2 and elsewhere is in the sophistication of the interaction and the corresponding quality of the customer experience. With N-B-A, it is now possible to build a mini-business case (in real-time when necessary) to determine the best action to take. Once the action is taken and the customer’s response recorded, the business case is immediately recalculated and the next best action is recommended. O2 and others are using sophisticated arbitration metrics to balance insight into customer interests, risks, loyalty, etc. with corporate priorities, such as revenues, costs, and branding. O2 has recognized that while the company’s overall goal is profitability, this does not mean that every interaction must be focused on selling. A better strategy is to continuously maximize the relationship throughout the customer lifecycle. For a very few customers, that may mean reaping as much as you can now as there will be little future in the relationship, but for most customers it means that every interaction, conducted through any channel, is used not necessarily to sell, but to nurture the relationship. The end result, as evidenced by the O2 case, is a stable increase in profitability and customer satisfaction. 1
  • 4. The Next-Best-Action Model To demonstrate how the N-B-A approach actually delivers, it makes sense to look at the benefits O2, an early adopter of both the methodology and the technology, has achieved. O2 was one of t The N-B-A model provides a corporate “Decisioning Hub” that guides each inbound and outbound customer action and communication for every channel and line of business. This centralized decisioning capability ensures the following benefits: “With N-B-A, it is now ``Consistency across channels possible to build a ``Collision avoidance mini-business case ``Cost alignment (in real-time when ``Optimization of interaction time necessary) to determine the best action to take.” ``Natural conversations ``Effective monitoring ``Total control Consistency and Collision Avoidance across Channels Absolute consistency, across channels and over time, is achieved because the Decision Hub executes a holistic strategy for each customer that recommends the best action. Any customer response or relevant event will have a bearing on what the next best action will be. For example, a customer who logs into a company’s web site or calls into the call center or is contacted through an outbound communication will be subjected to the same (best) action, assuming that the channel is not a relevant factor in the decision. The action may be to first address the customer’s question about an outstanding balance and then to offer her the Premium Platinum card, or 30 free minutes of international calling, but regardless of which channel is accessed for communication, the same messages are delivered to the customer. There is a second advantage to this approach—avoiding campaign collisions. In traditional campaign management, it is a challenge to avoid targeting the customer with two campaigns simultaneously. She may be selected for the Premium Platinum card as well as the higher interest Ultimate Savings card. Even if the two products are not mutually exclusive, they will compete for her attention. 2
  • 5. A traditional solution is to run a separate optimization after one campaign is executed to detect that the customer has received the Premium Platinum campaign and remove her from the Ultimate Savings campaign. With potentially hundreds of live campaigns, this is a cumbersome process. Alternatively, once the Premium Platinum proposition is executed, the customer may (temporarily) be on the suppression list for other campaigns. The question then becomes one of sequencing: what’s the best sequence of campaigns to maximize the customer’s contribution to the bottom line? The fact of the matter is that both of these solutions are trying to fix a problem that should not exist in the first place. Mortgage Mortgage Mortgage Figure 1: Propensity Eligibility Strategy N-B-A selling strategy example. Loans Loans Loans Retention Best Propensity Eligibility Strategy Matrix Offer Cards Cards Cards Propensity Eligibility Strategy 3
  • 6. The N-B-A model is an entirely different approach, representing a single, continuous campaign that selects the optimal treatment, one step, or decision, at a time. Arbitration between alternative treatments can be just-in-time, avoiding the time consuming post-processing required to assign the customer to a single campaign. With N-B-A marketing, the customer will be considered for a specific proposition or treat¬ment during real-time interactions or when generating the output file for batch processed outbound communications. Consider an N-B-A model as illustrated in Figure 1, a simple selling strategy that will be extended later in this paper. When fed to the Decision Hub, this strategy (or Decision Logic) will make a decision concerning the best offer to make. The decision is made in real-time, the moment the customer enters the website or contacts the call center. In batch, the decision is made when the output file is generated for outbound communication. In both cases the decision is made just-in-time and will select only one (the best) proposition out of the three that are possible, depending on the customer’s propensity to buy any of the three products, her eligibility to do so, the desirability of making the offer now, and the economics of the purchase, such as margin, contribution to branding, effect on market share, and so forth. The N-B-A strategy avoids collisions because under the organization’s arbitration scheme there can be only one best proposition. Assuming the Decision Hub is fast enough, it would be a waste of time, effort and data storage to set up three campaigns for mortgages, loans and cards and then apply the same arbitration afterwards. As will be shown later, a