My wife and I were shopping the other day and she asked,“ Why are these people always asking us if we want to get a credit card? They have to know we have a couple already, don’t they? ” While I can’t
pretend to know all the reasons why retailers train their customer - facing folks to push store credit cards, I do know that there are some informational advantages. Stores can use credit card data to determine spending pattern behavior. This in turn reveals the different charging patterns of different segments of customers, information that can be mapped to customer profitability. With the buying behavior captured, retailers can target their most profitable customers with a personalized campaign of couponing designed to stimulate behavior. Lot of retailers in India offer loyalty programs and cards. This program will tell you about purchases that were made at your store but Co-branded credit cards may also tell you about purchases that were made at your competition's stores.
All retailers have as one of their long - term goals the desire to keep profitable customers loyal to their store. Is there — via the wonders of analytics — a way for a retailer to know when a customer is on the verge of leaving your store and to do something to circumvent that decision? Analytically here you are asking two questions: How do I know if a customer is going to leave my store? What can I do about it?
Most people would develop an ad hoc report that says, “ Tell me all the people that provide $ X amount and if that sales volume has dropped by $ Y amount. So, if they used to buy $ 100 a week and now spend only $ 50 a week, maybe they ’ re leaving my store.” That was the traditional model. The challenge with that is that by the time that situation has occurred, that customer has likely already made the decision that they are leaving their store. What you want to do is to be able to predict that a customer is going to stop shopping at your store before it is noticeable in a loss of sales. So we did that in one of proof of concept at one the retailer.
We threw a whole bunch of data at the computer for a set of consumers who did lapse the store and for a whole bunch of people who haven’t. In this example, we did find that there was one key item that was a telltale product that if a customer used to buy this item, and then stopped buying this item, that there was a high percentage likelihood that this customer would stop shopping at that store. Do you have any idea what the product was? Believe it or not, it was salt.
Business Analytics helps you understand where the customer’s head at the time of contact/purchase. Understanding this would enable much more appropriate messaging and might enhance service recovery. Such situational awareness would allow airlines, when you check in at the kiosk, to say “ Oh, sorry about your delay yesterday, here’s a free Vada Pav or Idli or Coffee coupon. ”
Showing posts with label Business Analytics. Show all posts
Showing posts with label Business Analytics. Show all posts
Saturday, June 5, 2010
Saturday, April 17, 2010
Business Analytics Implementation Strategy - Part I
I had met several senior executives last month to help them create strategy for implementing business analytics framework. I would like to address some of frequently asked questions by them in this blog.
Q: How do I embark on Business Analytics Journey?
For companies just embarking on the analytical journey, a specific business problem may be a good initial target. Perhaps customers are complaining about service or quality, or performance benchmarks show that a business process is wasting resources, or a competitor has raised the bar and you need analytics to determine and execute a response.
For any analytics initiative to be successful, there are 3 pre-requisities
1. Definition of Problem
2. Availability of Good Quality data
3. Business Domain
There are several techniques available to address each business problem. Without having specific problem in mind, it is very difficult to determine which technique to apply on data. Quite often, organizations share their data with business analytics vendors and expect vendors to suggest suitable business analytics applications on their data. This approach is very time consuming and doesn't yield expected results as there is no problem definition.
Problem definition can be as simple as
1. Share of wallet is very low with existing customers
2. Frequent stock outs at stores or excess inventory in plant
3. Transportation cost is very high
Each of the above problems can be addressed using different analytics techniques.
To increase share of wallet, you need to do segmentation & use Cross Sell/Up Sell predictive techniques.
To prevent stock outs or excess inventory, you need to use different forecasting techniques to accurately forecast demand.
To optimize transportation cost, you need to use different optimization algorithms.
Q: I am still in process of implementing data warehouse. Can I implement Business Analytics framework without having data warehouse in place?
Yes. We can implement business analytics framework without having data warehouse in place. You need good quality data to perform any analytics. Richness of data is also very important to use any analytics techniques effectively.
You need to have single view of customer in place to use Cross Sell/Up Sell predictive techniques effectively. If you have duplicate customer information then you may end up sending two different offers to same customer.You need to have customer demographic information such as birth date and occupation filled up properly in your data to use segmentation techniques effectively. You need minimum 36 data points to use forecasting techniques effectively.
