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

Cartesian, the Precision Practice

Helping marketers bring precision to their initiatives

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

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%

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)

Year Month Mar to May'08, Aug'08, Nov'08

Publication HT

Creative

Category

10/16/20

11/16/20

12/16/20

10/16/20

11/16/20

12/16/20

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

10 Segment SummaryH

Vacation Mailer Campaign Analysis

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: ClustersSegment Logic ActionPizza 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 ActionAggregators 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 PredictedDecile 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

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

tRFM 50-50-50 2185Recent (R50), low value & freq (F,M = 10) 3628Lapsed (R=10), high value (M=50) 400Very frequent (F=50), Low value (M=10), R=30,50 41Birthday in next 30 days 2,961High RFM, Birthday in next 30 days 347Redeemer in May-Jun 2008 187Recent Enrolments with Birthday in next 30 days 149High value lapsers with birthday in next 30 days 37Members who have been active every month for 12 months 56Members who have been active every month with a tenure of 9 or more months

High No. of Store Brands (>=4) 4,252

Thank You!

www.cartesianconsulting.com+91 22 3016 3665

smittal@cartesianconsulting.com

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

cartesian consulting

reports profiling

standard reports

service reports

business userssales

precision marketinghow

marketing strategy

regression models

Documents

astell cartesian ethics

cartesian products

combinatory logic and cartesian closed...

cartesian genetic programming

cartesian coordinates

cartesian consciousness reconsidered...

iai cartesian ns_lsa_isa_catalog

cartesian cut cell

cartesian differential categories

dilation (cartesian)

pp100 industrial collaborative cartesian...

cartesian tensors

dennett cartesian theater

cartesian coordinates newsletter - spring 2016 · 4...

non-cartesian cnc cmm · xtreme cnc cmm provides a robust...

13.cartesian coordinates

cartesian coordination

cartesian rationalism

cartesian components

cartesian irf download