Database marketing

DATABASE MARKETING

also known as
Marketing Analytics
Invited presentation for
“BRANDWAGON”
March 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India

• ARUP GUHA
• ANALYSIS MANAGER
(INNOVATIONS)
• dunnhumby LTD

• TWO DEGREES in
ECONOMETRICS
• ZERO DEGREES in MARKETING


THE DATA SCIENCE
REVOLUTION

When the Sloan Digitial Sky Survey
started work in 2000, its telescope in
New Mexico collected MORE data in
a FEW WEEKS than in the entire
HISTORY of astronomy

Walmart, a retail giant, handles more
than 1M customer transaction
EVERY HOUR, feeding databases
estimated at more than 2.5
PETABYTES

Facebook, is home to 40B photos


SNOWBALLING DATA AND RELATED ISSUES
SPOT BUSINESS STORAGE, PRIVACY, SP
TRENDS, PREVENT URIOUS TRENDS
DISEASE, COMBAT
CRIMES
VAST
GETTING ORACLE,
VASTER IBM, SAP
INTERNALISATION
GADGETS,
MEASUREMENT MIDDLE CLASS
DATA SCIENTIST


DATA IS THE NEW ASSET OF BIG AND SMALL BUSINESS

ADVICE CUSTOMERS DECISION TO LEAVE IS MADE
WHETHER TO BUY AN BY CUSTOMERS MUCH BEFORE
AIRLINE TICKET NOW OR THEY ACTUALLY DO
WAIT FOR THE PRICE TO
COME DOWN INCREASING ARRAY OF POWERFUL
TECHNIQUES AND ALGORITHMS

MANAGERS PORED OVER
EVERY MACHINE TO MAKE IT
EFFICIENT. NOW STATISTICIANS
STOCK POPTARTS BEFORE PORE OVER THE DATA FROM
STORMS THE QUANTITATIVE ANALYST THESE MACHINES


THE AGONY OF ANALYSIS

ANALYTICS IS DATED IT SYSTEMS
IT INDUSTRY IS PERFORMING STORING “TORTURE THE DATA
PILING INTO STATISTICAL INNACURATE LONG ENOUGH AND
BUSINESS OPERATIONS FOR INFORMATION AT IT WILL CONFESS TO
INTELLIGENCE FORECASTING OR HUGE COST AND ANYTHING”
UNCOVERING SPACE USAGE
Hal Varian, Google’s Nestle sells IPCC reported only
Chief Economist CORRELATIONS 100,000 products in the influence of
predicts that the The Royal 200 countries using warming factors
job of the Shakespearian 550,000 suppliers. that are raising
quantitative Company sifted Half of its records global
analyst would be through 7 years of were obsolete or temperature
the sexiest around
IBM has invested sales data for a duplicated and without mentioning
$12B in last 4 years. marketing more than 15% the cooling
SAS, ORACLE, SAP all campaign that plain wrong forces, to make
have invested increased regular their figures look
heavily visitors by 70% more dramatic


DIVE DEEP INTO
MARKETING
ANALYTICS


EVOLUTION OF DATA BASED INSIGHTS IN MARKETING

Simulatio

Forecasti
ng
n
MARKETERS
Planning AND
Post
Analysis ADVERTISER
Descriptiv
es S ALWAYS
KNEW THAT
RESEARCH
WAS
THE MARKETING ANALYST
IMPORTANT


DESCRIPTIVES HELP SUMMARISE A DATASET AND
REVEAL UNEXPECTED PATTERNS

In 2004, Walmart peered into its massive databases and found that before a
hurricane strikes, there was a run on flashlights and batteries, as might be
expected, but also on pop tarts, a sugary American snack

product store shop Sum spend by product name for shop dates
name name spend units date corresponding to the week before hurricane
12-12-
telephone grimgold 4,000 345 2012
13-11-
sack ciplo 29,900 5214 2012
10-06-
ground beef mezol 48,57,659 245425 2012 product name spend shop date
12-12-
flashlight forte 3,84,734 976776 2012 flashlight 3,84,734 12-12-2012
04-04-
printer kong 2,84,859 43525 2012 batteries 2,84,85,905 12-12-2012
12-12-
batteries forte 2,84,85,905 523523 2012 Pop tarts 2,99,494 12-12-2012
04-02-
plugs ciplo 2,94,959 4576 2013
12-12-
poptarts brim 2,99,494 43124 2012