single Decision Logic strategy simplifies campaign management, reduces costs and IT effort, and, importantly, does not differentiate between inbound or outbound use. How to design and implement such an overall strategy in a multi-product organization will be discussed later in this paper. Cost Alignment Resources are scarce in every enterprise – both in terms of time spent in communicating and money spent in negotiating terms or incentives to make customers agree to a proposition. The ‘art’ in marketing is still choosing where to spend scarce resources. N-B-A strategies and their underlying predictive models2 enable an organization to be discerning in its use of resources. Given the prediction of the customer’s level of interest, some time can be justified in offering a proposition to those very likely to purchase if they are simply told about it. More time and an incentive can be justified for those less likely to be interested because they need to be persuaded. For those with even lower interest, time can be spent not on selling the proposition but on explaining it and developing the need or desire. Those with a minimal interest may fall below a threshold that prevents the proposition from being offered. This hierarchy of strategies for a proposition optimizes the use of scarce resources and aligns the cost of the effort to the likely value of the customer. 4
  • 7. The style of the relationship and the costs involved to both parties can be similarly managed and optimized over time. For example, during the initial phase of a relationship, the emphasis in the conversation may be on building a mutual understanding and level of trust. Early propositions can be structured to be particularly simple to understand and compelling to the customer, if not particularly profitable for the enterprise. When the value of the proposition is proven, the customer’s level of trust in what the enterprise offers increases, and later propositions can be more complex, requiring an element of faith on the part of the customer. Further in the relationship, when most needs have been met, the customer can be offered a trade-off in the style of relationship – lower fees or a discount in exchange for using a lower touch and lower cost channel. The N-B-A approach enables any and every aspect of the relationship to be explored and optimized. Optimization of Interaction Time The value of an N-B-A Decision Hub within the organization is that it can be used to intervene at any time and make recommendations concerning the best action. For example, it is estimated that the total customer interaction time is about 3 hours and 22 minutes per year for a multichannel bank (Figure 2).3 For the customer, much of this time is consumed by waiting, and it is during these waiting periods that the Decision Hub can step in and suggest the action that will most likely be meaningful to the customer. For the bank, this means that interaction time is used effectively. AVERAGE CHANNEL INTERACTIONS PER MONTH Figure 2: Yearly customer interaction time in banking. Branch 1.7 Average 3hr 22min interaction per year Web 1.8 Call 1.5 ATM 8.4 5
  • 8. It is estimated that marketing intervention during the 3 hours and 22 minutes can provide value equivalent to a 40-fold increase in the marketing budget.4 It also means that campaigns rarely need scheduling in the traditional sense of creating a fixed timeline for contacting the customer in predefined ways. Rather, a single, fluid, real-time campaign can be deployed that grabs available time for marketing during inbound contacts. In addition, the N-B-A Decision Hub can be used to decide on the best action for outbound communications, starting with the decision on whether or not to engage in outbound communication in the first place. If this decision for a particular customer is positive, the outbound proposition can be entirely consistent with the inbound proposition should the customer happen to engage in an inbound contact first. As a result, outbound campaign costs decrease for two reasons: ``Outbound communication becomes more effective by avoiding collisions; ``Inbound communication is used to make propositions that would otherwise be made in the outbound channels. Research has shown that customers are more receptive to offers made in the inbound channel. One reason for this is that customers choose their moments to engage with the company and are not contacted while relaxing at home or otherwise engaged. The second reason is that humans are more susceptible to offers made during a natural conversation, especially when the N-B-A Decision Hub guarantees a dynamic give-and-take process. Industry analyst firm Gartner estimates a factor-of-10 improvement in sales with a real-time, customer- initiated, relationship-driven conversation over traditional outbound mailings (Figure 3). All three features – real-time, customer-initiated, relationship-driven – illustrated in Figure 3 can be supported by the N-B-A Decision Hub. ENTERPRISE CUSTOMER CAMPAIGN-DRIVEN Loans Enterprise-Initiated, Loans “Intrusive” 1% to 5% Propensity Marketing-Driven Strategy Response Figure 3: Effectiveness of inbound versus outbound offers. EVENT-DRIVEN Loans Customer-Triggered Loans “Convenient” 5% to 25% Propensity Product as Service Strategy Response Leveraging Inbound Requests in Real Time Loans Customer-Initiated Loans “Appropriate” 10% to 50% Propensity Relationship-Driven Strategy Response 6
