Using Big Data & Analytics to Create Consumer Actionable Insights

Customer Analytics in Retail
Using 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 marketing
Cross-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.
Variables
Cluster number Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5
Number 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 1.8
Average # of purchased items per transaction 1.4 1.5 1.5 1.8 1.6
Weekend 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 15.805
2 02 44 9.407 13.731
3 02 45 9.407 12.883
4 02 48 9.407 11.291
5 02 61 9.407 10.678
6 04 01 6.109 22.132
7 04 04 6.109 16.464
8 05 01 6.108 24.252
9 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 winemaker
to become a wine connoisseur.
Professor Meng Xiaoli
(Harvard University)

Using Big Data & Analytics to Create Consumer Actionable Insights

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