Cartesian, the Precision Practice Helping marketers bring precision to their initiatives.
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Transcript of Cartesian, the Precision Practice Helping marketers bring precision to their initiatives.
Cartesian, the Precision Practice
Helping marketers bring precision to their initiatives
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 3
Precision Marketing Bringing back the left brain into Marketing
• How do you target, recognize patterns, find clusters, optimize• How do you explain what happened, and then use that insight to predict what will
happen• How do you identify, retain and build relationships with your best customers
=
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 4
Our work in marketing analytics
Forward LookingRetrospective
Exploratory
Control
• Dashboards• KPIs• Balanced scorecards
Real Time
Targeted• Root cause analysis• Hypothesis testing• Hypothesis driven surveys
• Predictive customer analytics• Modeling/ forecasting
• Performance projections• Target setting
Descriptive Customer analysis• Scoring• Segmentation & profiling• Pattern recognition
• Exception reports• Fraud detection
Data exploration• Data mining• Dimensional analysis• Data discovery• Regression
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 5
Our solution
Precision Marketing Infrastructure
Insight ready systems
Marketing Insight
Campaign design and management
Precision Marketing/ CRM/ Loyalty consultancy
Consult Implement Manage
How to set up a platform for precision marketing
How to capture and enrich data, make it insight ready, processes to follow
How to analyze the data and get insights that are actionable
How to design campaigns and implement them for ROI
How to create a marketing strategy for precision/ CRM
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 6
Steps to Analysis
• Infrastructure• Database Cleanup, standardisation,
dedupe, etc• Data enrichment
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 7
Step 4: Analysis
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 8
Three approaches to analyticsExploratory analysis, Data discovery
Statistical Model build
Analysts and business users browse data in a discovery mode, create and test hypothesis on the fly.
Who fits this target? How many such customer do I have? When is the last time…
MIS and Reports
Profiling, cross tabs, Venn diagramsNeeds high speed environment that allows flexible browsing of data
Business defines an objective that can be modeled. Statisticians select appropriate modeling technique, select variables, build and validate models, score databases.
Who will respond? Forecast sales. Segment customers.
Regression models, Logistic, segmentation models, forecasting models, market basket.
Given some recurring needs of business, standard reports are created and automated.
Standard reports can be batch-processed and output sent to Excel for easy use by business users
Sales reports, service reports, model-wise growth reports, loyalty points earn and burn reports, upgrade and down grade reports…
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 9
Nature of Analysis
• Predictive Models: Which of my customers is most likely to purchase
• Segmentation: What clusters exist that I can reach out to
• Hypothesis tests: What is the impact of a personalized mailer with a strong offer?
• Market basket: What is the next best product to cross sell/ up sell?
• Profiling: What profile of customers are likely to show this behavior
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 10
Step 5: Campaigns
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 11
Campaigns
• Creation of campaign calendars• Set up campaigns• Campaign cell creation• Control groups and seeding• Testing of media-message-offers• Response tracking
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 12
4 Major ProcessesCreate Unique Customer Master
Create customer one view
ETL, Merge files, cleanup standardize and dedupe, improve capture on ongoing basis
Pull in data from all available sources to create one view, aggregates, expressions, decodes to enrich view.
Profiling, segmentation, market basket, predictive models, response models, adoption analysis, store scoring, KPI setting…
Analysis and Insight Campaigns, ROI
Campaign management, design, control groups, multi-stage, multi-channel, response tracking, ROI measures
IT resourcesSQL/ Oracle Db, Harmony Software, Automated ETL to Alterian
AnalystsBusiness rules, scripting to automate expression creation. Alterian platform.
StatisticiansModelers, KXEN software in Alterian, SPSS where needed.
ConsultantsLiaise with all business groups, take briefs and design campaigns, support execution
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 13
Customer Segmentation Models
• Low value• Average ticket size of
Rs. 5037• Usually weekend
shoppers• Transact more at shop-
in-shop format stores
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 14
Adoption Analysis
1 mth 1-2 mths 2-3 mths 3-6 mths 6mth-1yr 1-1.5yrs 1.5-2yrs >2 yrs0%
5%
10%
15%
20%
25%
30%
35%
40%
Adoption CurveEarly
adopters
Early Majority
LateMajority LaggardsInnovators
Where are the innovators coming from?
