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Measuring Customer Value

As relationships with customers continue to change, now might be the time to revisit your customer profile, identify those that bring value to the business and then consider what actions you need to take to re-connect with those who will provide the income needed to sustain the business.

Measuring the value of individual customers, or segments of customers is vital to all business sectors, to understand where future profits lie and thus where to focus strategies. Businesses must retain their most valuable customers and devote less attention to the least valuable. However, deciding how to measure value can be daunting. 

There are a multitude of factors that can contribute to customer value, such as profitability, loyalty and retention; all of which are highly debated in themselves. This article outlines some of the different techniques, and the advantages and drawbacks of each. As a starting point, some definitions may be useful:

  • customer value – includes profitability and the secondary benefits a customer can bring to a company 
  • profitability – can be synonymous with value, but here is defined as the monetary value a customer provides following cost deductions 
  • customer retention – maintaining customers. This can be the outcome of high customer loyalty and satisfaction 
  • customer loyalty – here includes attitudinal loyalty (such as commitment), and not merely behavioural loyalty (purchasing behaviour) 
  • satisfaction – is how content customers are with current quality and price as well as prior period expectations 

These traditional methods calculate the absolute and relative profit of customers over a defined period and use these historical costs and revenues to predict a future value. Profitability profiles can highlight the interests of leading customers, which can assist in developing new products and improving existing services. However, such an approach needs to incorporate reliable revenue cost figures, future downstream costs and multiple periods. 

Three profit-based approaches are analysed below: Recency Frequency and Monetary Value (RFM), Customer Lifetime Value (CLTV) and Activity-Based Costing (ABC).

RFM – Recency, Frequency and Monetary Value: This method of profitability analysis concentrates on historical transaction data of individual customers to map purchasing cycles and to predict future behaviour. It regards recent, frequent purchasers and high spenders as the most valuable customers. RFM is beneficial as it broadly defines the customers and customer segments generating the most income, and reveals the consumers demonstrating recent activity. 

It also valuably highlights key events in a customer’s purchasing cycle, the effectiveness of marketing strategies and impacts on shareholder value.

Problems: 

  • The focus is on revenue, so the cost of acquiring, maintaining and keeping customers is ignored. This is problematic as some customers may be costlier to serve than others, e.g. those requiring bespoke work or specialist products. 
  • It assumes recent large purchasers are more likely to stay valuable than small purchasers some time ago, which is not always the case. 
  • Historical spending patterns can be misleading as the size and frequency of expenditures generally fluctuate with cash flow availability. 
  • RFM ignores the volatility of a customer’s past purchasing behaviour. 
  • This technique is not capable of considering a customer’s potential value, i.e. opportunities for cross-selling (selling the customer different types of product to their regular purchase) or up-selling (selling more of the same types of product to a customer).

CLTV – Customer Lifetime Value: This technique is often utilised by businesses. It focuses on the long-term value and predicted purchases of individual customers based on the entire lifetime of that customer. It calculates the value in today’s terms of those future purchases by considering revenue from the customer, cost of generating the revenue and projected lifetime value. Successful implementation requires accurate prediction of both a customer’s future spending patterns and the cost of acquiring future sales. The use of data warehouses, which record consumer activity, is particularly useful for this technique. Such warehouses are common in retail where information is collected on store cards, and used to analyse past transactions and develop risk strategies.

Problems: 

  • This technique incorporates many of the same problems as RFM because it too relies on historical spending patterns to predict future spending. This ignores cash flow fluctuations, volatility of past spending and potential value. It also regards customers as static entities incapable of change. Equally the company is passive and unable to influence future consumer behaviour through marketing, sales and product development. 
  • This technique is inappropriate for industries with tough competition and rapid changes, as historic patterns of buying will be short and thus less reliable and it does not include what may cause customers to change purchasing behaviour.

ABC – Activity-Based Costing: ABC is a financial tool developed to aid profitability analyses that focus on transaction size, level of purchases, changes in order volume and the cost of the customer. However, the latter is often ineffectively calculated. This is because cost of sales varies and customers use such resources as marketing and general administration very differently. ABC overcomes the problems of allocating costs by calculating the actual time spent on each customer, and multiplying this by the cost per hour. 

This tool is particularly relevant for industries with customers that exhibit very different purchasing behaviours, as it allows identification of demanding and costly customer segments. It also allows managers to identify the specific activities which are the costliest and can thus look at ways to reduce these costs.

Problems: 

  • ABC still does not examine potential customer value, and again regards both customers and companies as static and incapable of influencing change. 
  • It incorporates difficulties of acquiring (often costly) information.

