Customer lifetime revenue is the total amount of revenue that a customer is likely to get in for the company.

The computation of the customer lifetime revenue is relatively easy:

Customer lifetime revenue = average purchase amount x purchase frequency in a year x number of years customer is expected to stay

or in exponential form with a yearly discount rate

CLR = Total Revenue per client * (r/(1+d))

where r is the retention rate and d is the discount rate.

It is also important to keep in mind that if your product has serviced the customer well, there is a likely chance that the estimated volume of purchase per cycle and therefore the estimated average amount per purchase occasion increases over time. However, this is something that also depends on the category in question. The number of sanitary pads that a woman purchases in a month is not likely to increase just because she is satisfied with the product. However, the share of wallet on some multi brand categories is likely to increase over time with higher levels of customer satisfaction.

Customer Lifetime Value
Simply put, customer lifetime value of a customer can be defined as the value of the customer to the business. This pertains to the total value that the customer can bring to your business across a specific period of time. Factors that need to be taken into account to calculate the lifetime value of a customer include the amount of money being spent on the customer for acquisition and retention. In addition to that there is also the aspect of the referral value of a satisfied customer in terms of good word of mouth. The lifetime value of a customer therefore needs to be a summation of the profit that she or she is likely to bring to the business and the referral value too.

Computing the lifetime value of customer is not easy. It needs to take various aspects into consideration.
Estimated customer lifetime value – (Customer lifetime revenue – customer lifetime cost) + expected number of referrals x expected value of the referred customers)

Some businesses however do not like to add in the referral value of a customer in the overall computation since it can bring in duplication over a period of time. Therefore the calculation is limited to:

Estimated customer lifetime value – Customer lifetime revenue – customer lifetime cost

The customer lifetime cost can be calculated by looking at the profit per sale and the number of times purchase has been made.
Knowing the customer lifetime value helps in assessing the amount of money that you should be spending on certain segments of customers in order to retain them. It helps in ensuring that the return on investment of specific customers is high and in accordance with the kind of returns that the company is looking at.

A company can use the values of lifetime value by categorizing people into various groups based on the level of lifetime value – high medium and low. These people can be profiled based on their categorizations and once you know the specific types of people in each group, the company shall be in a better position to spend the marketing budget in the right direction. This data can also be used to plan invites to high profile events and loyalty programs.

Customer Lifetime Value or CLV is one of the best ways in which the objectives of the company can be defined for the year. Defining the company objective based on CLV can help in ensuring that the future of the company is also being taken into account and that the marketing strategies being developed are not merely short term and tactical.

Efforts of the sales force can also be defined in keeping with the customer lifetime value so that you can be sure that you are keeping the high value customers happy and content.

This post talks about the business intelligence industry and how some changes are needed to serve small and medium size businesses. It is written by our CEO Jaime Brugueras and posted on SoftwareAdvice.com. Enjoy!

http://blog.softwareadvice.com/articles/bi/whats-lacking-in-business-intelligence-software/

After being a marketing consultant for more than 5 years, I have come to the conclusion that reports are useless. Business intelligence companies are focused on providing “actionable insights” via web-based dashboards and charts, but very few times is this information used strategically.

Businesses spend thousands of dollars a year on solutions that create fancy reports and dashboards. These solutions are mainly queries about the current state of the organization like quarterly sales, the number of repeat customers, and customer satisfaction indices. Since they only provide the current state of affairs rather than predicting and alerting, the damage is already done. By the time you read the report sales are already down, customers that for some reason stopped buying from you are long gone, and dissatisfied customers already reviewed your service badly!

The Why Question
The “why” question is hardly ever answered in reports. Yes, sales are down I know, but why? Is it a service, product, distribution, demographic, economic, or inventory problem? By looking at raw counts the important answer as to why things happen can only be speculated.

Lack of Action
The lack of action in these business intelligence reporting solutions is the main problem. If data analysis can spot business opportunities, then why can’t one easily act on this information? Charts take time to decipher and a marketing strategy needs to be presented to higher executives in the hopes that something is done to improve a process. When businesses do not have an easy or automatic approach to act on the information, it becomes very hard to profitably use data as a competitive advantage.
When important information is aggregated to create KPIs, business opportunities are lost. For example, when one looks at a customer satisfaction index trend, one can easily miss that one of your most important clients was very dissatisfied with your product or service. Dig into the data and you will find your treasure, but don’t spend a lifetime because you might pay more to find it.

