The objective of a segmentation exercise is to take targetted action on each segment which would seek to alter behavior of shoppers in a desired direction. Here are 16 possible segment specific actions one can take
1) Waiving of loyalty renewal fees for active customers
2) Selective preview / samples of newly launched products
3) Invitations to specific launch events for valueable
4) Product recommendations based on past purchase behavior and segment to which the customer belongs to
5) Temporary price reductions on selected products to “valuable vulnerable” segment. Loyalty card holders who’s basket value is high but have not shopped off late
6) Bundle certain products together
7) Adapt the core product
8) Customize the product without modifying the core
9) Reconfigure the services you offer with the product
10) Modify the channels you use to go to market
11) Modify the pricing approach for the product
12) Modify the customer service levels you offer
13) Alter perceptions about you or your competitor’s product
14) Alter importance weights that customers attach to the various benefits
15) Make important benefits “table stakes”
16) Call attention to neglected or new dimensions
Friday, June 19, 2009
4 tests to test effectiveness of customer segmentation scheme
Since there are multiple ways to segment a customer population, how do we know we have arrived at the right behavorial clusters. Here are 4 simple tests to test segment for effectiveness
1) Is the segment actionable?
2) Does the segment have a critical mass of shoppers for the retailer to warrant a specific value proposition tailored to the segment?
3)Is the proposition required by each shopper cluster sufficiently different from that required by other clusters?
4)Is each cluster reachable through communication and sales channels the company can use and for communication purposes?
1) Is the segment actionable?
2) Does the segment have a critical mass of shoppers for the retailer to warrant a specific value proposition tailored to the segment?
3)Is the proposition required by each shopper cluster sufficiently different from that required by other clusters?
4)Is each cluster reachable through communication and sales channels the company can use and for communication purposes?
Segmentation variables in Retail industry
Here are some possible variables to segment customers using their behavorial profile
1) Customer Purchase Recency
The number of days which have elapsed since the customer last purchased from a store
Example : less than 30 days, less than 3 months, less than 6 months etc
2)Tenure
The number of months the customer has been a member of the loyalty card program
Some targeted campaigns could have increased loyalty subscriptions during certain periods
3)Average basket value
Average amount spent by the shopper during each visit to the store
Example : less than $ 50, 50à125 $, greater than 125 $
4)Average basket size
The number of items the shopper purchases during each visit
5)Spend dispersion
The % of spend dispersed across various categories of products like music, books, stationery items, perfumes etc
Example : 12 % on stationery, 35 % on books, 43 % on perfumes
6)Customer Purchase frequency
The number of times the customer purchases from the store in a year
Example : 10 times in a year
7)Overall spend dispersion profile
The % of deviation between spend of this customer and an average store shopper to benchmark the intensity of shopping on various categories relative to an average buyer
Example: An average Joe would spend lets say 20 % on stationery, 50 % on books and 30 % on perfumes. If David’s spend dispersion profile is 60 % on stationery, 35 % on books and 5 % on perfumes, his spend bias helps us understand his profile better relative to an average Joe.
8)Demographic spend dispersion
The % of deviation between spend of this customer and the demographic segment to which the customer belongs to
9)Range of products purchased
Out of the overall number of categories present in the store, what % of the categories has the customer purchased
10)Range of channels used
A product can be sold thru multiple channels – company owned store, franchisee store, web , phone/contact center
1) Customer Purchase Recency
The number of days which have elapsed since the customer last purchased from a store
Example : less than 30 days, less than 3 months, less than 6 months etc
2)Tenure
The number of months the customer has been a member of the loyalty card program
Some targeted campaigns could have increased loyalty subscriptions during certain periods
3)Average basket value
Average amount spent by the shopper during each visit to the store
Example : less than $ 50, 50à125 $, greater than 125 $
4)Average basket size
The number of items the shopper purchases during each visit
5)Spend dispersion
The % of spend dispersed across various categories of products like music, books, stationery items, perfumes etc
Example : 12 % on stationery, 35 % on books, 43 % on perfumes
6)Customer Purchase frequency
The number of times the customer purchases from the store in a year
Example : 10 times in a year
7)Overall spend dispersion profile
The % of deviation between spend of this customer and an average store shopper to benchmark the intensity of shopping on various categories relative to an average buyer
Example: An average Joe would spend lets say 20 % on stationery, 50 % on books and 30 % on perfumes. If David’s spend dispersion profile is 60 % on stationery, 35 % on books and 5 % on perfumes, his spend bias helps us understand his profile better relative to an average Joe.
