FT.com

Customer Insight analysis

The Financial Times website, FT.com is a premier source of online financial, industrial and economic news. FT.com has more than 55 million page views and over 2.7 million unique monthly visits. The site is organised into a number of areas either with free or subscribed access.

The challenge

FT.com generates revenue from subscriptions and advertising. With a large volume of daily readers to the free elements of the site and no information about these readers, revenue opportunities were being lost. FT.com needed to understand customer’s online behaviour in order to maximise revenue. They wanted to know why:

	Some prospects didn’t take up the free 15 day trial
	Some free trial subscribers went on to take a full subscription whilst others didn’t

Unlike print publications where little is known, the online world can provide intimate detail on reader behaviour. The challenge was making sense of these large tracts of web log data.

Both the content and the structure of the website were being constantly modified so the analysis needed to take this into account.

The solution

In order to understand what the leading indicators were for a prospect to move to the next level of subscription CACI needed to understand how the frequency and length of visit, services used, relationship length and web pages visited affected behaviour. Therefore we decided to investigate the use of web log data.

Although not user friendly it was possible to aggregate web log behaviour up to page viewed and time stamp level. Further variables were then defined in order to build up an understanding of how each user interacted with the site such as frequency of visits, entry and exit points, stickiness of certain sections, what pages were read and services used.

This data was then aggregated into key statistics for three groups, unregistered, trialists and subscribers. Dependent on the type of question being asked different samples of the data were used so only unregistered and trialists data was used to ascertain what drives prospects to trial.

The major behaviour groups were then identified through the use of clustering algorithms and associated visualisation techniques. FT.com can now look at all customer records and now know in a given time period how many prospects trialled and subscribed and how many trialled and cancelled.

The results and benefits

FT.com can now understand the value not only of each subscriber but the value of different content on the site to different subscriber groups. The findings have been used to assist site design improvements such as ease of navigation between frequently used sections as well as justifying investment in adding granularity of web log data to the marketing database.

In addition the information has been used to drive email campaigns to trialists and registered prospects where the message is based on the prospect’s segment. Such communication encourages the use of other services and has increased customer entanglement.

As the dynamic banner advertisement capability is rolled out across the site so the behaviour segments derived for each prospect/triallist will be used to drive the nature of the offer presented.

"The key benefit to the Financial Times has been a step change in the information available to the business on how people use our services which has allowed us to make business decisions based on real insight." - Daniel McPherson, Head of Database Marketing, FT.com

To find out more about our work with FT.com either call 020 7602 6000 or email info@caci.co.uk