Direct marketers are beginning to realise shiny new pieces of technology
have their limitations and cannot replace traditional methods of
analysis, writes James Curtis
There is no doubt that technology is driving direct marketing forward.
The ability to gather and analyse vast quantities of data is the bedrock
of all targeted marketing, allowing marketers to understand and predict
how their customers behave.
Technological developments have made it affordable for businesses to
capture and store data on customers and, as the volume of information
increases, marketers look more and more to technology to provide answers
to their questions.
As a result, the language of direct marketing is now peppered with terms
more suited to the bridge of the Starship Enterprise than marketing
departments. Massively parallel processing, data warehousing and neural
networks are some of the buzz words of high-tech marketing.
But has this head-long rush into high-tech happened too fast? There is a
growing consensus that direct marketing is concentrating too much on
buying new toys and not enough on using them effectively. As Barry Hill,
senior vice-president of product development at American Express says:
‘The technology is there before the marketing imagination has arrived.’
A classic example is neural networks. This is the technology that
supposedly imitates human thought by spotting patterns in data: they can
range unsupervised through information and draw conclusions. The
attraction in direct marketing is that they are good at identifying
trends in customer databases - usually a complex mix of demographics,
purchasing behaviour, lifestyles and mailing history. This can then be
used to forecast behaviour, analyse response or look for clusters.
For a while, neural networks were held up as the next-generation
modelling technique, an unbeatably powerful analysis tool. However, they
have failed to live up to their promise, with many businesses finding
that they get better results using traditional analysis techniques, like
The number of companies actively using them in direct marketing is very
small, although they are widely accepted in financial services as a risk
assessment and credit-scoring tool.
The problem many companies have found is that unless their data is very
well organised, clean and properly prepared, neural networks and other
leading-edge analysis techniques do not bring anything new to the party.
The boring fact of life is that the majority of time should be spent
preparing data for analysis, not analysing it. Edwina Dunn, managing
director of Dunn Hunby Associates, contends that 90% of companies have
poorly prepared raw data. While this is the case, investing in sexy new
technology is a waste of time. Dunn argues: ‘People are focusing on the
technique because it is easier to debate whether you should use this
system or that. But they are not answering the really important
question: have they got all the data?’
The same is true of data-mining techniques, such as MPP and PC-based
versions like Rapidus and Viper. These allow incredibly rapid access to
huge databases, but unless the marketer knows what he is looking for and
what to do with the information once he has found it, they are not worth
the expense, and, in the case of MPP, this is no small investment. Neal
Muranyi, marketing director of the Database Group, says: ‘You can buy
all these gizmos and still not get a result. You can get swamped with
information and not know what to do with it.’
The key requirement when considering new technology is to ask whether it
will improve understanding of customer behaviour and allow the company
to communicate with them more efficiently. If, instead of bringing
greater wisdom, new technology is clouding marketers’ understanding of
what they do, then there is a problem.
This is one of the oft-quoted problems with neural networks, in that it
is often hard to understand how the system arrived at a particular
conclusion. Tony Masters, director of the Computing Group, says
marketing people often ‘can’t see what is going on in the neural network
model. They don’t understand why some prospects are better than others
and so can’t adjust their marketing strategy to suit’.
The benefit of using traditional techniques is that the marketer can see
how every conclusion is arrived at and so gets a more thorough
understanding of the logic behind trends in the data. In a strange
admission for an IT consultant, Dunn admits that ‘properly-trained
humans are more expert than systems’.
An over-reliance on IT can make marketers forget the most basic
principles of what they are doing. It is perfectly possible to get lost
in the science of your technique but miss the point of why you are doing
it. Keeping a keen eye on what you want to achieve from the database is
key to managing it properly. Richard Knee, commercial projects manager
for Heinz, which has invested extensively in its database management
system, says that ‘if people understood what they wanted before going in
they could identify their target database very quickly’.
Profiling need not be a mind-blowingly high-tech exercise. An example is
the Pareto principle which states 20% of customers account for 80% of
profits. Getting some of the customers in the middle of the database
into the top 20% is often a question of overlaying the profiles of the
most profitable onto the rest of the database. Chris Morris, database
director at NDL International, says: ‘This can be done just as
effectively with standard techniques as with neural networks. You can
leverage profitability without getting anally retentive about what
package you use.’
Some companies, however, are making imaginative use of neural networks
and other hi-tech analysis tools without allowing it to drive their
strategy. American Express is taking targeted marketing one step further
by customising the promotions in its account statements to match the
requirements of individual customers. This information is taken from the
company’s European data centre in Brighton and analysed to identify
trends in customers’ buying patterns. An extra page is being included in
British card holder’s statements, containing offers designed to match
the spending patterns of the customer.
More than 80 UK retailers are participating in the scheme, which will be
extended later in the year to include money-off coupons for stores local
to the customer. The results so far are impressive: response rates for a
non-customised car rental offer were 0.06% but rise to 9.32% with the
Barry Hill, at Amex, says neural technologies ‘are playing a role but
are not the main thrust’ of the programme, which draws on ‘a salad of
techniques’ for analysing the data. ‘We are quite happy to experiment -
whatever works is great. There is rarely a consensus of what is the most
But Amex still employs traditional analysis techniques. Hill explains
that the company uses neural networks ‘upstream’ in the analysis
process: ‘If we have 12,000 variables we may use it to cut it down to
800 and then use classical methods to look at the rest.’
While accepting that many marketing people do not know how to use
technology, Hill argues they should be more relaxed about allowing
computers to do the spadework instead of getting paranoid.
‘People need to be more relaxed about technical invasions and adjust
their roles accordingly. They need to spend less time on methodology and
more on creativity,’ he says.
The example of Amex shows that neural and other high-tech analysis
techniques can be used successfully as long as the user does not allow
it to complicate their objectives. Neural packages designed specifically
for marketers will help simplify the process, but in the mean time,
direct marketers could benefit from doing the simple things better.