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Wednesday, February 23, 2011

Predicting the Stock Price using other Stocks

Predicting the stock value using RapidMiner

I have written a regression model to calculate / predict the stock value of David Jones Share on the basis of the data points collected from Yahoo finance website. The independent variable include Telstra, Wesfarmers, Virgin Blue and Harvey Norman share.

Model Formula:

1.114 * Harvey Norman  ^ 2.000
- 0.702 * MYER  ^ 1.000
- 10.383 * TELSTRA  ^ 1.000
- 62.099 * Virgin Blue  ^ 5.000
+ 26.308

The model is 91% accurate and after testing it in the last few days, I have got the best results of getting less than 2% close to the actual value.

Model tested on yesterday’s data:image

Please feel free to write to me, in case you want to model any other stock and /or you want the model for the above tutorial and I can send you the excel spreadsheet containing the model.

Thanks,

Shailendra Kumar

An Introduction to Statistical Modelling
Predictive Modeling in Disease Management, 2nd Edition



Sunday, January 23, 2011

Why Is Customer Profitability Measurement In Banking Today So Badly Flawed, Grossly Misleading and Virtually Worthless?

 

It's very simple. The Model currently in use is all wrong. It's badly flawed because it is incomplete and a fraud! A 20th Century Technology Model trying to do business in the new higher intelligence enabled 21st Century!

All current MCIF, Data Warehousing, Data Mart and Database Marketing Technologies that have for years, prided themselves and touted their software on the benefits of Customer Profitability Measurement are badly flawed and invalid; and materially misleading and fraudulent in their representations that lead one to believe that there is soundness to the model when, in reality, there is not! They attempt to disguise and hide the fact that an entire technology has gone far astray and bet a fortune on an out of date, obsolete 20th Century model that is woefully short on measurement content and in the end, produces meaningless results re: the profitability of any subject customer under measurement. In short, all current profitability measurement methodologies employed by current MCIF, data base marketing software and CRM technologies simply leave out the most important customer relationships, the most important profit enhancing customer behavioral disciplines and the customer's lifetime relationship value (past and future) so essential to the accurate calculation of customer profitability!

In other words, these costly, out of date and fast fading technologies are woefully wrong and invalid in that they take into consideration in the measurement of customer profitability, only those traditional banking relationships that relate to loan and deposit spreads and current account balances in relation to cost of funds, and a few consider other fee driven relationships. And the resulting distorted picture reflects net spread profitability only at the moment of the photo, and tells one nothing about past and future profitability and lifetime value of that customer! And, only a seasoned banker can even interpret the spread information. Not something that means anything to a teller or secondary line person in the bank in interacting with the customer.

Here's the problem. What's the real contribution to profitability of a home loan or a home equity loan or a jumbo CD if the home loan and the home equity loan gets refinanced by a competitor and moved to another financial service provider? And the CD customer moves to another investment source for a higher rate? And what effect does the balance of these loans and investment products have on anything if the bank has no real and permanent means of retaining these products and a long-term relationship with the customer?

So what are the most commonly omitted relationships that are worth more than all of the common elements of measurement used by most current day profitability measurement technologies? Well try these for starters! The cumulative value of lifetime Customer Referrals (Selling for you); the Customer Influence Value of the Seniors Club relationship; the Cumulative Lifetime Value of the number of years a customer has invested in your bank; the Retention Power and Lifetime Value of share ownership in the company; plus the Cumulative Lifetime Value of the insurance relationships, the brokerage relationships; the Lifetime Value of Positive Behavioral Disciplines of direct deposit relationships including payroll and pension and retirement direct deposit relationships: and even the value added fee producing relationships?

If the customer sells for you for a lifetime (referrals); lends his influence to your bank through a Seniors Club Membership; invests many years of his life with you (loyalty) to provide Lifetime Retention of all relationships; owns a piece of your company; banks "your way" by using your efficiency disciplines to reduce your service costs and enhance your retention control over the overall relationship; and uses the services of your insurance and brokerage affiliates as well, I submit that all of this is worth far more than any high-balance home equity loan that will soon be refinanced and moved elsewhere sometime soon!

A new customer-driven, customer-engaged marketing system is preparing to enter the marketplace to correct all of these missing elements and rectify the shortcomings of the current and woefully misguided "customer profitability model" in use in the marketplace, and render all of these technologies obsolete very soon. It's called Loyalty Banking and it engages the customer in a Marketing System, supported by current day technology to correct all of these here-to-fore wrongs in customer profitability measurement. And replace it all with a truly reliable and meaningful new intelligence that delivers real customer lifetime value measurement, and does it much more cost efficiently and without the current level of technology overkill.

And if the current customer profitability model employed by the banking industry is a fraud, where do you think this leaves similar technology used in other industries?

-- Shailendra Kumar

 

Customer Loyalty in Banking and Financial Institutions: Determinants in the Belgian Banking Sector
Beyond the Ultimate Question: A Systematic Approach to Improve Customer Loyalty
Customer Service: Career Success Through Customer Loyalty (5th Edition)

Tuesday, December 14, 2010

Managing Customer Relationships using Data Mining

 

The way in which companies interact with their customers has changed dramatically over the past few years. A customer's continuing business is no longer guaranteed. As a result, companies have found that they need to understand their customers better, and to quickly respond to their wants and needs. In addition, the time frame in which these responses need to be made has been shrinking. It is no longer possible to wait until the signs of customer dissatisfaction are obvious before action must be taken. To succeed, companies must be proactive and anticipate what a customer desires.
It is now a cliché that in the days of the corner market, shopkeepers had no trouble understanding their customers and responding quickly to their needs. The shopkeepers would simply keep track of all of their customers in their heads, and would know what to do when a customer walked into the store. But today's shopkeepers face a much more complex situation. More customers, more products, more competitors, and less time to react means that understanding your customers is now much harder to do. A number of forces are working together to increase the complexity of customer relationships:

  • Compressed marketing cycle times. The attention span of a customer has decreased dramatically and loyalty is a thing of the past. A successful company needs to reinforce the value it provides to its customers on a continuous basis. In addition, the time between a new desire and when you must meet that desire is also shrinking. If you don't react quickly enough, the customer will find someone who will.
  • Increased marketing costs. Everything costs more. Printing, postage, special offers (and if you don't provide the special offer, your competitors will).
  • Streams of new product offerings. Customers want things that meet their exact needs, not things that sort-of fit. This means that the number of products and the number of ways they are offered have risen significantly.
  • Niche competitors. Your best customers also look good to your competitors. They will focus on small, profitable segments of your market and try to keep the best for themselves.

