Any single retailer gets a very limited view of its customers’ lives – just the part of their lives that it helps them with. But deep insights need to be based on very broad views of customers’ personal contexts, which are understood by looking at lots of different aspects of their lives.
The social data that one firm generates by its own interactions with customers can be massively enriched by using data intermediaries that are gateways to other firm’s social data.
Let me explain. If you want to make relevant suggestions to your customers then you need to personalise your suggestions. But personalisation requires a deep knowledge of the personal context your customers.
Unfortunately, what you know about your customers is limited by what your relationship with them is about. Each aspect of your relationship is like a telescope that observes from only one angle. You can buy-in data but it needs to be linked to your actual customers which is hard for third parties to do. Also data from customers’ relationships with other firms will rarely be focusing on the precise things that interest you. It might also be aggregated or anonymized. For example, consumer classification services, like Mosaic, are really useful for getting a general profile of the people. But they cannot tell you what a specific customer is like. They enable you to take a bet with good odds, but it is still a bet.
So how can a firm with a limited view of their customers lives really understand them enough to make on target and relevant suggestions that are more than just repeat purchases? I’m talking about cross-selling and selling for different uses of their products. For financial services firms the relationship data that they generate is like looking at their customer lives through a key hole. So how can they learn enough about a specific customer to suggest different products or to suggest different uses for the same products? For example, how can an insurance firm know when a specific car insurance customer also needs pet insurance or even when they want to buy a new pet?
The answer is not scale, i.e. huge customer populations. Learning about your customers’ lives through their grocery buying habits has the same fundamental limits for Tesco as it has for a medium sized grocer. Tesco gets to learn a bit more because of the range of its offer – you cannot understand how a customer feels about electronic goods and music unless you sell a wide enough range for them to tell you their interests by what they buy and how they do so.
Customer buying data is like the tests that you go to the doctor for – they only answer the questions that you ask. If you want to check for a specific disease then you need to do that particular test. If you want to know a customer’s taste in music then you need to stock a detailed enough range of DVDs so that they can tell you what they like by buying specific DVDs. The granularity of the question enables the granularity of the answer, not the other way around.
The diversity of the different services that you provide for customers, and what you talk about with them, drives what you can learn from them. The more of their lives that you take part in then the more of their lives you can help them with – in terms of information, services and products.
One great way to get highly specific and up-to-date insights is to use social data. Social data can help with assessing sentiment, targeting communications, product and service innovation, identifying influencers and detractors, dealing with complaints and many other ways to support your marketing strategies.
Social data can be used on an individual level to communicate with specific customers. And even better, it can be used in a bottom-up way to build groups or segments to interact with at higher and higher levels. This bottom-up method is the complimentary but opposite way to segmenting top-down from you total customer population.
Best of all you can take individual level social interactions, analyse them on a segment level and then take action back down on an individual level with the personalised manifestations of a segment-level strategy.
But you only get into deep and meaningful conversations about specific subjects and specific products. Usually this means your own products, and usually it is either some way down the buying funnel or after purchase. So what you can figure out about a customer still depends on your own product range.
Which is why social data needs intermediaries – firms that help other firms to collect and most importantly of all to share social data. With the proper permissions and safeguards, of course.
Social data intermediaries can help firms to get many more observation angles on their customers’ lives. They are a way of sharing the perspectives that have been gleaned from more than one product range, which means from more than one customer relationship, i.e. from more than one angle of observing each customer.
Of course, for several brands to share data in this way there needs to be things like methods of introduction, frameworks for generating trust, ways of enforcing good behaviour, and platforms for orchestrating efficient collaboration. But intermediaries are good at these as well. And there are plenty of precedents in older areas of analytics – like the data sharing clubs that Experian and Equifax host to help manage risk for member firms.