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 15
Store/ Branch scoring models
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 16
Management Dashboards
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 17
Media Effectiveness Models
Mumbai
Circulation (in Thousands)
129.22; 207
Hue Colour
Weekday Mon, Wed, Thu, Fri
Ad Size in Sq. cm)
40
Year Month Mar to May'08, Aug'08, Nov'08
Publication HT
Creative
Category
5/16/2
007
6/16/2
007
7/16/2
007
8/16/2
007
9/16/2
007
10/16
/2007
11/16
/2007
12/16
/2007
1/16/2
008
2/16/2
008
3/16/2
008
4/16/2
008
5/16/2
008
6/16/2
008
7/16/2
008
8/16/2
008
9/16/2
008
10/16
/2008
11/16
/2008
12/16
/2008
1/16/2
009
2/16/2
009
3/16/2
009
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 18
Cleartrip examples
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 19
An example of segmentation
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 20
Premium airline one-off fliersX Customers. Rs. 5,500 Avg.
• Single airline• Single sector• 1-2 bookings• V low value
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 21
High ticket size, return bookersY Customers. Rs. 23,000 Avg.
• High ticket size of Rs. 15,000
• Mostly Return bookings – 75% avg.
• Long journeys, 7 days between first and last flight
• More than 1 segment• High incidence of intl
fliers (434)
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 22
Single budget airline, 1 segmentZ Customers. Rs. 10,400 Avg.
• Single budget airline flown on
• Single sector flown• Low incidence of
premium airlines• Low incidence of
return bookings
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 23
10 Segment SummaryH
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Copyright © 2010 Cartesian Consulting Pvt. Ltd. 24
Vacation Mailer Campaign Analysis
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 25
Recap- Targeted Mailers
Objectives• To increase repeat business and reduce
dependency on cash back and discounts • To use past purchase data to send
relevant, personalized communication
ExecutionLast week of August
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 26
Data Overview
• The data considered for the campaign was air bookings for travel during Diwali period in the previous year
• On average customers who traveled in Diwali and booked early (August/September) saved around 20%
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 27
Campaign Segments
• Vacation mailer targeted towards customers in the following segments:– Traveled during festive season last year but did
not save– Traveled during festive season last year and
saved– Did not travel during festive season last year– Registered non-users
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 28
Messaging Strategy
• For the customers who traveled during festive season the previous year, the savings they made (or failed to make) were highlighted
• For everyone else, average savings made by people traveling to specific segments were highlighted
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 29
Creative: Late Booker
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 30
Creative: Early Booker
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 31
Creative: Generic
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 32
Response
• Of those who had traveled and saved: 6.5% conversion amongst opens
• OF those who had traveled but not saved last year: Over 11% conversion amongst opens
• Typical conversion amongst opens is about 1 to 1.5%
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 33
Case: Dominos CRM Mailers
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 34
The brief
• Get Dominos customers to order more pizza
• Use our knowledge of their consumption habits to strike a chord and push up response
• Final metric to be monitored: Coupon redemptions
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 35
Approach
• Who to target: Score entire customer database with probability of response
• How many to target: Estimate most profitable depth-of-file to reach out to
• Segment: Create clusters of targets based on our understanding of their Pizza ordering behavior and personalize the communication and offer
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 36
Step 1: Model Build
• 3 different predictive models built and then blended to arrive at a list of best prospects to target
• Logistic regression used to build models
Past campaign base
Likely to respond
Model Build 1
Any couponrespondent
Coupon userprofile
Model Build 2
Orderpropensity
Likely toorder
Model Build 3
Score 1 Score 2 Score 3
Wtd. Score
Target list
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 37
Step 3: Clusters
Segment Logic Action
Pizza only Customer who have only ever bought Pizza (no side order/ garlic bread/ beverage)
Offer on fee garlic bread/ side orders to encourage trial
Big Spenders Customer who have recently done a single order of over Rs. X
Offer on “party” or big orders
Pasta Trialist – BPO increase
Customers who have tried Pasta and not decreased in BPO
Pasta offer
Pasta Trialist – BPO decrease
Customers who have tried Pasta and decreased in BPO as did not order Pizza
Strong Pizza offer
No Pasta Customers who have ordered recently but not tried pasta
Pasta offer as add on to Pizza
Coupon crazy Very high number of coupon transactions. Min 5 orders in last 6 months, at least 80% coupon based
Build your own coupon. 2 extra coupons.