Small businesses lack the resources and knowledge to implement these BI tools. Therefore, they only look at accounting reports: income statement, balance sheet, and cash flow. Although important, accounting cannot be the resource to find profitable opportunities and ways to generate profits. Larger business are using data to their advantage, but implementation of BI solutions takes months and usage of data to their advantage trails at least 60 days from the collection of the data.

A company who is acting on information in real-time is Harrah’s Casino. According to Thomas Davenport author of “Competing on Analytics”, Harrah’s uses predictive modeling to identify current customers who are most likely to stop being customers. Their customer loyalty management system, among other things, weighs a number of variables (including demographic variables and the amount of business the customer gives Harrah’s) in order to contact and recommend hotel activities to entice these disengaged customers back to their hotels.

Relying on reports to run a business is not a competitive advantage anymore. How a company acts on data is what helps a business make more money. Fancy business intelligence tools can provide pretty charts and entertainment to some, but in the end they are just useless.

About the Author
Jaime Brugueras, CEO and Co-founder at Mineful
Jaime graduated with a Ph.D. in Statistics from the University of Illinois and worked as a marketing consultant for more than 5 years. As a marketing analyst, Jaime saw a gap between analytics and actual benefits to a company. He co-founded Mineful to bridge that gap and help companies take action based on analytics.

Connect with Jaime
LinkedIn: www.linkedin.com/in/marketinganalytics
Twitter: @brugueras

About Mineful
Mineful is helps companies use data to retain customers. No reports! An analytical application that takes action when it identifies patterns of disloyalty and dissatisfaction. Mineful integrates with major CRM and e-commerce platforms, uses its algorithms to detect customers at risk, and then provides the communication tools needed to entice customers to buy back from you. If customer retention is important for your organization, Mineful is right for you.

Mineful
800.848.9317
www.mineful.com
@mineful

Business intelligence is becoming more important as organizations move from the “information age” to the “knowledge age”. Today, the focus is not on the information, but on what is done with the data you have at hand. Trends in the BI space include a growing need to have cloud based solutions, a need to integrate data accross departments, access the information on the go, and optionally act on the data automatically. Here we briefly discuss the latest trends we are seeing in many business intelligence software tools.

The latest trends in the business intelligence tools industry include:
- Self service – This is a trend that is emerging from a belief that business intelligence can be best used when it percolates the real user of the data. It obviates the need of a separate business intelligence department or the involvement of IT personnel who may otherwise not understand the business needs as well as the end user of the data.

- Integration – While integration of data has been a key focus for a few years now, the manner in which all data and delivery platforms are being merged has never occurred before. Consolidation of data occurs from various sources like sales, primary customer research, customer feedback and online reports and is delivered offline, online and via messages and alerts on mobile smart phones.

- Software as a Service – Offline business intelligence tools that sit on the server of a company have become passé. The new trend is for a solution provider to host an application on the cloud and allow access to its members. Data is uploaded and analyzed online negating the need for buying and installing any kind of software tool.

- Dashboards – This feature of business tools has allowed data to be presented in a dynamic manner without the need of any programming. In addition to the fact that the application is web based, the data can be seen in real time.

- Social Computing – Today most organizations are only beginning toanalyze the learnings from online conversation.Technologies such as social mining and social intelligence are used to understand customer’s opinions on products and services.

- Business Rules Engine – Business intelligence engines allow for analysis of streams of data, computing trends and outliers concerning business processes. Business rules engines allow to use this output to make faster response and automates decision making in a way that helps the business react to threats in profitability, customer attrition, marketing effectiveness, and much more.

- Mobile access – Wireless access to data and information is possible now with mobile devices that can be used to upload sales data, stock requirement and also to receive updates and reports on performance.

Most market research focuses on understanding customers — their wants and needs, their likes and dislikes. A relatively new tool takes a different approach. Instead of focusing on customers themselves, the buying process model studies the steps that customers go through in making a purchase. Buying process research attempts to identify steps in the process where problems occur, so that marketers can create strategies to overcome roadblocks.

