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Before a form: Use predictive analytics for sales

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This NADA presentation looks at how to attract leads before they fill out a form.

Publié dans : Industrie automobile

Before a form: Use predictive analytics for sales

  1. 1. Scott D. Meyer CEO/Brofounder 9 Clouds Sioux Falls, SD 855-925-6837 scott@9clouds.com 1 Your Photo Goes Here Before a Form: Predictive Analytics for Sales
  2. 2. The Marketing Cliff 2 Sources: Wisconsin State Law Library http://wsll.state.wi.us/newsletter/issue4.html, http://web.archive.org/web/20041130092642/http://www.spamfilterreview.com/spam- statistics.html, doubleclick, silverpop
  3. 3. 3 Customers don’t tell you what they want, they show you.
  4. 4. The Solution • Inbound Marketing • Predictive Analytics • Personalized Sales 4
  5. 5. The Solution • Double email open and click rates • Increase sale quota fulfillment 9.3 percent • Know customers are ready to buy before they tell you 5
  6. 6. 6 1. The problem with reactive marketing 2. Solving with predictive analytics 3. Integrating predictive analytics and personalized sales at your dealership
  7. 7. 7 The Problem With Reactive Marketing
  8. 8. Blast - Convert – Follow Up 8 Scott Meyer name@email.com Interested in a [insert car]
  9. 9. 9 This is desperate (and doesn’t work). Mass emails have half as many opens and clicks as personalized messaging.
  10. 10. 10 Source: https://flic.kr/p/MWDXK
  11. 11. 11 Buying Has Changed
  12. 12. 12 Conversion Rates by Traffic Source
  13. 13. 13 Source: https://flic.kr/p/aWjhUn
  14. 14. 14 Predictive Analytics
  15. 15. 15 Using information from existing data to determine patterns and predict future outcomes and trends. (It doesn’t tell you what will happen in the future but what is likely to happen) Source: https://flic.kr/p/2jXoKY Predictive Analytics
  16. 16. 16 Source: https://flic.kr/p/a8xcgs CRM
  17. 17. 17 • Number of days since last purchase • Mileage • Number of repair orders • Cost of last vehicle • Percentage of last vehicle paid off • Days until paid off
  18. 18. Three Steps to Predict the Future 18 1. Export CRM data 2. View key factors 3. Contact top prospects and create marketing/sales actions
  19. 19. Export CRM Data
  20. 20. DealerSocket 20
  21. 21. DealerSocket 21
  22. 22. ADP
  23. 23. ADP
  24. 24. Rey Rey
  25. 25. Rey Rey
  26. 26. Rey Rey
  27. 27. View Key Factors Time | Event | Forecast
  28. 28. Days Before Service Time-based Benchmarks Know how long before your customers visit the service bay. 1. Filter to view customers. 2. View purchase date and service date. Hide other columns. 3. Create a blank column next to purchase date and service date. 4. Create a formula to calculate the days between purchase and service. If your blank column is column D, you can use a typical subtraction formula such as: D1=B1-C1. 5. Find the mean number of days between purchase and service. Marketing action: Contact customers as they approach the average days before service.
  29. 29. Move customers into a new lease 1. Filter new customers by recent sales dates. 2. Subtract T1 lease end dates from sales dates. 3. This is the number of days/months left in the average customer’s lease when they lease a new vehicle. Marketing action: Contact customers approaching the average days left in a lease and encourage them to re-lease. Pull Ahead Leases Time-based Benchmarks
  30. 30. 1. Open your customer records in Excel. 2. Filter to only customers with sales dates (these are people who have purchased.) 3. View T1 mileage. (This is the mileage at trade-in.) 4. Calculate the T1 mean. This is your average mileage at purchase. Marketing action: Contact customers within 5,000 miles of your benchmark Average Mileage at Purchase Event-based Benchmarks
  31. 31. Calculate when customers will buy based on 1. Filter customers who have a purchase price and a buy back price. 2. Average the purchase price and the buy back price. 3. Divide buy back price by average purchase price. This is the average equity for your customers. Marketing action: Contact customers whose vehicle buy back divided by potential purchase equals your average (or ideal) equity number. Sales action: Monitor your store’s average equity. Increase equity month-over-month. Forecast action: Identify the number of customers whose buy back divided by purchase price is within 10% of your average equity number. Use this data to plan on used or CPO inventory. Vehicle Equity Forecasting Benchmarks
  32. 32. See the future of your store’s trade-in business. 1. Filter customers by the number of months left in their lease. For example, filter 72 months and write down the number of customers. Then 71,70,69, etc. 2. Crate a bar graph of how many customers are in each month. 3. Predict the future pull ahead leases based on your average pull ahead lease date. Forecast action: Predict whether you should expect a high or low number of pull ahead leases. Create incentives for sales consultants and customers based on what will happen. Competitive insight: If you are considering purchasing r consolidating with another dealership, use this data to understand the future health of the store. Lease Forecasting Forecasting Benchmarks
  33. 33. Contact Top Prospects and Create Marketing/Sales Actions
  34. 34. 34 Cars purchased: Days before first service: Days before next purchase: Average miles at trade-in: 5,084 238 346 88,179
  35. 35. 35
  36. 36. 36
  37. 37. 37
  38. 38. 38
  39. 39. Identify What Matters
  40. 40. Identify What Matters
  41. 41. Identify Who Matters
  42. 42. 42 Customers don’t tell you what they want, they show you.
  43. 43. Personalized Sales
  44. 44. “The Switch Factor” Ingredients: - Assigned salesperson - Interested model - Purchased model - Purchase date Serves: Sales management team Cooking Time: One hour
  45. 45. Accelerate Your Digital Sales Cycle Ingredients: - Number of page views per paying customer - Number of recorded social media clicks - Purchase dates Serves: Marketing team members Cooking time: 15 minutes
  46. 46. 46 Source: https://flic.kr/p/a8xcgs
  47. 47. The Solution • Double email open and click rates • Increase sale quota fulfillment 9.3 percent • Know customers are ready to buy before they tell you 47
  48. 48. Questions? 48
  49. 49. Scott D. Meyer CEO/Brofounder 9 Clouds Sioux Falls, SD 855-925-6837 scott@9clouds.com 49 Before a Form: Predictive Analytics for Sales Your Photo Goes Here Please visit the NADA University Online booth in the Expo Hall for information on accessing electronic versions of this slide presentation and the accompanying handout material, and to order the workshop video-recording.

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