Customer portfolio is a list segmented into the various groups of customer that a business should build relationships with. It is basically a tool that helps to keep track of customer activities and generate relationships with them (Homburg et.al., 2009). A customer portfolio or customer profile contains a collection of mutually exclusive customer groups that comprise a business’s entire customer base.
Customer portfolio helps businesses understand about the behaviour of its customer needs and demands and accordingly, are able to make categorization. By segmenting customers into portfolios, a business can better understand the relative importance, each customer represents relative to the total sales and profits. Such an understanding assist businesses not only in retaining valuable customers but also in creating additional value with these customers through relationship development (Brennan, 2014). Consequently, the businesses can methodically allocate resources and apply marketing strategies toward the retention and development of its most valued customers. Customer portfolios also assist companies in determining their priorities when selecting and developing their customer base.
Market segmentation for customer portfolio management
Market segmentation is a process through which the company divides the market into a group of homogenous small sets which helps in creating various value propositions (Buttle, 2013). The segmentation of customers enables companies to provide a multidimensional view of the customer. It also helps companies effectively leverage this information to create customer value, increase profit, reduce operational cost, and enhance customer service (Thakur and Workman, 2016). This approach also helps companies track costs to serve and revenue from different groups of customers, thereby enabling companies to determine the optimal allocation of scarce resources to maximize profit.
The segmentation of a customer is based on their degree of value to the business and corresponding costs to the firm in providing service to each customer within the matrix. This matrix is known as Customer Portfolio Management (CPM) matrix (Thakur and Workman, 2016). This matrix comprises of a four-cell matrix where the customers remain divided into four segments on the basis of the type of customer and services provided. The matrix helps in differentiating each customer within the portfolio matrix and allowing the businesses in allocating company resources accordingly to the type of customers.
However, every business has its own method of segmentation and evaluating its customers. For instance, there is a McKinsey customer portfolio matrix where the segment opportunity is rated against each attribute and a score is computed and then mapped accordingly. These matrices help assess the value in a market segment and select which markets to serve. Banks and other service industries use tools like MIMICS Customer Portfolio, soft Target’s iBalance and Oracle.
Businesses use this matrix to segment customers on the basis of the following variables;
- Consumer markets
- Business markets
- Account value
- Share of wallet (SOW)
Sales forecasting for portfolio management
Sales forecasting is the process which helps in the estimation of the future sales of the business. Using sales forecasting techniques it provides useful information for customer portfolio (Buttle, 2013). Businesses mainly use qualitative methods, time-series methods, and causal methods as forecasting techniques.
Qualitative methods likely use customer surveys to ask customers both existing and prospects to give an opinion on what they are likely to buy in the forecasting period. Data is obtained by inserting a question into a customer satisfaction survey and then indicate future buying intentions or proxies for the intention (Wang and Hong, 2006). Usually, businesses that opt for operational CRM systems support the qualitative sales forecasting methods, to prepare sales team estimates, the value of the sale, the probability of closing the sale and the predicted period to closure.
However, in time-series approaches businesses to take historical data and extrapolate them for forecasting assessments and trend analyses. Businesses with historical sales data, therefore, make assumptions about the future of the customer portfolio (Buttle, 2013). Businesses use methods like exponential smoothing, decomposition method, regression models and advanced algorithms like GM (1, 1) and Hidden Markov Model.Offer ID is invalid
Bivariate customer portfolio models
The bivariate customer portfolio model, by Benson Shapiro, based on cost-to-serve into the evaluation of customer value (Buttle, 2013). According to the model, customers remain classified for the price they pay and the costs incurred by the company to acquire and serve them. Customers remain identified as;
- newly acquired customers who are costly to serve but pay a relatively high price,
- passive customers,
- aggressive customers and,
- bargain basement, customers.
The important contribution of this model is that it recognizes that costs are not evenly distributed across the customer base. Some customers are more costly to win and serve and, if this is accompanied by a relatively low received price, the customer is unprofitable.
The strategic importance of a customer in this model remains determined by the value or volume of the customer’s purchases, length of the relationship, diversification of the supplier’s markets, potential and prestige of the customer, customer’s business attractiveness, geographical distance, and relative strength of the buyer-seller relationship from past transactions (Norouzi and Albadvi, 2016). However, the bivariate model later developed into a three-dimensional CPM framework that comprises of cost-to-serve, net price and relationship value-based dimensions.
Tools based on the bivariate model
There are various tools based out of the bivariate model such as;
- SWOT analysis
- PESTE analysis
- Five-forces analysis
- and BCG matrix analysis
SWOT is an acronym for strengths, weaknesses, opportunities and threats that explores the internal environment (S and W) and the external environment (O and T) of a strategic business unit in order to categorize its customers. The ultimate use of SWOT is to assess the market strength and market strength is made by the customers available for the particular market (Buttle, 2013). PESTE, on the other hand, is an acronym for political, economic, social, technological and environmental conditions that assesses the macro-environment causing an impact on the customers for portfolio creation and segmentation.
PESTE and SWOT are correlated because using the macro-environmental factors businesses assess the relevant strengths to exploit the opportunities open to them. Again, the macro-environmental factors also help in responding to external threats in the current markets by exploiting their strengths for diversification.
The BCG matrix or Boston Consulting Group matrix designed to analyse a company’s product portfolio with a view to drawing strategy prescriptions. However, now it remains used for customer portfolio, whereby the matrix categorizes products in a portfolio into one of four boxes and prescribes certain strategies. This is linked to the customer segmentation matrix explained in the previous section of the article. Integrating the BCG matrix, the portfolio remains constructed not just on the basis of customer and services provided, but the most profitable customers to the least profitable ones (Thakur and Workman, 2016). Again, the bivariate model for customer portfolio management remains integrated on the basis of customer values and relationship with the business. Thus, these tools act a the connecting dots between customer relationship building and customer portfolio management.
- Brennan, R., 2014. Business-to-business marketing (pp. 83-86). Springer New York.
- Buttle, F. 2013. Customer Relationship Management Concepts and Technologies. 2nd edn. Burlington: Elsevier.
- Homburg, C., Steiner, V.V. and Totzek, D., 2009. Managing dynamics in a customer portfolio. Journal of Marketing, 73(5), pp.70-89.
- Norouzi, A. and Albadvi, A., 2016. A hybrid model for customer portfolio analysis in retailing. Management Research Review, 39(6), pp.630-654.
- Thakur, R. and Workman, L., 2016. Customer portfolio management (CPM) for improved customer relationship management (CRM): Are your customers platinum, gold, silver, or bronze?. Journal of Business Research, 69(10), pp.4095-4102.
- Wang, H.F. and Hong, W.K., 2006. Managing customer profitability in a competitive market by continuous data mining. Industrial marketing management, 35(6), pp.715-723.