Should you disregard or include ex-customers in your marketing plans?

The previous post ‘How many customers do you have? Really.’ identified 8 distinct groups of customers based their current status. Two of these groups that rarely receive as much attention as they should are:

• Customers don’t exist – the customer company doesn’t exist for various reasons.
• Customers not using – customers that don’t use your product/service/solution.

Why should you pay attention to, and spend marketing resources on ex-customers? Because, firstly you can learn valuable insights and secondly there is potential revenue from your ex-customers. Most companies tend to disregard ex-customers because they view them as a lost cause and it’s too unpleasant or difficult to communicate or deal with ex-customers. But they’re missing out on a significant opportunity to improve their business by learning from ex-customers. And there is still potential revenue to harvest.

Customers that no longer exist – nothing you can do about customer companies that shut down, go out of business, merge, get acquired or cease to exist for whatever reason. However, there were people inside those companies that used your product, maybe even liked your product. These people have moved on to other companies, they know about your product/service/solution, you probably have their names in your database. Find out where they are now and reconnect. Someone with a positive previous experience with your product/service/solution can be a valuable contact to market and sell into the company where they currently work.

Customers that have stopped using your product/service/solution – this can happen for a variety of reasons including dissatisfaction, new management preferences, a competitive product/service/solution that better meets their needs or many other reasons. Losing customers is painful and costly and has a direct impact on Lifetime Customer Value (LCV). The customers you have lost can provide valuable insights to prevent losing more customers by applying those lessons for improving or correcting whatever caused them to defect. Proactively taking action based on empirical research and analysis from these ex-customers can greatly improve retention and loyalty for current customers.

“The Customers you lose hold the information you need to succeed.” – Frederick F. Reichheld

Just as most companies do a win/loss analysis on current sales deals, you need a continuing Customer Defection Analysis program to gather information from ex-customers for business and marketing plans:

• The first step is to categorize the reason for defection – let the information from ex-customers guide you to the right categories rather than preconceived internal opinions.
• Identify the underlying causes in the ex-customers’ context, that led to the defection. Look for commonalities and trends to determine appropriate corrective action to avoid or minimize future defections.
• Get information about when they stopped using your product, what they’re using now, whether they’ll make the same decision again, etc.
• Determine what other useful and relevant information to gather for the Customer Defection Analysis based on your specific business/ product/service/solution circumstances.
• Identify possible revenue opportunities with these ex-customers:
• Can you provide paid services to help them migrate from your system to their replacement system? I know this sticks in one’s craw, but getting someone from your company into the ex-customer environment to provide services can yield significant insights in addition to the services revenues.
• Can you sell them something that is either complementary to their new system or addresses a completely different functional area of the business?
• Put them on the contact list for appropriate marketing programs to stay in touch and consider your business/product/service/solution for future needs.
• Although gathering information from ex-customers may seem difficult, most people are usually willing to share the reasons for the decisions. Don’t be defensive, argumentative, judgmental or try to rectify the past – just listen and learn.
• Being understanding, supportive and helpful during this information gathering process can put your business in a more favorable position for possible business opportunities with ex-customers.

Are you currently disregarding or including ex-customers in your marketing plans? Has this article given you food for thought to reconsider your practices in this area? Your comments are always welcome.
Copyright © 2009 The Marketing Mélange and Ingistics LLC. http://marketing.infocat.com

Is the RFM customer analysis model relevant for B2B marketing?

Continuing the discussion about customer metrics and analysis from previous posts; this post explores the relevance of the RFM (Recency, Frequency, Monetary) customer analysis model for B2B marketers and businesses.

The RFM customer analysis model has been around for over 40 years and is commonly used by Retail, Database Marketing, Direct Marketing, Non-profits and other primarily B2C businesses and marketing organizations. I have personally only encountered minimal use of RFM in B2B marketing but believe there is value in using this model in some aspects of B2B marketing depending on the specific circumstances of a business.

