One of the simplest steps most oth can do is to segment their customers behavior data ( purchase, payments, claims, complaints, clickstream behavior etc )to find out what natural groupings exist and how this can be leveraged to drive segment specific interventions.
In the context of customers behavior segmentation a lot has been discussed around
a) CUSTOMER DATA DIMENSION : Experiences in the quality of the input customer data
- missing zip codes
- wrong customer classifications
- match merge customer identifiers
- enrichening customer profiles with credit rating and demographic information from experian etc
- identify derived behavior metrics
- rfm etc
b) STATISTICAL PROCESS DIMENSION : If the data hygiene dimension is stabilized, then the focus shifts to the segmentation process -
- Do we use K means cluster vs SOM/Kohonen method vs Hierarchial clustering etc ?
- How many clusters are ideal ?
- How many variables should we use to segment ?
- Which variables should we use ?
- How do we characterise the clusters ?
Assuming we get the data dimension and the segmentation process right, there needs to be a lot more conversation around ACTIONS to undertaken once this customer behavior segments are created. There have been situations where the customer behavior data was segmented but on account of poor 'actionability' framework the whole exercise collapsed.
Increasingly we are seeing 'ACTION' post the segmentation process being the weak link in the whole process.
Here are some actions we have seen working from a customer segmentation intervention point of view ( It has a predominantly retail and travel flavor :-) ...
1) Bundling multiple products and offering a discount to selective segments exhibiting certain behavior
2) Building multiple cross sell models to increase wallet share from certain segments
3) Reallocating marketing $ to run different kinds of campaigns for each behavorial segments.
4) Decisions on what kind of promotional stimuli to use for each behavorial segment. Some may respond well to a temporary price reduction, others may respond well to a coupon, some segments may respond well to a gift off on a product while others maybe open to an exclusive 'in store' event.
5) Each of these behavorial segments could be treated by different call center agents . For example the most valuable customers can get routed to the call center agents who are ranked high on performance . The cross sell customers can get routed to the agents who have a good track record of cross sell conversions etc
6) Channel decisions: Low value customers can probably be moved to the internet channel and the high value customers can have a dedicated relationship manager to offer personalized experience
7) Personalized gift with name printed could be offered to selective segments.
8) Steeper discount to customers who have generated value in other lines of products but have not tried out a new line of product
9) Personalized online portal for customers exhibiting certain behavior with customized recommendations
Since ,Data + segmentation + Segment intervention = Successful segmentation exercise
Wanted to understand from others their experential inputs on what segmentation interventions have worked for them .
1) What differentiated actions on customer behavior segments have worked to impact business outcome? ( Retail or Finance or Travel or any other industry is fine )
2) Is their a generic "actionability framework" which can be created which gives a menu card of differentiated actions to undertake on various segments ?
3) Are some interventions for customer behavior segments more effective than others.Are their innovative or effective actions which have been undertaken on segments discovered by segmenting customer behavior whichresulted in lift in sales or some business outcome metric ?
Wednesday, June 24, 2009
Friday, June 19, 2009
Monetizing from customer analytics in Travel industry
Every time you go to a travel agent to book a ticket on a flight, there are 2 broad kinds of transactions which are generated.
- Search request and response transactions
- Booking transactions
While most travel organizations have mined their booking transactions data, not many insights have been juiced out of the search patterns for air booking transactions.
For example , If you are a price sensitive tourist looking for the cheapest tickets between Bangalore and Colombo in Nov on Economy class on a Friday evening. Or you could be a value conscious business traveler seeking Economy or Business class tickets at the last minute to ensure that you are on time for a crucial business meeting in New York.
All the search requests and responses are captured in search log files and flushed out at regular intervals. These search logs which were traditionally seen as occupying a lot of disk space is suddenly viewed as a gold mine of interesting information. For example some interesting
- Which are the heavily searched destinations from Bangalore on weekends / Holidays where an say Singapore airline has no service?
o An airline could use this information to expand its fleet of services to destinations which it currently does not serve and increase its share of market
Another scenario consists of segmenting agents based on price conscious search versus value conscious search behavior. Business users are typically convenience shoppers (correct timing and service excellence is important) whereas holiday shoppers typically are price conscious. (Getting the lowest price to Colombo is more important than catching the flight at a convenient time)
- Search request and response transactions
- Booking transactions
While most travel organizations have mined their booking transactions data, not many insights have been juiced out of the search patterns for air booking transactions.
