turning database into profitability 1

8/3/2019 Turning Database Into Profitability 1

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TURNING DATABASE INTO PROFITABILITY

Internship Report

Amitabh Bhushan

Mirtyunjay Singh

Faculty GuideProf: Sushil Raturi

National Institute Of Fashion Technology, Mumbai

Company GuideVani Dixit

Head-Customer Relationship ManagementTrent Ltd A TATA Enterprise


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INDEX

1. Executive summary 3

2. Company Profile 4

3. Scope of project 5

4. Data Mining

Foundation of Data Mining 6

Need for Data Mining 7

Steps in Data Mining 8

Process of Data Mining 9

5. Application of Data Mining

Retail 10

Banking 11

In various other industries 12

6. Techniques of Data Mining

Market Basket Analysis 13-18

Recency Frequency Monetary (RFM) 18-27

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INDEX


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3

EXECUTIVE SUMMARY


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4

COMPANY PROFILE


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The objective is to understand & learn applications of data mining that can be used invarious industries including retail, banking, telecom, insurance etc.

The data mining techniques are very useful in different industries & in the field ofretail it is used extensively to handle & interpret the customer data. The customerdata available with the company is a very useful resource for the company. Byanalyzing & interpreting the data the company performs various tasks like,

promotion strategy, pricing, visual merchandising, product development etc.

The Indian retail sector is growing very fast. Many big as well as smallcompanies are venturing into retail. As a result they are also creating a hugedatabase of their customers. This database is very important for them becauseon the basis of this they are going to make future plans. Today its the era of database marketing. And to do the data mining they apply different techniques viz,Recency Frequency Monetary analysis, Market Basket Analysis, Clustering etc.

So by learning the above mentioned techniques we will be able to analyze thedata available with the company & come out with a suitable solution to the

problem.

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SCOPE OF PROJECT


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Foundation of data mining:

Introduction Simple statistical techniques to help make business decisions .

Data mining is the process of using raw data to infer important business

relationships.

Despite a consensus on the value of data mining, a great deal of confusion exists aboutwhat it is.

Data Mining is a collection of powerful techniques intended for analyzing large amounts

of data.

There is no single data mining approach, but rather a set of techniques that can be used

stand alone or in combination with each other.

DATA MINING helps to

Determine the behavior surrounding a particular lifecycle event.

Find other people in similar life stages and determine which customersare following similar behavior patterns.

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DATA MINING


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Need of data mining:

Changes in the Business Environment.

Databases today are huge.

Databases a growing at an unprecedented rate. Decisions must be made rapidly.

Decisions must be made with maximum knowledge.

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DATA MINING


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Steps in data mining:

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DATA MINING

Data Warehousing

Data Transformation

Data Mining

Interpretation/Evaluation

Data Selection

Data Preprocessing


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PROCESS OF DATA MINING

Build data mining database

Define Business Problem

Explore Data

Prepare Data for Modeling

Build Model

Evaluate Model

Deploy Model & Results


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RETAIL:

1) Performing basket analysis

Which items customers tend to purchase together. This knowledge can improvestocking, store layout strategies, and promotions.

2)Sales forecasting

Examining time-based patterns helps retailers make stocking decisions. If acustomer purchases an item today, when are they likely to purchase acomplementary item?

3)Database marketing Retailers can develop profiles of customers with certain behaviors, for example,

those who purchase designer labels clothing or those who attend sales. Thisinformation can be used to focus costeffective promotions.

4)Merchandise planning and allocation When retailers add new stores, they can improve merchandise planning and

allocation by examining patterns in stores with similar demographic

characteristics. Retailers can also use data mining to determine the ideal layoutfor a specific store.

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APPLICATION OF DATA MINING IN DIFFERENT FIELDS


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BANKING:

1) Card marketing By identifying customer segments, card issuers and acquirers can improve

profitability with more effective acquisition and retention programs, targetedproduct development, and customized pricing.

2) Cardholder pricing and profitability Card issuers can take advantage of data mining technology to price their

products so as to maximize profit and minimize loss of customers. Includesrisk-based pricing.

3) Fraud detection Fraud is enormously costly. By analyzing past transactions that were later

determined to be fraudulent, banks can identify patterns.

