Redefining The Single Customer View

Adding website visitor behaviour to the mix.

There’s nothing new about the concept of a single customer view. Everybody understands the benefits and many argue passionately about the insight that can be gained from a centralised view of customer contact and activity. Most successful organisations have implemented a system that provides this insight and there are now a number of agencies that specialise in helping businesses to achieve a single customer view. Within the retail sector in particular, the use of storecards has enabled a thorough understanding of buying patterns and helped some of the larger brands to grow and dominate.
 
Inevitably most of the data stored about an individual is transactional.  In addition to the basic contact and demographic information businesses store information about purchases made, items returned and marketing communications delivered. The use of storecards makes it relatively simple to integrate EPOS and CRM data to create a rich and insightful data set. Ecommerce businesses offer incentives to purchasers, or simply force them, to log in and share their identity so as to be able to record this information against the purchase (and the purchase against the individual’s record).

For businesses that sell a single or simple product it is often possible to predict future behaviour based upon past performance. It’s self evident that an annual insurance contract will be up for renewal in 12 months time. However businesses with more complex portfolios need a more complex understanding of their customers. New product launches may create new market sectors and the business that can quickly identify prospective customers and market to these successfully has an opportunity to expand and possibly even dominate the sector.

Most e-commerce businesses rely on commerce reporting provided by their back-end order processing systems to populate their single customer view.  This means that they fully understand the end result but have little information about what drove this and hence what might be improved. Enterprise class web analytics systems have tried to fill this gap, but while they provide valuable insight into how to improve a website and which marketing activities yield the best results, most work by aggregating data and providing an insight based upon “the wisdom of the crowd”. Few are able to identify and report upon the activities of an individual and hence integrate with CRM systems and become part of a single customer view.

The main problem is that traditional web analytics systems were built on the assumption that a “visitor” uses a single website access device. Nowadays nearly everybody has more than one device. As a result a single person can be represented by many “visitor” records making it impossible to get a holistic view of an individual’s website behaviour.
 

.

“	A single person can be represented by many 'visitor' records making it impossible to get a holistic view of an individual's website behaviour, if not using Cognesia.	”
	Mark Wilding Head of Product Development Cognesia Ltd

Cognesia Explore is unique amongst the enterprise analytics systems in that, as well as reporting on “visitors” it also reports upon the activities of “contacts”. It has also been designed from the bottom up to knit together the visitor information whenever a match can be achieved and integrate this “contact” information with CRM and email platforms.

Having a holistic view of a contact’s activities makes it possible to interpret activities and infer preferences. A person that has been on your site a number of times over the last couple of days and has viewed a number of pages on walking holidays is probably thinking of making a purchase in the very near future. If they didn’t buy a holiday then it’s probably the right time to send them an email offering special deals on walking holidays.Cognesia Explore keeps a lifelong history of the search terms used and pages visited by a person.  Adding this type of information to the single customer view on a daily or weekly basis enables a business to react based upon a person’s inferred preferences as well as their previous buying history. The result is massively improved conversion rates and an ability to react to a person’s apparent interest rather than simply reporting on their activities after the event.