Successful companies need to react to each and every one of these demands in a timely fashion. The market will not wait for your response, and customers that you have today could vanish tomorrow. Interacting with your customers is also not as simple as it has been in the past. Customers and prospective customers want to interact on their terms, meaning that you need to look at multiple criteria when evaluating how to proceed. You will need to automate:

  • The Right Offer
  • To the Right Person
  • At the Right Time
  • Through the Right Channel

The right offer means managing multiple interactions with your customers, prioritizing what the offers will be while making sure that irrelevant offers are minimized. The right person means that not all customers are cut from the same cloth. Your interactions with them need to move toward highly segmented marketing campaigns that target individual wants and needs. The right time is a result of the fact that interactions with customers now happen on a continuous basis. This is significantly different from the past, when quarterly mailings were cutting-edge marketing. Finally, the right channel means that you can interact with your customers in a variety of ways (direct mail, email, telemarketing, etc.). You need to make sure that you are choosing the most effective medium for a particular interaction.
It is important to realize, though, that data mining is just a part of the overall process. Data mining needs to work with other technologies (for example, data warehousing and marketing automation), as well as with established business practices. If you take nothing else from this book, we hope that you will appreciate that data mining needs to work as part of a larger business process (and not the other way around!).

What Is Data Mining?
Data mining, by its simplest definition, automates the detection of relevant patterns in a database. For example, a pattern might indicate that married males with children are twice as likely to drive a particular sports car than married males with no children. If you are a marketing manager for an auto manufacturer, this somewhat surprising pattern might be quite valuable.
However, data mining is not magic. For many years, statisticians have manually "mined" databases, looking for statistically significant patterns.
Data mining uses well-established statistical and machine learning techniques to build models that predict customer behaviour. Today, technology automates the mining process, integrates it with commercial data warehouses, and presents it in a relevant way for business users.
The leading data mining products are now more than just modelling engines employing powerful algorithms. Instead, they address the broader business and technical issues, such as their integration into today's complex information technology environments.
In the past, the hyperbole surrounding data mining suggested that it would eliminate the need for statistical analysts to build predictive models. However, the value that an analyst provides cannot be automated out of existence. Analysts will still be needed to assess model results and validate the plausibility of the model predictions. Because data mining software lacks the human experience and intuition to recognize the difference between a relevant and an irrelevant correlation, statistical analysts will remain in high demand.

An Example
Imagine that you are a marketing manager for a regional telephone company. You are responsible for managing the relationships with the company's cellular telephone customers. One of your current concerns is customer attention (sometimes known as "churn"), which has been eating severely into your margins. You understand that the cost of keeping customers around is significantly less than the cost of bringing them back after they leave, so you need to figure out a cost-effective way of doing this.
The traditional approach to solving this problem is to pick out your good customers (that is, the ones who spend a lot of money with your company) and try to persuade them to sign up for another year of service. This persuasion might involve some sort of gift (possibly a new phone) or maybe a discount calling plan. The value of the gift might be based on the amount that a customer spends, with big spenders receiving the best offers.
This solution is probably very wasteful. There are undoubtedly many "good" customers who would be willing to stick around without receiving an expensive gift. The customers to concentrate on are the ones that will be leaving. Don't worry about the ones who will stay.
This solution to the churn problem has been turned around from the way in which it should be perceived. Instead of providing the customer with something that is proportional to their value to your company, you should instead be providing the customer with something proportional to your value to them. Give your customers what they need. There are differences between your customers, and you need to understand those differences in order to optimize your relationships. One big spending customer might value the relationship because of your high reliability, and thus wouldn't need a gift in order to continue with it. On the other hand, a customer who takes advantage of all of the latest features and special services might require a new phone or other gift in order to stick around for another year. Or they might simply want a better rate for evening calls because their employer provides the phone and they have to pay for calls outside of business hours. The key is determining which type of customer you're dealing with.
It is also important to consider timing in this process. You can't wait until a week before a customer's contract and then pitch them an offer in order to prevent them from churning. By then, they have likely decided what they are going to do and you are unlikely to affect their decision at such a late date. On the other hand, you don't to start the process immediately upon signing a customer up. It might be months before they have an understanding of your company's value to them, so any efforts now would also be wasted. The key is finding the correct middle ground, which could very well come from your understanding of your market and the customers in that market. Or, as we will discuss later, you might be using data mining to automatically find the optimal point.