4) Predictive life-cycle management Data mining helps banks predict each customers lifetime value and to

service each segment appropriately (for example, offering special deals anddiscounts).

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0

10

20

30

40

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60

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Sales/Marketing

CustomerRetenr

BuyerBehavior

Cost/Utilization

QualityControl

OtherSales/Marketing

Inventory

Fraud

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APPLICATION OF DATA MINING ACROSS INDUSTRIES


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Technique Market Basket Analysis

Purpose

Market basket analysis, or MBA for short, is the process of analyzing transaction-leveldata to drive business value. At this level of detail, the information is very useful as itprovides the business users with direct visibility into the market basket of each of thecustomers who shopped at their store. The data becomes a window into the events as

they happened, understanding not only the quantity of the items that were purchased inthat particular basket, but how these items were bought in conjunction with each other.In turn, this capability enables advanced analytics such as:

Item affinity: Defines the likelihood of two (or more) items being purchasedtogether.

Identification of driver items: Enables the identification of the items that drivepeople to the store that always need to be in stock.

Trip classification: Analyzes the content of the basket and classifies the shopping

trip into a category: weekly grocery trip, special occasion, etc.

Store-to-store comparison: Understanding the number of baskets allows anymetric to be divided by the total number of baskets, effectively creating aconvenient and easy way to compare stores with different characteristics (unitssold per customer, revenue per transaction, number of items per basket, etc.).

How this technique is used?

In retailing, most purchases are bought on impulse. Market basket analysis gives cluesas to what a customer might have bought if the idea had occurred to them.

As a first step, therefore, market basket analysis can be used in deciding the locationand promotion of goods inside a store. If, as has been observed, purchasers of Barbiedolls have are more likely to buy candy, then high-margin candy can be placed near tothe Barbie doll display. Customers who would have bought candy with their Barbie dollshad they thought of it will now be suitably tempted.

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MARKET BASKET ANALYSIS


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But this is only the first level of analysis. Differential market basket analysis can findinteresting results and can also eliminate the problem of a potentially high volume oftrivial results.In differential analysis, we compare results between different stores, between customersin different demographic groups, between different days of the week, different seasonsof the year, etc.

If we observe that a rule holds in one store, but not in any other (or does not hold in onestore, but holds in all others), then we know that there is something interesting about thatstore. Perhaps its clientele are different, or perhaps it has organized its displays in anovel and more lucrative way. Investigating such differences may yield useful insightsthat will improve company sales.

Applications of Market Basket Analysis-

Affinity Analysis

Affinity analysis is used to determine the likelihood that a set of items will bebought together. There are natural product affinities in the market place. Forexample, it is very typical for people who buy hamburger patties to buyhamburger rolls, as well as ketchup, mustard, tomatoes and other items thatmake up the burger experience.While there are some product affinities that might seem trivial, there are someaffinities that are not very obvious.A classic example is toothpaste and tuna. It seems that people who eat tuna are

more prone to brush their teeth right after finishing their meal.So, why it is important for retailers to get a good grasp of the product affinities?This information is critical to appropriately plan for promotions because reducingthe price on some items may cause a spike on related high-affinity items withoutthe need to further promote these related items.A good understanding of the affinity of the items might lead to customer friendlyprogrammes by re-accommodating the products in the store. Already a numberof hardware stores stock items by "project" along with their regular categories.This facilitates things for beginners who are trying to do home improvementsproject themselves but are daunted by the thought of knowing what items to buyand where to find them in the store.

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Identification of Driver Items

Identifying the items that drive the traffic to the store is always a challenge. It isbecoming increasingly difficult to strike the right balance between product depthand breadth regarding inventory. With only a couple of units on the shelf, the

probability of running out of stock is very high. If a particular customer was drawnto the store for this particular item and there are none in stock, it is possible thatthis customer leaves the store immediately or makes a mental note not to comeback in the future.Identifying the driver items will also help to distinguish the main item from therelated items when doing product affinity.For example, discounting the burger patties might increase the sales of rolls,veggies and ketchup, but the reverse will not hold true as discounting theketchup will not bring additional sales.