  • 9. Natural Conversations For both the customer and the enterprise, achieving a natural conversation yields greater satisfaction, better use of time, and ultimately greater profits. It is also more conducive to having customers agree to a proposition because humans are accustomed to a give-and-take style of dialog. It is important that both the “giving” and the “taking” match customer interests and corporate objectives. “Fundamental to the N-B-A paradigm is that For example, achieving a natural conversation in a call center means recognizing there is no such thing as the customer’s context for the call and aligning the way the agent responds with the way the customer is thinking. When the customer asks about a specific one-size-fits-all.” product, the agent responds with a description of that product and others that are worthy of comparison. When the customer asks about a particular type of product, the agent responds with the products in that group. When the customer expresses a need to do something, the agent offers the relevant products from several product groups. And finally, when the customer describes some event he or she is planning or experiencing, the agent again offers the relevant products. This is more that just a simple prioritization of propositions—it is dynamically focusing the conversation on the way the customer is thinking. For many customers, a natural conversation would not stem from a “here’s what we’ve got” offer of various products. Yet that is typically what happens in call centers and particularly on websites. Given the context of the call or website visit, the N-B-A Decision Hub enables the best propositions to be offered and accepted or declined at an increasing level of specificity by initially offering the most relevant product groups and then the most relevant products within the selected group. Even at the product level, the recommended propositions can be compared, allowing the customer to dismiss some of them based on their features, while an individual proposition can be illustrated so that the customer can comprehend its terms and implications and can tune the proposition to best meet his or her needs. There can even be an element of negotiation, evolving towards a deal that is a win-win for both parties. 7
  • 10. Fundamental to the N-B-A paradigm is that there is no such thing as one-size- fits-all. This extends to the way the relationship is conducted. Some customers prefer to be presented with the relevant alternatives and to reach a decision for themselves. Some prefer a consultative relationship in which they explain their circumstances and needs so that the organization can recommend the appropriate solutions. Some desire a mutually managed relationship in which they negotiate everything – the deal, the products it contains, the terms and the pricing. A natural conversation will mean something different to each group of customers. Now that these kind of complex interactions can be expressed using Decision Logic, consumer psychology can play an increasingly important role in designing the optimal customer experience. By continuously suggesting ‘what to do next’ during the dialog, the N-B-A Decision Hub supports iterative and interactive forms of communication that customers recognize as natural. As a result, they know how to conduct their part of the communication and focus their attention on what is being offered rather than being distracted, confused, or annoyed by the process itself. Effective Monitoring When all customer-facing decisions are made by the N-B-A Decision Hub, the enterprise is in a good position to automatically capture every decision and the basis on which it was made. In fact, this is affordable only with a central decisioning capability, as the impractical alternative is to trace all legacy rules and other logic that is embedded in all the different customer-facing applications and processes. When the effect of the decision becomes evident (perhaps immediately when the customer clicks a button; perhaps a year later when the customer repays the loan), it is then possible to judge the performance of the Decision Logic strategies fed to the Decision Hub. Such a comprehensive monitoring environment plays three important roles as follows: ``It operates in real-time, giving management an up-to-date view on propositions made and accepted or rejected. This is a key capability as management (product, channel, CRM) can exercise total and immediate control over all aspects—or at least all marketing aspects—of customer interactions. ``It allows marketers and others involved in designing the customer experience to discover which elements of their strategies work and which do not. This becomes even more important when the Decision Hub is used–as it should be–for experimentation and trying out alternative strategies in a champion/challenger fashion. Such feedback can be at the level of whole strategies or individual business rules and predictive 8
  • 11. models that make up Decision Logic. Figure 4 illustrates the use of experimentation. In this case two retention strategies are randomly tested once the decision has been made to try to retain a customer. This decision, in turn, is based on the risk of defection and the expected customer value, the combination of which determines the retention budget, if any. ``Comprehensive monitoring stores each decision as well as the basis for that decision, allowing internal and external auditors to replay the interaction and assess the underlying policies and assumptions. For some areas of decisioning, this is becoming a legal requirement, such as the Basel II framework that governs capital requirements in banking. Retention Figure 4: Strategy 1 Experimenting with retention strategies via the decision hub. Value Retention Champion/ Prediction Matrix Challenger Exit Risk Retention Prediction Strategy 2 9
