PREDICTIVE ANALYTICS & CUSTOMER DATA: A LOOK AT THE RETAIL INDUSTRY PROF. GINO VAN OSSEL

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1 PREDICTIVE ANALYTICS & CUSTOMER DATA: A LOOK AT THE RETAIL INDUSTRY PROF. GINO VAN OSSEL 2 1

2 3 e-commerce in Belgium, Comeos, June

3 VALUE PROPOSITION market test: collection of heavy items 50% discount on cost of delivery 1 week time to come & collect normal delivery time 5 working days only for who s living <15 km from the depot 41% collects 5 DISTANCE TO THE DEPOT? 50% 40% 48% 40% 30% 20% 12% 10% 0% < 5 km 5-10 km > 10 km 6 3

4 AGENDA 1. big data? 2. pump up the volume 3. omni-channel retail 4. the single view of the customer 5. predictive analytics 7 6. conclusion 8 4

5 9 who are you? name address family composition age & gender tell me some more? do you have pets? select your hobbies tick special interests 10 5

6 relationship intensity? recency frequency monetary value what do you buy? basket analysis 11 customer data purchasing history 12 6

7

8 adepts 20 4 potentials unlikelies never evers 30 /

9

10 19 AGENDA 1. big data? 2. pump up the volume 3. omni-channel retail 4. the single view of the customer 5. predictive analytics conclusion 10

11

12 relationship intensity? recency frequency monetary value what do you buy? basket analysis how do you buy? 23 customer data purchasing history orientation behavior 24 12

13

14 Looking up more information on something I saw in a folder 73% Comparing prices of stores online 70% Comparing prices of products & services online Looking up online what's for sale in stores before I go shopping Consulting online products reviews by consumers Exploring the offer online before going to a store 70% 65% 57% 55% Consulting online store reviews by consumers 36% % somewhat to totally agree 27 source: Retail in Belgium, InSites/Vlerick, 2012 (n = +170 statement) plus d info sur

15 29 AGENDA 1. big data? 2. pump up the volume 3. omni-channel retail 4. the single view of the customer 5. predictive analytics conclusion 15

16 monochannel monochannel multichannel 16

17 33 34 Vlerick Business School 17

18

19 For technical & operational reasons it is unfortunately not possible to return the goods in a C&A store 37 monochannel multichannel crosschannel 19

20

21 41 OMNI-CHANNEL monochannel multichannel crosschannel omnichannel 21

22 customer centric commerce 44 22

23 growth non-food online growth overall retail online share of retail growth John Lewis online growth John Lewis click & collect +19.2% +1.8% +18.6% +22.6% +62% 45 source: British Retail Consortium / KPMG Dec. 13 (growth is vs. year ago) We are seeing about 34% of (pick-up) visits translating into additional sales in shop and that number is growing exponentially at the moment. It s typically or increasingly for purchases that the customer didn t think they would make. So it is quite out with whatever they were going to collect

24 the launch of a webshop compares to omnichannel, like a wedding 48 to life as a married couple 24

25 We started to think about ourselves as a pure play e-commerce company that happened to have really great, differentiated stores in 500 locations around the world 49 Matthew Kaness, chief strategy officer for Urban Outfitters AGENDA 1. big data? 2. pump up the volume 3. omni-channel retail 4. the single view of the customer 5. predictive analytics conclusion 25

26 SINGLE VIEW OF THE CUSTOMER: FROM SINGLE TO MULTIPLE TOUCHPOINTS 51 Source: The New Multi-screen World: Understanding Cross-platform Consumer Behavior, Google, August 2012 (n=1611 US consumers; online survey + 24 hr. log) SINGLE VIEW OF THE CUSTOMER: FROM SINGLE TO MULTIPLE TOUCHPOINTS 52 Source: The New Multi-screen World: Understanding Cross-platform Consumer Behavior, Google, August 2012 (n=1611 US consumers; online survey + 24 hr. log) 26

27 SINGLE VIEW OF THE CUSTOMER 53 SINGLE VIEW OF THE CUSTOMER 54 27

28

29 57 TRACKING 58 29

30 59 IDENTIFYING 60 30

31 AGENDA 1. big data? 2. pump up the volume 3. omni-channel retail 4. the single view of the customer 5. predictive analytics conclusion 62 31

32 63 SERVICE 2.0 pre-digital sales associate: knows little about a lot ignorant consumer: knows nothing digital sales associate: knows little about a lot informed consumer: knows a lot about little result conversion: increasing interactions: less & shorter satisfaction: decreasing (customer & staff!!) 64 32

33 from selling to helping to buy from selling towards helping to buy 65 SERVICE

34 AGENDA 1. big data? 2. pump up the volume 3. omni-channel retail 4. the single view of the customer 5. predictive analytics conclusion If we focus on the customer, the outcome will be right Jamie Nordstrom head of Nordstrom Direct 34

35 customer centric commerce customer data purchasing history orientation behavior 70 35

36 big VOLUME 71 big VARIETY 72 36

37 big VELOCITY

38 75 CONCLUSION Prof. Gino Van Ossel Retail management E-commerce & omni-channel Shopper & trade marketing Channel 76 38

39 CUSTOMISED MANAGEMENT PROGRAMMES UNLEASH THE TRUE POTENTIAL OF YOUR PEOPLE & YOUR ORGANISATION The Macintosh Academy allows my international key retail management to be ahead of the curve. Vlerick designed a programme that really broadened our retail perspective. Frank De Moor Chairman, Macintosh Retail Group Customised management programmes are impactful learning solutions meeting your specific business needs. The retail & consumer goods industry is one of Vlerick Business School s 4 strategic focus industries facts and figures: 121 customised programmes for 84 different companies participants in 14 countries worldwide above 80 % satisfaction rates Some of our Retail & Consumer Goods customers: Want to know more? Contact Eva Pelgrims

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