Beyond Segmentation

1. Beyond Segmentation The Challenge of Contact Optimisation Mike Talbot CTO/Founder Alterian

2. Agenda • Challenges for marketers • Traditional segmentation solutions • The multi-channel explosion • An introduction to contact optimization • Strategies for implementing Alterian Contact Optimizer

3. This presentation focuses on selecting recipients but your data is vital for both tasks Our Aim as Digital Marketers Increase Profitability by Matching People to Appropriate Products By either selecting the appropriate people or by designing more appropriate products

4. “Actually I shop there too, it’s right by my tube station” We miss significant minorities Traditional Marketing Problem… “Customers for our store are women in their 20’s, single, they drive a golf enjoy beauty products and read HEAT”

5. “Actually I visit too, it’s got a great series of links and the News page is on myfavorites list” We are still missing significant minorities Traditional Marketing Problem… “Vistors to our web site are women in their 20’s, single, they drive a golf enjoy beauty products and read HEAT. They mostly arrive at the home page from Google.”

6. But our customers are absolute individuals Our Challenge as Digital Marketers We have a picture of each customer

7. Traditional Approaches to Segmentation

8. Knowing our product, select the right individuals Simple Segmentation Geo Demographic Codes Personal Details

9. Bringing in Unique Data Gold/Silver/Bronze Good/Bad Customer

10. Available Data • Outbound Marketing • Web Clicks • Products • Households • Transactions • Items • Deliveries • Response • Customers • Invoices • Demographics • Stock • Complaints • Call Centre

11. Most unique corporate data assets are not used Dark Data Summary Or Sample • Outbound Marketing • Web Clicks • Products • Households • Transactions • Items • Deliveries • Response • Customers • Invoices • Demographics • Stock • Complaints • Call Centre

12. The facts are thrown away! • What happened just before a customer defected? • Did they complain? • Did we deliver an order late? • Did our product/service fail to work? • Did we over market them? • Did we price them out of the game? • Were they a valuable individual in the first place? • Did they cost more to service than they made in revenue? • What was their potential value to our company?

13. Manual segmentation rapidly becomes irrelevant if we use all the data The Data Challenge • There are 60 million people in Britain • Your company has 200 products/variations on sale • You capture Age/Gender/Marital Status/Occupation • Customers buy using cash or credit cards • 2 (Gender) x 4 (Marital Status) x 10 (Age Banded) x 25 (Occupation cleaned up) x 200 (Products) x 10 (Recency Bands) x 2 (Payment Types) x 20 (Total order value bands) = 160m possibilities* * (if they only buy one product)

14. Build Models to Utilise More Data Personal Details = 0.1342452121 Propensity to purchase product X Transaction Details Location Details

15. The Multi Channel Explosion

16. Squandered opportunities, no planning & silos are frequently the result The Multi Channel Explosion • Lots of contact points • Lots of data • Need for more creative • Some offers only available by some channels • Lots of contact rules • Lots of confusion

17. There’s no such thing as a free lunch • All of the new channels still demand quality segmentation and offer selection • Email and Internet are cheap to deploy but consider: • Customer fatigue • Damage to the brand • Missed opportunities • We have limited opportunities to connect with the customer, we must still select the appropriate product(s) when a customer visits and the appropriate customers when we market a product

18. Lots and Lots of Channels, Products, Rules, Segments, Data…

19. Contact Optimization + + Business Rules & Constraints Product Propensities Available Campaigns

20. What is ‘contact optimisation’ • Contact optimisation applications work by processing inputs — including customer data, global business rules, contact policies, predictive model scores, business constraints, and objectives — to identify optimal solutions. Vendor approach varies, but, at a conceptual level, the technology incorporates five key activities: • All possible contacts are calculated based on model scores, business rules, and campaign data. • Contacts that meet set exclusions or suppression rules are eliminated. • The impact of each contact on business objectives and constraints is calculated. This is expressed as a measure like profit or revenue. • Mathematical techniques are used to identify the best or closest solution in order to assign each customer to the right contact(s). • The optimized lists of customers per identified contact are produced — and often handed off to a campaign management system — for execution. Understanding Contact Optimisation Technology, Suresh Vittal, 20 Sept 2006

21. What problems does Contact Optimizer solve? Getting the best contribution from a direct marketing budget while: • Meeting minimum sales targets • Applying contact density rules over time • Avoiding sending customers conflicting messages • Respecting minimum ROI rules • Living within channel capacities • Resolving which channel is best for whom

22. Scientific Marketing • Phase One: • create or use models that predict the likelihood that an individual will respond positively to a particular communication • use predictors of both the product and the channel of communication • Now you are able to choose the best individuals for a particular marketing campaign