The steps in the process
Most descriptions of the purchase process include five steps:

1. Problem recognition. The consumer identifies a problem that could be addressed by making a purchase. The problem could be anything from “My feet are killing me” to “I don’t have anything to wear.” In fact, the “problem” might not be a problem at all, just a response to a marketing stimulus, such as a billboard or a television ad.
2. Information gathering. The consumer collects information about products, including such things as price, availability, options, and features.
3. Evaluation of alternatives. The consumer weighs the pros and cons of each alternative. Decisions at this stage of the process may be affected by budgets, personal likes or dislikes, and recommendations from family or friends.
4. Purchase decision. The consumer commits to the purchase.
5. Post-purchase behavior. The consumer takes some action related to the product. A satisfied customer might tell friends about the product. A dissatisfied customer might experience buyer’s remorse and return the product.

Not every purchase will proceed through all five steps. For example, impulse buys might move directly from problem recognition (“I need that candy bar”) to purchase decision.
Some consumers might decide to drop out of the process at one of the steps. These are the decisions that are especially important for marketers to understand.

Identifying roadblocks
A well-designed survey can identify potential roadblocks at each step in the buying process. Consider this example: You operate a web-based business that sells engineering software. You want to understand the process that customers go through in deciding to purchase your products. You could create a survey with questions like these:

• What kinds of engineering projects do you typically undertake? (problem recognition)
• Where do you get most of your information about engineering software? (information gathering)
• Rank the following features in order of importance to you. (evaluation of alternatives)
• How would you rate the convenience of purchasing products through our website? (purchase decision)
• Would you recommend our products to other people in your field? (post-purchase behavior)

By studying the responses to these and other similar questions, you can discover where the “sticking points” are in the buying process, and make the necessary corrections.
As an alternative, if you suspected that most of the roadblocks to purchasing were concentrated in one step, you might focus most of your questions on that step.

Why focus on the process?
The main reason to use the buying process model, as opposed to more traditional types of consumer research, is that it provides a more sophisticated understanding of consumer behavior. The model gives marketers a new way of thinking about the strengths and weaknesses of their campaigns so that they can keep doing what’s working well and change what isn’t. In this respect, it helps provide a greater return on a company’s marketing investment.

Business analytics are turbocharging the engine of business intelligence. Traditionally, business intelligence involved collecting data and using it to answer some simple questions:

• What’s happening in the business?
• What’s working well?
• What problems are we having?

Business analytics go a few steps further. Using statistical business analysis, they answer questions like:

• Why are these things happening?
• Where are these trends headed?
• What are the likely results of our actions?

The answers to these questions can help guide decisions about everything from manufacturing to marketing.

Changing the Odds
The gaming industry offers a good example of how business analytics can turn the odds in a company’s favor. The big casinos collect data about customers from many sources on the casino floor itself. Player cards and slot machines are just a couple of examples. They also conduct surveys and gather data from call centers and online reservation systems. Harrah’s, which operates 26 casinos in 13 states, decided to use business analytics to put their data to work.

First the company used its player card system to discover who its best customers were. It found that 26% of gamblers generated 82% of revenue. It also found that these folks were not the typical “high rollers” that casinos have always courted. Instead they were people who spent modest amounts of money on a regular basis.

Then the company conducted some experiments, using business analytics, to determine what would motivate their best customers to spend more. It found that free chips worked better than free rooms or meals to prompt more spending. This surprised some company executives, who assumed that “comping” meals and rooms was the best way to increase gambling revenue. The company took this experiment one step further, using business analytics to design a three-tiered rewards program based on customer spending.

Harrah’s has had sixteen straight quarters of revenue growth, and company executives believe that business analytics are one of the main reasons for this success.

Business Analytics Become Affordable
At first business analytics were used mainly by large corporations that had the expertise and the budgets to make major investments in these powerful tools. Now, companies like Mineful are making analytical engines widely available to small and medium-size businesses as well.

Basically, analytics allow a business to use its data in new ways. For example, a chain of donut shops could use analytics to predict how opening an hour earlier would affect sales. A hotel chain could use analytics to predict how occupancy rates would be affected by offering a free breakfast. A home improvement store could apply analytics to survey results to determine why sales of appliances have declined.

Effective use of business analytics requires careful planning of the whole process, including:

• What data to collect.
• How to collect it.
• What questions to ask about the data.
• How to use the answers.

A well-planned effort in business analytics can generate a large return on a relatively small investment.