DESCRIPTIVES PACKED INTO A REPORT ARE A VERY
POWERFUL DECISION MAKING TOOL
Best Buy, an American electronics retailer, found that 7% of its customers
account for 43% of its sales. So, it executed a customer survey and
reorganised its stores to suit their needs and tastes
100%

SIMPLE TRANSACTIONS TABLE

CUMULATIVE SALES
Customer
ID spend

12345 4,000
43%
12346 29,900 WHO ARE MY BEST
21385 48,57,659 CUSTOMERS?
496848 3,84,734

3988476 2,84,859
7% 100%
`2367 2,84,85,905
CUMULATIVE CUSTOMERS
1345987 2,94,959

236567 2,99,494

DESCRIPTIVES HELP COMBINE PATTERNS AND FINALISE
BUSINESS STRATEGY

PRODUCT SEGMENTATION GERMINATING PRODUCT STRATEGY

BEST BEST
CUSTOMER CUSTOMER BEST CUSTOMERS LIKE BEST CUSTOMERS DONT LIKE
BEST CUSTOMERS

MOST THAT MUCH
S DON’T S LIKE It is good idea to remain Why they don’t like these products?
LIKE MUCH MOST competitive on these Is the price wrong? Is the quality
products, give frequent wrong? Can be use bundling? What
offers, display widely is happening in the outside world?
Are they niche products?

NOT BEST NOT BEST
NOT BEST CUSTOMERS NOT BEST CUSTOMERS LIKE
CUSTOMER CUSTOMER DONT LIKE MUCH MOST
S DON’T S LIKE Why are they here? Again is Are my stores inadequate in
LIKE MUCH MOST the pricing wrong? Just get rid these products? What is the
of them attitude of my best customers
towards these products? What
would be the conversion if I
focus on these products?

PURCHASE FREQUENCY


POST ANALYSIS HELPS UNDERSTAND WHAT WORKED
AND WHAT DID NOT
40% of all purchases in US supermarkets take place under some kind of
promotion. Are they Effective?
DID IT WORK?

IF WE DO IT AGAIN WILL IT
WORK AGAIN?

DID IT HAPPEN BECAUSE OF
THE EVENT OR SOME OTHER
FACTOR?

WILL IT WORK IN ALL
GEOGRAPHIES? FOR ALL
CUSTOMERS?

ANSWER
ANCOVA
ANSWER


ANALYTICS FOR PLANNING HELPS UNDERSTAND THE
DRIVERS OF OBSERVED TRENDS
Cablecom, a Swiss Telecom operator, reduced its annual attrition from 20%
to 5% by crunching numbers
FIRST, THE SECOND, THE THIRD, THE
TREND WAS DRIVER WAS STRATEGY WAS
IDENTIFIED UNCOVERED FORMULATED

25%
ARRESTING
20% CUSTOMER ATTRITION
Send special deals to
ATTRITION

15%
The X or the best customers at risk during
10%
indicator of the 7th month into their
5% attrition was found subscription
to be the number of
calls to customer
0%
0 2 4 6 8 10 12 14 16
support services. ANSWER
This fell around the SURVIVAL ANALYSIS
MONTHS