For example , If you are a price sensitive tourist looking for the cheapest tickets between Bangalore and Colombo in Nov on Economy class on a Friday evening. Or you could be a value conscious business traveler seeking Economy or Business class tickets at the last minute to ensure that you are on time for a crucial business meeting in New York.
All the search requests and responses are captured in search log files and flushed out at regular intervals. These search logs which were traditionally seen as occupying a lot of disk space is suddenly viewed as a gold mine of interesting information. For example some interesting
- Which are the heavily searched destinations from Bangalore on weekends / Holidays where an say Singapore airline has no service?
o An airline could use this information to expand its fleet of services to destinations which it currently does not serve and increase its share of market
Another scenario consists of segmenting agents based on price conscious search versus value conscious search behavior. Business users are typically convenience shoppers (correct timing and service excellence is important) whereas holiday shoppers typically are price conscious. (Getting the lowest price to Colombo is more important than catching the flight at a convenient time)
User engagement segmentation
The objective of executing the engagement segmentation process was to understand the behavioral profiles of the people who accessed the micro site focused on the new product being launched. There are 5 dimensions to the behavior of the customer which are related to price sensitivity, network referral effect, purchase, configuration chosen and any sentiment he/she has expressed. The input for the process was user wise metrics which indicate the intensity of user behavior as outlined below
Input data for segmentation process
Column
Input data
Use
User id
Landpgcnt
No of times user has landed on new product micro site home page
Pricepgcnt
No of times he/she has checked the pricing page
Configcnt
No of times he/she has configured the product
Friendscnt
No of friends referred or emailed to
Purchcnt
No of purchases made
Purchval
Average purchase value
Sentcnt
No of sentiments expressed by registered user
Input data for segmentation process
Column
Input data
Use
User id
Landpgcnt
No of times user has landed on new product micro site home page
Pricepgcnt
No of times he/she has checked the pricing page
Configcnt
No of times he/she has configured the product
Friendscnt
No of friends referred or emailed to
Purchcnt
No of purchases made
Purchval
Average purchase value
Sentcnt
No of sentiments expressed by registered user
Analytics for launching new products
The traditional marketing research techniques relied on survey data and techniques like conjoint analysis, whereas integrating customer interaction data whether in the form of configuration data, digital interaction data or unstructured blog sentiment data for new products going to be launched in real time is becoming a priority for CPG and Retail firms. New sources of data along with techniques like text mining, structural equations modeling, can guide organizations in taking more informed decisions on the 6 NPD decisions outlined earlier. The web provides an immersive environment for consumers to play around with new products which are launched. Example: Let’s say a apparel designer conceives of a design. He/She creates a mockup of that design using 3dimensional virtual reality tools and publishes this product on the internet. Giving consumers the ability to rotate the dress, examine the apparel from multiple angles, mails the configured product to his friends in a social network which also serves as a platform for blogging about the new product … As the consumer takes action on each product, the information can be logged and analyzed for degree of engagement matrix
6 questions while launching new products in CPG industry
In a recent survey * of CEO’s from Retail industry, an important question posed to them was- “Which one of these potential opportunities for business growth do you see as the main opportunity to grow your business in the next 12 months? “. The top 5 responses ranked on the frequency of importance are: (i) better penetration of existing markets (ii) NPD, (iii) geographic expansion (iv) Mergers &Acquisitions and (v) new JV’s or strategic acquisitions.