8)Demographic spend dispersion
The % of deviation between spend of this customer and the demographic segment to which the customer belongs to
9)Range of products purchased
Out of the overall number of categories present in the store, what % of the categories has the customer purchased
10)Range of channels used
A product can be sold thru multiple channels – company owned store, franchisee store, web , phone/contact center
6 steps in customer segmentation in Retail industry
Having understood some of the business questions which can be answered using a loyalty segmentation framework lets explore the step by step process. There are about 6 broad steps as outlined below
Step-1: Identifying the specific context for shopper segmentation within the organization
Step-2: Creating a universal customer behavior record of the customer from POS, Loyalty and store profile information
Step-3: Configure the segmentation parameters like cluster count, cluster algorithm, maximum input attributes etc
Step-4: Execute the shopper segmentation model and characterize the segments from a business perspective
Step-5: Conduct workshops with business users to explore possibilities of differential treatment of customer segments
Step-6: Having decided on specific segment specific treatment strategies it is necessary to operationalize it in CRM and other campaign management systems and monitor business impact
Step-1: Identifying the specific context for shopper segmentation within the organization
Step-2: Creating a universal customer behavior record of the customer from POS, Loyalty and store profile information
Step-3: Configure the segmentation parameters like cluster count, cluster algorithm, maximum input attributes etc
Step-4: Execute the shopper segmentation model and characterize the segments from a business perspective
Step-5: Conduct workshops with business users to explore possibilities of differential treatment of customer segments
Step-6: Having decided on specific segment specific treatment strategies it is necessary to operationalize it in CRM and other campaign management systems and monitor business impact
Customer segmentation in Retail industry
Most retail outlets these days have invested heavily in Point of sales systems to capture purchase transactions of customers. Some retailers have gone a step ahead and invested in a loyalty card program and incentivising buying behavior by giving points to increase “stickiness” with the store thereby stimulating increased repeat purchase behavior. But most organizations have not used the millions of POS transaction, loyalty card data, and redemption data to understand customer behavior at a deeper level.
Segmentation is a basic first step a retail organization can undertake to understand the behavioral characteristics exhibited by the shoppers and to build a comprehensive behavioral portrait of the customers shopping at the store. Segmentation is basically the process of dividing shoppers into meaningfully distinct groups. Once shoppers are grouped into distinctive segments each group can be offered a different marketing mix plan depending on behavioral characteristics they exhibit. Segmentation is both an art and a science where behavioral niches are identified and pin pointed marketing actions are initiated as opposed to “carpet bombing” the entire customer base. It is a very selective demand stimulation strategy which the retailer can adopt. Before getting into the actual process of segmenting shoppers in your store it makes sense to get an understanding of the flavor of business questions which can be answered using a loyalty based segmentation framework based on raw POS, Store and Loyalty card data.
What are some interesting business questions which are available in the raw data but remains unanswered in most organizations
1) Do you know the behavioral portraits of your customer? Are they price sensitive? Brand conscious? Convenience shoppers? “Once a fortnight” weekend grocery shopper?
2) Which customer segments drive repeat purchase behavior?
3)Which customer segments exhibit propensities to you strategic categories and brands?
4)Do you know what the drivers of behavior are for each of your customer segments?
5)Do you know what behavior discriminates one customer segment from another?
6)How are segment memberships changing over time ? What does it tell us about our product mix, price and any competitive activity in the market where the store is located?