9th month for
attritors ANSWER


ANALYTICS FOR PLANNING HELPS IDENTIFY VALUABLE
TRENDS FOR MARKETING
Airline cost management improved because analytical techniques helped
uncover the best predictor that a customer would actually catch a flight he
had booked: that he had ordered a vegetarian meal
Travellers who
PROBABILITY OF FLYING order a
vegetarian meal
have a higher
probability of
actually flying
Customers who
have not ordered
a vegetarian meal
have a low
probability of Vegetaria ANSWER
actually catching n meal LOGISTIC REGRESSION
N Y
the plane ANSWER


PATTERN MINING HELPS IN REAL TIME ANALYTICS AND
MORE
Amazon, Flipkart, provide real time recommendations. Practically all sites
rearrange their links according to past browsing behaviour. Retailers give
bundled offers. Shopper’s Stop rearranged its store layout
MAGIC FORMULA If most people who have bought book B also
bought DVD A, then maybe you would like it
too?
You always go from one formal section to
another, so we will skip the links for the casuals?
P(A|B)
P(A) I am getting a higher cut from lipstick X but
lipstick Y is what most people buy. There is a
high chance of buying lipstick after mascara.
What if I package lipstick X with that popular
mascara brand?
Women have a high tendency to buy shoes after
salwar kamiz and in my store they are at the
opposite ends. Wonder how much sales uplift I
can get by bringing them next to each other?
ANSWER
ASSOCIATION ANALYSIS
ANSWER


MORE VISUAL PATTERN MINING TECHNIQUES HELP
COMMUNICATE BETTER
Apparel Retailer J. C . Penney shifted to an EDLP program while its biggest
customer group liked coupons
ALL
CUSTOM Y
ERS You Call That a
Strategyeeeeee !!!!
MALE FEMALE
40% 60% Y

<30 30+
20% 80% Y

NO
COUPONS
COUPON
USERS
ANSWER
10% 90% DECISION TREE
ANSWER


FORECASTING ALLOWS US TO CAST THE SERIES OF
INTEREST MULTI-PERIODS INTO THE FUTURE
Forecasting New Product Demand over time for future saves a fortune in
stocking and advertising costs

A relevant curvature is assumed
for the diffusion curve which is
dependant on two parameters

Based on Sales data we estimate
two spread parameters: “word of
mouth” and “advertising”

These parameters enable us to
forecast future sales for new and
ANSWER
similar products
BASS MODEL
ANSWER


ADVANCED SIMULATION TECHNIQUES ARE USED TO
SIMULATE OUTCOMES TOO COMPLICATED TO MODEL
After remaining dormant for several decades, Hush Puppy Sales suddenly
skyrocketed in early 1994 Heterogeneous
AGENTS, Complex
interactions, Dynamics

Just enter simple rules for agents
to interact
Watch complex unimagined
macro outcomes appear

Software: Netlogo

Unlike in traditional modelling you
ANSWER
don’t have to know the macro
outcome and the interaction is too
AGENT BASED MODELS
complex to model analytically
ANSWER


SOCIAL NETWORK DATA HAS GIVEN BIRTH TO
NETWORK ANALYSIS WHICH ANALYSES INTERACTION
Store redesign for a major retailer was left to individual departments to
coordinate and execute. It was taking too long Heterogeneous
AGENTS, Complex
EXPECTED ACTUAL interactions, Dynamics

Enter the past interaction data
between agents and see the
network formed and uncover
mavens, masterminds, strength of
connection

Software: Gephi
EVERYONE WAS THEY WERE
EXPECTED TO INTERACTING ANSWER
INTERACT WITH ONLY THROUGH AGENT BASED MODELS
EVERYONE ELSE THEIR ANSWER
MANAGERS


BIG DATA AND ANALYTICAL
SOFTWARE

R is open source, freely
downloadable, rapidly updated software with
cutting age techniques

Between them, SAS and SPSS has 90%
of the world analytical software market share

MATLAB is best suited to financial
analysis but it can also be used for other
data analyses

For cutting edge work, most people still
write C++ codes

Hadoop uses others computers to process
huge data for free

THANK YOU


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