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"It IS possible to connect with your customers in a way that's more meaningful to them and more profitable to you" - Zoe Vine, Head of Data, The Trading Floor

The "one size fits all" approach has long been considered an outpost of modern marketing. Now more than ever, businesses are turning to strategic data analysis to gain more actionable insight into what is motivating and driving a purchasing decision.

My letter box is piled high with so much direct mail that my postman leaves me notes begging me to clear the mailbox. Telesales calls are met with the "reject call" button. Me - I'm your traditional time-poor, cash-rich kind of girl (keep this in perspective, I don't have a fleet of Porsches lining the sweeping driveway, this is Yorkshire you know) - and for the last four years I have almost exclusively bought every product and service online, often at strange hours of the day in between working, walking dogs, entertaining clients and trying to get some sleep.

Similarly, the products I buy do not necesssarily relate to my lifestyle or lifestage - last month alone I spent £130 on baby products, resulting in a glut of sales messages for multipurpose vehicles and holidays with creche facilities in that poor over-loaded letter box. Said purchase of baby products didn't mean a new addition to my household, more a spate of present buying for friends recently visited by that pesky stork.

So why do companies find it so hard to engage my interest in products that are meaningful and through a method I find acceptable?

Management by fact

Too many companies choose to focus on analysing a customer's past behaviour, when what they are really interested in is how that customer will behave in the future. The gap in knowledge between knowing how someone has previously behaved and predicting how they will behave is massive. The data that companies hold may be transactional, it may even be clean and recent, but it won't necessarily lead to any meaningful insight. Customers always act within a context, and this constantly changes as new products emerge, events happen and life stages change. Historical data can give you the what they did, but rarely the why they did it.

Piecing together the customer intelligence puzzle requires assimilation and evaluation of all the demographic, attitudinal and transactional data sets available to build your process and techniques. Three key steps help to paint this picture of the consumer's buying habit:-

Asking the questions

What data do you hold on each customer? What information can be deduced from each piece? What's the best way to harness this information for future sales? Management by fact is crucial to all these questions - basing your answers on a set of out-dated assumptions renders these questions near enough redundant.

Question the answers

Identify the key triggers within the data what happened at that point in the customer cycle to generate the sale of that product at that time? Does a shift in behaviour in one sector automatically lead to a shift in others? Adopting a variety of clustering and profiling techniques enables clear identification of customers with similar behaviour patterns and demographic trends. Drive robust statistical data sets to generate a variety of predictive models for cross and up selling purposes.

Mining the answers

Don't just ask the questions once - ask them frequently. An answer given 12 months ago may not necessarily be true of the individual if the same question was asked today. Similarly - ask WHY the answer is different - establish the cause for change driving the difference and back up an isolated piece of information with others that work to support or deny the information in question.

Reducing the assumptions made on a customer is the holy grail data-wise in the 21st century - just as a customer's purchasing decision is unlikely to sit in isolation from external events, so too that in order to examine context and behaviour a customers data needs to be cross-pollinated against external sources.

The Trading Floor recently worked with a well-known Travel company to supplement their existing knowledge by appending indications of recent significant expenditure from insurance and finance companies (purchase of a new house or a new car) against lapsed customers of their top of the range holidays, to drive financial triggers as to the likelihood of that customer to next spend with the brand. Examining travel insurance purchases have also helped the brand to identify where lapsed customers have booked through a competitor instead.

Partnership data pools are increasingly well recognised within industries as being more cost-effective, better targeted and more accurate means of reaching customers and prospects. Alongside The Trading Floor's multi-industry, multiple-channel pool, the Mail Order market has its pools with Abacus and T