When it comes to big data, analytics, contextual, spur of the moment offers, the room seems to become slightly cloudy, a state of unreality edges in. We nod, we ponder the enormity of being able to offer millions of people that spur of the moment, non intrusive, ‘makes complete sense, thank you’ offer. And then get back down to whatever it was we were doing before. Yet, 50 percent of operators surveyed by Openet recently want to implement the functionality that will allow them to do exactly that.
Like everything, common sense is the best approach. Forget big data, big analytics, big anything and take the contextual idea back down to the basics. Martin Morgan gives us the classic example of customers in McDonalds and the immortal words, ‘would you like fries with that?’ It is, as Morgan says, ‘relevant, personalised and immediate.’ It is also not intrusive, it is helpful and generally will have made goodness knows how many extra trillions of dollars for McDonalds. It is also very easy to implement.
As an industry, we get into a frame of mind that says ‘because we have millions of customers it is difficult to personalise offers.’ The thing is, we have to start somewhere. The other thing is that we can learn from other people’s mistakes. And offers should be based on what operators know, not what they think they know.
Two classic examples of basing offers on what companies think they know have crossed the desks of BillingViews operatives in the past few months. The first was the ‘train of thirst’ example. A bar in [insert name of somewhere hot and quite dusty] decided that a way of getting people into the bar was to offer a [technical term coming up] location based lure that said, ‘it is hot and if you’re passing, pop in and we’ll give you free cold drink.’ Not an offer to be passed up, frankly, if you happen to be in [hot and dusty] in August. The only downside was that the bar was 150 yards away from the railway track. So, hundreds of people heard the familiar ping, tweet or whoosh of their phone telling them they had a message, looked at it and saw that they could have a free cold drink at the local bar. Mouths watering, they might just have been in time to see the bar as it disappeared over the horizon as their train sped on to towns less hot and dusty.
The other favourite was the couple that were just about to get married and for some reason decided to re-engage with Facebook, which they had not done for some time. When statuses were updated to say they were engaged to each other, dozens of adverts were suddenly in their timelines offering wedding venues, dress makers and wedding cake manufacturing. All of which had been organised months before.
Good examples, however, are emerging. Operators know where their customers are, what device they are using and whether they use a little or a lot of data. Even these three basic data points could provide enough knowledge to know that this customer, while at the airport might appreciate a special deal on data while he is away. Add a relationship with a travel booking website, and the operator knows where the customer is going, how many there are in the party and whether it is a holiday or business trip (to a pretty high level of accuracy).
It is not rocket science. It is common sense, and technology is there to scale it up.