The idea of having a tool that can help organisations forecast
the future is an appealing one, especially when living through the
uncertainty of today's recessionary climate. But predictive
analytics technology, which aspires to do just that, has been
around in one shape or form for years and, while in steady growth
mode, has failed to set the world alight to date.
According to IDC, the global
predictive analytics (PA) market grew 12.1% in 2008 to $1.5bn and
is expected to grow at a compound annual rate of 7% over the next
five years. This compares with the more commoditised end-user
query, reporting and analytics (QR&A) sector, which was worth
$6.3m in 2008 and grew by 10.3%.
The big difference between the two segments, meanwhile, is that
QR&A involves working with historical data to identify trends
or patterns, while PA takes large volumes of both historical and
real-time information from different internal and third-party
sources, puts it through a model, and predicts likely outcomes
based on a range of causal factors.
The technology is used mostly by large data-rich enterprises
such as financial services firms. The most common applications in
this context are to analyse whether customers pose a potential
credit or insurance risk and/or to establish whether they are
attempting to undertake fraudulent activity. Prior to the credit
crunch, PA tools were also used to improve customer acquisition
rates, although the focus is now more on retaining the most
profitable ones at the expense of those providing a lower
return.
Other key sectors, however, include telcos, which generally
employ the software to predict likely customer activity such as
churn and/or payment rates, and pharmaceutical companies, which use
it in areas such as drug discovery.
Retail and leisure companies also comprise other core markets.
They invariably use such systems to try to forecast demand for
given products and services at different times of the year based,
for example, on seasonal factors such as holidays or the weather.
Another use case involves predicting whether individual customers
will be interested in specific special offers in order to maximise
contact with call centre agents via cross- and up-selling
activity.
Prime example
Center Parcs is one example of a PA user that benefited to the
sum of £2m during the first year of implementation. The provider of
holiday villages always used to undertake two bulk marketing
mailings to its customers each year. But after rolling out
DataDistilleries' PA tools in 2002 as part of an overhaul of its
marketing function, it introduced more frequent campaigns targeted
only at those customers it believed were most likely to
respond.
In the first year alone, the move enabled the company to cut
mailing costs by more than 50%, saving it £1m. It was also able to
boost revenues by the same amount as a result of increasing
occupancy rates and of selling guests a wider range of sports and
leisure facilities.
Another organisation that has used PA offerings for almost a
decade, however, is Procter & Gamble. It employs SPSS' software
to help it better understand and predict customers' probable future
buying behaviour in order to market new and existing brands to them
more effectively.
As John Hagerty, a research fellow at AMR Research, explains:
"Predictive analytics tends to be geared towards outward
customer-facing activity. It's primarily used to drive additional
revenue creation and sometimes for optimisation. But it's only used
in pockets and not in back-office operations much."
This is because the software is complex and has traditionally
been used by a specialist team of statisticians who are employed
for specific purposes in a large enterprise context. An expert team
is required because practitioners need to understand which
algorithms to use, in what context, and on which data sets.
But key vendors such as SPSS and the SAS Institute have been
trying to both make such tools easier to use and simpler to make
changes to underlying data and programming models, which can
currently take days. According to Alys Woodward, an IDC programme
manager, however, there's still a long way to go.
"Advanced analytics tools are more expensive than QR&A ones,
but they're not crazy. The market's not inhibited by price, but
more by the fact that you really need to understand this stuff and
people don't know what to do with it to take the next step. So
users are doing great things with it, but it's only about 5% of
what they could do," she explains.
Underlying information
Another issue is the need for underlying information, which is
generally housed in a large data warehouse for analytics purposes,
to be complete, consistently defined and accurate. But to achieve
such a state generally requires massive data cleansing and
integrity exercises, which involve the IT department and data
owners working together to undertake what is often quite a manual
task.
Hagerty warns: "Without good data, predictive analytics is
useless because it's the bedrock that everything else sits on top
of. You can build a fancy system on top of a rotten core, but the
results will be garbage. So it's about ensuring that you have
common data that's clean and defendable."
As to what the future holds, although predictive analytics is
likely to remain somewhat of a niche technology into the
medium-term, it could well start to broaden out in usage terms over
time.
Woodward, for example, believes that OEM arrangements that SPSS
has signed with QR&A vendors such as Business Objects and
Cognos could lead the charge to a widespread embedding of the
technology over the next couple of years, not only in QR&A
tools but also in other applications such as CRM.
Hagerty, on the other hand, expects to see a "real flowering of
analytics" in a general sense over the next three to five years,
with predictive analytics playing a key part in this.
"Once the technology evolves and more use cases emerge that are
valuable and cost-effective, barriers will start to fall. It's
already started as the concept of analytics has increasingly taken
hold, but it will definitely become a more accepted way for
organisations to exploit their data more fully," he concludes.