Doubling the Profits from CRM
Using Market Research to Optimise Customer Database Strategies
In 1898 the British Navy found that for every 1000 rounds fired by its battleships only about 17 ever hit the target. So Admiral Percy Scott studied the techniques employed by his gunners and discovered the most accurate were those who were better at allowing for the pitch and roll of the ship. Using this knowledge, he designed an automated aiming mechanism that took their hit rate to over 50%.
Direct marketing practice needs a similar leap of thinking and making better use of research into customer understanding is key.
Retailers have developed loyalty scheme programmes as a potentially groundbreaking weapon in the battle for customers. 25% of all marketing spend is now on direct marketing and this is increasing. Unfortunately, because modern CRM systems mean consumers can be bombarded with communications, many are now being used to do just that. Consequently there has been an explosion in the number of messages being sent to consumers with no corresponding increase in response per communication. In fact, one could make the case that response per message is now lower than when the industry was simpler!
More retailers are therefore recognising that the depth of their customer understanding is something that they need to use to differentiate their DM programme from the competition: everything else is technology-driven and hence can be copied by competitors. This makes it the best (or only) way of achieving sustainable competitive advantage. Their customer communications can then be targeted and tailored to address individuals’ emotional and personal needs and hence add value.
The potential benefits are huge and often run to tens of millions of pounds: a combination of additional sales through improved response rates and/or reduced costs through more effective use of the CRM budget
The key to leveraging customer databases is not just analysing how different groups of customers behave but understanding why they behave in the way they do and therefore how to influence their behaviour.
This depth of understanding can only be achieved through combining transactional data with research data. Models of how attitudes and circumstances motivate customers to behave can then be developed to identify the best customers, when, where and how to talk to them and what to talk about.
For the last 5 years, RedRoute has successfully applied a structured framework (CVM) to do this. A database containing information on behaviours, attitudes and circumstances for a large sample of customers is compiled to both segment customers and to develop motivational models of behaviour. By revealing which behaviours most closely correspond (given each customer’s circumstances) to particular attitudes, their likely response to new propositions and their future needs can be predicted more accurately.
RedRoute has proved that consumer purchase behaviour can be predicted using 5 common-sense motivations:
– The Relevancy of the Product or Service on Offer
– Identification with the Brand or Company providing the service or product
– The Accessibility of the Brand or Product
– Its perceived Value for Money
– Consumers’ Confidence they will be satisfied with what they will get
The information on attitudes and circumstances comes from a survey. Having obtained a respondent’s explicit consent, their market research data is either directly cross-matched with their behaviour and characteristics from the client’s database or data fusion is used to “marry” respondents from survey research with surrogate donors from the database. Often a mixture of both approaches is used.
As well as developing motivational models of behaviour, the measures of customer behaviour that are most predictive of differing customer attitudes can be identified. This enables us to project attitudinal data accurately across the entire database without having to survey to every customer.
The principle of using research data to understand customer motivations was used by RedRoute to develop a segmentation of a leading supermarket’s Savercard database. The effective use of market research has played an important part in the five-year turnaround programme started four years ago to rejuvenate the supermarket group. It is perhaps no surprise then that the company was quick to recognise the value of linking research data to their card database to understand their customers much better.
The supermarket launched its Savercard customer loyalty programme nationally in the in 2004. Currently, there are some four million customers signed up for the card and more than 60% of sales are tracked by it. The resulting database provides the company with a basis to analyse shopping behaviour, fine-tune the product mix in individual stores and refine marketing initiatives.
RedRoute interviewed 2,000 shoppers at exit interviews across 32 stores. These interviews used a bespoke questionnaire to capture data across all the key dimensions and potential motivational drivers: shoppers’ reasons for choosing where they shop, attitudes to grocery shopping, attitudes to food and life, level of satisfaction with The supermarket, mix of shopping missions at The supermarket, etc. It also captured a range of information about the shopper’s circumstances that were not already held on the Savercard database (eg other store cards held and demographic details). The interviews also included a number of non-Savercard holders to provide comparisons and benchmarking for the Savercard sample.
For each individual interviewed, the responses to this survey were then linked to that individual’s transactional data on the Savercard database. Using our CVM framework, RedRoute was then able to measure and validate relationships between key attitudinal characteristics and shopping behaviours (with particular regard to the types of products purchased). In doing so, behavioural measures were determined that could be used to estimate these key attitudinal characteristics, not just for customers in the research sample, but across all four million customers on the database! Hence a customer segmentation was created for the entire database which reflected the relationships between behaviours and attitudes.
The Head of Consumer Insights for the group, recognised the value of this and commented that: “The result is a customer segmentation that we can monitor and track, together with a much greater understanding of our customer base. This is a tremendously powerful base from which to develop business applications”.
The segmentation that this approach provides is a true motivational segmentation: ie not just attitudinal or behavioural. However, the segmentation is only the starting point.
Models can be developed which, for each individual customer on the database, score the relative importance of each motivational driver. Communications can then be targeted and tailored to groups of individual customers based on their potential receptiveness to a proposition and couched in terms they find most motivating.
Often some highly predictive attitudinal or circumstance variables cannot be proxied using data already on the database. In this case the client can put mechanisms in place to obtain this information continuously and cheaply and do so just for those customers for whom the information will make a difference to effectiveness. This saves money by avoiding the need to run regular large surveys.
Other initiatives, such as gathering potential leads from contact databases, web-site referrals and so on, can be analysed and profiled using the same techniques. Their potential can be better judged and, more importantly, the investment needed in CRM capabilities to exploit them can be justified. One client, with a direct sales force and call centre operation, used the model to better predict what type of proposition would be most motivating and whether a prospect was likely to convert. This helped prioritise staff time on the best opportunities and increased conversion rates by 38%.
Whether the customer data is derived from on-line or off-line transactions, or even if it only contains quite basic information about customers, the same approach can be used.
Naturally a company will only wish to enhance their targeting in this way if the rewards are worthwhile. To make the communications more relevant and to target customers who are more likely to respond, the model places greater emphasis on Relevance and on targeting people with greater Brand Identification. They are less price sensitive and have potential for increased spending. More value-driven messages will be directed at price-sensitive customers who are most likely to both switch and repeat. The result is more sales-effective and more profitable marketing campaigns.
The immediate benefit, typically a step-change in response of more than 50%, generates additional profits that often exceed the required project investment by a factor of 500! Overall returns from the CRM marketing budget can be doubled and evidence provided to justify continued investment in database marketing that Finance Directors can easily accept and support.
In summary, using market research in developing one-to-one targeting models is both feasible and, if done correctly, highly productive. It requires consideration of the five key drivers of response and using the data to model the relationships between attitudes, behaviours and circumstances. In so doing it provides a new “ABC” of targeting effectiveness.
This approach tells companies how to mine their data: what to look for, what extra data to collect and what themes and messages would be most beneficial in driving response rates.
Nowadays, RedRoute often analyses cross-matched survey and customer data for clients to understand both the “What?” and “Why?” of consumer behaviour. The learnings directly impact on the client’s database marketing strategies. Such analyses will only increase in the future as more and more digital communications are used for customer contact and tracking.
For the benefit of all concerned, and for long term consumer trust, what is needed is to replace what some commentators have termed “Carpet Bombing” with a situation where we know how each communication we send fits into the life of the consumer, what they are going to think when they get it, and where the expected response is 98% rather than 2%. The benefit of taking this more considered approach will be a step-change in targeting accuracy, and an improvement in effectiveness that even Admiral Scott would be proud of.