  • 12. Total Control As is the case at O2 and other companies, strategies fed to the N-B-A Decision Hub are developed and maintained by line of business management rather than IT. The advantage of this approach is that the company can adapt its strategies (and, by implication, its profitability and aspects of branding) on the fly. It is important to note that this ability does not mean strategies should be implemented casually. They will need to be tested before they go into production as in any normal business practice. However, with the Decision Hub as the central location for making all customer-facing decisions, changing the instructions changes the way the company does business. For example, O2 is making changes to their extensive customer experience strategies a few times each week for a variety of reasons such as: ``Experiments (Figure 4) have shown that a particular challenger strategy should be promoted to the default strategy. ``Agents in the call center complain about a conversational flow (script) that is not producing the desired effect and requires modification. ``The strategy needs to be changed in reaction to an action by a competitor and requires a same day response. O2 can do all of these things because line-of-business management, including CRM and marketing managers, have total control over the customer strategies. While many changes require redeployment and retesting, some tactical changes need to take immediate effect. Suppose a company is running out of stock for a particular incentive. Operational management needs to be able to change the Silver Pen incentive to the Leather Wallet instantly. Similarly, if a flu epidemic strikes the call center staff, strategies may need to be adjusted to route inbound contacts to other channels or reduce call handling time by shifting to faster-to- complete propositions. With an N-B-A Decision Hub, such tactical changes can be made by adjusting certain control parameters that are part of the Decision Logic itself. Thus, rather than changing the structure of the strategy to any degree, parameters used within the strategy are modified. The Decision Logic does not have to be redeployed, as the extent of adaptation has been predefined and pre-approved, and the effects are merely tactical.10 Similar to the modern Fly-by-Wire aviation approach where a pilot moves the joystick to steer the plane without interfering with the mechanics, the company becomes an agile and adaptive Fly-by-Wire enterprise with all checks and balances firmly in place. 10
  • 13. N-B-A Marketing Realized — Shifting Operational Paradigms The benefits of the N-B-A paradigm as discussed above are extremely valuable to any customer-focused organization. And as the approach has been proven by some of the world’s most successful brands, implementation is clearly feasible, and will be discussed later in this paper. However, as with all market- driven paradigm shifts, there must be similar shifts in operational paradigms within your company to fully realize the potential benefits. Both technical and organizational in nature, these shifts must be examined in some detail as many large companies are organized in a way that resists this type of innovation and change. Certain areas of the business will be impacted the most and may need to modify their processes, and often, their ways of thinking. These areas include: ``Marketing & Organizational Structure ``Outbound Marketing ``Call Center ``Enterprise IT Marketing — A Collaborative, Customer-centric Approach Marketing faces perhaps the most important paradigm shift of all. Marketing strategists have to abandon their natural product focus and adopt customer- centric thinking. This has been the CRM mantra for some time, but the truth is that existing CRM technology has never allowed a truly customer-centric approach. N-B-A marketing, in contrast, forces everyone in the organization to put the customer first. All staff involved in thinking about customer interaction strategies, including branding, product marketing, risk management and CRM, will need to answer this Critical Question: What is the approach we will take to maximize the relationship with each customer when contact occurs? The answer triggers further questions and the full strategy unfolds. From the start, this rigorously top-down strategy design is at odds with the traditional product-first approach when setting up campaigns and selecting customers for the propositions. The answer to the Critical Question immediately requires collaboration between departments that may hardly interact. This lack of collaboration comes at a great cost, confusing the brand and bypassing potential revenues. Without this collaboration, the first decision made will be owned by the business function that happens to own the interaction. Customer-centric interactions demand that a company agree on the answer to the Critical Question. The first step to answering the Critical Question is for the company to agree on its priorities for the business. Is risk more important than selling? Is selling more important than retention? Consider the simple N-B-A logic example in Figure 5 that could be executed in any channel to answer the Critical Question and the 11
  • 14. Is there a credit risk, and, if so, Credit Credit Risk Strategy how to respond? Retention Strategy 1 Is there an exit risk? If so, should we invest Value Retention Champion/ NEXT BEST in retention? If so, Prediction Matrix Challenger how to retain? ACTION Exit Risk Retention Prediction Strategy 2 Mortgage Mortgage Mortgage Propensity Eligibility Strategy If there are no risks, what proposition should Loans Loans Loans Retention Best we make and how Propensity Eligibility Strategy Matrix Offer should we make it? Cards Cards Cards Propensity Eligibility Strategy Figure 5: Building customer centricity into interactions. 12