Unlike filler items, shoppers are usually very brand sensitive when buying the

driver items. If retailers are planning to introduce private labels, this informationwill be critical to determine the initial price point and the target market for theseprivate items, otherwise they run the risk of one failed retailer who wanted todisplace the leading brand of detergents with a product of "similar" quality at thesame price point. Needless to say the results were a disaster; the national branddid not loose any market share and this retailer was eventually forced to severelydiscount their private label. It was not until someone realized that they hadpositioned the product for the wrong market and changed the market strategy toposition the product for consumers with low and moderate incomes that theprivate label started moving at a decent pace.

Trip Classification

The concept of basket or trip classification is not new, but it has receivedrenewed interest over the last couple of years as retailers struggle to determinethe format for their new stores. There is no magic behind trip classification. Itrequires a real understanding of how to properly classify the contents of thebasket to profile the shopping trip. Taking into consideration variables such astotal basket value, number of items, number of category A vs. category B items,rules can be derived that help map each of the baskets to a previously definedclassification.Understanding what kind of shopping trips a customer performs at a particularstore at a particular time is critical for planning purposes. This data provides aunique window into what is happening at the store and enables advancedapplications such as labor scheduling, product readiness and even temporarylayout changes.Let's take for example a grocery store, given that most of the grocery items havea short shelf life - it is important for the store manager to understand when theitems are going to be consumed to have enough products in stock. With some

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preliminary analysis he learns that not many people will buy beer during the earlypart of the week As a result, he calls the beer dispatcher and asks him to stopdeliveries on Monday and Tuesday and come twice a day during Saturdays(when he is always running out).Other retailers are using MBA to understand their customers shopping behavior

during a particular day of the week and at various times from morning toafternoon. A particular hardware store used MBA to analyze the demand oncertain consumer departments and found out that on certain days of the week,some employees were sitting idle while the contractor department was shortstaffed. By implementing on-demand systems (e.g., call buttons), this retailer wasable to reduce labor costs by redirecting the employees to where they wereneeded and keep an electronic eye for any customers outside of the pattern.

As a Basis for Store-to-Store Comparison

This is a very simple but effective use of MBA - count the total number of basketsfor each of the stores and use metrics that can be normalized so stores can becompared to each other.Let's take, for example, a retailer that has big stores and small stores; the bigstores have more employees, more customers and more sales than the smallerones. One day this retailer decides to create a contest across the whole chainwhere all the stores will compete against each other on dollar sales and volume.The store managers for the small stores do not want to play ball, arguing thatthey will never be able to compete with their big brothers. An analyst reviews thisconcern and finds it to be valid.Fearing a showstopper for the contest, the analyst remembered that he read anarticle about MBA where the author suggested dividing store metrics by the totalnumber of customers per store. This metric could be used to compare resultsfrom store to store independently of the size. The analyst explained the idea tothe disgruntled store managers with a practical example: Assume store A sold$540 worth of product x, and store B only sold $188. At first glance it seems thatstore A did three times better than store B. However, once you factor in MBA -you discover that store A had 400 baskets (customers) while store B only had 80customers. This changes things. If you divide the $540 for store A by 400, youget $1.35 per basket; store B divides $188 by 80 for $2.25 per basket. Store B isgetting a full dollar more per customer than store A. The store managers are notdisgruntled anymore; corporate found a way for all the stores to compete on thesame basis so every customer matters.MBA is indeed a great capability that can revolutionize the retail business as wenow know it. MBA provides an excellent way to get to know the customer andunderstand the different behaviors. This insight, in turn, can be leveraged toprovide better assortment, design a better Plano gram and devise more attractivepromotions that can lead to more traffic and profits.

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Example/ case study

Developing insight into what customers buys and in what combination enablesthe promotion of more profitable products and encourages customers to buyitems that might have otherwise been overlooked or missed. The oft-quoted

example of what can be achieved is the case of Wal-Mart where an observantstore manager discovered a strong association for many customers between abrand of babies nappies (diapers) and a brand of beer.

Analysis of purchases revealed that they were made by men, on Friday eveningsmainly between 6pm and 7pm.

