Applying Predictive Sciences To Sales
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Dave Elkington is the CEO and founder of InsideSales.com, a predictive sales technology company that has experienced strong revenue growth over 4 years. The company applies machine learning and predictive analytics to sales data to help salespeople identify the most qualified leads, prioritize activities, and improve performance metrics like close rates, deal size, and revenue. InsideSales.com's platform collects and standardizes different types of sales data which is then analyzed using machine learning techniques to provide real-time recommendations and insights to sales teams. Customers have experienced significant improvements across various key performance indicators through the use of InsideSales.com's Neuralytics technology.
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Applying Predictive Sciences To Sales
- 1. Dave Elkington, CEO & Founder APPLYING PREDICTIVE SCIENCES TO SALES
- 2. INSIDESALES.COM - ABOUT US 40KUsers 3KCustomers 2011 2012 2013 2014 100% Revenue Growth (4yrs) x 2x 2x 2x 100 Top #5 Job Creation 25 Patents Employees 675 + Private Companies
- 3. LEADERSHIP TEAM Brent Peters Jim Steele Lindsey Armstrong David Rudnitsky Ken Krogue Mick Hollison
- 4. INTERNET DISRUPTION Key Disruptions • Commerce • Communication • Collaboration
- 5. SALES REVOLUTION Inside Sales • Data Driven • Process Driven • Analysis Driven
- 6. RISE OF DATA Data Types • Observed Data • Stated Data • Derived Data
- 7. SALES PROBLEM Survival Instinct • Pipeline Reports • Activity Reports
- 14. APPLYING MACHINE LEARNING 4 Steps 1. Ask Right Questions 2. Capture Data 3. Standardize Data 4. Perform Analysis
- 15. APPLYING MACHINE LEARNING TO SALES
- 16. ASK THE RIGHT QUESTIONS Sales Given … 1. Buyer & Seller Profile 2. Activity Performance 3. Situational Relevance 4. Sales Performance
- 17. CAPTURE DATA Technologies 1. Marketing Automation 2. Lead Management 3. Sales Activity Automation 4. Customer Relationship
- 18. STANDARDIZE DATA Categories 1. People & Co. Data 2. Process Data 3. Interaction Data 4. Contextual Data
- 19. Compute 1. Excel / Tableau 2. R / Stata 3. MLlib / Mahout 4. Neuralytics ANALYZE DATA
- 20. INSIDESALES.COM PLATFORM People Prospects Performance NEURALYTICS™
- 21. INSIDESALES.COM PLATFORM +57% Increased Close Rate Neural Prioritization Activity + 38% Neural Motivation +42% Quota Attainment Neural Hiring + 24% Conversations Neural Sort
Editor's Notes
- 70% Director Level or above.
- The world of sales changed with the internet. Sales is changing, disruption because of the internet, Retails to marketing to sales. Brings data into play with each case. Two kinds of data >> old school data is observed data. An example would be a pipeline. Internet Disruption Sales Revolution Problem ----- Meeting Notes (4/20/15 12:47) ----- Ecommerce Communication Speed
- The world of sales changed with the internet. Sales is changing, disruption because of the internet, Retails to marketing to sales. Brings data into play with each case. Two kinds of data >> old school data is observed data. An example would be a pipeline. Internet Disruption Sales Revolution Problem ASK EDDIE ABOUT SLC OFFICE >> ANY PICUTURES? 1. Please join my meeting. https://global.gotomeeting.com/join/952365029 2. Use your microphone and speakers (VoIP) - a headset is recommended. Or, call in using your telephone. Dial +1 (571) 317-3122 Access Code: 952-365-029 Audio PIN: Shown after joining the meeting Meeting ID: 952-365-029 GoToMeeting® Online Meetings Made Easy®
- We are still treating data the same way we did for the last 30 years, which is activity and pipeline data. We aren't actually using the data. We are using technology to help us sell, but we aren't using the data. Revolution in sales, born out of disruption of the internet. Two kinds of data >> old school data is observed data. An example would be a pipeline. The problem: We've evolved in our activity, but not our data analysis. The new way uses predictive science. By evolving, we can't process enough data. Use some kind of brain image. There is a stat about how much you can actually process.
- The world of sales changed with the internet. Sales is changing, disruption because of the internet, Retails to marketing to sales. Brings data into play with each case. Two kinds of data >> old school data is observed data. An example would be a pipeline. Internet Disruption Sales Revolution Problem ASK EDDIE ABOUT SLC OFFICE >> ANY PICUTURES?
- Handedness of the Pitcher, Speed of the pitch, type of the pitch, and
- AKAS: Batter | Pitcher | Batting Average How do I generate Sales given …
- Screenshot of Renaissance ----- Meeting Notes (4/20/15 11:11) ----- How do they do it in helicopter, some of the technologies. How do they analyze the data. Show stata, R, video of R processing What does this look like on a sales floor? Predictively find the right sellers. Survey the people. This is what we do. WE NEED TO END WITH "MACHINE LEARNING IS LEARNING"
- Introduce Neuralytics Three levers to pull Sales experience optimization Customer Experience Optimization
- Neural Hiring = Only 58% of Reps are hitting Quota Neural Motivation = Immediate 38% bump when turning on PowerStandings NeuralSort = Using things like weather, previous interaction history, holidays, time of day etc we can increase contact rates by 24% NeuralPrioritization = Your best prospects close at over 10x the rate of your worst leads. By prioritizing your effort in the right place you can see close rates increase by up to 57%
- Neural Loop The system continues to learn We are getting smarter, better.
- Predictive Systems to find the right people you can increase pipeline = 50% increase in attainment Gamificaiton = 37% InsideSales logo Videos
- Introduce Neuralytics Three levers to pull Sales experience optimization Customer Experience Optimization
- AKAS: Batter | Pitcher | Batting Average The right questions: What is the right profile? PICTURE FOR ASKING THE RIGHT QUESTIONS
- Screenshot of Renaissance ----- Meeting Notes (4/20/15 11:11) ----- How do they do it in helicopter, some of the technologies. How do they analyze the data. Show stata, R, video of R processing What does this look like on a sales floor? Predictively find the right sellers. Survey the people. This is what we do. WE NEED TO END WITH "MACHINE LEARNING IS LEARNING"
- The world of sales changed with the internet. Sales is changing, disruption because of the internet, Retails to marketing to sales. Brings data into play with each case. Two kinds of data >> old school data is observed data. An example would be a pipeline. Internet Disruption Sales Revolution Problem ASK EDDIE ABOUT SLC OFFICE >> ANY PICUTURES?