What Leaders Still Get Wrong About Customer Portfolio Management

The concept of customer portfolio management, or CPM, is intuitive. We are hardly the first to suggest that organizations can improve their financial performance by focusing on all the customers in their portfolio. Yet business literature is replete with strategies that oversimplify the management challenge.

CPM is not just about creating volume through a large customer base. Nor is it just about creating more satisfied and loyal customers. It’s about how to create value with all the customers in a portfolio over time — to view a company’s market strategies as long-term investments in the strength of relationships over an entire portfolio of current and future customers.

The image of a large leaky bucket illustrates both the theory and complexity of CPM. Consider the choice between two very different buckets, or portfolios, of customers: (1) a smaller, watertight bucket of loyal and profitable customers, or (2) a larger, albeit leaky bucket of customers that includes both stronger and weaker customer relationships. Our research and applications of CPM have taught us that it is typically more profitable in the long run to pursue a larger, leaky bucket.

In this article, drawn from our book, Customer Portfolio Management: Creating Value With a Large Leaky Bucket of Customers, we explain how companies can understand what types of relationships dominate their customer bases so that they can identify strategies for portfolio growth.

Looking Beyond Customer Needs

In traditional market segmentation, unique populations of customers are segmented and targeted using differentiated products and services. The segments are based on differences in customer needs, wants, or benefits sought, be it a soup that is thicker and tastier or a bank that is friendlier. This “needs-based segmentation” has been a cornerstone of marketing management for decades and remains an extremely valuable approach and embedded within CPM.

The limitation of traditional needs-based segmentation, however, is its focus on a particular product or service category and brand. It is a relatively static approach that presumes customers are in a particular needs-based segment.

The reality is that customer behavior is dynamic, where a company’s or brand’s customers are active in multiple needs-based segments within and across product or service categories. Customers use a portfolio of brands that evolves over time and depends on the context or usage occasion. A Marriott Bonvoy customer may, for example, book more self-service brands such as Residence Inn or Fairfield Suites early in their relationship and migrate to more luxury brands such as Ritz Carlton or J.W. Marriott over time. They may seek an upscale or distinctive brand for special occasions or business travel while preferring select service brands when traveling with their kid’s sports team. Similarly, a banking customer may look to the bank with the lowest interest rate and best payment terms when they need a loan, migrate to a different bank with better wealth management services as they accumulate savings, or use more than one bank at the same time for different needs.

In our applications of customer portfolio management, we have found that the best place to start understanding customers is to segment them based on the strength of their relationship with a brand.

In the framework of CPM, “acquaintances” provide both a source of future loyal customers and a basis for scale economies, while “friends” and “partners” provide greater margins and future cash flows. (See our 2022 MIT Sloan Management Review article, “Manage Your Customer Portfolio for Maximum Lifetime Value,” for details on the progression of consumers from strangers to acquaintances to friends to partners and the importance of relationship leverage and relationship defense.)

Stronger relationships increase customer expectations, brand preference, usage, and resulting customer satisfaction. As satisfaction and relationship strength grow, so does a customer’s willingness to share knowledge and adapt to a brand’s systems, services, and brand extensions, thus increasing margins and lowering costs per customer.

The Segmentation Process and Lifetime Value

Categorizing customers into relationship segments is the result of a segmentation process. There are a variety of approaches to sorting existing customers into acquaintances, friends, and partners. We recommend using a combination of measures that include customer preference, satisfaction, purchase volume, and gross margins to classify customers into segments.

In many B2B settings, we have found that individuals inside a company are so familiar with individual customers and their purchase histories that they can reliably classify customers as acquaintances, friends, or partners. The labels may differ, as some firms label customers as levels 1, 2, and 3, or A, B, and C, but the meaning is the same. In the case of parts suppliers in the auto industry, for example, we have observed that Level 1 customers work closely with the supplier and share information to develop and provide highly customized parts, services, and delivery systems in large volume (partners). Level 2 customers are companies that regularly purchase a volume of more standardized products out of inventory (friends), and Level 3 customers buy products on occasion, such as from the company’s catalog or website (acquaintances).

Another general approach to relationship segmentation is to derive segments through statistical methods. A study of telecom customers provides a good example. The study used survey measures of overall satisfaction and switching costs (the perceived economic costs of switching brands) along with measures of actual prior churn (frequency of switching behavior over the four months leading up to the survey) to predict future churn (frequency of switching behavior over the nine months following the survey).

As expected, increased levels of both satisfaction and switching costs decreased subsequent churn. The largest single predictor of future churn was prior churn. Put simply, the biggest predictor of future behavior was past behavior, where some customers are inherently more prone to switching than others. This observation led to one of the more interesting findings, which was a significant interaction involving prior churn and customer satisfaction when predicting future churn. Interactions occur when the impact of one variable on another variable is, in part, dependent on the value of a third variable.