Rip Van Winkles Customers who recently woke up after a long slumber. Min 6 months between last transaction and second last transaction.
Welcome back offer + on “what’s new”
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 38
Variables/ Segments to consider
Segment Logic Action
Aggregators Customers who show “aggregator” behavior – frequently order 3 or more Pizza Qty.
Coupons driving large order sizes (% off on value etc., 2 free pastas on order of Rs. 600 and above etc.)
Need Guidance Customers who haven’t ordered what we consider are “hot menus”
Special “hand picked by our experts” offer
Past CRM responder
Anyone who was targeted last time and used even one coupon
Copy to reference last time usage and encourage more
Past heavy responder
Anyone who’s used 4 or more of the last campaign coupons
Free gift (?) and new coupon set. Reference in the copy to “you seem to have enjoyed the coupons we sent you last time…”
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 39
Creative routeBy Black Swan Life – the ideas generation agency
• A final list of 12 clusters were created
• Each cluster was assigned a “Pizza Sign” based on their Pizza behavior
• The message and offer were tailored to the cluster and put across in a highly engaging piece of communication
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 40
Nostalgios: someone who’s been missing for a while
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 41
Loyalos: they have a favourite Pizza
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 42
Nightos: they tend to order at night
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 43
Partios: Have placed “party sized” orders
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 44
Impact
• Over 30% coupon redemptions
• Targeted customers on average did over 50% more sales than non-targeted control group of similar customers
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 45
Examples of Analytics led Marketing
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 46
Case: Pantaloons EOSS
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 47
Case: Pantaloons EOSS
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 48
Situation
• Pantaloons, one of India’s largest apparel retailers has a bi-annual EOSS (End Of Season Sale) cycle
• Pantaloons also runs a loyalty program Pantaloons Green Card for its best customers
• Green Card members get invited to an exclusive EOSS “preview”
• The task was to select Green Card members to send a direct mailer to such that– Those with best propensity to shop during the EOSS
would respond– Cost of mailers is high so there was a need to optimize
budgets
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 49
Approach
• A predictive model was built using various behavioral variables. Over 50 variables were used, of which after iterations about 14 were deemed important
• Logistic regression, using Robust regression principles
• Outputs of: A list of who to target, how many to target, and information on why targeting them makes sense
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 50
Outputs and implementation
• Direct mailers sent to 85,000 members selected from the model
• Control group held out to test response
• Two versions of mailing ensued, the more expensive DM and the cheaper non-personalized “Inland letter”
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 51
Actual Vs Predicted
Decile Predicted % Targeted Transacted Actual %
20 44%-100% 56%
19 33%-44% 37%
18 28%-33% 30%
17 22%-28% 26%
16 21%-22% 21%
15 20%-21% 18%
14 13%-20% 15%
13 12%-13% 12%
12 11%-12% 10%
11 8%-11% 13%
10 5%-8% 0%
Total 25%
In line with predictions
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 52
Highlights
Targeted Transacted % Transacted
TARGETED 25%
CONTROL GRP. 7%
TOTAL 14%
> 3 times the non targeted group
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 53
Campaign details
Type of DM Targeted Transacted % Transacted
DM 40%
Inland 17%
Total 25%
Tier Targeted Transacted % transacted1 Star 17%3 Star 34%5 Star 47%7 Star 59%Total 25%
DM Vs Inland
Tier Wise
Higher than Avg
Higher than Avg
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 54
Case: Reporting for Levis
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 55
Filters applicable at SBU – State – City –
Store level (also Month in other pages)
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 56
Key-Indicators Sheet (with lots of filters applied)
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 57
More details below + in an attachment
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 58
Selections for ActionSegment Coun
t
RFM 50-50-50 2185
Recent (R50), low value & freq (F,M = 10) 3628
Lapsed (R=10), high value (M=50) 400
Very frequent (F=50), Low value (M=10), R=30,50 41
Birthday in next 30 days 2,961
High RFM, Birthday in next 30 days 347
Redeemer in May-Jun 2008 187
Recent Enrolments with Birthday in next 30 days 149
High value lapsers with birthday in next 30 days 37
Members who have been active every month for 12 months 56
Members who have been active every month with a tenure of 9 or more months
296
High No. of Store Brands (>=4) 4,252
Copyright © 2010 Cartesian Consulting Pvt. Ltd. 59
Thank You!
www.cartesianconsulting.com+91 22 3016 3665