Relevance to a Business Process
For data mining to impact a business, it needs to have relevance to the underlying business process. Data mining is part of a much larger series of steps that takes place between a company and its customers. The way in which data mining impacts a business depends on the business process, not the data mining process. Take product marketing as an example. A marketing manager's job is to understand their market. With this understanding comes the ability to interact with customers in this market, using a number of channels. This involves a number of areas, including direct marketing, print advertising, telemarketing, and radio/television advertising, among others.
The issue that must be addressed is that the results of data mining are different from other data-driven business processes. In most standard interactions with customer data, nearly all of the results presented to the user are things that they knew existed in the database already. A report showing the breakdown of sales by product line and region is straightforward for the user to understand because they intuitively know that this kind of information already exists in the database. If the company sells different products in different regions of the county, there is no problem translating a display of this information into a relevant understanding of the business process.
Data mining, on the other hand, extracts information from a database that the user did not know existed. Relationships between variables and customer behaviours that are non-intuitive are the jewels that data mining hopes to find. And because the user does not know beforehand what the data mining process has discovered, it is a much bigger leap to take the output of the system and translate it into a solution to a business problem.
This is where interaction and context comes in. Marketing users need to understand the results of data mining before they can put them into actions. Because data mining usually involves extracting "hidden" patterns of customer behaviour, the understanding process can get a bit complicated. The key is to put the user in a context in which they feel comfortable, and then let them poke and prod until they understand what they didn't see before.
How does someone actually use the output of data mining? The simplest way is to leave the output in the form of a black box. If they take the black box and score a database, they can get a list of customers to target (send them a catalogue, increase their credit limit, etc.). There's not much for the user to do other than sit back and watch the envelopes go out. This can be a very effective approach. Mailing costs can often be reduced by an order of magnitude without significantly reducing the response rate.
Then there's the more difficult way to use the results of data mining: getting the user to actually understand what is going on so that they can take action directly. For example, if the user is responsible for ordering a print advertising campaign, understanding customer demographics is critical. A data mining analysis might determine that customers in New York City are now focused in the 30-to-35-year-old age range, whereas previous analyses showed that these customers were primarily aged 22 to 27. This change means that the print campaign might move from the Village Voice to the New Yorker; There's no automated way to do this. It's all in the marketing manager's head. Unless the output of the data mining system can be understood qualitatively, it won't be of any use.
Both of these cases are inextricably linked. The user needs to view the output of the data mining in a context they understand. If they can understand what has been discovered, they will trust it and put it into use. There are two parts to this problem:

1) presenting the output of the data mining process in a meaningful way, and

2) allowing the user to interact with the output so that simple questions can be answered.

Creative solutions to the first part have recently been incorporated into a number of commercial data mining products. Response rates and (probably most importantly) financial indicators (for example, profit, cost, and return on investment) give the user a sense of context that can quickly ground the results in reality.

Data Mining and Customer Relationship Management
Customer relationship management (CRM) is a process that manages the interactions between a company and its customers. The primary users of CRM software applications are database marketers who are looking to automate the process of interacting with customers.
To be successful, database marketers must first identify market segments containing customers or prospects with high-profit potential. They then build and execute campaigns that favourably impact the behaviour of these individuals.
The first task, identifying market segments, requires significant data about prospective customers and their buying behaviours. In theory, the more data the better. In practice, however, massive data stores often impede marketers, who struggle to sift through the minutiae to find the nuggets of valuable information.
Recently, marketers have added a new class of software to their targeting arsenal. Data mining applications automate the process of searching the mountains of data to find patterns that are good predictors of purchasing behaviours.
After mining the data, marketers must feed the results into campaign management software that, as the name implies, manages the campaign directed at the defined market segments.
In the past, the link between data mining and campaign management software was mostly manual. In the worst cases, it involved "sneaker net," creating a physical file on tape or disk, which someone then carried to another computer and loaded into the marketing database.
This separation of the data mining and campaign management software introduces considerable inefficiency and opens the door for human errors. Tightly integrating the two disciplines presents an opportunity for companies to gain competitive advantage.

How Data Mining Helps Database Marketing
Data mining helps marketing users to target marketing campaigns more accurately; and also to align campaigns more closely with the needs, wants, and attitudes of customers and prospects.
If the necessary information exists in a database, the data mining process can model virtually any customer activity. The key is to find patterns relevant to current business problems.
Typical questions that data mining addresses include the following:

  • Which customers are most likely to drop their cell phone service?
  • What is the probability that a customer will purchase at least $100 worth of merchandise from a particular mail-order catalogue?
  • Which prospects are most likely to respond to a particular offer?

Answers to these questions can help retain customers and increase campaign response rates, which, in turn, increase buying, cross-selling, and return on investment (ROI).

Scoring
Data mining builds models by using inputs from a database to predict customer behaviour. This behaviour might be attrition at the end of a magazine subscription, cross-product purchasing, willingness to use an ATM card in place of a more expensive teller transaction, and so on. The prediction provided by a model is usually called a score.
A score (typically a numerical value) is assigned to each record in the database and indicates the likelihood that the customer whose record has been scored will exhibit a particular behaviour.
For example, if a model predicts customer attrition, a high score indicates that a customer is likely to leave, whereas a low score indicates the opposite. After scoring a set of customers, these numerical values are used to select the most appropriate prospects for a targeted marketing campaign.

The Role of Campaign Management Software
Database marketing software enables companies to deliver timely, pertinent, and coordinated messages and value propositions (offers or gifts perceived as valuable) to customers and prospects.
Today's campaign management software goes considerably further. It manages and monitors customer communications across multiple touch-points, such as direct mail, telemarketing, customer service, point of sale, interactive web, branch office, and so on.
Campaign management automates and integrates the planning, execution, assessment, and refinement of possibly tens to hundreds of highly segmented campaigns that run monthly, weekly, daily, or intermittently. The software can also run campaigns with multiple "communication points," triggered by time or customer behaviour such as the opening of a new account.

Increasing Customer Lifetime Value
Consider, for example, customers of a bank who use the institution only for a checking account. An analysis reveals that after depositing large annual income bonuses, some customers wait for their funds to clear before moving the money quickly into their stock-brokerage or mutual fund accounts outside the bank. This represents a loss of business for the bank.
To persuade these customers to keep their money in the bank, marketing managers can use campaign management software to immediately identify large deposits and trigger a response. The system might automatically schedule a direct mail or telemarketing promotion as soon as a customer's balance exceeds a predetermined amount. Based on the size of the deposit, the triggered promotion can then provide an appropriate incentive that encourages customers to invest their money in the bank's other products.
Finally, by tracking responses and following rules for attributing customer behaviour, the campaign management software can help measure the profitability and ROI of all ongoing campaigns.