After some serious thinking; the supermarket figured out the rationale was thatbecause diapers are voluminous, the wife, who in most cases made thehousehold purchases, left the diaper purchase to her husband. Being the end ofthe working week, the husband and father also wanted to get some beer in for

the weekend.

What did the supermarket do as a consequence?

They put the beer display next to the diapers - not regular beer but the premiumbrands, the more profitable ones!

The result was that the fathers buying diapers and who also usually bought beernow bought the premium beer (the up-sell), as it was so conveniently placed nextto the nappies.

Significantly, the men that did not buy beer before began to purchase beer whenit was so visible and handy - just next to the nappies (the cross-sell).

Apparently beer sales skyrocketed.

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How to code database

To do RFM analysis, all customer records must contain certain purchase history data, andbe properly coded. This is true of telephone companies, oil companies, retail stores whichuse a proprietary card, catalog mailers, insurance companies, travel and leisure, banks

and many others: probably more than half of the database marketing situations.

In each customer record you must maintain three pieces of information:

a) The most recent date that the customer has requested a change in his service,purchased a discretionary item, etc.

b) A counter for the frequency - the number of times he has made a purchase, orcontinued his service with you. For a telephone company, for example, it might be thenumber of months of continuous service; for a retail store, it would be the total number ofstore visits. This counter is incremented by one every time a purchase is made.

c) A counter for the monetary amount - the total Rupees amount the customer haspurchased from you since the beginning of time.

Constructing a Recency Code

To create a recency code, sort all the records in your database by most recent date, withthe most recent at the top and the most ancient at the bottom. Once have done this, divide

RFM (recency, frequency, monetary) analysis is a marketing technique used todetermine quantitatively which customers are the best ones by examining how recently acustomer has purchased (recency), how often they purchase (frequency), and how muchthe customer spends (monetary). RFM analysis is based on the marketing axiom that"80% of your business comes from 20% of your customers."

For more than 30 years, marketers have used an informal RFM analysis to target theircustomers. The reasoning behind RFM was simple: people who purchase once weremore likely to purchase again. With the advent of e-mail marketing campaigns andcustomer relationship management software, RFM ratings have become an importanttool. Using RFM analysis, customers are assigned a ranking number of 1,2,3,4, or 5(with 5 being highest) for each RFM parameter. The three scores together are referredto as an RFM "cell. The database is sorted to determine which customers were "thebest customers" in the past, with a cell ranking of "555" being ideal.

Although RFM analysis is a useful tool, it does have its limitations. A company must becareful not to over solicit customers with the highest rankings. Experts also cautionmarketers to remember that customers with low cell rankings should not be neglected,but instead should be cultivated to become better customers.

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RECENCY FREQUENCY MONETARY


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the database into five exactly equal parts (quintiles):

Figure 01 Building Recency Quintiles.

To the top group (most recent) assign and insert in each customer record the number 5.To the next group, a 4, etc. Everyone in your database then has a recency code of 5, 4,3, 2, or 1. Stick with exactly equal quintiles, assigned by a computer program.

If we were to do a promotion to your customer base, you fill find a very interestingbreakdown of responses by Recency Code. It will probably look something like this:

Figure 02 Response by Recency

There are very few absolutes in marketing, but one of them is this: the people most likelyto respond to a new offer are those people who have made a purchase from us mostrecently. There is something about people's psychology that makes them more likely toopen your envelope and act on what is inside if they have recently had a satisfactorytransaction with you. This is true of retail stores, software houses, automobile companies,and insurance firms. It is a universal phenomenon.

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Constructing a Frequency Code

We about constructing a frequency code in exactly the same way. Sort our entire file frommost frequent down to least frequent. Apply frequency codes to each quintile, so everyrecord now has a frequency code of 5, 4, 3, 2, or 1. Put the frequency code in customerrecords right next to the recency code. In effect, we have created a two digit code in everycustomer record, which varies from 55 (most recent and most frequent) down to 11 (mostancient and least frequent). Each group will have exactly the same number of records.

Figure 03 Responses by Frequency.

We note that the difference in response between quintile 5 on frequency is not as great asthe difference in the first and second recency quintiles. Because recency is a morepowerful predictor of customer response than frequency.

