Merging multiple sources
The major advantage we can offer to clients is the ability to merge a huge range of multiple external data sources and data feeds with the intelligence we’ve built up ourselves – which is extensive – or had sight of in the wider Capita arena. That’s an enormous amount of information when clients themselves may only be working with one or two insight partners, typically Experian, Equifax or similar.
What that enables us to do is fill in many of the gaps in knowledge that clients have about potential customers, and segment more effectively as a result.
Our approach will vary from client to client, but typically we would take a broad set of their existing customer data and then build what we call multi-dimensional profiles on the back of it, using all the additional intelligence we’ve gathered and models we’ve constructed. So for example, we’ll have a telephone model that will tell us if this customer likes to interact by phone, have they done it in the past, have they purchased by this channel before, are they likely to do so again?
We’ll also have a lot of life-stage data: have there been any major events in somebody’s life such as a home move, the birth of a child, a new car purchase, significant investments, movements within pension pots, kids coming of age for school or university, etc.
Because we amalgamate from so many different sources, we often have sight of this data in advance of when clients may see it. A good example might be someone about to move home. We’ll know that at the point they instruct an estate agent or ask for a valuation, which will be ahead of when the home itself goes on the market.
Timing communications around life-stage behaviours
Events like that can initiate a series of timed communications, and we can be prepared in advance. We know that when someone moves into a new property, they’ll generally be decorating and buying new furniture in the first eight weeks; within the first 6 to 12 months they’ll often consider moving energy or broadband supplier; further out at 6 to 18 months it will be home improvements such as windows, conservatories or loft conversions. In a different example, if someone has a child about to reach secondary-school age, they’re more likely to take out a mobile phone for that child, and have a higher propensity to look at tablets, laptops or other tech-related products.
So when you merge all these multiple external data sources and feeds with the intelligence we’ve built, you arrive at an optimal segmentation model. Some of it is based on propensity, some based on actual behavioural history, but together it makes a very powerful way of ensuring a product or service a customer might want is delivered at the right time, via the right channel, at precisely the moment they should be most receptive to it.