  • 15. subsequent questions that follow that first answer. Figure 5 illustrates how to fit a single campaign to the customer, not fit the customer to the campaign as with a traditional approach, where prospects are assigned to predefined marketing segments. Segmentation, even when based on a thorough customer analysis and not on ill-founded assumptions, is the ultimate bottom-up approach to customer interactions. It is not uncommon, for instance, to see organizations define hundreds, even thousands of customer segments, while their options to differentiate propositions represent only a fraction of that. Instead, if companies addressed the Critical Question first, segmentation would naturally follow from the level of customer differentiation that is actually operationally supported, reducing costs and enhancing transparency. With the Critical Question as its starting point, the N-B-A Decision Hub applies its strategies repetitively to the same customer. As the customer’s state is likely to be affected by the recommended action, the next-best-action will be different with each application. To maximize bottom-line benefits, the Decision Hub considers operational constraints as well as customer inputs to sometimes defer potential propositions. At any time, the next-best-action may be determined to be no action. Perhaps the Decision Hub decides it is best to wait until a later contact moment or not to burden the customer with a proposition that is likely to be perceived as annoying. Inside a company, strategy teams from multiple disciplines must come together to help determine the answer to the Critical Question. Priorities should be based on expected profitability, operational constraints, branding considerations, relevant regulations, and company policies. Having agreed on the Critical Question, the representatives of the different departments then work on their internal Next-Best-Action sub-strategies, such as for arrears management or sales. Within the department, this process should begin with the runner-up question. For example, if the answer to the Critical Question is to sell, which offer should be made first? Each following question is answered until the sub-strategy is developed to the degree possible. Not all sub-strategies need to be determined up-front. In fact, even the perfect strategy would only be perfect for a limited period of time, as the market is a fluid environment. Detail to basic assumptions can be added later, as the N-B-A paradigm is perfectly suited to address a single business issue. For example, the organization can start with N-B-A for fraud detection with the first decision during an interaction about the likelihood of fraud. If the risk is deemed to be satisfactorily minimal, traditional customer processes take over. N-B-A strategies or sub-strategies for retention, arrears management or selling may be added later, one at a time. O2 has been in production with N-B-A logic since early 2004 and has achieved impressive results from the start with proposition- 13
  • 16. to-acceptance conversion rates in the 50 percent range. Even so, O2 continuously improves the logic to address more customer issues in greater detail. Results continue to improve with conversion rates now in the 70 percent range. Once the virtual strategy team signs off on the sub-strategies, they are assembled into a corporate strategy. Technology helps to make this potentially hazardous task safe and quick.5 The strategy can then be deployed and go live. (See Implications for Enterprise IT Solutions section.) The monitoring environment allows different departments to assess and calibrate the performance of their respective strategies. (See Effective Monitoring section.) The team discusses results and decides on strategy corrections and extensions, priority changes and responses to feedback from monitoring or user comment. New versions of the strategy, which are typically subtle changes, can be deployed any time. This is all possible because sophisticated N-B-A Decision Logic can be safely created and maintained by non-technical users as it allows them to intuitively combine predictive models that were once the realm of statisticians with a completely new breed of user-oriented business rules that no longer require IT ownership. Traditional marketing campaigns are hardly portable because they start with the detail and are very product specific. In contrast, N-B-A campaigns start with a holistic view of the customer and add the detail last. As a consequence, the skeleton logic does not vary much from one telecom company to another, or from one bank to another bank. The implication is that best practice blueprints can successfully be used to fast track the implementation of N-B-A marketing solutions. Indeed, implementation time for early adopters of 8 months to recuperate costs and 18 months for a full multichannel rollout, has shrunk to about half that because of the availability of N-B-A blueprints. In addition to the enterprise strategy team, switching to an N-B-A marketing paradigm will require a high-throughput customer analytics department. High throughput is necessary to drive successful prediction-based strategies. There will be a large demand for high quality predictive models to help make decisions about likely customer interests (in buying products, channel preference, likely amount spent, etc.), risks (late or no payments, exit risk, fraud, etc.) and values. With recent advances in predictive technologies, it is now less important that 14