23. Selected Customer Cheques Retail Loans ATI House Customer Score 15 0.99 0.85 1.00 0.68 0.14 0.99 20 0.99 0.42 0.98 0.01 0.11 0.99 5 0.99 0.60 0.54 0.98 0.74 0.99 3 0.98 0.20 0.09 0.25 0.46 0.98 19 0.97 0.01 0.47 0.48 0.60 0.97 12 0.84 0.05 0.90 0.90 0.45 0.84 4 0.75 0.23 0.84 0.53 0.51 0.75 14 0.69 0.55 0.50 0.92 0.20 0.69 6 0.64 0.34 0.39 0.08 0.52 0.64 17 0.59 0.07 0.71 0.23 0.07 0.59 9 0.48 0.21 0.22 0.59 0.77 0.48 18 0.46 0.94 0.82 0.32 0.33 0.46 10 0.26 0.05 0.91 0.57 0.33 0.91 13 0.39 0.57 0.91 0.60 0.33 0.91 11 0.10 0.10 0.69 0.21 0.88 0.69 16 0.33 0.83 0.57 0.57 0.93 0.57 2 0.20 0.81 0.49 0.55 0.82 0.49 1 0.12 0.43 0.34 0.15 0.92 0.92 7 0.13 0.37 0.15 0.50 0.79 0.79 8 0.14 0.31 0.35 0.84 0.20 0.20 Expected 9.37 3.57 1.91 14.85 sales Traditional Model Driven Campaign Selections Campaign oriented selections pick the ‘best’ customers for each campaign, in order of execution First Selection Second Selection Third Selection

24. Traditional Model Driven Campaign Selections • Traditionally the best recipients are picked for the campaigns you run – however, these are not necessarily the best communications for the recipient! • Contact Optimization helps you choose the recipients for whom your Campaign is the most appropriate

25. Selected Customer Cheques Retail Loans ATI House Customer Score 15 0.99 0.85 1.00 0.68 0.14 1.00 20 0.99 0.42 0.98 0.01 0.11 0.99 5 0.99 0.60 0.54 0.98 0.74 0.99 3 0.98 0.20 0.09 0.25 0.46 0.98 19 0.97 0.01 0.47 0.48 0.60 0.97 12 0.84 0.05 0.90 0.90 0.45 0.90 4 0.75 0.23 0.84 0.53 0.51 0.84 14 0.69 0.55 0.50 0.92 0.20 0.92 6 0.64 0.34 0.39 0.08 0.52 0.64 17 0.59 0.07 0.71 0.23 0.07 0.71 9 0.48 0.21 0.22 0.59 0.77 0.59 18 0.46 0.94 0.82 0.32 0.33 0.94 10 0.26 0.05 0.91 0.57 0.33 0.91 13 0.39 0.57 0.91 0.60 0.33 0.91 11 0.10 0.10 0.69 0.21 0.88 0.88 16 0.33 0.83 0.57 0.57 0.93 0.93 2 0.20 0.81 0.49 0.55 0.82 0.82 1 0.12 0.43 0.34 0.15 0.92 0.92 7 0.13 0.37 0.15 0.50 0.79 0.79 8 0.14 0.31 0.35 0.84 0.20 0.84 Expected 18.37 sales How Contact Optimization Works Selections based on optimum contribution Contact optimization picks the ‘best’ campaign for each customer It next applies the constraints you set it It then translates that result into individual selections or a campaign plan In this case the optimization improvement is 24%

26. What is the optimization based on? For each customer or prospect on your database, we calculate for each of the propositions or offers you could make them, a single score P V C - x Propensity Value Cost Using our Contact Optimizer software we can then pick the best contributing treatment, or we can just pick the one with the highest response propensity

27. Contact Optimizer • Only when you base your campaign planning on the knowledge of individual customer and prospect propensities can you optimize the result • Yet you cannot simply give every customer your best proposition for them all the time because: • There are specific product sales targets • Customers prefer a mixed diet • Outbound channels do not have infinite capacity • A need to live within a budget • The role of Contact Optimizer is to optimize return while satisfying the business constraints

28. Contact Optimizer • Allows you to simulate your business in multiple scenarios, each of which can define • How often you can contact customers about particular products or using particular channels • How many products you want to sell • What capacity your communication channels have • Mandatory communications • Compare multiple scenarios at a point in time • Project the results of strategies over time

29. Case Study – Automotive Services Company This client has a contactable base of customers and enquirers of 10m, makes 40m selections each year for direct mail, telesales, and e-mail, for 30 core campaigns

30. Selections can be driven directly into automated marketing campaigns Scenarios, Selections, and Plans Scenarios Customer Data Input Contact Optimizer Contact Optimization Plans Selections There are three distinctly different types of output from contact optimization

31. Strategy for Implementation

32. Implementation Challenges • Models are required • You can start with very crude models and refine them over time, but you do need to be able to build models and be prepared to drive segmentation using them. • The Organization must change • Once you are driving your marketing processes using optimization it may change the way in which marketing functions interact • Changes to product management • Changes to budget allocation

33. Start Simple • Rather than trying to boil the ocean use Contact Optimization to: • Choose the products to be included on a daily email newsletter • Choose between two offers being made to a customer • Contact Optimization allows you to model Business As Usual to see how big improvements are

34. Add Complexity • As you add complexity you must be aware that time will be needed to model BAU and verify new models • Start by managing more products within a single business unit • Add multiple channels of communication • Implementing CO is a process and has a methodology delivered alongside the software

35. Conclusion • Contact Optimization allows data rich businesses to maximize the use of their data assets • Contact Optimization allows marketers to model and assess the financial impact of different strategies • Contact Optimization is a practical, comprehensible solution that drives directly at profitability and customer satisfaction.