Constructing a Monetary Code

Construction of a monetary code is exactly the same as the previous two. Sort our entire

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file by rupees spent with the greatest rupees amounts at the top. Assign a 5 to the topquintile, 4 to the next, etc. If you measure your promotion response by monetary amount,you are very likely to get a response pattern that resembles this:

Figure 04 Responses by Monetary Amount.

Put monetary codes right next to the frequency codes. Everyone in database will now havea three digit code in their customer record, from 555 down to 111. There are 125 RFMCells in all. We should recalculate and revise your RFM cell codes every time that updateyour database -- typically once a month. Provide a space in your customer record forprevious RFM cell so you can measure how people have moved during the past month.You may want to keep track of their RFM cell even earlier -- six months ago, for example.

first step is to select your 40,000 customers from your database using an Nth. This is acomputer program that automatically selects every Nth record for your test. To determinean Nth, divide the number of records in your test group into the number of records indatabase universe. If have 800,000 in your customer database, dividing by 40,000 is 20.That means that select every 20th record. You will pick the 1st, the 21st, the 41st, etc.When do this, the 40,000 tests will be an exact statistical replica of the main database --no matter in which order the main database had been sorted (alphabetical, by zip code,

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customer number, etc.). There will be an exact statistical sample of the RFM codes aswell. There will be 125 different RFM cells, and each one will have exactly the samenumber of customers in it.

Let us assume that you make an offer to these 40,000 customers. Each business isdifferent. For our example, let us assume that you offer something that costs about Rs120,and that the net variable profit from a successful sale is Rs35. The cost of the mailing(including creative, printing, personalization, and postage) is Rs0.62 per piece. Here iswhat the response to your offer might look like:

Response to 40,000 Test Mailing

Cell Position RFM Cell Number MailedNumber

ResponsesResponse Rate

A B C D E

1 555 320 31 9.69%2 554 320 30 9.38%

3 553 320 28 8.75%

4 552 320 20 6.25%

5 551 320 19 5.94%

6 545 320 26 8.13%

7 544 320 20 6.25%

8 543 320 18 5.63%

9 542 320 16 5.00%

10 541 320 12 3.75%

11 535 320 14 4.38%

12 534 320 10 3.13%

13 533 320 10 3.13%

14 532 320 9 2.81%

15 531 320 7 2.19%

16 525 320 13 4.06%

17 524 320 10 3.13%

18 523 320 8 2.50%

19 522 320 8 2.50%

20 521 320 7 2.19%

These are only the first 20 cells. There are 125 in all. Lower cells usually have lower

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response rates. Those towards the bottom may have no response at all.

Why are all cells of exactly equal size? Why are not some slightly larger or smaller thanother cells? Because of the method by which cells are created. Each quintile (R, F, and M)is exactly 20% of the entire file. It follows that each of the 125 cells will be exactly 1/125 ofthe entire file. If the coding is done correctly, there will be no larger or smaller cells.

Cell Personality

There are many other uses of RFM. If, for example, you have decided to create specialcustomer segments which will get special treatment (gold card holders, for example) RFMis an ideal way to find out who should go into your top category.

Each RFM cell has a personality of its own. All new customers, for example, enter as511's. They are the most recent, but usually the least frequent and have the lowest dollaramount. After they enter, they can either move up or down, based on their subsequent

behavior. It is a good idea to track where people were last month. You can set goals foreach cell, to get new customers to advance in their second month from a 511 to a 512, forexample, rather than moving down to a 411.

The lowest group, 111's may not be customers at all. You may want to archive the lowestcells, taking them off your database, after trying a reactivation mailing on them. To keepthem on your database year after year can just waste your money, and their time. Don'tdrop your 155's without a struggle, however. These are great people on whom you shouldlavish special attention.

