SDI CHICAGO World Premiere Episode

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A case study story about how to use analytics to make the most of sales data. This {fictional} company grappled with how to be productive in contacting the 80% of customers who produce 20% of revenue. What did they do?

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SDI CHICAGO World Premiere Episode

  1. 1. World Premiere Episode
  2. 2. "Questionable Contact"
  3. 3. Deep faceless voice intones solemnly …
  4. 4. Any similarities between real life and the fictional company and characters in this story are unintentional. 
  5. 5. However these positive returns of predictive analytics are absolutely possible …
  6. 6. In the offices of a $200MM B2B supplier of equipment to companies that do polls and surveys … "Questionable Contact"
  7. 7. Hearts are pounding Palms are sweaty It’s flight or fight
  8. 8. Because there’s declining revenue, a projected shortfall  in meeting fiscal year goals … what to do …
  9. 9. Sales team decides strategy <ul><li>ACTION PLAN </li></ul><ul><li>Contact each of the top 20% of customers. </li></ul><ul><li>These customers produced 80% of revenue. </li></ul><ul><li>These are best bets to bring fast sales. </li></ul>
  10. 10. Sales team decides strategy Sales team gets to work contacting customers <ul><li>ACTION PLAN </li></ul><ul><li>Contact each of the top 20% of customers. </li></ul><ul><li>These customers produced 80% of revenue. </li></ul><ul><li>These are best bets to bring fast sales. </li></ul>
  11. 11. However. Results did not hit goals. 
  12. 12. However. Results did not hit goals. 
  13. 13. This is usually the time for a commercial break .
  14. 14. One theory to explain the shortfall in meeting goals: <ul><li>EXPLANATION </li></ul><ul><ul><li>The sales team already has </li></ul></ul><ul><ul><li>close relationships with </li></ul></ul><ul><ul><li>these accounts. </li></ul></ul><ul><ul><li>They already speak </li></ul></ul><ul><ul><li>and visit with these  </li></ul></ul><ul><ul><li>customers often. </li></ul></ul><ul><ul><li>So an additional contact didn't bring much sales gain. </li></ul></ul>
  15. 15. Faced with contacting the remaining 80% of customers … the team grappled with how to best identify who to contact. Thousands of customers are in this group!
  16. 16. And if the top 20% of customers brought disappointing results, can the remaining 80% make up the shortfall? And, who to contact?
  17. 17. Contacting even a targeted list of the 80% would require substantial resources. And these customers produce just 20% of the company's revenue. How to justify this effort?
  18. 18. <ul><li>JUSTIFICATION </li></ul>???
  19. 19. Cue some music that conjures “save the day” feelings.
  20. 21. There’s a will. There must be a way. There must be a method to identify the best group of customers to contact among these thousands of customers. Let’s examine …
  21. 22. What can solve this complex problem? Predictive analytics. Perfect for this situation. Why?
  22. 23. Predictive analytics finds patterns in sales data. Patterns that only powerful analytics can see.
  23. 24. These patterns predict upcoming customer actions. Predictive analytics finds patterns among hundreds of variables in sales data that predict customers' future behavior, and identifies the customers who are most likely to buy right now. Predictive analytics are able to tell exactly who to call and when.
  24. 25. This company’s previous strategy of contacting the top 20% of customers was based on historical purchase data and review of business intelligence and sales analysis reports. The past doesn't always predict the future, particularly in changing economic conditions.
  25. 26. Plus, if you think about it, wouldn't many of the &quot;top 20%&quot; customers have once been among the &quot;bottom 80%&quot; until they were cultivated as high-value customers?
  26. 27. Plus, if you think about it, wouldn't many of the &quot;top 20%&quot; customers have once been among the &quot;bottom 80%&quot; until they were cultivated as high-value customers? So who among the 80% are next to emerge as major accounts?
  27. 28. This potential is what predictive analytics can identify. Wouldn't this team surely want to be calling upon those customers!
  28. 29. After implementing a predictive analytics retention model, calls by the sales reps brought a 10% return and $4 million in incremental revenue  during the first phase of customer contacts.
  29. 30. What's more, this required no extra sales team resources -- the model simply suggested which customers should be contacted each day.
  30. 31. The team did wish they had seen more revenue results, but adoption was not 100% -- not all sales reps fully followed the model's recommendations. Of course. That's understandable.
  31. 32. They could get skeptical about yet another campaign.
  32. 33. However this was different. It’s not an ordinary campaign.
  33. 34. However this was different. It’s not an ordinary campaign. Word is spreading among the sales reps that their colleagues who &quot;mined the gold&quot; are developing new and stronger customer relationships. They’re selling more. They’re golden.
  34. 35. Sales management intend to have sales reps continue to contact customers based on model recommendations daily, and this should further increase the annual incremental return.
  35. 36. Thus, a happy wrap-up by the end of the episode.
  36. 37. It’s not over yet though. Thought you were getting away 100% commercial-free?
  37. 38. Someone’s gotta type all this stuff. And find all the pictures. And make it look presentable.
  38. 39. Brought to you by Valgen. Sales productivity solutions using predictive analytics. Fast, easy and SaaS simple. Find out how at www.valgen.com

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