O.R. within Consumer Marketing - from simulation to optimisation

1. O.R. within Consumer Marketing - from simulation to optimisation Chris Doel – Head of Marketing Analytics, Virgin Media

2. What will be covered… The Operational Research Society is currently debating how far to include Analytics within its remit • Provide some examples of scientific approaches I have encountered within what is branded analytics • Mathematical Modelling • Simulation • Clustering • Regression analysis • Optimisation

3. Virgin Media – The UK’s leading entertainment & communications company The first company in the UK to offer TV, Broadband, Phone and Mobile - all from one place • Formed in February 2007 from a merger of ntl, Telewest & Virgin Mobile • UK’s largest fibre optic cable network • Around 8 million customers across Cable, National and Mobile offerings • Clear leadership in Broadband • Market-leading multi-product take-up

4. 1 Forecasting Call Centre Demand using Mathematical Modelling

5. Call centre forecasting Providing call number forecasts to aid daily roster planning in the call centres • VM undertakes hundreds of different campaigns each month • We could forecast from the top down using time series techniques or from the bottom up using mathematical modelling • The call numbers are affected by factors such as: • The number of marketing contacts made • What channel the contact is made through • The effectiveness of the contact (e.g. what incentives are being offered, the size of the letter…). • The timing of the contacts with seasonality and day of week

6. Modelling the time delay of response Response curves • Calls received follow a skewed bell curve from the date of contact • We build up separate response curves for direct mail, door drop, text and email. • When building the response curves we have to account for the fact that not all contacts happen on the same day

7. Modelling the day of week response Dealing with Multiple Effects • We have to deconstruct the response curves using a least squares method • We also have to account for the fact that customers call in at different rates by day of week

8. The model All campaigns have to be aggregated in their effects • For each day the number of calls expected is the sum of the expected responses from each campaign for that day • Factors affecting overall response rate such as incentives and letter format estimated in their effect • Legacy calls from previous months are also accounted for

9. Performance Forecast accuracy is acceptable… Daily view

10. 2 Simulation of Call Centre Performance

11. Issues simulation can address Call centres have targets to meet on call delay times and the percentage of abandoned calls whilst meeting budget constraints and ensuring the staff are motivated Cost Service Quality Employee Satisfaction

12. Call centre model Example simplified call centre structure - stochastic system with multiple queues Queues Agents Random call duration Sales Random arrival Automatic Call Distribution Calls Service Disconnect Leakage Leakage

13. Issues simulation can address Business issues have to investigated within these remits • What would be the effect on customer service if we amalgamate two call centres into one? • Can we meet the target on call delay if the number of lines is reduced by X? • What will be the effect on call delays and abandoned calls when a new offer is introduced that lengthens the time each call requires? • Can we optimise shift patterns to improve response times? • Can we prioritise high value customers in the queues without large adverse impacts on the remainder?

14. Analysts role The analyst has an instrumental role in this process: • Consult with the business on what issues should be investigated • Create an appropriate design for the simulation • Agent skill definitions • Queuing logic • Agent shifts and activities • Parameterise the model • Estimate call volumes and determine stochastic distributions and parameter values • Validate the model • Perform what-if analysis to address the issues • Communicate the results to influence decisions

15. 3 Segmenting the Customer and Prospect Bases using Clustering

16. What might a segmentation look like? Established Families - High Income Young Families Baby Boomers Established Families - Low Income Young Singles Older Retired Older Singles

17. What data may be available for such a clustering? Bought in demographic data (mostly derived from the census) • Household composition, age, household income, etc.. Customer Usage data • Internet • BB uploads and downloads amounts by time of day • TV • Relative likelihood of having Pay TV • Relative likelihood of having PVR • Relative likelihood of having HD • Relative likelihood of having premium TV services (e.g. Sport & Movies) • Phone • Fixed line usage and spend • Mobile voice, SMS and data usage and spend • Main reasons for use of these services • Time spent on those services (focusing on on-line social network behaviour) • Usage by time of day and day of week split by voice, SMS, MMS and data • Relatively likelihood of owning different mobile phone types

18. How is the clustering structured? • N dimensional, centroid based least distance approach • Aim to have 6-10 segments • Make sure no segment is less than 5% of the base. • Use profiling to understand the segments

19. Illustrative results Higher Established Families - High Income Young Singles Older Singles Digital Engagement Baby Boomers Older Retired Lower Value for Money Motivation Quality Time