I shall address the following questions in my next blog
1. How do I uncover Analytics Problem? I do not have analytics expertise in house.
2. Should I outsource Analytics work or should I build that capability inhouse?
3. How do I go about setting up Analytics CoE?
I had received execellent response for my earlier blog "Business Intelligence Vs Business Analytics". Thanks all for your encouraging commments. Do let me know if you want me to address any questions/doubts that you may have about business analytics.
Q: How do I embark on Business Analytics Journey?
For companies just embarking on the analytical journey, a specific business problem may be a good initial target. Perhaps customers are complaining about service or quality, or performance benchmarks show that a business process is wasting resources, or a competitor has raised the bar and you need analytics to determine and execute a response.
For any analytics initiative to be successful, there are 3 pre-requisities
1. Definition of Problem
2. Availability of Good Quality data
3. Business Domain
There are several techniques available to address each business problem. Without having specific problem in mind, it is very difficult to determine which technique to apply on data. Quite often, organizations share their data with business analytics vendors and expect vendors to suggest suitable business analytics applications on their data. This approach is very time consuming and doesn't yield expected results as there is no problem definition.
Problem definition can be as simple as
1. Share of wallet is very low with existing customers
2. Frequent stock outs at stores or excess inventory in plant
3. Transportation cost is very high
Each of the above problems can be addressed using different analytics techniques.
To increase share of wallet, you need to do segmentation & use Cross Sell/Up Sell predictive techniques.
To prevent stock outs or excess inventory, you need to use different forecasting techniques to accurately forecast demand.
To optimize transportation cost, you need to use different optimization algorithms.
Q: I am still in process of implementing data warehouse. Can I implement Business Analytics framework without having data warehouse in place?
Yes. We can implement business analytics framework without having data warehouse in place. You need good quality data to perform any analytics. Richness of data is also very important to use any analytics techniques effectively.
You need to have single view of customer in place to use Cross Sell/Up Sell predictive techniques effectively. If you have duplicate customer information then you may end up sending two different offers to same customer.You need to have customer demographic information such as birth date and occupation filled up properly in your data to use segmentation techniques effectively. You need minimum 36 data points to use forecasting techniques effectively.
I shall address the following questions in my next blog
1. How do I uncover Analytics Problem? I do not have analytics expertise in house.
2. Should I outsource Analytics work or should I build that capability inhouse?
3. How do I go about setting up Analytics CoE?
I had received execellent response for my earlier blog "Business Intelligence Vs Business Analytics". Thanks all for your encouraging commments. Do let me know if you want me to address any questions/doubts that you may have about business analytics.
Wednesday, March 10, 2010
Loyalty Programs: Derive more value from loyal customers
Today, I have become a member of loyalty program of a big retailer. I was very happy to be member of loyalty program as they were offering instant discount of 10% on my total bill. This is my 8th such membership. I am already a member of Kingfisher, Jet Airways, Cross word, Lifestyle, Shoppers Stop, Pantaloon and Park Avenue loyalty programs.
Today, there is a wide array of reward programs in virtually every industry segment. It has become defacto standard in Airlines and Retail industries.As membership in such programs continues to increase, many firms are left wondering whether their programs buy loyalty and increase customer value, or simply add costs without securing repeat business.Lot of organizations have started offering this programs as their competition is also offering such programs.They do not know whether customer loyalty/ reward programs actually influence consumers to change their behaviors, and if so,which factors of a program have the greatest influence.
I am a loyal customer Life Sytle(Retail Chain in India) for past few years, and I am also a member of their loyalty program. I haven't seen any special treatment given to me as their loyal customer in past few years. You are treated like any other customer in the store. Probably, they are offering this program because their competition is also offering similar programs. I like to buy from Life Style because of variety of brands they keep in their stores. One of the striking difference between Life Style loyalty program and other programs is that there is no loyalty program tier like Gold, Platinum, Silver etc. I think program tiers can be powerful incentives and a good way to reward your best customers with the best rewards.
I am also a member of Pantaloon loyalty program in India. They do have various loyalty program tiers. They offer discount based on program tiers. I buy things from Pantaloon just because it is not available in a nearby retail store.They are offering discount on things which I have any bought from Pantaloon.Loyalty programs like this turns loyal customers into price sensitive customers,who are then more likely to defect for a lower priced offer.
