When it comes to the customer benefits of CRM, the main questions is what the customer value in the program. This is also argued by Sigala (2006) raising questions like, “why does guests’ want to engage and take part in a CRM program. Does the CRM increase the perceived quality of service they receive? ” (Sigala, 2006) This is definitely one of the key factor as no matter how well-designed and thought out the program is when it comes to the decision deciding how much time and resources you as a customer are going to invest in something is up to the customer to participate and engage in the use of it.
In the world of customer retention there is no standardized metric or magic formula. The methods that do exist vary with the trends, the goals and the objectives of the company’s strategic plan. Nevertheless, these metrics have become very important in order to benchmark the company brand both internal, as well as external against the competitors
Goals and productivity have become more and more important in business and the debate around the concept of loyalty and relationship metrics has been going for some time now. As the technology gets more sophisticated, -automated and –integrated the old algorithms change and new possibilities appear. Nevertheless, there are a three metrics that gives more tonality to the discussion and are used more often in the industry, as well have been supported in the literature.
The first one is RFM (Recency, Frequency and Monetary Value). This metric justifies customer loyalty purely by spend; how often customers buy, what they buy, the more you buy and the more expensive goods or services you buy the better score depending on how the company has chosen to weight the numbers. However, Reinartz (2002) argues that, “One problem is that patterns of buying behavior for frequently bought goods are quite different than those infrequently bought goods and RFM cannot distinguish between them.” (Werner Reinartz, 2002)
Event history modeling is the second method which Reinartz (2002) describes as, “a purely statistical technique. It figures out the probability that some future event will occur based on statistical patterns observed either theoretical or empirically in the past. Unlike RFM, this approach is particular good at predicting how quickly a customers purchasing activity will drop off as the probability of their being active in the future drops steeply with time. This model is therefore good for preventing heavy over investment in profitability but disloyal customers” (Werner Reinartz, 2002)
All the metrics have different advantages and disadvantages, although this dissertation support Reinartz (2002) when arguing that, “no matter how complex the software that a company is uses to do the math, the analysis is very easy to implement, since all such probability models depend on three pieces of information that any customer database stores: When did the customer buy for the first time? When did he/she purchase the last? And when did he/she purchase in between? ” (Werner Reinartz, 2002)
Nevertheless, as measurements have to be able to fit and measure specific goals and objectives of the CRM long term strategies. Most metrics used are tailor-made and tweaked versions of the three main customer loyalty metrics, RFM, Event History or CLV to best comply with the specific goals of the company.