APPLICATIONS OF RFM

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RFM is an indispensable tool in retail business and can be used in the following. Itassists company in understanding:

Consumers

Consumer perceptions of a brand Product concepts Advertising creative Price points Promotional channels The required message

Qualitative Research

Customers perception of the company Potential customers awareness of the company Gathering market information/perceptions from key influencers

Future expectations/requirements Advertising opportunities

Quantitative & Statistical Analysis

Consumer awareness of the company/brand Buying habits Brand tracking

Brands

Developing brand positioning Determining where brand should be seen Communicating the brand internally and externally Researching brand and your potential brand positioning Liaising with designers for artwork and design Policing the brand Advertising campaign awareness

Marketing

Implementing promotional/communication plans Creating and updating databases for existing customers Reviewing & updating website content Identifying trends in the marketplace

Sales

Development of sales strategy Draft mail/email sales letters

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Planning of sales activity Identification of further sales prospects Key sales support for your team

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RFM EXAMPLE

The Bookbinders Book Club

The Bookbinders Book Club sells specialty books and selected other

merchandise through direct marketing. New members are acquired by advertising inspecialty magazines, newspapers and TV. After joining, members receive regularmailings offering new titles and, occasionally, related merchandise. Right from its start,Bookbinders made a strategic decision to build and maintain a detailed database aboutits club members containing all the relevant information about their customers.Initially, Bookbinders mailed each offer to all its members. However, as Bookbinders hasgrown, the cost of mailing offers to the full customer list has grown as well. In an effort toimprove profitability and the return on his marketing dollars, Stan Lawton, Bookbindersmarketing director, was eager to assess the effectiveness of database marketingtechniques. Because of direct marketers long history of success with RFM and itsrelative ease of use compared with more sophisticated modeling approaches, Standecided to test the RFM approach.

Stan proposes to conduct live market tests, involving a random sample ofcustomers from the database, for new book titles in order to analyze customers'response and calibrate a response model for the new book offering. The responsemodel's results will then be used to "score" the remaining customers (i.e. those notselected for the test) and to select which customers to mail the offer to. Bookbinderscustomer database provides a complete record of purchasing history for each customer.This includes how long they have been a customer, the specific titles ordered andsummary totals by category such as cooking or childrens books. Of direct relevance forRFM analysis, Bookbinders keeps a record of the number of months since lastpurchase, the total number of purchases made as well as the total dollars spent by eachcustomer. With these three pieces of information for each customer, Stan can easily testthe RFM approach.

The Art History of Florence Offer

Stan conducted a test to verify RFM. He had a random sample of 50,000customers drawn from Bookbinders customer database. By selecting a random sampleof customers, Stan could be confident that all types of customers would be represented:both recent and not-so-recent purchasers, frequent and infrequent purchasers andcustomers spanning a range of total dollars spent. This random sample of customerswas mailed an offer to purchase The Art History of Florence and their response eitherpurchase or no purchase was recorded. Now for each customer in the test, Stan knewhis or her values for the recency, frequency and monetary variables at the time the offerwas mailed and he knew the response. (Note that the recency, frequency and monetaryvalues are at the time the offer was sent and, for this analysis, have not been updatedfor those who did buy The Art History of Florence. Had they been updated then all thebuyers would fall into the most recent category! What we want to know is whetherrecency (and frequency and monetary) values at the time the offer was mailedare usefulfor predicting who will respond.) Stans objective is to use the results of the test mailingto identify which groups of customers are more likely to respond. Then, for the rolloutmailing, he will only target customers who fit the profile of those more likely to respond.By carefully targeting which customers to mail the offer to, Stan hopes to reach the

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majority of the responders while significantly reducing costs by not mailing tothose with a low likelihood of responding. A secondary benefit is thatcustomers with little interest in a title such as The Art History of Florence willnot get the mailing which, had they received it, may leave them wonderingwhy they are getting such unappealing offers.

The ResultsStan began by comparing those who bought The Art History of

Florence to those who didnt in terms of the recency, frequency andmonetary variables. Exhibit 1 reports the averages for number of monthssince last purchase (recency), total number of purchases (frequency) andtotal dollars spent (monetary) for the two groups of customers: those who didbuy The Art History of Florence and those who did not. The results areconsistent with what proponents of RFM analysis would predict. Those whodid respond to the offer was more recent purchasers (8.6 months comparedwith 12.7), more frequent purchasers (5.2 purchases compared with 3.8) andhad spent slightly more in total ($234 versus $206).

turning database into profitability 1

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