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

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PREDICTIVE ANALYTICS & CUSTOMER DATA: A LOOK AT THE RETAIL INDUSTRY

PROF. GINO VAN OSSEL

[email protected]

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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

5

41%

collects

DISTANCE TO THE DEPOT?

6 0% 10% 20% 30% 40% 50% < 5 km 5 - 10 km > 10 km 48% 40% 12%

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AGENDA

1. big data?

2. pump up the volume 3. omni-channel retail 4. the single view of the

customer

5. predictive analytics 6. conclusion

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9

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who are you ?

name

address

family composition

age & gender

 tell me some more ?

 do you have pets ?

 select your hobbies

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relationship intensity ?

recency

frequency

monetary value

 what do you buy ?

 basket analysis

customer

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13

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“adepts”

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“potentials”

“unlikelies” “never evers”

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17

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AGENDA

1. big data?

customer

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21

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relationship intensity ?

recency

frequency

monetary value

 what do you buy ?

 basket analysis

 how do you buy ?

customer

data

purchasing

history

orientation

behavior

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25

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36% 55% 57% 65% 70% 70% 73%

Consulting online store reviews by consumers

Exploring the offer online before going to a store

Consulting online products reviews by consumers

Looking up online what's for sale in stores before I go shopping Comparing prices of products & services

online

Comparing prices of stores online Looking up more information on

something I saw in a folder

% somewhat to totally agree

source: “Retail in Belgium,” InSites/Vlerick, 2012 (n = +170 statement)

27 |

plus d’info sur

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AGENDA

1. big data?

customer

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mono- channel multi- channel mono- channel

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33

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“For technical & operational reasons it is unfortunately not possible to return the goods in a C&A store”

cross- channel multi- channel mono- channel

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29/01/2014 21 41 | OMNI-CHANNEL omni- channel cross- channel multi- channel mono- channel

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customer centric

commerce

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29/01/2014 23 45 growth non-food online +19.2% growth overall retail +1.8% online share of retail +18.6% growth John Lewis online +22.6% growth John Lewis click & collect

+62%

source: British Retail Consortium / KPMG – Dec. 13 (growth is vs. year ago)

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“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

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“the launch of a webshop compares to omnichannel,

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“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”

Matthew Kaness, chief strategy officer for Urban Outfitters

AGENDA

1. big data?

customer

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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

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SINGLE VIEW OF THE CUSTOMER

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SINGLE VIEW OF THE CUSTOMER

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TRACKING

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AGENDA

1. big data?

customer

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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 !!)

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from selling towards helping to buy

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from selling to helping to buy…

SERVICE 2.0

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AGENDA

1. big data?

customer

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Jamie Nordstrom head of Nordstrom Direct

“If we focus on the customer, the outcome will be right”

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customer centric

commerce

70

customer

data

purchasing

history

orientation

behavior

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big

VOLUME

big

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big

VELOCITY

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CONCLUSION

Prof. Gino Van Ossel

Retail management E-commerce & omni-channel Shopper & trade marketing Channel management

@ginovanossel

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