The premise of the RFM model is straightforward:

• Recency – when did a customer last buy? Research shows that Customers who purchased recently are more likely to respond to an offer than those who purchased some time ago.
• Frequency – how many times has a customer bought? More frequent buyers are more likely to buy again.
• Monetary value – what is the value of their lifetime actual spend? Big buyers are more likely to spend more than small buyers.

The RFM analysis ranks each customer for each RFM factor on a 1 to 5 scale (5 is highest). The 3 scores together are the RFM ‘cell’ for each customer ranking their historical propensity to buy with a 555 customer ranking being the best.

The RFM model has limitations and risks such as:

• Historical behavior does provide indicators for future behavior, but it’s not truly predictive.
• Continually targeting high-scoring customers could annoy or alienate them.
• Neglecting lower-scoring customers that should be nurtured.
• Just analyzing the numbers without relating the RFM score to specific business, product and marketing events and circumstances.
• The 125 cell (5x5x5) RFM model is too granular – rather group scores into clusters or bands to get a better picture of what the data are communicating.

The RFM model could be a valuable marketing analysis and segmentation tool to complement and qualify other analysis and segmentation tools used by B2B marketers:

• Relating customer RFM scores to lifetime customer value (LCV) can provide insights for developing and improving revenues from existing customers.
• In addition to the RFM score, the trend or migration between cells over time can provide further actionable information for marketing.
• The RFM score trend over time for major customers or segments of similar customers can provide insights into changing buying behavior and revenue performance.
• Relating RFM scores to results for various campaigns can provide insights into the effectiveness and appeal of particular campaigns for different RFM segments of customers.
• Relating RFM scores to products or product categories. For example, if a customer buys something in a product category do they usually buy more in that category or does it lead to cross-sell opportunities in other categories. Or if they buy something of low monetary value does that lead to buying something of higher monetary value or vice versa.

Do you use a RFM analysis in B2B marketing and if so, how has it worked for you? Your comments are always welcome.
Copyright © 2009 The Marketing Mélange and Ingistics LLC. http://marketing.infocat.com

Aligning marketing investment and campaigns with customer segments

Following on from the previous post ‘How many customers do you have? Really.’ which discussed basic customer count and group segmentation; this post explores some ideas for analyzing the segmentation for more effectively aligning marketing investment and campaigns. This diagram depicts the previously discussed basic customer count segments:

The fundamental customer objectives for any business are straightforward – acquire new customers, retain existing customers and grow revenues from existing customers. The challenge for marketing is how to effectively do this within budget and resource constraints.

Given this simplistic overall view, the next step is to categorize customers by value. One measure of customer value is how much revenue you have generated from a customer versus the total potential revenue for that customer. Let’s call this Realized Value – the percentage of the potential revenue already realized. We can now categorize customers by realized value:

• Most Valuable – customers with 75%* or greater Realized Value. These are the customers you most want to retain and keep active.
• Most Potential – customers with 25-75%* Realized Value. These are the customers you most want to grow, keep active and increase buying frequency.
• Marginal – customers with less than 25%* Realized Value. Although these customers may have lots of Realized Value upside, it’s a more difficult group to develop.
• Least Valuable – these are the customers from hell – the one’s that cause more problems, are never satisfied and cost more to manage than the revenue they produce. They could fall anywhere on Realized Value scale.

*suggested percentage – use appropriate measures relative to your business specifics.

These four categories should provide a good indication which marketing approaches would be most appropriate for each within the context of your business and market.

Now overlay these four Realized Value categories with the customer count segmentation discussed in the previous post and you’ll have an interesting matrix of customer insights to make objective marketing decisions:

For each intersection in the above matrix you would define specific marketing objectives, engagements, campaigns and execution programs. That should provide targeted alignment to most effectively align your marketing investment to produce better results from your existing customer base.

The concept of Realized Value is related to Lifetime Customer Value (LCV) which was previously covered in several posts; How to determine Lifetime Customer Value, Strategic Insights from Calculating Lifetime Customer Value and Impact of Customer Retention on Lifetime Customer Value.