Loyalty and reward programs are typically designed to achieve four objectives: increase customer spending, improve retention, maintain competitive position and capture new customer data. But do such programs actually achieve those aims? There’s no doubt that today’s programs yield useful customer data, but what about the other objectives?
In order to achieve other objectives of loyalty programs, you need to analyze transactional data of loyalty members and tweak programs so that you can achieve maximum results.
Typically, BI query and reporting system will help you answer the following questions.
How do people behave in a loyalty program over a long period of time? Do things change as they move through tiers? Does their spending accelerate or decelerate? How do these trends align with customer demographics?
You need business analytics system to address the following questions
"Which segment of customer is most profitable?"
"What products can you cross sell and up sell to this segment?"
"How do you retain most profitable customers and let go non profitable customers?"
"How do I efficiently attract most profitable customer to become member of loyalty program?"
The answers to above questions will help you build loyalty of your customers around brand and the buying experience they have with your organization. This will in turn help you increase revenue from loyal customers.
Today, there is a wide array of reward programs in virtually every industry segment. It has become defacto standard in Airlines and Retail industries.As membership in such programs continues to increase, many firms are left wondering whether their programs buy loyalty and increase customer value, or simply add costs without securing repeat business.Lot of organizations have started offering this programs as their competition is also offering such programs.They do not know whether customer loyalty/ reward programs actually influence consumers to change their behaviors, and if so,which factors of a program have the greatest influence.
I am a loyal customer Life Sytle(Retail Chain in India) for past few years, and I am also a member of their loyalty program. I haven't seen any special treatment given to me as their loyal customer in past few years. You are treated like any other customer in the store. Probably, they are offering this program because their competition is also offering similar programs. I like to buy from Life Style because of variety of brands they keep in their stores. One of the striking difference between Life Style loyalty program and other programs is that there is no loyalty program tier like Gold, Platinum, Silver etc. I think program tiers can be powerful incentives and a good way to reward your best customers with the best rewards.
I am also a member of Pantaloon loyalty program in India. They do have various loyalty program tiers. They offer discount based on program tiers. I buy things from Pantaloon just because it is not available in a nearby retail store.They are offering discount on things which I have any bought from Pantaloon.Loyalty programs like this turns loyal customers into price sensitive customers,who are then more likely to defect for a lower priced offer.
Loyalty and reward programs are typically designed to achieve four objectives: increase customer spending, improve retention, maintain competitive position and capture new customer data. But do such programs actually achieve those aims? There’s no doubt that today’s programs yield useful customer data, but what about the other objectives?
In order to achieve other objectives of loyalty programs, you need to analyze transactional data of loyalty members and tweak programs so that you can achieve maximum results.
Typically, BI query and reporting system will help you answer the following questions.
How do people behave in a loyalty program over a long period of time? Do things change as they move through tiers? Does their spending accelerate or decelerate? How do these trends align with customer demographics?
You need business analytics system to address the following questions
"Which segment of customer is most profitable?"
"What products can you cross sell and up sell to this segment?"
"How do you retain most profitable customers and let go non profitable customers?"
"How do I efficiently attract most profitable customer to become member of loyalty program?"
The answers to above questions will help you build loyalty of your customers around brand and the buying experience they have with your organization. This will in turn help you increase revenue from loyal customers.
Monday, March 1, 2010
Business Intelligence Vs Business Analytics
Last week I was presenting business analytics framework to a large audience at one of the partner organizations. There were a lot of questions around business analytics and business intelligence reporting. I think there is lot of confusion between business intelligence & business analytics. I would like to address some basic questions about business analytics in this blog.
What is difference between Business Intelligence(BI) & Business Analytics(BA)?
Business Intelligence word was first coined decades ago. Business intelligence converts data into information. It includes query & reporting, OLAP, interactive dashboards and alerts. It's about analysis on past events, and more reactive in nature. It helps you address the following questions
1. What happened?
2. How many, how often, where?
3. Where exactly is the problem?
4. What actions are needed?
This is a first step towards creating intelligent enterprise.Today, there are many big organizations who are struggling to establish enterprise wide business intelligence reporting platform. It's not enough to compete using BI in today's economic scenario. You need much more than BI to create differentiation against your competitors in today's market place.