7)How can customer behavior portraits be used to drive customer treatment strategies and targeted outbound campaigns?
8)How can customer behavior portraits be used to drive in store experience and merchandise mix?
9)How can you use customer behavior portraits to increase the intimacy level with the customer?
Segmentation is a basic first step a retail organization can undertake to understand the behavioral characteristics exhibited by the shoppers and to build a comprehensive behavioral portrait of the customers shopping at the store. Segmentation is basically the process of dividing shoppers into meaningfully distinct groups. Once shoppers are grouped into distinctive segments each group can be offered a different marketing mix plan depending on behavioral characteristics they exhibit. Segmentation is both an art and a science where behavioral niches are identified and pin pointed marketing actions are initiated as opposed to “carpet bombing” the entire customer base. It is a very selective demand stimulation strategy which the retailer can adopt. Before getting into the actual process of segmenting shoppers in your store it makes sense to get an understanding of the flavor of business questions which can be answered using a loyalty based segmentation framework based on raw POS, Store and Loyalty card data.
What are some interesting business questions which are available in the raw data but remains unanswered in most organizations
1) Do you know the behavioral portraits of your customer? Are they price sensitive? Brand conscious? Convenience shoppers? “Once a fortnight” weekend grocery shopper?
2) Which customer segments drive repeat purchase behavior?
3)Which customer segments exhibit propensities to you strategic categories and brands?
4)Do you know what the drivers of behavior are for each of your customer segments?
5)Do you know what behavior discriminates one customer segment from another?
6)How are segment memberships changing over time ? What does it tell us about our product mix, price and any competitive activity in the market where the store is located?
7)How can customer behavior portraits be used to drive customer treatment strategies and targeted outbound campaigns?
8)How can customer behavior portraits be used to drive in store experience and merchandise mix?
9)How can you use customer behavior portraits to increase the intimacy level with the customer?
Customer sentiment analysis using text mining
Text mining can be used to cull out customer comments from user generated content like website comments to give an idea of how customers feel about the experience they had at a store or with a product. For example www.yelp.com has a lot of comments about customer experiences of a product, service etc. This can be mined say using Oracles text miner to understand keywords which are used to express a sentiment and rank their frequency. Some questions which an organisation can answer using text mining are
1) What are the top 3 keywords which occur frequently online when a sentiment is expressed ?
2) Is their any affinity between choice of keywords & product,age,progression,location etc ?
3) Which are the keywords growing fastest in the last 3 months in terms of frequency and what does that tell us about product or service we deliver and the process of delivering that experience ?
4) Is the overall sentiment trending favorably or is their reason to be concerned ?
5) How are we doing vis a vis competitition from a buzz perspective ?
6) What are the top 3 keywords which are used to express a sentiment about competition online ?
7) How can we create a strategy to respond to what we are hearing based on online buzz / feedback ?
There are few sentiment related key performance indicators which can be used to answer some of the above questions. They are
1) Sentiment velocity : Figuring out the direction of the sentiment
2) Positive Sentiment index : Ratio of positive vs negative sentiments
3) Buzz index : No of entries by source
4) Keyword : Top 5 positive and negative keywords used
5) Sentiment sales co-relation index : Is online sentiment impacting sales yet ?
6) Competitive Sentiment Position : Where are we with respect to consumer sentiments on competition
7) Volume of discussion : More discussion means more buzz, positive or negative needs to be drilled down
8) Ratio of your entries with respect to competition :
9) Competitive sentiment ratio : Ratio of your sentiment index vis a vis competitive
10) Buzz velocity : Rate at which entries are coming up
The basic process of text mining consists of the following
Step-1 : Using a data extraction adaptor to pull entries which meet a certain criteria ( date , keyword ) from a URL
Step-2 : Indexing - Splitting the sentence into token words. Ex : "I liked the Dockers trousers" is broken down into "I" , "liked", "the" etc
Step-3 : Filter stop words - Words like "I", "the" are weeded out using a filtering process
Step-4 : Stemming . Words like 'analyze', 'analysis', etc are collapsed together into one keyword
Step-5 : Generate themes and identify co-relations among keywords, between keywords and products
1) What are the top 3 keywords which occur frequently online when a sentiment is expressed ?