I have more ideas to share on the customer analysis topic in upcoming posts. How does this approach relate to what you’re currently doing? Do you think this approach could improve your marketing results? Do you use a Relative Value type of analysis? Your comments are always welcome.
Copyright © 2009 The Marketing Mélange and Ingistics LLC. http://marketing.infocat.com

Impact of Customer Retention on Lifetime Customer Value

Continuing the series of posts about Lifetime Customer Value (LCV), this post looks at how Customer Retention impacts LCV.

Everyone probably agrees that customer retention is important and we know intuitively that better retention is a good thing. But just having better customer retention isn’t worth much unless you have relevant programs to monetize existing customers. Do you know specifically how a change in customer retention will manifest in financial terms at your company?

That’s where having factual data that measures LCV comes into play. Measuring LCV provides the means to do modeling on possible scenarios and then track performance. Using the same LCV example from the previous posts, this would be the scenario if we increased retention to 95% with a 10% increase in retention costs (changes in red):
Assuming the same sales penetration rate for existing customers, revenues and gross profit would increase by 41% and 34% respectively during years 2-5 (the retention period). The LCV revenue and profit per customer would increase by 9.1% and 9.3% respectively over the full 5 year lifetime period. In this example the 10% increase in retention cost represents 1.05% of total costs. Therefore an investment of 1.05% returns 9.3% additional LCV profit – a good deal by any measure. Would you like to do this for your company?

Unless you have factual LCV baseline data to model possible improvement scenarios, you’re just guessing that the outcome would be favorable. In this example, spending 28% more on customer retention produces a negative return – do you know what your rate of return would be for various spending levels?

“Old age marketers like photo shoots and they believe in their intuition. But new age marketers believe their job is allocating assets in order to achieve desired business results, such as increasing revenue or customer retention.” – Jeff Levitan

As mentioned earlier, we probably all agree that improving customer retention is a good thing, especially in the current recessionary business environment where finding new customers is tough. Retaining more customers should reflect positively on Lifetime Customer Value, but it’s not a straightforward certainty:

• What’s the value proposition for customers to stay with you longer than they have been?
• How much can you spend to retain more customers?
• If you can retain more customers, how are you going to monetize it? There’s no point in just retaining more customers if you don’t have a plan to market and sell something to them.
• What other benefits can you reap from retaining more customers? For example, a larger pool of candidates for references and case studies.

Hopefully this series of blog posts has provided some ideas and food for thought to make better decisions and improve business results using Lifetime Customer Value data.

Your comments are always welcome.
Copyright © 2009 The Marketing Mélange and Ingistics LLC. http://marketing.infocat.com

Strategic Insights from Calculating Lifetime Customer Value

Last week’s post looked at the calculations and mechanics of developing a Lifetime Customer Value (LCV) model. This post reviews some of the insights that can be determined from LCV calculations for better marketing, sales and business decisions.

Consider the following sample analysis using the same LCV model example from last week's post for reference.

Acquisition costs for a new customer are high and profitability in the first year is usually low or possibly negative. In the example, the ROI in the first year of acquiring a new customer is ~26% whereas the ROI for the next 4 years for those same existing customers is ~92%. More remarkable is the Sales & Marketing ROI in the acquisition year is ~42% and ~297% over the next 4 years. While this insight may be intuitively obvious in general, it is knowing exactly what your numbers are that will enable you make informed strategic decisions for improving performance.

“If you cannot measure it, you cannot improve it.” – Lord Kelvin (William Thomson)

Consider LCV per customer – in the example, total revenue over 5 years is $88,279 of which $12,000 (13.6%) is realized in the first year. LCV per customer in profit terms is $26,093 over 5 years of which $2,500 (9.6%) is realized in the first year. The point here is that many B2B companies invest a huge amount of resources, time and effort to acquire new customers, but rarely seem to show that same determination and enthusiasm for generating returns from existing customers in subsequent years. But the vast majority of the revenue potential and profitability is only realized in those subsequent years.

