JAYWING CREATIVE. DATA. SCIENCE.

Transcription

1 JAYWING CREATIVE. DATA. SCIENCE.

2 600 GREAT MINDS that bring together data, insight, planning and creative to deliver emotive and engaging customer experiences.

3 50 DATA SCIENTISTS Analysts and modellers who are commercially astute and pragmatic experts with practical, client-side experience.

4 SOURCE: THE IBM GLOBAL CMO SURVEY 2014 WHAT S WORRYING OUR CLIENTS? Social Media Complexity Big Data Channel Proliferation

5 BETTER PERFORMING CLIENTS ARE......trying to understand and communicate with individuals...determined to drive value from Big Data...focusing on relationships, not just transactions...struggling to measure ROI SOURCE: THE IBM GLOBAL CMO SURVEY 2014

6 THERE S NEVER BEEN A BETTER TIME TO BE JAYWING

7 Up close and

8 PERSONALISED MARKETING HAS ALWAYS BEEN ABOUT The Right Product Place Time Creative Salutation

9 Received over 200 marketing s Searched for and visited over 30 websites Last week Spent over three hours on Twitter Served over 70 ads and over 100 suggestions Spent over five hours on Facebook Received 60 promo tweets Received six pieces of personally addressed direct mail Served over 50 ads or suggested posts 11 pieces of unaddressed mail Received three unsolicited calls

10 Most involved some level of rudimentary personalisation many got it wrong Just how personal? Retail websites and s delivered by far the best personalisation

11 MICRO PERSONALISATION The Right Product Place Time Creative Salutation

12 MICRO PERSONALISATION The Right Product attributes Places Point in the journey Content Salutation Offer or price

13 SO WHAT can we learn from online retailers? can online retailers learn from others? should marketing teams be doing now?

14 ONLINE RETAILERS have a wealth of data SKU codes with associated product attribute data Browsing history engagement Transactional history Some customer data

15 Better retailers have built a data infrastructure to harness the power of this data

16 Sophisticated retailers have invested in analysis to develop algorithms to power their micro personalisation

17

18 BUT WHAT IF YOU RE NOT AMAZON (OR BLACKWELL S)?

19 WHAT IF YOU RE A TRAVEL COMPANY?

20 COMMERCIAL OBJECTIVES IN THE TRAVEL SECTOR Selling a route or means of travel Selling destinations Selling ancillary services

21 GETTING THE DATA IN ORDER

22 CREATING A CUSTOMER LEVEL VIEW

23 CONDUCTING THE ANALYSIS AND DELIVERING THE INSIGHT

24 IF YOU KNOW SOMEONE HAS VISITED MALAGA

25 OR AMSTERDAM

26 OR HAS BOOKED THIS COTTAGE

27 ...OR THIS ONE

28 HOW CAN THAT KNOWLEDGE BE USED TO IDENTIFY WHERE THEY MIGHT GO NEXT? Or identify other people who might want to visit there?

29 WE USE AN APPROACH CALLED ATTRIBUTE MODELLING. What destination attributes are most closely linked to customer behaviour and their demographics?

30 ATTRIBUTE MODELS PROVIDE MANY OPPORTUNITIES Rank destinations by customer appetite Target distressed inventory Assess opportunities and risk of current portfolio Target customers most interested in new inventory Evaluate new business opportunities

31 TYPICAL ATTRIBUTES

32 Industry knowledge is used to identify the key attributes that describe a series of destinations and to allocate each attribute to each destination.

33 FEATURES? Cultural? Beach? Mountain/Country? Regional Gateway? Major Attraction? Sport Adventure? ACCOMODATION? Hotels Available? Mature Self Catering? All Inclusive? Cruise Ships? Package Holiday? Independent Holiday? DURATION? Long Stay Destination Short Stay Destination TRAVEL REASONS Party Destination? Family Destination? Business Destination? Resort? COST Expensive? Long/Mid/Short Flight? Cheap Destination? PERCEPTION OF DESTINATION? Well Known? Established? Competitive?

34 Product THE TRANSLATION GRID Attribute Each attribute has a separate model where the predictive variables link to past booking and research behaviour

35 PREDICTING ATTRIBUTES FOR CUSTOMER SEGMENTS

36

37 key attributes for segment B

38 key attributes for segment B key attributes for segment E

39 PREDICTING WHO WILL GO WHERE COMBINED ATTRIBUTES

40

41 Customer with these scores will generate the most sales

42 PROPERTY EXAMPLE

43 PROPERTY EXAMPLE

44 SEGMENTING PROPERTY TYPES BASED ON SCORES Mainly families, Large luxury complex, indoor pool Mainly families, 6+ people, frequently Very large multi-family Scottish Holidays Adults only, 1-3 people, without pets Families, 3-4 people

45 ATTRIBUTE ANALYSIS PROVIDES A bank of potential decisions WHO? WHAT? WHERE? WHEN? HOW? What destination attributes are most closely linked to customer behaviour and their demographics?

46 OUR LATEST INNOVATION An approach that takes the best aspects of attribute models and collaborative filtering Identifies patterns in purchase behaviour Dynamically updates based on actual purchase behaviour Works well on large product inventories Incorporates customer demographic information Secure cloud-based Big Data processing platform

47 DECISIONING & DEPLOYMENT: DELIVERING MICRO PERSONALISATION

48 MOVING FROM MARKETING CAMPAIGNS TO ACTIVITIES

49 CAMPAIGNS Campaigns support the strategic agenda Campaigns are planned, and run at fixed points in the year Campaigns, if targeted are relevant but not necessarily timely ACTIVITIES Activities support tactical marketing An activity looks to identify the people on that day who are most likely......to be upsold...to be cross sold...to book...to lapse... to book particular distressed inventory

50 Actual Customer information Modelled customer information Offers Decision Engine Thousands of variants for each send Rule

51 THE RESULTS

52 30% uplift in cross sales purchased pre-trip Doubled Package holiday bookings in one year

53 950% increase in credit card sales Trebled sales of additional products

54 10% lower cost of sale amounting to an initial 245% return on investment

55 WHAT CAN ONLINE RETAILERS LEARN FROM OTHERS?

56

57 We helped Aviva change from a product to customer centric approach with our model office. It achieved a 67% uplift in cross sales and 47% improvement in NPS. STRICTLY CONFIDENTIAL

58 WHAT SHOULD MARKETING TEAMS BE DOING NOW?

59 Enhance product attributes Map out customer journey Build or borrow appropriate data infrastructure Engage experts in developing algorithms to drive micro personalisation Bin campaigns in favour of data driven activities Think omni-channel

60 CAN YOU AFFORD NOT TO GET PERSONAL?

61 Thank You JAYWING.COM