Combining Data Mining and Campaign Management
The closer data mining and campaign management work together, the better the business results. Today, campaign management software uses the scores generated by the data mining model to sharpen the focus of targeted customers or prospects, thereby increasing response rates and campaign effectiveness. Ideally, marketers who build campaigns should be able to apply any model logged in the campaign management system to a defined target segment.

Shailendra Kumar

Data Mining with R: Learning with Case Studies (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
Data Mining: Concepts and Techniques, Second Edition (The Morgan Kaufmann Series in Data Management Systems)
Managing Customer Relationships: A Strategic Framework


Tuesday, November 16, 2010

CUSTOMER RETENTION, DEVELOPMENT AND ACQUISITION FOR FINANCIAL SERVICES

 

Driving Customer Retention, Development, and Profit

For traditional Financial Services Institutions (FSI’s), it is becoming increasingly difficult to capture competitors especially since competitors are becoming larger, and more non-FSI’s are offering financial services and products. A steady flow of new, innovative services and delivery channels is usually necessary to build market share. Of course, these can be costly to develop and execute.

However, with a greater customer focus, greater emphasis on relationship marketing (the leaky bucket concept) and effective customer retention plans, you may discover greater profitability within your existing customer base.

In the past, it was always easier to attempt to “poach” your competitors’ customers. However, studies have shown that companies spend five times more money on acquiring new customers as they do on retaining those they already have. Further studies demonstrated that: “As a customer relationship with a company lengthens, profits rise.” And not just a little. Companies can boost profits by 100% by retaining just 5% more of their customers. This does not necessarily specifically apply to banking, but Pareto’s Law still applies. One Bank found that the top 20% of its customers made all the profit, while the other 80% cost it money. The question is, have you considered the lifetime value of each of your customers? And, who makes up the 20% or less that accounts for the majority of your current and potential future profits?

The key to marketing success is the ability to ask the right questions that relate to a well thought out and actionable strategy. These are usually fundamental questions, and should be answerable with available data. Unfortunately, finding that data turning into information and getting the answers you need is typically impossible with the way information systems are structured.

A customer knowledge infrastructure with a data warehouse at it’s heart provides an opportunity to ask those difficult questions with a good chance that most of them will be answered. For example, with better information about just who are the FSI’s customers, you are better able to serve and keep them.

A bank in the United Kingdom attempted to find just how many customers it had. They derived a number from a range of disparate computer systems that provided different bank services. That number was twenty five million. When they finally cleaned up the data and used matching software to eliminate duplicates and triplicates, they actually found they had seven million distinct customers. There were customers in different databases that were not stored by name but by account number. In addition, incomplete records existed in many systems that focused on products. To make specific offers to your customer base’ you have to know who they are. Without a clear picture of who your own customers are, it is just as easy to pursue your competitors customers.

Rudimentary Segmentation

To be able to do even rudimentary segmentation, you need to know at least your customers’ names, addresses, ages, sex, etc. Yet, traditional marketing segmentation methods — demographics, psychogaphics, geodemographics, behavioural, and clusters — are just coming into play in some financial institutions and they are not enough.

In many cases, the data does not reflect household-by household information. Profiles taken of particular types of households don t represent the individuals and their likely propensities to act in particular ways.

A reliable predictor of future behaviour in humans is their actual past behaviour, which is why transactional information is key. The future success of your financial institution depends on knowing as much as you can about each individual customer, rather than what you know about all of your customers. A culture must be fostered that continuously gathers customer specific information that enhances your customer information database. Every encounter must be seen as another opportunity to find out additive information. This information forms the basis of a FSI’s real assets; its relationships with its customers.

Few banks have this depth of information to mine. And even if they did, there was little they could do with it. The sheer amount of data they had could not be turned into answers because of duplications of inaccurate information, the lack of focused base data, and the weakness of current technology.

At last a Customer Focus

To overcome this, some banks now use customer knowledge infrastructures that include data warehouses/data marts with a customer focus that sets them apart from their traditional operational databases. Operational and often organisationally disparate databases, by the very nature of their data structures, predicate against easy customer focused analysis. The data warehouse has become the basis upon which specific business aims can be achieved and predictive models can be built. They do not just store data.

Today, large successful banks have been able to consolidate names and addresses using matching software to clean their data. With this “one version of the truth” they are now able to identify customers as individuals.

How long has that customer been with the bank? Are there any other accounts in the same family? Are they a one, two or many product family? The questions keep coming. The difference is that the answers are at hand. Through data mining, predictive models can be built for database marketing, as opposed to decision support.

When you can see who the customers actually are and what they do with the institution, then there is an opportunity to more effectively market to them. This approach has even changed the traditional description of marketing - “The Four P’s” - to demonstrate a move towards a greater customer focus - “The Four C’s”

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The move to a greater customer focus is an attempt to see things from a customer perspective. Always keep in mind that the purpose of marketing is to generate customer-perceived value, profitably When looking at retention programs, you must appreciate who you want to retain and why. What would you plan for the marginal customer and those on which you make a loss? Customer retention planning is where the solution begins, and its implementation is where you add to bottom line profitability Loyal customers are typically more profitable customers. There are no acquisition costs. They tend to buy more services and try out new ones. Their growing number becomes an annuity. You can manage the costs of servicing their needs. And, they act as word-of-mouth marketers for you.

Customer Retention Management

Customer retention management is achieved by building a deep insight into who your customers are, then developing and using models that will predict which customers are likely to defect. By using these models, along with profitability potential models, you are able to make informed decisions about which customers to attempt to retain. You could also look at their individual profit improvement potential and make decisions about how you can meet their needs.