Business Analytics converts information into knowledge.It's about predicting future using past data and current events. It's more proactive in nature. It helps you address the following questions
1. Why is this happening?
2. What if these trends continues?
3. What will happen next?
4. What's the best that can happen?
Business analytics can directly impact top & bottom lines. It helps you to
1. Identify segment of people who are more likely to buy your product
2. Identify the best offer among the list of potential offers
3. Identify potential customers who can buy more products and services from you
4. Retain most profitable customers
5. Optimize resources based on various contraints
None of the above is possible using business intelligence tools. All of the above requires usage of statistical algorithms and processes.
BI will give you information like number of stock outs in a store where as BA will give you knowledge like optimal quantity of stock that you need to keep in your store to prevent stock out situations and minimize inventory cost. BI will give information like amount of withdrawals and cash out instances of a particular ATM where as BA will give you knowledge like optimal amount of cash that you need to keep in your ATM based on location, and withdrawal patterns so that you prevent cash out situations and minimize cost. There are several such examples available.
Do you need a Data warehouse to implement Business Analytics framework?
No. It's not necessary to source data from data warehouse for business analytics. You can apply business analytics techniques on data which is directly extracted from source system. You do not have to wait till your data warehouse is implemented as it usually takes anywhere from 12 - 24 months. One of pre-requistite for business analytics is availability of good quality data. It's doesn't matter where it comes from.
How much data do you need to perform Business Analytics?
It's depends on type of analytics that you want to perform. Typically, business analytics techniques requires data from last 3 to 36 months. As mentioned earlier, you need to have good quality data to derive effective results out of business analytics techniques.
According to me business intelligence is a subset of business analytics framework. You must have strategy in place to implement business analytics framework to compete in today's economics conditions. Business Intelligence is just not enough. Be aware of vendors who supply query & reporting tools in name of "Analytics".
You can find more info about business analytics @
http://www.sas.com/businessanalytics/
What is difference between Business Intelligence(BI) & Business Analytics(BA)?
Business Intelligence word was first coined decades ago. Business intelligence converts data into information. It includes query & reporting, OLAP, interactive dashboards and alerts. It's about analysis on past events, and more reactive in nature. It helps you address the following questions
1. What happened?
2. How many, how often, where?
3. Where exactly is the problem?
4. What actions are needed?
This is a first step towards creating intelligent enterprise.Today, there are many big organizations who are struggling to establish enterprise wide business intelligence reporting platform. It's not enough to compete using BI in today's economic scenario. You need much more than BI to create differentiation against your competitors in today's market place.
Business Analytics converts information into knowledge.It's about predicting future using past data and current events. It's more proactive in nature. It helps you address the following questions
1. Why is this happening?
2. What if these trends continues?
3. What will happen next?
4. What's the best that can happen?
Business analytics can directly impact top & bottom lines. It helps you to
1. Identify segment of people who are more likely to buy your product
2. Identify the best offer among the list of potential offers
3. Identify potential customers who can buy more products and services from you
4. Retain most profitable customers
5. Optimize resources based on various contraints
None of the above is possible using business intelligence tools. All of the above requires usage of statistical algorithms and processes.
BI will give you information like number of stock outs in a store where as BA will give you knowledge like optimal quantity of stock that you need to keep in your store to prevent stock out situations and minimize inventory cost. BI will give information like amount of withdrawals and cash out instances of a particular ATM where as BA will give you knowledge like optimal amount of cash that you need to keep in your ATM based on location, and withdrawal patterns so that you prevent cash out situations and minimize cost. There are several such examples available.
Do you need a Data warehouse to implement Business Analytics framework?
No. It's not necessary to source data from data warehouse for business analytics. You can apply business analytics techniques on data which is directly extracted from source system. You do not have to wait till your data warehouse is implemented as it usually takes anywhere from 12 - 24 months. One of pre-requistite for business analytics is availability of good quality data. It's doesn't matter where it comes from.
How much data do you need to perform Business Analytics?
It's depends on type of analytics that you want to perform. Typically, business analytics techniques requires data from last 3 to 36 months. As mentioned earlier, you need to have good quality data to derive effective results out of business analytics techniques.
According to me business intelligence is a subset of business analytics framework. You must have strategy in place to implement business analytics framework to compete in today's economics conditions. Business Intelligence is just not enough. Be aware of vendors who supply query & reporting tools in name of "Analytics".
You can find more info about business analytics @
http://www.sas.com/businessanalytics/
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