Here, the impact of satisfaction on future churn was dependent on the overall level of prior churn. For customers who were less frequent switchers before the survey, customer satisfaction had a significantly greater impact on retention after the survey. For customers who were more frequent switchers, customer satisfaction had a lower impact on retention. The analysis thus revealed very different relationship segments based on customers’ predisposition to churn and level of satisfaction. The implication for portfolio management is that efforts to increase customer satisfaction are better targeted toward customers who are more predisposed to stay loyal to a brand.

Loyalty programs can be another basis for relationship segmentation, but with caveats. Based on points earned for hotel stays and related purchases, Marriott’s Bonvoy customers earn up to five levels of elite status. Similarly, Delta Air Lines’ SkyMiles customers are identified by four status levels. Recognize, however, that as the segments may be based on purchase volume, higher-level customers may include both high-volume acquaintances and true friends or partners. The number of levels in these loyalty programs also likely map into fewer relationship segments, namely acquaintances, friends, and partners.

A Retail Application of CPM

A CPM analysis for a European retail brand of building materials illustrates how one company used relationship segments to identify strategies for portfolio growth. The first source of data was a survey of a representative sample of the population of customers who had bought building materials from this brand during the previous two years. These customers were asked if they perceived the brand to be better than, equally good as, or worse than the competing brands they were familiar with. Next, customers were asked to provide an estimate of the share of wallet from this brand relative to other brands. Customers also indicated their total purchase volume, perceived strengths and weaknesses of the brand, and loyalty intentions.

Discussions with the management team zeroed in on customer preference and share of wallet to define four types of customers:

1. Partners, who are 20% of customers and who perceive both the brand to be better than competitors and purchase more than 50% of their share of wallet from the brand.
2. Friends, who are 6% of customers and who perceive the brand to be better than competing brands but purchase less than 50% of their share of wallet from the brand.
3. High-volume acquaintances, who are 18% of customers and who rate the brand as equal to or worse than other brands yet purchase more than 50% of their share of wallet from the brand.
4. Low-volume acquaintances, who are 56% of customers and who rate the brand as equal to or worse than other brands and purchase less than 50% of their share of wallet from the brand.

The first insight from the analysis is that the majority of relationships in the portfolio are weak (74%), with most of those relationships at a low volume. The second insight came from estimating the lifetime value of the average customer in each of these segments. The expected cash flow from a partner (3,144 euros) was more than six times the value of an acquaintance with less than 50% share of wallet (466 euros) and more than one and a half times the cash flow from an acquaintance with more than 50% share of wallet (1,847 euros). The expected cash flow from a partner was also three times greater than that from a friend (1,054 euros).

Given the percentage of customers in each segment, partners account for 45% of the total cash flow value of all customers but only 20% of the portfolio. Our analysis revealed that if acquaintances and friends developed into partners, it would increase the overall value of the brand’s portfolio by 122%.

The most important finding for the retailer boiled down to differences across retail locations. We found a very strong connection between relationship strength and individual store performance. Stores with more friends and partners performed significantly better than those with more acquaintances. However, the strength of customer relationships varied widely across the 29 stores in the chain. Although the stores had identical product assortment and store layout and followed the same marketing programs, their relationship strength varied considerably.

For the best-performing store, 78% of customers were friends or partners, whereas the weakest-performing store had just 13%.

The explanation for these differences was that both local management and local competition varied considerably from store to store. Some store managers were better able to deliver and exceed customer expectations, while in some markets competitors were better able to fulfill and exceed customers’ expectations instead.

This information provided the management team with powerful facts and set the retailer in a new direction regarding its growth strategy. First, members of the team knew which stores had the highest potential for growth through relationship conversion. Second, they developed more diagnostic information about which specific elements in their offerings were important to customers, yet underperforming. A weakness in service culture was identified as an important competitive vulnerability in the analysis. Third, management was able to borrow best practices from more effective store managers.

The management team developed a dynamic and detailed monitoring system for its customer portfolio, collecting data from different data sources, including transaction data, loyalty program data, CRM data, and customer surveys. This, in turn, helped the retailer identify key performance indicators, provide strategic direction for store managers, and improve overall performance.

Customer portfolio management balances the need to build markets in the short term by converting strangers into acquaintances with the longer-term value of turning acquaintances into friends and friends into partners. The framework of CPM rests on three key building blocks: relationship segmentation, customer portfolio lifetime value, and the management decisions that impact portfolio growth and profitability. The most important takeaway from the framework is that segmenting customers into strangers, acquaintances, friends, and partners is a robust approach to market segmentation. It effectively categorizes customers based on the strength of a brand’s value proposition.

The creation of partners in business-to-business relationships is well established. Less so is the management of an entire portfolio of weaker to stronger relationships for profitable growth. Partnerships in business-to-consumer markets, meanwhile, are growing as customers adapt to digital platforms and engage in brand communities, recognizing that platforms and communities may require very different management.

As our retail application shows, application of the framework has the potential to turn the complexity of CPM into straightforward and manageable opportunities for growth, in this case the borrowing of best practices from stores with stronger and more profitable relationships and applying them to less profitable locations.