20. Illustrative results Higher Meet segment needs over time as motivation changes and customer lifetime value increases Build products and services to retain and cross-sell into these segments Established Families - High Income Young Singles Older Singles Digital Engagement Baby Boomers Older Retired Lower Value for Money Quality Time Motivation

21. Other uses for segmentations

22. 4 Understanding the Effectiveness of Marketing using Econometrics

23. The marketing feedback loop • If we know who we are contacting, we can set up a feedback loop to track the effectiveness of our campaigns • If we know the cost of our campaigns and the revenue/margin generated through linked sales we can work out return on investment • However, this loop breaks down for TV, radio, outdoor and press media 23

24. Marketing based econometrics The application of statistical and mathematical methods to help quantify the effect that different types of internal business activities (e.g. spend on DM, product pricing) and external factors (e.g. competitor activity, consumer confidence) have on key company objectives. With these relationships defined, a process of optimising marketing spend can be undertaken to more efficiently meet our targets.

25. Econometrics inputs Competitor Products and Pricing Economic variables Sales VM Pricing & Offers Direct Marketing Availability and Delivery Public Relations/Events Advertising 25

26. The model This can be formulated as a multiple linear regression • Independent variables • Spend/contact volume on direct mail • Price differentials with competitors • Market saturation • Views of TV advertising • Dependent variable • Sales • Calls • Customer satisfaction • Disconnections (churn)

27. Functional forms • Diminishing returns Returns rise at increasing rate as campaign builds towards critical mass Returns start to diminish as reach of advertising is exhausted and potential to generate returns starts to diminish Optimal Spend Range

28. Modelling persistent effects • Ad stocks 0% Memory 28

29. We use an algorithm to test all possible memory processes on advertising between 0 and 100% 40% Memory 29

30. We use an algorithm to test all possible memory processes on advertising between 0 and 100% 60% Memory 30

31. When the correct memory process is applied to the model, there is no longer a consistent over-prediction 80% Memory No pattern in residuals 31

32. Note that if the memory process applied is too high the model will not fit correctly either 99% Memory 32

33. Building the model • ~150 data points for the outcome measure (weekly measures over 3 years) • Over 1000 independent variables to be assessed • Interaction effects investigated • Variable statistical significance and r2 used to direct modelling Treat conclusions with fair degree of caution and verify findings through testing 33

34. Illustrative results • Large unexplained element • Clear effects of seasonality • Large r2 • Relative contributions of marketing apparent but unverified 34

35. 5 Optimising Customer Contacts

36. Campaign?(hundreds) Channel?(multiple) Timing?(any day/time) Customer?(thousands/millions) Offer “A” Minimum 32,000 leads Transaction trigger Preference? Offer “B” Saturation? 100,000 mail volume £450,000 budget End of Term Product “C” Wrong timing? Contact frequency? Up-sell opportunity Product “D” 25,000 volume Competitor product renewal Channel usage? Action “E” Missed opportunity? 10% ROI Channel preference? Recent contact Action “F” 1,200 sales The customer management dilemma • How to maximise return across a universe of customer contact plans whilst managing day to day business constraints?

37. Variety of Offers

38. The goal Max Value = P*V-c Business Needs Relevance constraints Channel constraints Budget constraints Creative constraints Frequency constraints Sequence constraints Solution Optimum communication mix (who, with what, when and how) Contact Optimisation The problem Millions of customers Hundreds of offers Multiple channels Any time Any combination Any sequence

39. Analysts role The analyst has an instrumental role in this process: • Consult with the business on what issues should be investigated • Create an appropriate inputs for the optimisation • Logistic regression models to estimate response probabilities • Incremental value models for the value of a response • Define contact rules that are in use in the business • Setup the model within our optimisation software MarketSwitch. • Validate the model • Perform what-if analysis to address the issues • Communicate the results to influence decisions

40. MarketSwitch A powerful optimisation tool • Has the capability of working with millions of customers and dozens of potential offers • Will only return feasible solutions • Uses genetic algorithms to search through the solution space • Usually returns results within minutes but can run on samples of the customer set to speed up what-if analysis • Can run with mixed objectives to define maximum efficient frontiers - for example when comparing max. sales vs. max. profit

41. Final Remarks Will Rogers "Let advertisers spend the same amount of money improving their product that they do on advertising and they wouldn't have to advertise it."