One of the big assertions that have been made by people working in the social media Analytics space is that social media data can be used to predict individual purchasing behaviour.
At an intuitive level this is appealing. If my friend recommends a product then if I have the same need I will be more likely to consider that product as a solution to my need. Furthermore, I may signal that I have that need by Tweeting that I have (or indicate I am likely to have) such a need.
But from a brand owner’s perspective there is still a fundamental conceptual issue here, as well as a whole host of practical issues. The most obvious practical issue is “Which database do I use to manage the customer?”. The only set of universally-correctly linked set of data is the one that is owned by the individual themselves. Only they know the myriad of on-line identities that they have chosen for themselves.
Brand owners are very aware that whilst systems such as Facebook and Twitter have millions of users, there are still people out there (and we have to remember that on a global basis it is still the majority) who do not, even though they may be economically active in many other ways.
This leads to a second practical issue that is often glossed-over but which is a fundamental prerequisite – how can I link together data from disparate systems and know that I am linking the right data to the right individual? There are many ways it can be done but it adds time, cost and complexity – all of which undermine the very concept of achieving ‘close to real time response’.
What is more, however, these issues are common to every marketer – there are, at the end of the day, only one set of individuals (even if they may well have multiple on-line personalities). So in principle it makes no economic sense for each company to create ‘a single view of the customer’ because at best whatever they create will always only be a partial view. Each company that is trying to sell goods/services to an individual is only interested in information that influences their needs for those particular types of goods/services. The volume of data about other areas is just noise that needs to be simplified – at least in their view, that is. Yet as the social network data experts will tell you – seemingly unrelated data can be connected.
My purchase of an airline ticket may well be of Interest to the coffee shop chain that I use because they will know that at some point I will be going to the airport. So they can trigger a communication to ensure I use their outlet/brand of coffee at the terminal.
Which brings us back to my original assertion – what we are describing is a multi-dimensional view of the individual, not a “single customer view”. But generating that multi-dimensional view can only ever be done by an independent direct marketing (data) company. There are already many out there – and the strongest players will, ultimately, prove to be those like Nectar and Tesco who already have millions of individuals on their databases.
Unfortunately there will be many discussions ahead about data privacy, data linking, data cleaning, meta measures, and many other detailed considerations and that the average marketing director will find extremely tiresome. All they want to do is get their message across to the right people, at the right time, in the right way. Nevertheless these detailed issues will have to solved / handled if the concept of the “connected world” (as trumpeted by companies such as IBM) is to become a reality in any meaningful way. Needless to say, for the best solutions to these problems we should look to companies such as Amazon as the most likely source of the innovation rather than IT manufacturers. Their views on the world are fundamentally different.
Meanwhile, back in the real world of today, what should the marketer do to achieve his/her more immediate objectives?
The first thing to do is to make sure that you partner with a good direct marketing company – and one that spans both online and offline communications. Without this you run the risk of running “silo communications” and that totally undermines the principle of a customer-centric approach.
In addition, make sure the agency has their own consumer database, one where they are bringing together data on consumers from a variety of sources, even if those sources are not necessarily complete. You do not need them to have a full picture of the consumer, just enough to cover the key dimensions. If you need advice on who to choose, please let us know.
Secondly, if you have your own internal CRM customer database then supplement it with appropriate meta-data measures (even if they are only fused or projected) taken from as wide a cross-section of sources as are relevant. For example, in the DIY sector, data on house moves is always useful, as is data on leisure activities.
Better still, although it is harder to achieve in the short term, is to become a trusted adviser. For example, if you manage an airport, do not just focus on information about your own location but become a mine of information about all airports in that region of the world. Establishing a reputation for providing accurate, authoritative, advice about a whole category will make web site attract people back time-and-again and that will, in turn, create deeper customer loyalty and greater revenues.
Thirdly, use econometric modelling at both a strategic and operational level to maximise the returns from your marketing activities.
At the operational level this means creating appropriately-extended customer propensity models from all the measures in your customer database. In particular, calculating an “Effective Net Preference” (ENP) score for each and every customer. This will give you an instant understanding not just of their likelihood to respond to your offers but also what you need to change about your offer (other than a cheaper price) to increase that likelihood and reduce their likelihood to defect.
At the strategic level, it means understanding how much money to allocate to different types of marketing activities by understanding the levels of reach and frequency they generate. This is important because online communities generate lots of seemingly actionable data but often for only very small proportions of the customer base. To know how to optimise the media mix you need to step back and look at the net reach and frequency different types of activities are able to deliver – individually and when working in concert. Econometric modelling both describes the mechanisms involved and also measures and correctly allocates their net sales effects – providing you with the ROI’s from each of the differing parts of your marketing mix.
With this framework in place you can then, overtime, improve the data sources you have, improve your customer and prospect targeting, improve your offer(s), and improve the effectiveness of your marketing communications programmes.
It is not exactly rocket science, more a methodical approach that remains consistent over time but without becoming repetitious. This are the benefits that our clients derive from using our services and if you would like to know about it then please let us know.