Channels, operations and marketing

1. Channels, operations and marketing • Connecting to drive customer experience M@ KuperholzBrisbane, 10 May 2012

2. In God we trust…

3. In God we trust……everyone else bring data

4. The sexiest job in the next decade will be…

5. The sexiest job in the next decade will be…… statistician

6. The Customer Experience

7. Between the dawn of civilisation and 2003, we only created five exabytes of information…

8. Between the dawn of civilisation and 2003, we only created five exabytes of information……now we create that amount in less than two days

9. Between the dawn of civilisation and 2003, we only created five exabytes of information……now we create that amount in less than two days …by 2020 every hour

10. Data is an asset

11. BIG DATA

12. big data

13. Data is an asset

14. Huge amounts ofData is an asset

15. Data is an asset Win : Win

16. Data is an asset which can improve customer experience • Acquire • Store and access • Prepare and structure • Analyse • Validate and interpret • Report and implement • What? • When? • Where? • How? • Who? • Why? • What next?

17. Channels • Physical presence • Live events • Mail • Telephone • Web • Email • Social • Online chat • Mobile

18. Operations • Human resources • Customer care / contact centre • Production • Supply chain / logistics • Store placement / layout • Delivery

19. Marketing • Word of mouth • In store promotions • Sponsorship • Billboards / print • Mail • Cinema • Radio • Television • Web • Email • Web 2.0 • Social • Mobile

20. Enriching with external data • Census / ABS • Property details • Household expenditure / net worth • Geodemographics / Segmentation • Loyalty Programs • Data aggregators • Market research

21. 2012 : GaSoLoMo

22. Socialytics • “The Big Shift” and increasing social capital • Social network analytics • Unstructured and structured data

23. Data is an asset which can improve customer experience • Acquire • Store and access • Prepare and structure • Analyse • Validate and interpret • Report and implement • What? • When? • Where? • How? • Who? • Why? • What next?

24. Text analytics

25. Social Network Analysis

26. Reporting, statistical analysis and geospacial

27. Artificial Intelligence and other advanced techniques

28. MAKES PURCHASE

29. ACTIVE CUSTOMER MAKES PURCHASE

30. ACTIVE CUSTOMER IS MALE MAKES PURCHASE

31. BRAND PERCEPTION HOURS USED CATEGORY SPEND

32. ACTIVE CUSTOMER IS MALE MAKES PURCHASE BRAND PERCEPTION HOURS USED CATEGORY SPEND

33. ACTIVE CUSTOMER IS MALE MAKES PURCHASE BRAND PERCEPTION HOURS USED CATEGORY SPEND

34. Segmentation • Holistic and simultaneous consideration of all attributes • Assumption free • Hierarchical clustering : GRANULARITY aligned with capacity to act

35. Self Organising Map (Artificial Intelligence) videos • Technical • http://bit.ly/saxaHK • Business • http://bit.ly/Kla4Z5

36. Data is an asset which can improve customer experience • Acquire • Store and access • Prepare and structure • Analyse • Validate and interpret • Report and implement • What? • When? • Where? • How? • Who? • Why? • What next?

37. Test and Learn

38. Data is an asset which can improve customer experience • Acquire • Store and access • Prepare and structure • Analyse • Validate and interpret • Report and implement • What? • When? • Where? • How? • Who? • Why? • What next?

39. Large Financial Institution : target marketing customer case studyProfile, Segment, Target : 10,000,000 customers based on 17,000 metrics derived from data assets relating to Channels, Operations and Marketing • Deliver 17 targeted lists and models for Bank • Retention • Upsell • Cross-sell • Each model consisted of a marketing objective, primarily a targeted cross-sell product or service

40. Granular segmentation assigned customers into 40 segments C39 C36 C30 C22 C25 C18 C34 C23 C7 C6 C26 C35 C4 C11 C10 C20 C38 C40 C17 C37 C24 C15 C3 C9 C13 C16 C1 C29 C2 C21 C8 C33 C32 C12 C5 C27 C14 C19 C31 C28

41. Tenure, gender, age • Less affluent customers tend to be found along the bottom part of the SOM model. • New and young customers are predominantly found in the bottom right hand side of the SOM model. • Two main typologies of TD WaterHouse customers

42. Product holdings Investments Demand Loans • Generally the customers with investments are high value, older and have a longer tenure – but there are pockets of younger/newer customers who also have investment products. Mortgages Line of Credit Visa • Visa product held by both high and low profit customers as well customers with both deep and very shallow banking relationships.

43. Risk and value Beacon Score NSF Fees Other FI Utilization • Highly utilized, high NSF fees, high risk customers Total average chequing balance - 6 month average Total Money In Total Money Out • Investment customers with high total money-in balances • Older and long tenure customers tend to have higher than average chequing account balances

44. Changes over time Ezyweb transactions as a percentage of total channel usage (Historical 6 months) Future log increase in product holdings Future log increase in investment balance • This cluster of customers have the number of product holdings increase over the one year period. They are also younger customers who have some investments. • The usage of Ezyweb as a percentage of total channel usage increase over the year for this cluster. This top cluster is majority TDWH customers. There are several smaller clusters as well. • These are a cluster of older customers who are taking money out of their investment accounts probably to spend during retirement. Ezyweb transactions as a percentage of total channel usage (Future 6 months) Future log increase in chequing balance Future log increase in Visa balance • New customers tend to increase their chequing balance the most, the customers who reduce their balance tend to attrite • There are strong clusters of customers whose visa balance has increased over the year.

