By TLF Research
Customers tend to benchmark organisations very widely, comparing them with their experiences across many different sectors.
Often customers’ recent experience is limited to one organisation per sector. They simply don’t currently deal with more than one local council, one mortgage lender, one mobile phone provider or one housing association. At other times, however, customers are much more active in making comparisons; when they bought their house and needed a new mortgage for example or when their annual phone contract or insurance policy was due for renewal. In some markets customers may habitually use one supermarket, for example, or drive one make of car for three or four years before replacing it, but are nevertheless frequently making comparisons between competing suppliers even though they are not switching.
In other markets customer promiscuity is much more prevalent. In most industrial supply markets, for example, dual or multiple sourcing is widespread. In the leisure sector customers often frequent more than one restaurant, tourist destination or theatre. It is therefore very useful for some companies to understand how customers make comparisons and choices between competing suppliers.
For companies in competitive markets where churn is common, a survey must cover all the factors that influence customers’ choice and evaluation of suppliers enabling the company to see how it compares against its competitors on all the most important supplier selection criteria used by customers. (See Figure 1)
Provided the factors have also been scored for importance, a weighted index can be calculated for each supplier, the outcome providing an accurate reflection of how the market perceives the relative standing of the competing brands. Since customers’ attitudes precede their behaviours, Figure 2 will provide a reliable guide to future customer behaviour in the market and its consequent impact on market share so provides a sound basis for decisions about how to improve competitive positioning.
To make the most impact on improving the satisfaction of its own customers, XYZ should focus on addressing a small number of PFIs (priorities for improvement) based on its biggest satisfaction gaps (i.e. where it is least meeting its customers’ needs. So based on Figure 3, XYZ should be prioritising stock availability.
However, in a highly competitive market there is also another dimension to consider; XYZ’s relative performance compared with its main competitors, especially its biggest threat, Competitor 1. Figure 4 shows the competitor gaps between XYZ and Competitor 1.
There are significant differences between figures 3 and 4. Stock availability is the obvious PFI for XYZ if based on the satisfaction gaps but queue times are a much bigger area of under-performance against Competitor 1. Since in the real world there would probably be at least 20 important customer requirements covered on the survey, and all companies have finite resources, XYZ may have to make choices between increasing the satisfaction of its own customers or closing the gaps with Competitor 1. Putting the two sets of data together into a competitor matrix would be a useful start for making this decision.
Requirements closest to the top left hand corner of Figure 5 represent XYZ’s main areas of weakness, in terms of failing to satisfy its own customers and under-performing its main competitor. Whilst ‘stock availability’ would emerge as XYZ’s main PFI based on measuring the satisfaction of its own customers, the data from across the market suggests that improving ‘queue times’ would make a bigger difference to XYZ’s market position against Competitor 1.
The main characteristic of very competitive markets is the prevalence of switching. Customers see changing from one supplier to another as relatively easy so often feel it is worth switching for even a small increase in satisfaction. They may even switch just to find out if an alternative supplier is better, since it is easy to switch back if it isn’t. In highly competitive markets this promiscuity reaches its height when customers switch simply for a different customer experience, e.g. visiting a new restaurant ‘for a change’. Hofmeyr1 calls this ‘ambivalence’ and points out that in some markets customers are loyal to more than one supplier. They will sometimes visit a different restaurant even though they are completely satisfied with their favourite restaurant. In some markets therefore, companies need a much deeper understanding of customers’ loyalty attitudes and behaviour.
A competitor analysis must identify the customers most and least likely to switch2. This should include the company’s own customers and competitors’ customers since the company must understand how to defend its own vulnerable customers as well as how to target and attract its competitors’ most vulnerable customers. This is done by dividing one’s own and the competitors’ customers into loyalty segments as shown in Figure 6.
Few companies have the resources to successfully implement acquisition and retention strategies across all segments. Figure 7 illustrates the situation for a supplier with one competitor but in a very promiscuous market there will be several competitors, each with their own strengths, weaknesses and customer profiles. The starting point for strategic decisions on retention and acquisition strategies is therefore to understand the distribution of the customer base across the four loyalty segments.
Figure 8 depicts a company with a very secure customer base, which should take steps to reward and protect the loyalty of its many faithful customers, whilst implementing strong measures to attract any of the competitors’ available and flirtatious customers, provided they have a suitable needs profile.
By contrast, the supplier shown in Figure 9 has a customer base that is typical of a company devoting too much resource to winning new customers at the expense of satisfying and retaining its existing ones. This company needs to seriously re-think its strategic priorities. An example is the MBNA case study from Harvard, where the company was not keeping its customers long enough for them to become sufficiently profitable. MBNA’s ‘zero defections’ strategy based on delivering exceptionally high levels of service to targeted customers, moved the company from the 38th to the largest bank card provider in the USA over two decades3,4.
To optimise strategic decisions of the type outlined in Figure 7, a company must segment customers and build detailed profiles of the predominant types of customer in its own and its key competitors’ loyalty segments.
