using big data & analytics to create consumer actionable insights

Customer Analytics in RetailUsing Big Data & Analytics to Create Consumer Actionable Insights

Presented by Olivier Maugain at ad:tech China 2015 Shanghai, 16th April 2015

Customer Analytics Applied – Example

Using Big Data & Analytics to Create Consumer Actionable Insights 2

3

All brands, products and customers appearing in this case study are fictitious.

Any resemblance to real brands and products, living or dead, is purely coincidental.

Background

Challenges

Slowing growth rates(other brands in the group were growing

much faster)

Lack of deep understanding of customer needs and behaviours(no analytical processes or

tools in place)

Dependence on skincare category(although the brand is global leader in make-up products)

No cross- or up-sell strategy

(CRM activities stuck in the customer acquisition mode)

The client was facing a number of challenges, as the brand was underperforming compared to the other brands in the group.


Proposed solution: Next-best-action marketingCross-selling campaign promoting make-up products and accessories to loyal skincare customers


Objective: Personalisation of communication……By providing the right message…

…With the right

offer

…Through the right

channel…

…At the right time…

…To the right person…


R

F

M 1

5

5

Area 555: The VIP Corner

Area 111:→ Ask yourself whether to keep them

5

Area 155:→ Send reminder (promotion, information, etc.)

Area 515:→ Design a loyalty plan for them

Area 551:→Cross-sell / up-sell products and services


Step 1: Select the best target – Who shall we go after?Search for the most desirable customers, in terms of loyalty, value, etc. via RFM (Recency-Frequency-Monetary) analysis

Outcomes:• Generation of scores for each individual in the customer base (about 100’000 records)• Ranking of the customers based on the score• Separation of the “ideal” customers from the rest (via definition of a threshold) -> from 100k to 14k targets

Step 2: Zero in the right segment– Which groups of customers are we targeting?


A cluster analysis (two-step algorithm) helped us create a limited number of customer groups that were distinct enough to be treated individually.

VariablesCluster number Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5Number of cases in the cluster 3,465 2,946 3,331 1,972 2,267 % within the cluster 24.8% 21.1% 23.8% 14.1% 16.2%% within the total population 4.8% 4.0% 4.6% 2.7% 3.1%Total money spent (RMB) 8,704 7,554 10,833 8,856 9,110 Total # of different items ever purchased 2.6 2.8 3.0 3.1 2.7# of transactions 1.7 4.3 2.4 2.5 1.8Average # of purchased items per transaction 1.4 1.5 1.5 1.8 1.6Weekend shoopers (%) 0% 73% 100% 0% 0%Daytime (9am - 6pm) shoppers % 99% 32% 85% 97% 2%Masks customers (%) 35% 2% 10% 1% 32%Cleansers customers (%) 12% 4% 37% 3% 27%Moisturizers customers (%) 4% 74% 11% 89% 30%

Skin care only


Step 2: Zero in the right segment– Which groups of customers are we targeting?

Outcomes:• Generation of 5 separate groups, or customer personas, with specific characteristics • Precise description of each group both in quantitative and qualitative terms• Customisation of marketing activities and campaigns for each of these 5 segments (instead of “one-

size-fits-all”) → definition of a “campaign theme” for each persona

Persona 1 Persona 2 Persona 3 Persona 4 Persona 5

Spending

Product diversity

Avg transaction size

Shopping day Week day WE WE Week day Week day

Shopping time Day time - Day time Day time Evening

Preferred product Various (masks top)

Mostly moisturizers

Various (cleansers top)

Mostly moisturizers Various

Label “Cautious” Housewives

Moisturizer-focused

White-collars

Wealthy weekend shoppers

Moisturizer-dedicated

Housewives

Office ladies, frequent buyers

Highest

Lowest

Step 3: Assess propensity to respond for each target – Is Channel A the right way to communicate with Customer X?


Using classification techniques (decision trees), we profiled each individual in the customer base, and were able to predict their inclination to redeem an MMS coupon.


Outcomes:• Generation of 50+ such profiles (business rules) for all the clusters identified before…• …resulting in the selection of about 8’500 targets for the next campaign (MMS coupon for cross-selling

make-up products)

Step 3: Assess propensity to respond for each target – Is Channel A the right way to communicate with Customer X?

Model accuracy of 73.252%

Interpretation (simplified):• 73.2% of all the customers who purchased more than 3 skincare products, more than 1 Cleanser and more than 5

masks in the past, responded to an MMS coupon campaign in the past.• Accordingly, we can assume that 73.2% of customers with this profile would respond a similar campaign. We should

include them into the next campaign.


Outcomes:• For each target, selection of the make-up product line (foundation, eye shadow, lipstick, etc.) most likely to be

purchased.

Step 4: Identify loyalty reinforcing products– Which category should we pitch to the selected targets?

Interpretation (example):• Among all the customers who bought skincare and make-up products in the past, Lines 02, 12, 44, 45 and 48 are

often purchased together.• Accordingly, Lines 12,44, 45 and 48 can be considered as loyalty reinforcing lines (as 02, masks, is already defined

as the loyalty generating line), and constitute good candidates for cross-selling offers.

Association techniques (Market Basket Analysis, Sequence Analysis) were employed to determine which products were often purchased together, during the same transaction or sequentially.

Rule Antecedent Consequent Support % Confidence %

1 02 12 9.407 15.8052 02 44 9.407 13.7313 02 45 9.407 12.8834 02 48 9.407 11.2915 02 61 9.407 10.6786 04 01 6.109 22.1327 04 04 6.109 16.4648 05 01 6.108 24.2529 05 12 6.108 17.053


Outcomes:• Lift of the model: 2.70 (=13.5% / 5%)• Cost savings: 300’000 CNY• Additional value generated: 1’192’500 CNY• ROI: 3.7x

Outcome

Current campaignIn the past (example)

¡ Number of ads sent: 15'000

¡ Cost of each ad: 100 CNY

¡ Cost of the campaign: 1’500’000 CNY

¡ Value of a positive response: 3’000 CNY (= lifetime value of a multi-category customer)

¡ Response rate: 5.0%

¡ Number of positive responses: 750

¡ Value generated from the campaign: 2’250’000 CNY

¡ ROI of the campaign: 50.0%

¡ Number of ads sent: 8’500 (about 60.8% of the selected population of “best” customers)

¡ Cost of each ad: 100 CNY (unchanged)

¡ Cost of the campaign: 1’200’000 CNY (850’000 + 350’000 for modelling and other technical costs)

¡ Value of a positive response: 3’000 CNY (unchanged)

¡ Response rate: 13.5%

¡ Number of positive responses: 1,148

¡ Value generated from the campaign: 3’442’000 CNY

¡ ROI of the campaign: 186.9%

Concluding words…

You don’t need to become a winemakerto become a wine connoisseur.


Professor Meng Xiaoli(Harvard University)

using big data & analytics to create consumer actionable insights

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