45. All Offers A2, Z3, E2, E4 – - Cluster 1 A2, Z3, E1, E4 - Cluster 1 A3, Z3, E1, E4 – Cluster 1 E4, E1 –Cluster 1 A1, D1, E1, E3 - Cluster 1 E1 –Cluster1 A1, A3, E1, E3 – Cluster 1 A1,A2,A4,Z3,E1,E2,E3,E4 – Cluster 1 A2, A3, E2, E3 – Cluster 1 A1 – Cluster 3 E3 – Cluster 1 Z3, E1, E4 – Cluster 1 Z3, E1, E4 –Cluster 2 A3, B1, E2 - Cluster 1 A1, A4, Z3, E2 – Cluster 1 A1, A3, E1 – Cluster 1 A1, A4, Z3 – Cluster 1 A1, Z3, E1, E2, E3 - Cluster 3 A1, E1, E2 Cluster 1 B1 –Cluster 2 A1, E1, E3 – Cluster 1 A1 – Cluster 1 A1, Z3 - Cluster 1 E4 – Cluster 2 A1, A3 - Cluster 1 A2, Z3, E1, E3 – Cluster 1 Z1, Z2, E1 – Cluster 1 A1, A2, Z3, E3 - Cluster 1 B1, Z1, Z2, E1 - Cluster 1 B1, Z1, Z2 – Cluster 2 A1, A3, E1, E3 –- Cluster 2 B1, Z1, Z2 - Cluster 1 Z1 – Cluster 2 A1, A4 – Cluster 1 B1, Z1, Z2 - Cluster 2 E1 – Cluster 3 Z1, Z2 – Cluster 1 A1, A3 –- Cluster 2 A1, D1 – Cluster 1 Z1, Z2 – Cluster 2 A1 –Cluster 2 A1 – Cluster 4 D1 – Cluster 2 A4 – Cluster 2 B1 – Cluster 1 D1 – Cluster 1 B1, D1 – Cluster 1 Z1, Z2, E1, E3, E4 – Cluster 1

46. Large Global FMCG Organisation – Promotional, marketing and pricing Analyse & Interpret Design Interventions Execute Campaign • We identified segments of opportunity • Segments identified based on sales, response to marketing, product, pricing and brand performance • We then characterised the segments defining their distinguishing attributes, example: Problem Statement SituationPricing / marketing strategy to maximise return on promotion spend GoalMake decisive marketing, promotion and pricing choices in order to grow ChallengeWhat drives customer purchases? Where are we over-investing? Where should we invest? How? • We helped embed the model into their marketing and sponsorship plan • We have embedded the model with the marketing and strategy teams who refresh the model on a regular basis to measure performance. Segment 5 Segment 4 • Lower sales • Year on year decline • Categories 1, 2, 3 over represented • Low volume per household • High advertising activity (TV and radio) with lower incremental return • Weak competitor brand positioning • Higher income earners • Above average membership of Segment Y • Reasonable sized sales • Year on year growth in value and volume • Generic split of categories • High volume per household • Low advertising activity on television with high return • Competitor brand positioning growing • Lower income earners • Above average membership of Segment X Granular customer insights We modelled the purchase patterns of 10,000 stores with ~ 700 attributes simultaneously to identify 46 granular segments, with a focus on return on promotional spend. We also overlayed promotions, marketing, pricing, TV, radio, online, social media, weather data and more to identify and predict effects of marketing Interventions were Identified to reallocate marketing spend, channel mix and pricing adjustments • Withdraw promotional spend from X segments with, low market share and no clear intervention strategy to rectify • Pricing adjustments in areas with low price elasticity • Channel and promotional brand and sponsorship mix tuned based on granular segment insights A market of no excuses – how do we negotiate and drive improved distribution? How best to improve Marketing ROI? An unforeseen opportunity– how should we engage the distribution channel and end consumer to capitalise on the growth?

47. Airline targeted marketing Granular Insight Strategic Alignment Agile Execution & Measurement • Setting Growth Priorities • We aligned market growth priorities to the organisation’s overall strategic objectives • Prioritising which segments to target for maximum growth • Quantifying growth goals against segment priorities Problem Statement Situation: Incumbent under threat Goal: Increased share of wallet Challenge: Who is the true customer? What is their travel behaviour? How can we improve loyalty? We Defined the Intervention Program We defined a program of personalised campaigns for customer acquisition over a 12 month program Granular customer insights 2.7m customers, 30 months of history to trace customer behaviour: Domestic, international, leisure business; corporate, SME, retail Product (fare, route, Valet, QC) Value (revenue, price sensitivity)Booking (ltime, channel, checkin) Points usageCampaign response 5. We refresh, test & refine We refresh the segmentation to measure program effectiveness, respond to shifts in behaviour & re-align for sustained growth

48. Telcommunications company marketing strategy for digital adoption Granular Insight Agile Strategy Targeted Execution & Measurement • Defining adoption strategy • We prioritised key segments & defined relevant strategies for online adoption: • We articulated the value proposition that best aligned with customer needs & behaviour; • We tied this to quantified benefits for lowering cost to serve. Problem Statement Situation: Increased competition Goal: Improve service whilst lowering cost to serve Challenge: How do we best meed the needs of customers to drive online adoption? We designed the Interventions We improved specific business processes and online user experience flows to support key market interventions for adoption Granular customer insights We developed a granular segmentation model to understand the long-tail of cross-channel customer behaviour: across all aspects – transaction, product, channel, attitudinal & demographic 5. We designed targeted campaigns for execution We drove into the segmentation model to select customer lists and execute targeted cross-channel campaigns for adoption

49. Questions?

50. Channels, operations and marketing • Connecting to drive customer experience M@ KuperholzBrisbane, 10 May 2012