PREDICTIVE ANALYTICS & CUSTOMER DATA: A LOOK AT THE RETAIL INDUSTRY PROF. GINO VAN OSSEL GINO.VANOSSEL@VLERICK.COM 2 1
3 e-commerce in Belgium, Comeos, June 2013 4 2
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
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
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
relationship intensity? recency frequency monetary value what do you buy? basket analysis 11 customer data purchasing history 12 6
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adepts 20 4 potentials unlikelies never evers 30 / 400 16 8
17 18 9
19 AGENDA 1. big data? 2. pump up the volume 3. omni-channel retail 4. the single view of the customer 5. predictive analytics 20 6. conclusion 10
John.Doe@gmail.com 21 22 11
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
25 26 13
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 www.carrefour.eu/tv 28 14
29 AGENDA 1. big data? 2. pump up the volume 3. omni-channel retail 4. the single view of the customer 5. predictive analytics 30 6. conclusion 15
monochannel monochannel multichannel 16
33 34 Vlerick Business School 17
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For technical & operational reasons it is unfortunately not possible to return the goods in a C&A store 37 monochannel multichannel crosschannel 19
39 40 20
41 OMNI-CHANNEL monochannel multichannel crosschannel omnichannel 21
customer centric commerce 44 22
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. 46 23
the launch of a webshop compares to omnichannel, like a wedding 48 to life as a married couple 24
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 50 6. conclusion 25
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
SINGLE VIEW OF THE CUSTOMER 53 SINGLE VIEW OF THE CUSTOMER 54 27
55 56 28
57 TRACKING 58 29
59 IDENTIFYING 60 30
AGENDA 1. big data? 2. pump up the volume 3. omni-channel retail 4. the single view of the customer 5. predictive analytics 61 6. conclusion 62 31
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
from selling to helping to buy from selling towards helping to buy 65 SERVICE 2.0 66 33
AGENDA 1. big data? 2. pump up the volume 3. omni-channel retail 4. the single view of the customer 5. predictive analytics 67 6. conclusion If we focus on the customer, the outcome will be right Jamie Nordstrom head of Nordstrom Direct 34
customer centric commerce customer data purchasing history orientation behavior 70 35
big VOLUME 71 big VARIETY 72 36
big VELOCITY 73 74 37
75 CONCLUSION Prof. Gino Van Ossel Retail management E-commerce & omni-channel Shopper & trade marketing Channel management gino.vanossel@vlerick.com @ginovanossel 76 38
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