Scoring your customers

Companies must continue communicating with their customers through a consistent strategy that is driven by customer insight. Rating current customers is not solely based on how much they spend but has as its basis the classic RFM (or RFV in the UK) model that creates a score based on recency, frequency and monetary value. My recommenation is not to stop there but to add more elements such as Returns, Complaints, number of enquiries, length of time they have been a customer and create a contact plan that reacts to the dynamics of these scores.

Often we will identify cohorts of customers that represent bad business. The solution can either be to review the business process for how you do business with them, e.g. relegating them to an ‘exclusive’ on-line relationship where the cost of managing them is reduced rather than taking up the time of a salesman or telephone agent; alternatively the bullet might have to be bitten and you resign the account.

However, building segmentations or communities of valuable, profitable customers by profile and comparing their behaviour will also drive the communication; but don’t just use it to determine when to make contact. Customer insight should also drive the ‘next best proposition’ for each customer so that the sales person can be proactive in establishing opportunity. What you know about your customers can also be used to drive new customer acquisition by comparing prospects’ profiles with your customers and determining the best proposition.

Customers rate companies with whom they deal by the quality of the communications and this means relevance, personalisation and timeliness.

The extended 3-letter acronym to drive retail direct sales

Analytics software is useful, but some concept of what should be analysed is also of value. Retailers will often create a RFV model, combining Recency (of last purchase), Frequency (regularity of purchase) and Value (either total spend or average order value, for example) to create a score for each customer.

We have found that adding additional elements expands the score from the 3-letter acronym to a multi-letter acronym such as RFVRCA where you add on the instances of Returns, Complaints and Abandoned on-line baskets committed by the customer.

This score then becomes a comparitor between customers, a selection criterion for campaign or proposition and changes in the individual’s score over time become a valid measure of success of the relationship.

Applying demographic, lifestyle and psychographic profiling provides an even more granular segmentation and the ability to apply the concept to prospects, with the benefit of making relevant offers to convert them to customers.

Such models can be created without investment in analytical software and can be applied in rules-based workflow and business process, with the dynamics regularly reviewed and the model(s) enhanced.

E-communication underlines the need for greater control of the Campaign Management cycle

The growth of the internet has implications on both sides of the data-driven communications equation. On the one hand, it provides an exciting and convenient medium for delivering a message to customers and prospective customers. On the other, it presents a channel for data collection that makes possible concepts that were only hypothetical in the past.

The beginning of the 1990s saw the introduction of data to drive variations in customer magazines and newsletters across a variety of media channels, bringing lifestyle “versionalisation” and reader personalization.

However, in all of these cases the systems that drove them relied upon pre-selection and qualification of the data. The influence of the internet imposes new complexities to the concept of the strategic cycle.

In a traditional, tactical direct marketing scenario, you can start with planning a campaign, which will generate a response, which, in turn can then be analysed (even a zero response can be analysed!). This analysis of the performance of the communication can then be used to fine-tune the communication strategy for the future, and so the process continues. One needs to imagine this in 3-D, more of a spiral than a circle, because, if all turns out well, the initiative will be moving the enterprise forward while the strategic cycle turns.

The key element here is control. The introduction of the campaign to the marketplace is controlled: The marketer knows when this has been done; and the expectation of response is controlled, because the execution of a campaign will generate response over which the marketer has control in terms of channel and timing.

The internet has imposed new, reduced timescales between those points in time when the relationship is measured and tested or where opportunities for collecting, verifying or qualifying information are encountered. Previously, the direct marketer could establish when a mailing or phone call was executed and within some degree of control anticipate when a response would be generated. Access to the web has reduced this element of control. A marketer can no longer engineer when a contact may encounter his message, proposition or brand. One now has to consider the implications of both broader data source opportunities and communication media. With the web and the attendant swing toward a demand-led marketing environment, with its greater customer expectation of availability, the response is no longer controlled, and your strategy must account for that factor.

For years marketers strove for one-to-one relationships relying on the traditional tactical spiral model discussed above, and its success has been commendable within the context of the available techniques. New concepts, tools and expertise now help deliver an outcome that is closer to the vision. The development of the web has coincided with an increased awareness of the availability of data within organizations and the appearance of new, intuitive data analysis tools, employing techniques that provide for the derivation of information and the definition and interpretation of patterns within timeframes conducive to achieving the required dynamics.

As companies seek to integrate their suppliers, their customers and their marketing partners in complex relationship structures, new quantities of data are becoming accessible for exchange and sharing, and the value of the data as a corporate asset is increasing.

The income of inquiries, web site hits, new data, business intelligence and market data can have a real-time effect on the way the strategy proceeds, and so the strategic cycle becomes more complex. The marketer must now be prepared for uncontrolled response arriving from customers and prospective customers drawn to or discovering the company’s web site. Similarly, as new information is acquired internally into the data warehouse from, say, the accounts system or externally from suppliers of market information and competitive intelligence, the data has to be analysed and its value to the strategy interpreted and used to automate variations in the strategy. This ensures relevance of message, continuity of relationship and maximal effectiveness of any marketing communication, getting ever closer to the “holy grail” of real one-to-one marketing.

The strategic cycle, therefore, takes on new elements, to account for the uncontrolled data entering the process and the need for aligning its value to the strategy and translating it into rapid response in terms of proposition and delivery of a communication.

The sheer wealth of information available now flowing from the web, the ability to identify and acquire it and the tools now available to manage it in all its varying formats and structures all mean that disparate data, wherever it may reside, can be made available to add qualification, enhancement or verification to a marketer’s database. The natural corollary to that is a keener degree of customer profiling and targeting, a greater level of personalisation of message, proposition and presentation, resulting in improved marketing effectiveness and efficiency.