Customer Retention

All the above examples have based customer value on the profits they can generate. However, research has demonstrated that customers can in fact create additional benefits, and organisations may occasionally retain less profitable customers [1]. In 1990, Professor W Earl Sasser Jr, and Frederick F Reichheld published an article in the Harvard Business Review which suggested that a 5% increase in customer retention leads to a profit increase of between 25-85% [2]. Customer retention is thus an important consideration when analysing value.

Benefits of customer retention:

  • Revenue grows from repeat purchases. 
  • Serving an experienced customer can often be more efficient as they may not demand the information and extra service required by new customers. 
  • Learning from existing customers can benefit process effects as feedback and suggested innovations can reduce costs of product development and can inspire new products. 
  • Some companies retain customers who will ‘stretch’ them. These are more demanding and thus push the company to continually improve. 
  • Existing satisfied customers will generate new business through referrals. This strategy is valued as shown by the emergence of ‘family and friends’ deals. 

Retention is often calculated by the percentage of customers repeat buying within a specific time, the cumulative value of purchases over a specific time or the percentage of customers repeat purchasing from year to year. However, measuring retention is difficult as many complex factors such as loyalty and satisfaction will interact to determine whether a customer will stay with a particular brand or company. These can be difficult to quantify.

Customer Loyalty

Along with satisfaction, customer loyalty is often used as a measure of retention rates, and is equally as problematic to quantify. It is often calculated by repeat purchases, cross-selling, multiple purchases and referrals. However, loyalty is a multi-dimensional concept involving behavioural elements (purchasing patterns) and attitudinal elements (commitment, trust, emotional attachment), therefore measuring just repeat purchases cannot fully capture the motivation behind consumer loyalty.

‘Loyalty’ is a dubious term, as customers demonstrating loyal behaviour may not always be showing true commitment to the brand or company, but merely repeat purchasing out of convenience. It is therefore important to consider why customers may stay ‘loyal’:

  • monetary incentives 
  • convenience 
  • no alternative 
  • may see no difference among alternatives 
  • avoid risk 
  • high switching costs 
  • loyalty initiatives 
  • true attitudinal loyalty 

Customer satisfaction and loyalty are difficult to incorporate into valuation methods, but understanding how these contribute to customer purchasing patterns is vital for managers. Understanding these factors can provide managers with information, such as which customers are most likely to continue purchasing and why they remain loyal to a particular company. This allows implementation of targeted strategies to keep the desired customers, attract new business and develop successful products or services.

However, deciding which customers to retain and which customers to attract is far from simple, even with the above factors accounted for. Dhar and Glazer suggest ‘hedging’ customers can overcome such issues. To do this, a manager must assess the customer base as a whole and gain a spectrum of customers between the high profit, highly volatile and the low profit, low volatility customers. This means that any new customer must be judged in accordance with the existing portfolio to strike a balance between volatility and expected return. This is beneficial as high spenders are often unpredictable so should not be relied upon exclusively, but steady spenders may not generate sufficient profits. Instead of always trying to attract big spenders, it can also help to keep some reliable customers around. Managing a customer portfolio in this way brings stability to cash flows as the collective impact is always accounted for [4].

Conclusions

New technologies and analysis methods (such as ABC) are allowing more accurate calculations of customer profitability that can be used to predict future purchasing behaviour. However, profit-based analyses omit the human elements that determine what makes a person buy into a product or service. Therefore, they remain useful as a base for decision-making, but should not viewed as an exact reflection of future customer behaviour. It is important to understand the motivations behind consumer patterns, as it can be useful to direct marketing, develop products and retain customers. Businesses need to retain their most valuable customers, but as this can include more than just monetary gains, care needs to be taken over the method of analysis used.

[1] Lynett Ryals ‘Are Your Customers Worth More Than Money?’ Journal of Retailing and Consumer Services, Vol 19 Issue 5 (September, 2002) pp 241-251.

[2] W Earl Sasser Jr & Frederick F Reichheld, ‘Zero Defections: Quality Comes to Services’, Harvard Business Review, Vol 68, No 5 (September, 1990), pp 105-111.

[3] E Grigoroudis & Y Siskos analyse national satisfaction barometers spanning various countries and sectors in: E Grigoroudis & Y Siskos, ‘A Survey of Customer Satisfaction Barometers: Some Results from the Transportation-Communications Sector’, European Journal of Operational Research, Vol 152, Issue 2 (January, 2004) pp334-353.[4] Ravi Dhar & Rashi Glazer ‘Hedging Customers’, Harvard Business Review (May, 2003) pp86-92.


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