Lucian Camp is a financial services brand consultant, copywriter, author and blogger. He co-presents the On The Other Hand podcast.
I saw a news report the other day about a hotel in Bournemouth which is currently accommodating asylum seekers. The article seemed to say that at the same time, it’s still open to paying customers, which surprised me – so I looked it up on Booking.com and Trip Advisor to check.
In fact it isn’t – I’d misunderstood – but that’s not the point. The point is that every day since, I’ve been carpet-bombed by emails from both firms excitedly encouraging me to start preparing for my trip to Bournemouth, and reminding me that – on the basis of the dates I’d typed in – it’s really getting very soon now and if I don’t get my skates on I might miss out.
Sorry if this is a touch pedantic, but let me just pause for a minute to consider the number of levels on which this data-driven, rifle-shot, personalised one-to-one marketing has got me wrong.
- Of all the holiday destinations in the world that I might consider, Bournemouth is definitely bottom-quartile – probably bottom-decile.
- At the moment I made the enquiry, I was in fact already on holiday and not at all interested in booking another one.
- As I’ve just explained, my enquiry had nothing at all to do with making holiday plans – I was following up an article about asylum seekers.
- I’d noticed that the hotel is part of the Britannia Hotels group, which I know comes out every year in the annual survey of UK hotel groups as the worst in the country.
- Both sites’ suspicions might have been aroused by the fact that although I’ve used them both on a number of occasions (Trip Advisor a pretty large number), I’ve never used either to book one-star UK seaside hotels.
Nevertheless, we’re now up to a total of eleven follow-up emails, and each one of them serves to confirm my impression that neither organisation has the faintest idea of who I am, what my needs might be and how they might be able to help me in the future.
The data-driven marketing era wasn’t supposed to be like this. It might be worth reminding ourselves that the smart use of big data was supposed to lead us into the sunlit uplands of relevance, personalisation and ever-closer relationships between customers and their understanding, empathetic, responsive providers, who bring them welcome, timely and relevant offers at regular, if slightly too frequent, intervals.
You may well recall, as I do, one of the first great use cases that explained all this to us. Tesco Clubcard introduced us to the awesome power of people’s main-shop till receipt, and how much insight it can provide into their lifestyle and their future wants and needs. (I can’t remember the particular changes in shopping behaviour that flagged the imminent arrival of a new baby, but I do remember the stunning results from follow-up offers on Pampers and Calpol.)
That must have been 30 years ago now, so there’s been plenty of time to fix the teething troubles in such systems. But in fact, what those 30 years of experience have taught us is, first and foremost, that creating accurate insights out of limited and ambiguous data is a very great deal more difficult than it looks – and the harder you try, the more likely you are to find new ways to fail.
It’s also taught us – or, if I may say so, in my case it’s reminded me – that the effect of a misfiring insight isn’t just neutral: it is in fact highly counter-productive. Knowing that Booking.com thinks of me as a customer who’s planning a holiday in a one-star Britannia hotel for asylum seekers in Bournemouth doesn’t make me likely to pay much attention when it comes to the next proposition they have for me. If they understand me as badly as that, they’d be better off trying to reach me only as part of the target audience for mass-market communications like advertising campaigns – at least I don’t expect them to know anything about me.
The objection to the argument I’m making here is that I’m only noticing the bad apple – that I’m failing to notice the scores, maybe even hundreds of smart, well-targeted, insightful propositions that deliver great results to the firms making them, and great satisfaction to the customers on the receiving end.
I do wonder, though, how those percentages really stack up. I probably receive somewhere between 50 and 100 more-or-less data-driven propositions a day, from organisations wanting me to buy or do something. Typically you might expect the 10-80-10 rule might be a good way to separate them – 10% useless and embarrassing, 80% sensible enough but not actually something I want to act on right now and 10% bull’s-eyes. Actually, though – and judging fairly harshly, as customers often do – I suspect it’s more like 50-45-5, and inevitably the most fun is to be had – or the most irritation generated, depending on my mood – by analysing the problems that sank the 50. To take a few recent examples of data-driven insight failures in my inbox, there have been:
- The firm that sent me offers on embroidery products for years after I made one purchase, once, of something as a present for my grandma;
- The firm that sends me armfuls of messages about buying reading glasses the week after I just bought six new pairs;
- The restaurant in the Lake District which I visited once, over ten years ago, that still sends me weekly details of its changing menus;
- The garage that’s very much better at adding car details to its consumers’ records than removing them, and so contacts me about twelve times a year to tell me that each of my last six cars is due for its MoT/service/whatever;
- And talking of cars, the stand-alone GPS provider who still sends me details of offers and upgrades without seemingly having noticed that I’ve had no use for their products since all cars started having built-in satnavs about 100 years ago.
I could go on and on and on (and of course if I could be bothered I could also unsubscribe from most of these timewasters) but I think that’s enough.
As far as I can see, there are only two significant conclusions that can be drawn from all this. One is that 30 years after Clubcard (I checked – it launched in 1995) the teething troubles are rather trickier than originally anticipated; and the other is that even if, on a case-by-case basis, the numbers do stack up and the grumbles of a few cynics don’t really matter, the geese that lay the golden eggs are gradually becoming more and more jaded with the whole business.
“I am not a number, I am a free man!” shouted Patrick McGoohan’s character in the classic 60s dystopian fantasy The Prisoner. “We might not mind being numbers,” we reply 60 years later, “If only you could get our bloody number right!”