Experience has shown that banks with a focus in this area have achieved significant improvement in retention levels, retaining up to 35% of the customers who would ordinarily have defected.

The key is to identify the changes of the customers behaviour that could indicate a potential defection - change of address, regular credit lines cancelled, complaints made, account balances decline - and then to take some action in order to prevent the potential attrition.

However, key to the profitable success of this action is to be able to assess which customers are worth retaining, from a current or potential profitability standpoint. There is a theory that customers decide to defect many months before they actually leave, so it is important to recognise their behaviours and their profitability potential at the point of their decision, not necessarily at the point of attrition.

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Promotion Communication

There is also strong evidence that retention increases in direct proportion with the number of products held by a customer.

Other evidence shows that nearly 75% of all defectors held only one product for a full year before leaving. It is partly for this reason that many banks have introduced “loyalty” programs, where customers gain value through discounts on the bank’s products. These programs are intended to strengthen the relationship by making it broader, either through more products being acquired or through greater expenditure on a revolving credit facility.

To be successful in this arena, it is essential to understand which customers are to be targeted and to measure their reaction to the offer. Using your marketing budget to solicit unprofitable or marginal customers is simply not good business. By leveraging information assets, banks can become attuned to customers’ needs, which translates into more responsive services, which leads to improved customer satisfaction.

By better understanding the customer, a bank is able to more effectively target the right promotion, to the right prospects, at the right time through the right channels. By using combinations of internally created information, web gathered and outside sourced information, the cost of customer acquisition can be reduced by the use of more effective customer knowledge. FSI’s that have adopted customer knowledge infrastructure as a support tool in their approach to marketing have been able to grow their businesses and increase income, while reducing costs. The tangible benefits can be clearly measured, as can the spin-off benefits relating to enhanced customer service and staff morale.

The typical results of customer knowledge-based marketing activity show a 200% to 400% improvement in converted response rates. The actual results vary by product, but as a “rule of-thumb” expect the rate of response before this approach to be multiplied by a factor of 2 to 4 times.

Some can do it

A UK bank developed a complete array of models that offered the capability to predict each customer’s propensity to buy each product in the consumer range. The bank was able to pre-score all customers for each of four lines of credit - overdraft, credit card, unsecured loan and debit card. They considered recent behavioural patterns, cross-sell propensity models and also considered pre-approving applications. This combined approach to marketing and risk management enabled the bank to start thinking about a new way of planning to get a greater customer wallet share, and to deliver a more focused way of achieving its revenue targets.

On the planning side, the models could be used to predict the mix of products that could be sold over the early part of the plan - say the first year of a three-year forecast. This same exercise helps the bank identify gaps in its product range, as well as customers with little potential. A more intelligent set of targets can then be built into the plan for sales and marketing.

On the target marketing side, the more timely and relevant the contact, the more likely customers will respond and buy a “new” or “improved” product or service.

Two specific campaigns can be cited as examples of the successes for one bank. The typical response rate to its campaigns designed to attract checking account holders to take out a personal loan had been 1%. This usually cost justified the campaign. Following their switch to focused database marketing, the campaign generated a 3% to 4% converted response.

Similarly, when making guaranteed offers of a debit card, the bank was able to generate responses between 20% and 25%, compared to previous bests of around 8%. Given the increasing success of these database approaches in creating better quality and higher response rates, this bank expects to spend more on direct marketing than on above the line television advertising.

The Journey

To get from where you are now to where you could be, is not a trivial undertaking. It is however, a step you will have to make to remain competitive. You have the customer data. All you need to do is manage its transformation into marketing information.

Unfortunately, an effective CRM implementation supported b a data warehouse based customer knowledge infrastructure is not an off-the-shelf solution. You need to clearly define the business problems you hope to solve, the strategic imperatives you want to satisfy and then work with consultants and vendor s that can provide the technology infrastructure, the services and the experience that is so critical to success.

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Furthermore, building a customer knowledge infrastructure just like implementing CRM is not a destination, but rather a journey. It requires a long-term commitment to be truly effective. The process is not a project but a major programme that includes some technologies and lots of change management. But once you have started the journey, you will find a wealth of information that will help you drive customer retention and profitability.

 

- Shailendra Kumar

Marketing Management: A Relationship Approach
The Lifebelt: The Definitive Guide to Managing Customer Retention
The Right Customers: Acquisition, Retention, and Development
Rethinking Retention In Good Times and Bad: Breakthrough Ideas for Keeping Your Best Workers


Saturday, November 6, 2010

PREDICTING AND USING CUSTOMER LIFETIME VALUE TO IMPROVE PROFITABILITY

1 WHY CALCULATE CUSTOMER LIFETIME VALUE?

1.1 The Value of Customer Loyalty

Companies worldwide are investing billions of dollars in Customer Relationship Marketing (CRM) strategies and the technology to deliver their objectives of becoming customer focussed. Their main aims are generally to improve customer loyalty, contain costs of acquiring and serving customers and improve margins. In the process of re-orientating the business around the customer, companies are increasingly realising that not all customers are equal. Different customers provide different revenues to the organisation, they choose its products and services for different reasons and they defect for different reasons. This range of customer behaviour results in widely differing values across the customer base, in terms of customers’ future revenue to the business.

To maximise profitability, it is important for companies to determine the lifetime value of customers and prospects so that they can differentiate their marketing activity and business processes.

Successful CRM depends heavily on recruiting “The Right Customer” in the first place. Calculating customer lifetime value helps to ensure high value prospects (i.e., prospects who are likely to turn into high value customers) are the priority target for new customer acquisition.

1.2 The Balance between Customer Acquisition, Retention and Development

Adoption of CRM as a business strategy is often based on nothing more than the premise that retaining customers is ‘A Good Thing’. No doubt it is; a 5% increase in retention per annum will double your customer base in 14 years, assuming all else is equal.