This raises some interesting questions:

1. Are your marketing and sales resources aligned with generating the maximum lifetime value from your customers relative to your specific LCV data?
2. Are these resources appropriate relative to the lifetime values? The type of marketing campaign and sales activity to acquire a new customer is quite different from marketing and selling to existing customers.
3. Knowing your LCV revenue and profit profiles, are you using the right marketing and sales channels?
4. In last week’s post I recommended calculating LCV by customer or market segment. You may also consider a separate dimension by product line if applicable. Having the market segment and/or product line slices of the LCV data provides more granular and specific insight for making even better and more relevant strategic decisions.
   

Customer retention is a significant factor in determining Lifetime Customer Value revenues and profitability – I’ll explore that in the next post.

Your comments are always welcome.
Copyright © 2009 The Marketing Mélange and Ingistics LLC. http://marketing.infocat.com

How to determine Lifetime Customer Value

I’ve mentioned Lifetime Customer Value (LCV) in several previous posts. Subsequent discussions around the topic of LCV indicated that while many people talk about it, few actually know the information for their business. Seems that there are perceptions about lack of data, complexity, calculation formula, etc. that get in the way of determining LCV. There’s tremendous value in going through the process and determining LCV for your business. Keep it simple to start, then refine and improve data and calculations over time.

You can find many LCV calculation models on the Internet. For the purposes of this discussion, we’ll use this simplified model I developed:This post will focus on the model and mechanics for calculating LCV using this simplified example. I’ll review analysis and usage in next couple of posts. The following bullet numbers refer to the subscript numbers in the first column of the spreadsheet image above:

1. Unless you only sell one type of product/service/solution to one market segment, you should do LCV calculations by customer or market segment – this will provide much better information and insights to make good strategic decisions. LCV information averaged over all customers in a multi-segment business tends to obscure the nuggets that can make a huge difference.
2. The number of new customers in that customer or market segment acquired in a recent year.
3. What percentage remain active customers in each subsequent year (year over year %).
4. Calculated from the initial number of new customers and subsequent retention rates.
5. Total product/service/solution revenues from these customers each year. Year 1 is when they initially buy, subsequent years are additional purchases.
6. If applicable, the annual license/maintenance/service/support/hosting/etc. fees the retained customers pay.
7. Divide total annual revenue by number of retained customers for each year.
8. Cumulative total revenue divided by initial number of customers. This reflects the LCV in Revenue terms for each customer. In this example each of the initial 1,000 customers that produce $12,000 in revenue in year 1 from the initial sale, produce $18,253 in revenue over 3 years and $22,603 over 5 years.
9. Your total sales costs for acquiring the customers in year 1 and additional purchases in subsequent years.
10. Your total marketing costs for acquiring the customers in year 1 and additional purchases in subsequent years.
11. Your cost of goods sold (COGS) – this example uses a 25% of revenue flat rate.
12. The costs for retaining customers and generating continuity sales(6) – such as support, product updates, services, etc.
13. Total revenue minus total costs.
14. Cumulative gross profit divided by initial number of customers. This reflects the LCV in Profitability terms for each customer. In this example each of the initial 1,000 customers that produce $2,500 in gross profit in year 1 from the initial sale, produce $5,505 in gross profit over 3 years and $7,587 over 5 years.

For added financial accuracy, the Revenue and Profit LCV over the extended period should be calculated in Net Present Value (NPV) using the discount rate (based on prevailing interest rates and risk) to calculate future revenue and profits in today’s value of money. For simplicity, NPV is not included in this example. The time span for calculating LCV should be based on your typical customer life cycle longevity.

What analyses and insights can you glean from this Lifetime Customer Value example that would be beneficial to know in you business?

The next post in this series reviews some of the insights that can be determined from LCV calculations.

Your comments are always welcome.
Copyright © 2009 The Marketing Mélange and Ingistics LLC. http://marketing.infocat.com