Predict 2014, SiriusDecisions Kerry Cunningham
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Increasing the odds: The conditions and correlates for predictive lead scoring success
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Predict 2014, SiriusDecisions Kerry Cunningham
- 1. Increasing the odds: The conditions and correlates for predictive lead scoring success Kerry Cunningham Research Director, SiriusDecisions
- 2. #predict2014 SiriusDecisions, Kerry Cunningham Research Director, SiriusDecisions – 20 years Lead Development & Management – Research methods and analytics 5 years social science research – Propensity modeling behavior • Spending/ Consumption • Employee performance • Personality correlates of well-being, Book Chapter, September, 2014
- 3. Agenda • Why is predictive necessary? • 4 Principles of Prediction • 5 Building Blocks for Predictive lead scoring success • 4 Keys to Success with Predictive Lead Scoring
- 4. #predict2014 Why Predictive Is Needed To better understand why prediction is called for…
- 5. #predict2014 Why Predictive Is Needed Conversion % AQL > TQL 10%-30%
- 6. #predict2014 Why Predictive Is Needed Conversion % AQL > TQL 10%-30% TQL + TGL > SQL 5%-10%
- 7. #predict2014 Why Predictive Is Needed Conversion from AQL to SQL = 0%-3%
- 8. #predict2014 Why Predictive Is Needed Today, most of that qualification involves teleprospecting and sales calls
- 9. The Future of B-to-B Lead Development The Role of Data Science Find clues that exist out in the world, which reliably point to qualifying criteria & buying signals you would ask the decision-maker about if you could get him/ her on #predict2014 the phone?
- 10. #predict2014 The Promise of Predictive 7 5.25 3.5 1.75 0 MAP-based PLS Satisfaction
- 11. #predict2014 4 PRINCIPLES OF PREDICTION
- 12. #predict2014 Conditions For Good Predictions Past behavior >> Future performance But… 1
- 13. #predict2014 Conditions For Good Predictions The Muni Problem 2
- 14. #predict2014 Conditions For Good Predictions Are your sales cycles snowflakes? 2
- 15. #predict2014 Conditions For Good Predictions Sirius Perspective: Predictions made by the model need to make a real difference Wins Against Replacement Player 3
- 16. #predict2014 Conditions For Good Predictions Sirius Perspective: Time is the enemy of prediction. 4
- 17. 5 BUILDING BLOCKS OF PREDICTIVE MODELS #predict2014
- 18. #predict2014 Model Use Case Starting Point Entity Predicted Source of Predictors Data Building a Model
- 19. #predict2014 Sirius Data Perspective: Prediction begins with data that is related to the outcomes that are to be predicted. Digital artifacts 1
- 20. Data Sirius Perspective: Modern data science can reach deeply into online digital artifacts to unearth evidence of business problems and buying initiatives. #predict2014 • Corporate websites • Press releases • Job postings • Application signatures 1
- 21. #predict2014 Use Cases Use Case Find new businesses that have a high propensity to buy from me 2
- 22. #predict2014 Use Cases Find new businesses that have a high propensity to buy from me 2 Score and prioritize businesses already in my database on their propensity to buy from me
- 23. #predict2014 Use Cases Score and prioritize existing customers for their propensity to buy other products and services we sell Find new businesses that have a high propensity to buy from me Score and prioritize businesses already in my database on their propensity to buy from me 2
- 24. #predict2014 Starting Point Historical Data Prospects that: • bought or not • converted or not • responded or not Didn’t Become Customers Data that clearly distinguishes the two groups 3
- 25. #predict2014 Starting Point No Historical Data Prospects that: • Have a business problem • the motivation and resources to solve it Fit the profile Don’t fit Data that clearly distinguishes the two groups 3
- 26. Sirius Perspective: What is likely to be most predictive may be at the contact or the account level, and gleaning information from both is normally important. #predict2014 Source of Predictors Top down Bottom up The best models typically include both prospect and account level predictors 4
- 27. Entity PredictedSirius Perspective: Data science can reach deep into a contact’s world to determine who is most likely to be involved in a buying cycle. #predict2014 Company Contacts Job Role Common Titles 5
- 28. #predict2014 Entity Predicted Contacts | Accounts Company Hiring Tech Ecosystem Job Role Common Titles Content Engagement Prof. Communities Social Media Interaction MAP PLS 5
- 29. #predict2014 In the organization 4 KEYS TO PREDICTIVE LEAD SCORING SUCCESS
- 30. #predict2014 4 Keys to Success with Predictive Lead Scoring • Realistic Expectations • Pilot | Champions • SLAs • Feedback | Iteration
- 32. #predict2014 Realistic Expectations If it actually goes to 11, great, but…
- 33. #predict2014 Realistic Expectations: The Nature of Prediction SiriusPerspective: Complexity is the (other) enemy of prediction, but the reality in b-to-b selling. B-to-b selling, like human behavior, is complex
- 34. #predict2014 Realistic Expectations • Experience and expression of emotions • Personality & Well-being • Spending and money • Biological basis of behavior • Time perspectives • Statistical modeling of employee performance and attrition • Propensity modeling - predictive lead scoring
- 35. Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 #predict2014 22% 23% 12% 9% 13% 20% Realistic Expectations Expectation
- 36. Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8 #predict2014 Realistic Expectations Reality Not measured
- 37. #predict2014 Realistic Expectations 50% Happiness How I feel right now. Subjective Wellbeing How I think my life is turning out.