One of the earliest academic authorities on customer segmentation was Yoram Wind5, who suggested some less commonly used segmentation variables, which, in his view, often provided more insight than standard classification data such as demographics. Wind’s preferred segmentation criteria included:
- Needs segmentation
- Product preference
- Product use patterns
- Switching behaviour
- Risk aversion
- Media use
- Loyalty behaviour
In B2C sectors classification data can include demographic, geographic, behavioural, lifestyle and psychographic details. In some markets, such as pensions or health care, customers’ attitudes and behaviours are heavily influenced by demographic factors. In others, such as groceries and cars, a more complex level of attitudinal and psychographic profiling is often necessary to fully understand the differences between loyalty segments. These may include core values such as the importance placed on individual liberty, health and fitness and family values or deeply held beliefs such as commitment to the environment, fair trade food or specific political or charitable causes. Sometimes, the best way to profile customers is to start with their tangible behaviour such as when they buy, how they buy (channel), how often they buy and how much they buy, then search for demographic, psychographic or geographic differences within the behavioural segments.
For even more intelligence, a company can often draw insightful conclusions about customers’ loyalty by asking them questions about their behaviour in other walks of life. Media usage is an obvious example. Some people are promiscuous users of media, hopping across many TV, radio and internet channels, whilst others may get their information and entertainment from one newspaper, one or two radio stations and a small range of TV channels. Rather than asking its customers direct questions about their behaviour in its own market (e.g. likelihood of renewing their policy), an insurance company might ask about their media usage and shopping behaviour. Customers that use a very small range of media and are highly loyal to one supermarket for their grocery shopping are displaying a more favourable loyalty personality than those who often shop at three or four different supermarkets and have very diverse media habits. Whatever they say about their intentions to renew their policy, customers demonstrating strong loyalty behaviours in other markets are more likely to be loyal insurance customers.
In B2B, readily available demographic criteria include company size, usage volume and industry segment but B2B companies can often gain an insight advantage over competitors by adopting the kind of loyalty personality techniques described in the previous paragraph. A good example would be risk aversion. De Bruicker and Summe classified buyers into segments such as ‘inexperienced generalists’ and ‘experienced specialists’6. The former are less knowledgeable and less confident and therefore less price sensitive but much more demanding in terms of support. By contrast, experienced specialists are less interested in customer service and relationships but will unbundle and haggle over all aspects of a purchase.
Decision tree analysis
A good way of using survey data to identify loyalty segments, decision tree analysis identifies the biggest differences between segments by sequentially dividing a sample into a series of sub-groups with each split chosen because it accounts for the largest part of the remaining unexplained variation. The easiest way to understand this process is to work through the decision tree shown in Figure 10. (See Figure 10)
The process starts with the entire sample, indicated by the 100% above the first box, which is numbered 1 in its top right hand corner. The 81.3 refers to the customer satisfaction index for the sample in question. This could be the entire sample, or, more usefully a sub-set of it, such as the ‘flirtatious’ segment, or a competitor’s ‘available’ segment. The data examined does not have to be overall satisfaction. It could be a loyalty index, a Net Promoter Score or an individual factor such as ‘quality of advice’. The process then looks for the single dichotomous variable that accounts for the biggest difference in satisfaction variation across the sample and, in this example, finds that it is age. It can split any variable into only two groups at each stage, and in this example the two age segments that account for the biggest variation across age groups are over- and under-55s, which now become boxes 2 and 3. And so it goes on.
This makes it possible to profile the most secure customers, the most flirtatious or any other loyalty segment. The company concerned would be well advised to target retired over 55s on modest incomes outside London. As well as having very high levels of satisfaction with the benefits delivered by the company they also account for a sizeable 19% of customers in the target market.
For many companies, satisfying and retaining their existing customers is only half the battle. In a highly competitive market a company could be achieving high levels of customer satisfaction but still losing market share if a competitor is acting on better brand perception and competitor benchmarking insight. A competitor who understands the priorities and profiles of a rival’s flirtatious and available loyalty segments and invests in delivering and promoting appropriate benefits to them will always have the edge. To succeed, companies in competitive markets must therefore use surveys to understand market perception and should develop loyalty segments for their own and competitors’ customer bases.
- Hofmeyr, Jan (2001) “Linking loyalty measures to profits”, The American Customer Satisfaction and Loyalty Conference, American Society for Quality, Chicago
- Rice and Hofmeyr (2001) “Commitment-Led Marketing”, John Wiley & Sons, New York
- Heskett et al (2003) “The Value-Profit Chain”, Free Press, New York
- Heskett et al (1997) “The Service-Profit Chain”, Free Press, New York
- Wind, Yoram (1978) “Issues and Advances in Segmentation Research”, Journal of Marketing Research, August
- De Bruicker and Summe (1985) “Make sure your customers keep coming back”, Harvard Business Review, January-February