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However, CRM should be about increasing profitability for the organisation through customer management – not just ‘customer retention’ per se. Unless analysis of customer lifetime value has been carried out, it is difficult to identify which customers should be retained. Indeed it would be presumptuous even to assume that customer retention should take priority over customer acquisition.

Too often, CRM strategies are justified on the basis of the simplistic generalisation that:

“It costs 5 times more to recruit a new customer than it does to retain an existing one”

Not only is this factually incorrect for most organisations, it is largely irrelevant. The important question to ask is:

“How much it is worth spending to recruit each new customer compared to retaining or develop existing ones, in order to optimise profitability?”

Customer lifetime value is the measurement that provides the answer to this question.

1.2.1 The Value of Customer Retention

Customer retention happens in most companies with virtually no special effort or expenditure on retention marketing or business processes. In planning a retention marketing strategy, the questions to be addressed are:-

  • “What increase can I make in customer retention through active intervention?”
  • “What is that retention going to be worth to the bottom line?”,
  • “Which customers should we be targeting for increased retention?”,
  • “What do I have to spend on marketing or business processes to get it?”

Lifetime value predictions for customers provide the basis for answering these questions.

1.2.2 The Value of Customer Development

Similarly, customer development can also be evaluated in terms of changes to predicted revenue, when varying factors such as cross-sell, up-sell, referral rate, frequency of purchase, etc.

1.2.3 The Value of Prospect Acquisition and Conversion

Lifetime value can also be calculated for prospects, using predicted prospect conversion rates and subsequent customer retention rates to map out the likely revenue from each prospect.

By varying prospect acquisition rates, cost-per-sale values, etc., the changes in lifetime value of prospects can be evaluated.

1.2.4 Optimising Future Revenues

Comparing the effect on lifetime value by changing the criteria which determine prospect acquisition, customer retention and customer development, enables proper comparisons to be made between the profitability of different marketing strategies.

Calculating lifetime value for customers and prospects makes it possible to plan and implement differentiated marketing activity for different customers based on their value (and potential value) to the organisation. Different tactics can then be used to maximise return on investment.

2 WHAT DO WE MEAN BYCUSTOMER LIFETIME VALUE?

Customer lifetime value is a summary of the net present value of future contributions to profits and overhead that a company expects to make from a customer, from the present time until the customer ends the relationship.

‘Net present value’ can be thought of as simply adjusting future cash flows into today’s monetary value.

Lifetime value is determined by predicting the various future ‘Events’ throughout a customer’s lifecycle with the organisation, and allocating the relevant revenues and costs to those Events.

3 BENEFITS OF PREDICTING AND USING CUSTOMER

LIFETIME VALUE

Measurement without action = A waste of money

Action without measurement = A gamble

3.1 New Customer Acquisition

  • Define the optimum balance between acquisition and retention marketing activities, based on comparative payback to the business’.
  • Set value thresholds for acquiring prospect data, such as list rentals, data lease, list swaps, affinity partner data exchange, etc.
  • Recruit more prospects which “look” like high value customers.
  • Set customer acquisition budgets to optimise profitability over a defined payback period.
  • De-select prospects from campaigns where their lifetime value falls below profitable payback levels.
  • Allocate prospects to different sales channels based on their lifetime value.
  • Route enquirers to different call handling resources in call centres based on the value.
  • Evaluate the cost-benefit of using third party data (geo-demographics, lifestyle data, business data overlays, etc.) in terms of that data’s ability to enhance the differentiation of prospect values.
  • Use “What If” scenarios to identify the payback of increasing prospect conversion rates.
  • Put accurate values on customer referrals – set budget thresholds for profitable recruitment through this channel.

3.2 Customer Retention

  • Differentiate CRM activity according to customer value.
  • Use “What If” scenarios to identify payback for increases in purchase frequency and/or customer retention – from direct sales as well as from indirect causes, such as improvements to customer satisfaction.
  • Ensure retention of high value customers where they are under competitive threat.
  • Allocate customers to different sales channels based on their lifetime value.
  • Route customers to different call handling resources in call centres based on the value.
  • Create “Share of Wallet” models to compare each customer’s lifetime value with the customer’s potential value with the organisation.
  • Profile high value customers – look for more people with those characteristics Identify product portfolios of high value customers and protect / promote these products 1.
  • Track customer lifetime value over time – identify trends in customer segments and segment migration.
  • Use customer lifetime value at individual customer level for improving decision making by Customer Services for goodwill payments, reimbursements, upgrades, etc.
  • Identify prime targets for loyalty scheme membership, or customers suitable for loyalty scheme membership upgrades in advance of their ‘transaction’ thresholds.
  • Aggregate individual customer values to company totals to create company lifetime values.

3.3 Cross-Sell & Up-Sell

  • Identify revenues to be gained from specific upgrade and cross-sell actions – both for ‘internal’ products, as well as third party or affinity products.
  • Identify revenue growth through increasing frequency of purchase.
  • Compare potential increases against “share of wallet” estimates at customer or customer segment level.

3.4 Campaign Management

  • Use customer lifetime value as a decision criterion in campaign selection or de-selection.
  • Use customer lifetime value for automated decision making in lead management and campaign automation tools.
  • Track the change in values over time of customers and prospects within campaign cells compared to control cells.

