IN ANY potential war situation, it is wise to gather all available data on enemies, their capabilities, disposition of people, weapons and systems. But it is even more essential to assess their overall capability and willingness to engage. Estimating the likelihood of a win, should hostilities break out, is the goal.
Generally speaking, a relative superiority of threefold or more would be taken as statistically sufficient to win a conflict outright. But the confirming steps would involve a series of battle plans, computer models and simulation trials. If these look good, the ultimate decision can be taken: go or no go.
Sad to say, the business world is generally far less methodical and diligent. Practices remain from a simpler era than that experienced by the military. Until recently, this old world of business could be blamed on a lack of raw information, a paucity of suitable models, trials, testing and decision support systems. This is no longer true.
Personal, professional and transactional information is increasingly available. The companies that exploit social networks are extracting the behavioural statistics of individuals, groups and markets to not only ‘steal a march’ on the old players – they are stealing complete markets. They have recognised that the use of social networks lead to new purchasing patterns.
A good example is the way we buy music. Only 20 years ago, we would purchase a CD of 19 tracks at £15 when we only wanted two songs. The hit parade reflected this pattern, with the sales of numbers 1, 2 and 3 being 20 or 30% apart by sales volume. Now we buy online by the track for a few pence and the hit parade looks very different. Numbers 1, 2 and 3 are separated by 100 and 200% or more by volume, while the rest fall into an exponentially collapsing long tail.
Today, likely buyers are targeted directly, and if they like the track, they tell their friends via Twitter, Facebook et al, and the viral sell begins.
So how do these new boys on the block do all this? Data, lots of data, as well as analysis, market and individual modelling, real-time monitoring and feedback. They access every data source and monitor every move and purchase we move. They know and exploit the early adopters and key communicators – the super nodes of the purchasing world, if you will. They understand the market of one and target us all with specific ads, but – better still – they have our history and habit information. At the leading edge, they know our preferences and they know when we last purchased new pants, and how long that will last, and they advertise and tempt us appropriately – at the right time with the right product.
This is all way beyond ‘people like you bought this product so you might like it too’. It is anticipatory. They know what we want and when, and they predict what will tempt us most. And they do this on an individual and at a market level. But as they do, they continually hone their models to add even more dimensions. This is just one of the many reasons that data about you and me and what we do and buy is so valuable and exchanges hands for substantial amounts of money. Data now equates to sales – exponential sales.
So how about the high street? Many are dying a slow death and most are struggling, but some are getting their act together with full ‘in-store’ 3G coverage, free WiFi, and BlueTooth product data. This is not an act of altruism. They are gathering data: where did you walk; who did you talk to; did you go online to do a price comparison; and what did you purchase? And then they extract the patterns of an entire shopping population to optimise store layouts, product placement, stocking and delivery. The lessons of the online world of marketing and selling are migrating to the real world of traditional retail.
In the next phase, active tags on all products will go beyond the boundaries of the physical store and distribution, and the marketing world will lurch further to yet another mode: there is money in data, but even more in meta-data. ?
Peter Cochrane is an IT consultant and former BT chief technologist
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