2) Is their any affinity between choice of keywords & product,age,progression,location etc ?
3) Which are the keywords growing fastest in the last 3 months in terms of frequency and what does that tell us about product or service we deliver and the process of delivering that experience ?
4) Is the overall sentiment trending favorably or is their reason to be concerned ?
5) How are we doing vis a vis competitition from a buzz perspective ?
6) What are the top 3 keywords which are used to express a sentiment about competition online ?
7) How can we create a strategy to respond to what we are hearing based on online buzz / feedback ?
There are few sentiment related key performance indicators which can be used to answer some of the above questions. They are
1) Sentiment velocity : Figuring out the direction of the sentiment
2) Positive Sentiment index : Ratio of positive vs negative sentiments
3) Buzz index : No of entries by source
4) Keyword : Top 5 positive and negative keywords used
5) Sentiment sales co-relation index : Is online sentiment impacting sales yet ?
6) Competitive Sentiment Position : Where are we with respect to consumer sentiments on competition
7) Volume of discussion : More discussion means more buzz, positive or negative needs to be drilled down
8) Ratio of your entries with respect to competition :
9) Competitive sentiment ratio : Ratio of your sentiment index vis a vis competitive
10) Buzz velocity : Rate at which entries are coming up
The basic process of text mining consists of the following
Step-1 : Using a data extraction adaptor to pull entries which meet a certain criteria ( date , keyword ) from a URL
Step-2 : Indexing - Splitting the sentence into token words. Ex : "I liked the Dockers trousers" is broken down into "I" , "liked", "the" etc
Step-3 : Filter stop words - Words like "I", "the" are weeded out using a filtering process
Step-4 : Stemming . Words like 'analyze', 'analysis', etc are collapsed together into one keyword
Step-5 : Generate themes and identify co-relations among keywords, between keywords and products
Tuesday, June 16, 2009
Are all business problems "modelable" statistically ?
With the increased awareness of statistics, many organisations are looking to deploy statistical models to optimize their business processes . Some examples of this are pricing models, cross sell models, forecasting models etc. But are all business problems 'modelable' ? Are their scenarios when a process cannot be optimized by statistical techniques. In this context 2 very important questions must be posed
a) Is the problem 'modelable' ?
b) Is the past a reliable indicator of future ?
For example can a statistical model succesfully be trained to predict stock market behavior ?
Given the plethora of factors which influence shareholder sentiments is it even worth attempting to do this.
Even if it is worth modeling the problem, is the past a reliable indicator of the future ?
For example if a model was trained on historical data before the economic crisis kicked in, its ability to forecast future behavior is tremendously affected
Given the fact that there are constraints in statistically modeling every business problem it is prudent to ask the 2 most important questions before starting the exercise
1. IS THE BUSINESS PROBLEM STATISTICALLY MODELABLE ?
2. EVEN IF IT IS MODELABLE IT THE PAST A RELIABLE INDICATOR OF THE FUTURE ?
a) Is the problem 'modelable' ?
b) Is the past a reliable indicator of future ?
For example can a statistical model succesfully be trained to predict stock market behavior ?
Given the plethora of factors which influence shareholder sentiments is it even worth attempting to do this.
Even if it is worth modeling the problem, is the past a reliable indicator of the future ?
For example if a model was trained on historical data before the economic crisis kicked in, its ability to forecast future behavior is tremendously affected
Given the fact that there are constraints in statistically modeling every business problem it is prudent to ask the 2 most important questions before starting the exercise
1. IS THE BUSINESS PROBLEM STATISTICALLY MODELABLE ?
2. EVEN IF IT IS MODELABLE IT THE PAST A RELIABLE INDICATOR OF THE FUTURE ?
Subscribe to:
Posts (Atom)