3.5 CRM Operations / Database Marketing / Database Management

  • Provides the business case for implementing a CRM strategy.
  • Track the future value of the total customer base over time – it is effectively the value of the company!
  • Provides the means to target and reward Customer Management staff in CRM orientated businesses, where customer value, rather than product sales and product revenues, are appropriate measurements of success.
  • Identify customer groups within the customer base responsible for changes in overall database value– isolate the factors causing the changes and take action to either address problems or exploit opportunities.
  • Run “What If” scenarios to test the effect on customer value where:-
    • New competitor products or services are launched which are likely to steal customers or “Share of Customer”.
    • Withdrawal from existing products or markets.
    • Enter new markets or launch new products.
Converting Customer Value: From Retention to Profit
Relationship Marketing: Gaining Competitive Advantage Through Customer Satisfaction and Customer Retention
Database Marketing: Analyzing and Managing Customers (International Series in Quantitative Marketing)

Shailendra Kumar



Thursday, October 14, 2010

Do You Think Your Customers Are Loyal? Think Again

 

It is critical to understand the multitude of variations of customer loyalty if a company is to win customers, increase market share and achieve high performance.

You’ve gone to great lengths to identify and nurture the most valuable segments of your customer base. You’ve closely monitored them through surveys and focus groups, and you know they consistently indicate they are “highly satisfied” with your company and its products. But … are they loyal?

If you’re like most companies, you don’t really know – at least, not for sure. And that’s a problem.

The loyal customer is perhaps the most elusive subject in all of management science. And a recent customer loyalty study suggests that the psychology at the heart of customer buying patterns and preferences is far more complex than previously thought. Different variations of customer loyalty must be understood if a company is to win the long term battle for customers and market share.

Loyalty and High Performance

Understanding, nurturing and ultimately serving these different forms of loyalty is essential to a company’s “market focus and position” – one of the three building blocks of high performance.

Through their market focus and position, high performers achieve a kind of strategic decision making capability that enables them to compete in the best markets and maximize growth opportunities, without reaching or scaling beyond their capability to do so. Companies with an overly simplistic view of loyalty and of their customers are likely to have a misguided market focus and position, taking them down errant and expensive paths that can leave them poorly equipped to compete.

What makes customer loyalty such a vexing matter? Some loyalty challenges are inherent in the current market. Customers, for example, are harder to reach and impress than they used to be. Many traditional marketing channels have been weakened as consumers pursue various “market of one” activities: iPhones, iPads, iPods, video games and movies on demand that allow commercial skipping. In the hypercompetitive Internet age, customers also have more pricing information and more buying options than ever before.

But a number of misconceptions about loyalty have also led companies to make misguided investments in customer management programs.

The notion that loyalty is all about improving customer “satisfaction” is perhaps the most common mistake. The frustrating truth is that what customers say about being satisfied turns out to be a poor indicator of loyalty. In fact, a consistent finding from customer research is that 60 to 80 percent of lost customers across all industry segments reported on surveys just prior to defecting that they were “very satisfied” or “satisfied.”

Another misstep is thinking that because a company has a loyalty program in place, it is doing all it can to improve customer loyalty. Loyalty programs are one part of an overall loyalty strategy, but they lack the nuance that gives companies the ability to target the most profitable segments.

Senior managements have also diluted efforts to encourage customer loyalty through the attitude that “it’s marketing’s job.” Marketing has a vital role to play, to be sure. But developing customer loyalty is a team sport. Human resources, product development and pricing, operations, and sales and service must all be pulling in the same direction to generate the kind of loyalty that produces high performance.

The True Drivers

To attract and retain the most loyal and profitable customers, a company must first understand the true drivers of loyalty: the customer attitudes that, in turn, drive the different types of behaviors that must be understood and nurtured.

To enable better analysis of different kinds of loyalty drivers. At the heart of the model is a better delineation of the different types of loyalty customers exhibit. These types can be understood as spectrums of attitude and behavior along three dimensions.

Involvement With the Product or Service Category

How interested are customers in the category’s products and services? Are they active and engaged participants in loyalty programs? Are they “heavy” users of the category and enthusiastic about the category in general?

Commitment to the Brand

How passionate are customers about the brands they buy? Do they identify themselves with a brand and develop deep ties to it? Do they care about the fate of the brand? Are they willing to pay a premium for the brand? Are they advocates for the brand with people within their family and social network? Much as educators have discovered that teaching a subject helps a person understand the content more deeply, leading companies have discovered that the very process of advocating a brand to others creates deeper loyalty to that brand.

Likelihood to Re-evaluate

How prone are customers within a particular product or service category to re-evaluate their current buying choices? What are the most important shopping triggers? What barriers exist or might be erected against switching brands? For example, do changes in personal circumstances, such as income level or moving to a new home, trigger a re-evaluation of alternatives?

By analyzing the behavior and attitude indicators of this three-part model, different loyalty segments emerge, each with its own distinct loyalty drivers. Companies that recognize these nascent segments can improve their market focus and position by identifying previously unseen markets within markets. They can then design marketing, sales and service strategies to deliver a unique customer experience within each sub-segment.

Applying the Loyalty Model

The following examples demonstrate how this loyalty model can help companies compete on customer loyalty.

Variety Seekers

With the equivalent of 550 billion bottles sold annually worldwide, beer is a $1000 billion global business. The marketing efforts of many of the largest brewers have, for the most part, been aimed at retaining brand loyalists by appealing to their competitive nature, asking them to identify with a brand much as they would with their favorite sports team. Using our three-part loyalty model, this means beer companies have been targeting customers with high category involvement (that is, they like beer), high brand involvement (that is, they are loyal Heineken or Fosters or Blue Tongue drinkers, for example) and a low likelihood to re-evaluate.

Yet a marketing approach targeted primarily at brand loyalists ignores a significant sub-segment of the “high category involvement” customer base: drinkers of micro-brewed, or “craft” beers. These customers are after not only quality but also variety. They are loyal, not in the sense that they can be counted on to order their favorite brand of beer again and again, but in their commitment to a diversity of experience. Such buyers constitute a growing market.

By meeting a customer’s need for variety within the overall brand, companies can build greater long-term loyalty in a segment that might otherwise have seemed to defy loyalty.