  • 17. the analysts have a PhD in statistics than that they fully understand the business purpose of the analytics. Using these business-friendly tools, companies can easily integrate the customer analytics department with the marketing or CRM department. A company like O2 easily requires a hundred different predictive models, each of which would take weeks to develop using traditional tools, while they take only hours using the latest technology, which is more business oriented and offers a model factory rather than a model laboratory. The demand for predictive models can be particularly great in sectors like financial services and telecom where products are increasingly defined just-in-time as a dynamic combination of features. Working together, the business analysts and strategy team will form this model factory that can replace or augment the current modeling capability relied on by many large organizations. Fueled by the predictive model factory, an N-B-A marketing strategy will enable companies to market to consumers based on anticipated behavior. Traditional attribute-driven segmentation can only tell marketers how to sell to a homogeneous group, it cannot describe exactly who to sell a specific product to. With behavioral segmentation, an individual can become a “Segment of One” in which non-linear interplay between individual dimensions can be used to select exactly the right action or offer for exactly the right individual. For example, someone making $100,000 per year in Oklahoma will probably have a different spending pattern than someone with the same salary in Manhattan. From a marketing perspective, salary cannot be linearly interpreted in order to understand a particular person’s behavior. Similarly, a set of parents of a certain age may buy a rubber duck for their child. The same couple may purchase a rubber duck again for their grandchild. Segmenting this couple into a single “rubber duck–buying” age category eliminates the possibility of selling to the grandparents. To create a successful N-B-A strategy, marketers will have to change their thinking, from traditional attribute-based segments to multi-dimensional behavioral segments, that are actionable, and exactly what’s needed to drive N-B-A strategies. 15
  • 18. The Role of Outbound Marketing In traditional marketing organizations, outbound marketing is an independent marketing silo tasked with increasing sales by getting product propositions in front of prospects. In an N-B-A-centric marketing organization, this is considered one (important) element of the total customer experience. The dialog with the customer will touch many channels, and the outbound channel is one of them. But rather than just being a direct sales channel, its job should be to encourage customers to engage with the more effective inbound channels. The real-time inbound channels, as was discussed earlier, are more suitable for natural conversation and to exploit the fact that the customer, not the company, has chosen the moment to interact. Because of this, inbound is more effective for conducting the real business. In general, the customer experience is the result of continuous interaction across channels and over time. Directed by the Decision Hub all channels will ‘know’ what role to play and how to carry on with the conversation. For instance, an inbound interaction may conclude with the customer’s agreement to be contacted later. Outbound communication is required to keep that promise towards the customers and ensure from a company perspective that leads are properly managed and not wasted. As another example, certain lifecycle events like a birthday may trigger an outbound action by the Decision Hub. Some enterprises even choose to consolidate these kinds of triggers along with most of their outbound campaigns into a holistic outbound customer contact strategy. Rather than having a January mailing for product 1, a February mailing for product 2, etc., all campaigns are competing for a customer (to be decided in true N-B-A fashion) in a single monthly outbound campaign. Next-Best-Action in the Call Center McKinsey6 states that in a bank that realizes a ‘core product conversion rate’ of 4%, adding 5 extra agents to the call centre adds additional sales power equivalent to that of a mature bank branch. A sales force to behold. The call center is only one of the channels used by large organizations, but the effects of N-B-A marketing can be felt perhaps most directly in this area. Where previously call center agents could be dedicated to a task (service, selling or retention), the N-B-A paradigm mandates that the customer take center stage. This means that the agent will need to follow the lead of either the customer or the pro-active recommendations from the company’s N-B-A Decision Hub. The first action an N-B-A Decision Hub could take is to intercept the call while it is being handled by the voice response system and route the call to the 16
  • 19. appropriate queue or agent.7 Or, the Decision Hub can be implemented to get involved by guiding the agent only once the agent has taken the call. Once in place, the N-B-A Decision Hub becomes the agent’s partner8 in making each customer experience as effective as it can be. A good N-B-A strategy represents the call center’s best practices, which empowers agents, especially those who are less experienced. With the high turnover in call center staff, this is an important feature in its own right. In addition, all agents like recommendations that meet the customer’s needs most precisely as well as helping those with a sales incentive meet their targets. The Decision Hub can alert less experienced agents of promising propositions they do not know about, while reminding highly experienced agents of propositions that are forgotten, easily overlooked or only rarely relevant. As a result, customers and agents experience much higher levels of satisfaction. By receiving continuous recommendations for the next best action from the N-B-A Decision Hub, each interaction becomes a meaningful conversation that fills gaps that normally occur when the agent is waiting for data from the back- office. Because of this, average call handling time is not significantly affected, even when a whole series of recommendations is made to the agent during the call. Another aspect outside the scope of this paper but worth mentioning is that current technology allows for a call center application that is almost 100% driven by Decision Logic, including presentation elements. This means that agents can not only give input on the recommendation logic and expect a quick turnaround (because of the independence of IT), but also on the look and feel of their desktop. Implications for Enterprise IT The Next Best Action paradigm stands on the shoulders of three important IT breakthroughs: 1) the adoption of enterprise-wide application servers like IBM’s WebSphere™ and BEA’s WebLogic™, and the Service-Oriented Architecture (SOA); 2) the speed of even modest hardware today being sufficient to meet real-time demands; and 3) the coming of age of artificial intelligence techniques, allowing for the aforementioned model factory to help achieve better alignment between IT and business. Implementing the N-B-A model has many implications for IT, most of them quite technical and out of scope for this paper. For the skeptical reader, it helps to realize that several large companies have already implemented the concept to its fullest extent. For the sake of brevity, the IT implications discussed here will be 17
  • 20. limited to two—opening up operational processes for business intervention and the data logistics necessary to support an N-B-A driven enterprise. Effect on operational processes. Perhaps the most significant change to IT after implementing an N-B-A Decision Hub is that business can take ownership of operational processes. Or more accurately, intervene in operational processes for marketing purposes.9 Although this intervention is done in a carefully controlled manner, it still encroaches on IT realms that previously required hard programming or business rule development (assuming the operational CRM systems allowed significant customization in the first place). In an N-B-A-based organization, IT is able to give business the means to take control of the dynamic, decision-driven part of the process while they continue to own the process flow itself (and the operational system interfaces defined by IT.) If IT implements a Decision Hub and a trusted way of deploying Decision Logic to this Hub, business can fulfill its requirements and adapt the Hub when appropriate. However, business may need to do so more often than IT might appreciate because market dynamics are more volatile than the corporate IT infrastructure can afford to be. In fact, such business agility is a key contributor to the success of the company. To maintain appropriate checks and balances, IT must define Decision Logic criteria in advance and establish a process that forces business to adhere to them. In brief, this problem is solved using meta-data, such as advanced data dictionaries, to force business staff to design Decision Logic that will be safe to deploy from a data logistics point of view. Data logistics required for N-B-A. The other major implication from an IT perspective is in data logistics. As the N-B-A model puts great value on exploiting interaction opportunities in real-time channels, the biggest challenge is to find a cost-effective way to manage real-time data. While the details are not in scope for this paper, a few key observations can be made: ``For real-time decision support, not all data needs to be available in real-time. More precisely, the data needs to be delivered in real-time but does not always need to reflect the up-to-the-second customer situation. A distinction between current real-time, near real-time and non real-time data requirements is important. A pragmatic approach extends the scope for real-time over time and starts with just the minimally required data elements. Even this modest scope produces many of the benefits discussed earlier. This is because customers are usually most sensitive to the company’s response to the most recent information they have given. For example, try repeating an offer if the customer just said ‘no’. Fortunately, the in-session data created during the interaction itself 18
  • 21. is easier to come by than some of the data that needs to be retrieved externally, such as the current account balance. In summary, the reason is that the customer touch point itself collects the intra-dialog data and can send it to the Decision Hub (at the same time as making a decision support request for example.) Successive drops of the project will see a migration of static customer data from near real-time to current real-time, but only where the advantages of doing so are previously known in order to weigh the costs.10 Data logistics roadmaps are available that reflect best practices in this area. ``Contrary to expectations, making decisions, supporting calculations and predictions just in time (realtime), is likely to make data logistics easier, not more complex. If this seems paradoxical, consider dining in a restaurant. The traditional prepare-in-advance approach (or ‘data warehousing’ in IT terms) would be comparable to cooking all possible courses in advance, ready for when a customer chooses that particular dish. If this sounds far fetched and unappetizing, consider that product propensities are usually calculated in advance and then added to the customer data warehouse. The N-B-A alternative is to calculate the same propensity to buy in real-time. This not only avoids the need to store all propensities in a huge database, but also means that the calculation will be made only for that customer for whom the propensity is relevant. Moreover, it is possible to make the calculation using the very latest information provided by the customer—a negative response to the previous offer, the call reason or answers to questions. The reason decisions, or more basic output like predictions and scores, are usually calculated in advance with only the data retrieval as a real-time process, is that not so long ago there was no other choice from a performance perspective. Today, decision engine computations are of the same order as database access in terms of speed. In other words, if data needs to be retrieved from a database to arrive at a decision, the actual making of the decision does not add significant overhead. In summary, it appears the N-B-A way of thinking about customer interactions has significant technical implications, but in the end will make life easier for IT. They will become less of a bottleneck for marketing where agility is so very necessary and the lack of it a significant source of frustration and a drain on revenues. 19