- 39. Pilot teams should be comprised of lead recipients who can be expected to perform like typical end-users #predict2014 Pilots | Champions Pilot projects help develop realistic expectations, reveal process flaws, and develop in-role champions.
- 40. #predict2014 Pilots | Champions Lead recipients normally have compelling reasons to find defects in lead scoring, and they will.
- 41. #predict2014 Pilots | Champions A strong pilot team will help you socialize the project effectively, in terms the end-users understand and trust.
- 43. #predict2014 SLAs World’s Most Popular Lead Scoring System
- 44. #predict2014 SLAs Timing Effort SiriusPerspective: SLAs need to include accountability for both the timing and the amount or degree of follow-up.
- 45. #predict2014 Feedback | Iteration
- 46. #predict2014 Feedback | Iteration Part of every SLA should include specific, actionable feedback to marketing, enabled via technology not (just) word of mouth
- 47. Key Take-aways • Current lead scoring does not account for enough of the variance in lead conversion • Modern data science can generate proxies for questions your best salesperson would ask prospects if he/she could reach them all • It is possible to model contacts, accounts and even existing customers • Prediction requires good, relevant data related to consistent processes and outcomes • Unrealistic expectations will kill even the best modeling efforts • Pilot projects will establish realistic expectations and develop internal project champions
Notas do Editor
- So, first, I want to touch on some principles for making predictions of any sort, and I draw on my experience both in the b-to-b arena and from my academic research experience.. <<Advance slide>>
- To begin with, the best data we have to predict the future is past behavior. But there are important limitations. <<Advance slides>>
- Another way to think of it is as the Muni problem… I live in san Francisco and I take public transportation to work. Here, the subway system is notoriously unreliable with respect to schedule. Most subways run on time, because there are not many variables at play underground. They run on a schedule, and unless something breaks, there’s little to mess up the schedule. But here, the subway cars also run above ground for part of their journeys and are subject to the same traffic issues drivers face. So, there are a lot more variables at play, a lot more can go wrong, and so as any MUNI rider can tell you, these predictions are barely more than hopeful guesses… So, the more variables there are impacting an outcome, the less predictable it will be. The more those variables change, the less predictable the outcomes will be. <<Advance slide>>
- In another recent modeling effort I ran, we were interested in predicting which small business owners were more likely to buy advertising from us. As I already mentioned, we started with a data set consisting of two kinds of prospects we had tried to sell in the prior year – those who bought, and those who didn’t. Then we added data that we acquired from a third party, which told us whether those companies were listed on Yelp, Craig’s list, Angies list, etc. Those that were listed on one particular online destination and had lots of reviews were 23X more likely to have bought from us the prior year. In other words, if we had only called companies like that, we would have been 23X more successful. Most models product lift numbers in the low single digits, so that is an exceptional find. Your mileage will vary! <<Advance slide>>
- Predicting stage 4 from stage 3 is going to work better than predicting it from stage 1. More time allows for the introduction of more variables, more change, and so time is the enemy of good prediction. It is harder to predict a 2 years sales cycle than a 2 day sales cycle. <<Advance slide>>
- So, now that we understand a bit more of the mechanics of prediction, let’s talk about some of the considerations you should have in mind when thinking about whether and how to engage in a predictive lead scoring effort. <<Advance Build >>
- The use cases – when and why you would employ predictive lead scoring What you will need as a starting point Which entity you are predicting – is it a contact or a company? And then, where the predictor variables, or clues, come from <<Advance slide>>
- But, for some of you, particularly those with large addressable markets, what matters most is finding the right accounts to be marketing to, and finding them at the right time. <<Advance Build >>