Habitual Buyers

Consider another category of buyers whose loyalty patterns can fool companies: habitual buyers. If your company sells soft drinks or snack foods, for instance, you can easily take for granted the steady business from your retail outlets. But as one soft drink maker found out several years ago in a spat with a leading Supermarkets in the United Kingdom, nothing is a sure thing. They pulled the brand from its shelves and substituted its own private-label brand. Within months, the private-label brand was one of the best-selling soft drinks in the United Kingdom. What happened? Shoppers were more loyal to the store than to the soft drink brand.

In the context of our loyalty model, the brand commitment for these customers appeared high, but for a substantial segment of the market it was, in fact, quite low. Because re-evaluation was also quite low, it was only when customers were forced to rethink their purchase that the true nature of this segment’s loyalty became apparent. So if loyalty comes more from purchase habit than brand preference, what becomes crucial is the third dimension of the loyalty model: that is, what the company must do to reduce the likelihood of re-evaluating.

One method used by market leaders to lower the risk of re-evaluation is to embed themselves inside the supply chain of buyers through approaches like vendor-managed inventory and continuous replenishment. These suppliers restock store shelves (for example, snack foods in grocery aisles) and companies’ production inventories (say, cooking oil) on a continual basis, relieving the buyer of the need to constantly reorder to maintain supply. Becoming a quiet but essential player in the supply chain not only adds value for buyers in terms of ease of management, it also avoids the risk of buyers rethinking their supplier choices every time they fill out a purchase order.

Loyalty Engineering

Creating the right loyalty capabilities in a company, and then effectively managing customer loyalty, demands what might be called an “engineering” perspective. That is, it requires a data-driven approach that enables a company to analyze and understand the different configurations of loyalty drivers among its customers, and that supports long-term initiatives to shift and evolve the market focus and position of the company based on those customer configurations. It is important to combines detailed steps involving insight, strategy, execution and measurement, as well as essential enablers such as leadership, technology and organizational design.

Some practical steps for creating differentiated customer loyalty-building capabilities:

Understand Loyalty in the Context of Your Business Model

Most organizations continue to place more emphasis on customer acquisition than on customer loyalty. That can be an expensive mistake. A rigorous analysis of the comparative costs of acquiring new customers and retaining existing ones can be eye-opening. For example, one telecommunications service provider recently conducted an analysis of different customer churn models, mapping various churn percentages to the number of customer acquisitions that would be required to offset the losses. The company found that more effective customer loyalty programs were more important than customer acquisition programs.

Only by challenging long-held beliefs with hard facts and figures rooted in the company’s business model and financial targets will change be effective. It is absolutely imperative that the full costs and benefits of a revitalized loyalty strategy are explored across all aspects of the organization to ensure that initiatives gain senior level endorsement as part of the company’s overall growth plan.

Develop a Detailed Mapping of Loyalty Drivers

Understand the different types of loyalty that exist within your current customer base and across the wider market, and how each type influences the risk of defection and the possibility of achieving even greater loyalty than you currently have. Leverage loyalty-driver insights to plan distinctive customer experiences that create and sustain each type of loyalty.

Plan a Comprehensive Response, Integrated Across All Relevant Dimensions of Your Company

Use an integrated approach to generating customer loyalty that involves a coordinated series of initiatives across the following five dimensions.

Insight – Use sophisticated data mining tools, behavior analyses and external research to understand different segments of the customer base over time.

Strategy – Align loyalty drivers to the customer experience, and ensure that the brand values are consistent with the needs of targeted customer segments. Define loyalty metrics and who owns them.

Execution – Implement customer treatments across channels, organizational boundaries and technologies. Make sure that a rewards based loyalty program delivers clear business benefits and that it is flexible over time. Appoint a clear owner of the entire “customer experience.”

Measurement – Track loyalty measures at each customer touch point. Proactively address the causes of excessive customer churn. Make sure that loyalty programs have a clear purpose within the loyalty strategy and that the return on investment for such programs can be measured.

Enablers – Build technology capabilities that enable data mining and cross-organization integration. Align leadership, culture and values toward a customer-centric approach. Implement governance and journey management capabilities to ensure that silo-based turf warfare does not derail the necessary change.

Test New Loyalty-Building Programs Before Scaling Them Across the Company

A common practice among loyalty leaders is to pilot loyalty-building initiatives to prove their value and gain broader organizational buy-in before making sizable investments in long-term programs. The key is to start with a high-impact first phase and then scale quickly.

A Comprehensive Approach

Loyalty is the result of multiple factors involving brand, customer characteristics, category involvement, cultural issues and a myriad of other considerations. Companies that continue to base their market focus and position on a monolithic and overly simplistic view of customer loyalty may be investing in the wrong things for the wrong reasons. Even well-intentioned loyalty programs can hamper competitive effectiveness if they are not based on the right customer loyalty drivers.

The advantage of the approach to building customer loyalty recommended here is that it integrates strategy, analytics and measurement to quickly put into practice the strategic changes that can have the greatest impact on business performance. By using this approach, companies can shape and deliver optimal customer experiences based on the unique loyalty characteristics of a complex customer base.

This approach can help companies better understand what their customers are thinking and what motivates their purchasing decision – and, thus, can help companies keep those profitable customers. It’s a far better method than basing strategic decisions on how customers have acted in the past, which is often not the best predictor of future behavior. In the end, companies may find that the loyal customers they seek have been there all along, hiding in plain sight, simply waiting to be identified, understood and marketed to in the right ways.

 

Shailendra Kumar

 

A Complaint Is a Gift: Recovering Customer Loyalty When Things Go Wrong
Customer Satisfaction is Worthless, Customer Loyalty is Priceless: How to Make Them Love You, Keep You Coming Back, and Tell Everyone They Know
Measuring Customer Satisfaction and Loyalty, Third Edition: Survey Design, Use, and Statistical Analysis Methods