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

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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


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


Steps to Analysis

• Infrastructure• Database Cleanup, standardisation,

dedupe, etc• Data enrichment


Step 4: Analysis


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…


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


Step 5: Campaigns


Campaigns

• Creation of campaign calendars• Set up campaigns• Campaign cell creation• Control groups and seeding• Testing of media-message-offers• Response tracking


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


Customer Segmentation Models

• Low value• Average ticket size of

Rs. 5037• Usually weekend

shoppers• Transact more at shop-

in-shop format stores


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?


Store/ Branch scoring models


Management Dashboards


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

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6/16/2

007

7/16/2

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8/16/2

007

9/16/2

007

10/16

/2007

11/16

/2007

12/16

/2007

1/16/2

008

2/16/2

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3/16/2

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4/16/2

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5/16/2

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6/16/2

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7/16/2

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8/16/2

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10/16

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11/16

/2008

12/16

/2008

1/16/2

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2/16/2

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3/16/2

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Cleartrip examples


An example of segmentation


Premium airline one-off fliersX Customers. Rs. 5,500 Avg.

• Single airline• Single sector• 1-2 bookings• V low value


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)


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


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Vacation Mailer Campaign Analysis

Tapan Khopkar

this was our most successful campaign for Cleartrip.


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


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%


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


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


Creative: Late Booker


Creative: Early Booker


Creative: Generic


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%


Case: Dominos CRM Mailers


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


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


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


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”


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…”


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


Nostalgios: someone who’s been missing for a while


Loyalos: they have a favourite Pizza


Nightos: they tend to order at night


Partios: Have placed “party sized” orders


Impact

• Over 30% coupon redemptions

• Targeted customers on average did over 50% more sales than non-targeted control group of similar customers


Examples of Analytics led Marketing


Case: Pantaloons EOSS


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


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


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”


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


Highlights

Targeted Transacted % Transacted

TARGETED 25%

CONTROL GRP. 7%

TOTAL 14%

> 3 times the non targeted group


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


Case: Reporting for Levis


Filters applicable at SBU – State – City –

Store level (also Month in other pages)


Key-Indicators Sheet (with lots of filters applied)


More details below + in an attachment


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


Thank You!

www.cartesianconsulting.com+91 22 3016 3665

[email protected]

http://www.cartesianconsulting.com/

mailto:[email protected]

cartesian, the precision practice helping marketers bring precision to their initiatives

Documents

analysis slide

cartesian consulting

data enrichment slide

reports profiling

precision crm slide

marketing analytics

standard reports

service reports