  • 22. Conclusion Next-Best-Action solutions for marketing and other business areas represent a necessary paradigm shift for companies that hope to increase profitability while maintaining high levels of customer satisfaction. Technological advances in predictive analytics and interface presentation have enabled new levels of customer-centricity within an organization. As recent projects in this area have been completed well within time and budget constraints, the biggest remaining hurdle for companies in implementing such a radical shift in business focus might be the new ways of thinking and new levels of intra-company collaboration that are required. In spite of these potential challenges, the quantitative, tangible business benefits described in this paper are of such magnitude, that quick success will inevitably engender even more enthusiastic and profitable collaborations between departments. In fact, the first implementation of an N-B-A-centric marketing organization achieved significant returns on investment within eight months. It is too early to tell, but this paradigm shift may finally dissolve the artificial walls that exist around departments and move customer experience to the forefront of corporate awareness for entire industries. Finally, Next-Best-Action thinking coincides nicely with the general trend of using inbound channels to extend and partly replace outbound channels. Customers are more likely than ever to choose their own moment and their own channel to contact the company, then select, negotiate, and configure the product or service of their choice. Analyst firm Gartner is not only anticipating the growing use of inbound channels, but also an explosive demand for solutions that leverage interactions through these channels to create an intelligent inbound channel (Figure 6). As a Next-Best-Action solution adds value to every customer decision, its bottom line contribution goes up with both the number of interactions and their complexity. It appears this revolutionary approach will land on fertile grounds. Implementation should have a high priority for organizations that want to carefully control the customer experience and fulfill their brand promise as well as for companies that are looking to make the most of customer interaction time to increase revenues and optimize the use of resources. Footer Notes 1. This high number can in part be attributed to the type of propositions made, such as selling a relatively inexpensive texting (SMS) subscription. Perhaps more telling is the change in conversion ratio compared to O2’s traditional approach using the same propositions: a factor-of-15! 2. Predictive models are essentially mathematical expressions that relate known customer data, such as age, income, gender, and so forth, to expected behavior, such as the customer will buy product x. Such models are usually developed by statistical or data mining software. 3. Banco Espirito Santo, Presentation at the annual Dutch Dialogue Marketing Association conference, September 2001. 20
  • 23. Figure 6: TRADITIONAL INBOUND Growth in inbound channel interactions. INTELLIGENT INBOUND NUMBER OF CUSTOMER INTERACTIONS EVENT-DRIVEN CAMPAIGN-DRIVEN TIME 4. By making extensive use of data dictionaries and other meta-data to make sure the sub-strategies can be safely blended. The technical aspects of this are outside the scope of this paper. 5. This could go as far as having the Decision Hub predict likely call reasons to optimize the voice response menu structure in real-time and minimize customer navigation time. This use of real-time N-B-A Decisioning is outside the scope of this paper. 6. The McKinsey Quarterly, May 2006: Using call centers to boost revenues. 7. This could go as far as having the Decision Hub predict likely call reasons to optimize the voice response menu structure in real-time and minimize customer navigation time. This use of real-time N-B-A Decisioning is outside the scope of this paper. 8. AOL, after a first implementation of this new paradigm in Germany, calls this form of advanced decision support the ‘call center buddy’ (American Marketing Association webcast, June 8th, 2006). 9. This paper emphasizes marketing aspects. However, an N-B-A Decision Hub can be and is used for different purposes like intelligent lending, collections, fraud, etc. 10. This cost/benefit analysis is greatly facilitated by the fact that all customer experience logic has been consolidated. Simulating the business benefits of using up-to-date customer data in making N-B-A recommendations would otherwise be next to impossible.
  • 24. About Pegasystems Pegasystems, the leader in business process management and software for customer centricity, helps organizations enhance customer loyalty, generate new business, and improve productivity. Our patented Build for Change® technology speeds the delivery of critical business solutions by directly capturing business objectives and eliminating manual programming. Pegasystems flexible on- premise and cloud-based solutions enable clients to quickly adapt to changing business conditions in order to outperform the competition. For more information, please visit us at www.pega.com. Copyright © 2010 Pegasystems Inc. All rights reserved. PegaRules, Process Commander, Pega BPM and the Pegasystems logo are trademarks or registered trademarks of Pegasystems Inc. All other product names, logos and symbols may be registered trademarks of their respective owners. 2012-4