Clearly organizations are looking at new product development process as a critical component to deliver breakthroughs in the market space. With every launch organizations spend millions in dollars in researching new products; test marketing it and releasing it to the broader market. If the new product launch succeeds, it will result in significant enhancement to their revenue stream. If it fails it could result in millions of dollars going down the drain. It is a high risk, high returns game. Industry figures reveal that there are about 150,000+ new CPG products launched globally every year. Only 4 % of new product launches achieve success. (Source: Information Resources Inc. ). Also given these troubled economic times, organizations may want to be more cautious with their new launch initiatives as if the product 'bombs' in the market it could have disastrous financial consequences. This paper attempts to introduce rigorous analytical techniques and processes along with new sources of data which would help optimize the 6 critical decisions which are undertaken when a new product is launched.
1)Which products from the labs need to be launched and which products to terminate launches?
2)Which product configurations are resonating in the market?
3)What messages regarding product attributes does marketing need to amplify for each of the launched market and for each product launched?
4)What products would find acceptance in launched market?
5)What price to sell?
6)Whom to influence?
Analytics and statistical processes can help answer each of the above 6 questions optimally
Clearly organizations are looking at new product development process as a critical component to deliver breakthroughs in the market space. With every launch organizations spend millions in dollars in researching new products; test marketing it and releasing it to the broader market. If the new product launch succeeds, it will result in significant enhancement to their revenue stream. If it fails it could result in millions of dollars going down the drain. It is a high risk, high returns game. Industry figures reveal that there are about 150,000+ new CPG products launched globally every year. Only 4 % of new product launches achieve success. (Source: Information Resources Inc. ). Also given these troubled economic times, organizations may want to be more cautious with their new launch initiatives as if the product 'bombs' in the market it could have disastrous financial consequences. This paper attempts to introduce rigorous analytical techniques and processes along with new sources of data which would help optimize the 6 critical decisions which are undertaken when a new product is launched.
1)Which products from the labs need to be launched and which products to terminate launches?
2)Which product configurations are resonating in the market?
3)What messages regarding product attributes does marketing need to amplify for each of the launched market and for each product launched?
4)What products would find acceptance in launched market?
5)What price to sell?
6)Whom to influence?
Analytics and statistical processes can help answer each of the above 6 questions optimally
Social network analysis from Call Detail Records ( CDR) transactions in Telecom industry
"Who talks to whom ?" is an important question marketers in the telecom industry can ask to discern customer behavior. Social network analysis can be done by analyzing call behavior data. There are 4 essential steps in doing this
Step-1:
Extract CDR information and summarize it for each unique combination of caller and called number
Step-2:
For each caller and called number, count the frequency of calls made, the number of smses sent, the number of prime time calls etc
Step-3:
Use this model to develop call behavorial profiles to target . Example : Friends and families program
Step-4 :
Interrogate the social network database for specific behavior . For example : Who are my existing customers who make more than 20 % of calls to competitive networks during peak time and either the call duration for day exceeds 90 minutes or number of calls per day exceeds 12 ?
Can we target them with a ‘friends and families’ scheme to bring their most frequent called numbers into our network fold and incentivise them in the process
Step-1:
Extract CDR information and summarize it for each unique combination of caller and called number
Step-2:
For each caller and called number, count the frequency of calls made, the number of smses sent, the number of prime time calls etc
Step-3:
Use this model to develop call behavorial profiles to target . Example : Friends and families program
Step-4 :
Interrogate the social network database for specific behavior . For example : Who are my existing customers who make more than 20 % of calls to competitive networks during peak time and either the call duration for day exceeds 90 minutes or number of calls per day exceeds 12 ?
Can we target them with a ‘friends and families’ scheme to bring their most frequent called numbers into our network fold and incentivise them in the process
3 outcome metrics to track effectiveness of segment specific actions
How does one track effectiveness of a segmentation strategy . The best way to do this is to quantify metrics before and after a customer segmentation strategy was put in place
Here are 3 outcome metrics to assess the effectiveness of the segmentation framework
1) % increase in revenue from customers who have not shopped in last 6 months
2) % increase in breadth of categories shopped
3) Measures which track intensity of shopping behavior – average basket size change and changes in purchase frequency
Here are 3 outcome metrics to assess the effectiveness of the segmentation framework
1) % increase in revenue from customers who have not shopped in last 6 months
2) % increase in breadth of categories shopped
3) Measures which track intensity of shopping behavior – average basket size change and changes in purchase frequency
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