18 Jan Improving Customer Relationships Through Complex Event Processing
I do like it when customers talk publically. So I’m delighted that DBS Bank, based in Singapore, has been talking to ZDNet about what its been doing with Apama (amongst other software vendor products). I recommend you listen to what David Gledhill, head of technology at DBS, has to say. The article and video can be found here.
DBS is a good example of how organizations in many industries – banking, telco and retail as examples, are moving from conventional customer relationships to those that are more personalized and interactive, to increase revenues and increase loyalty. And a key technology in enabling this is Complex Event Processing (CEP).
Consider the targeting of marketing promotions. Customer segmentation through profiling is the start of this process, although in itself it’s nothing new. Segmentation involves understanding customers’ past spending patterns, combining this with other financial information that may exist or can be implied, and further combining this with other socio-economic information. This is matched with marketing promotions which are then sent out to the customer in the hope of a positive response. And it is more hope than expectation. Even using sophisticated predictive analytics technology to determine what offer to send leads to very low response rates, usually less than 1%.
There are many reasons for this low response rate but one that can be tackled and that is key to DBS’ strategy is ensuring that the offers are contextually relevant. Gledhill gives an example of adding context to an offer by using the location information implied by the use of an ATM machine in a shopping centre. Perhaps the offer gives the recipient the chance of 20% off lunch in a nearby restaurant. If delivered very soon after the ATM transaction (within a few seconds) it will be known that the customer will be physically near the restaurant in question. The offer has far more contextual relevance to the customer to an offer conventionally delivered hours or days later when any context will have been lost.
The above example uses location to provide physical context. But there’s an even more important factor at work here – time (and for people moving in particular, time and location are, of course, inextricably linked). Responding at the right time is critical to providing context. Imagine another example, this time from the world of mobile telco. During a phone call the caller exceeds the number of free minutes within their price plan. At the moment the call is terminated they receive a text telling them this and also offering them an upgrade to a plan including more minutes. Intuitively one would expect a much higher response rate than if this offer was sent hours or days later. And practise bears this out. Turkcell, Turkey’s biggest mobile telco, and a Progress customer I’ve written about before, has found it typically receives 10 times the number of positive responses when such offers are sent in real-time than when sent hours or days afterwards.
Responding fast is critical in both these cases. Recently, I’ve started using the term intervention window to describe the time window that exists to effectively respond to a business event that occurs. This is illustrated below:
Intervention window is a term borrowed from medicine where it describes the time available between a diagnosis and an effective medical intervention. It is intended to describe the maximum time that is available to respond effectively to some kind of business event, whether an ATM withdrawal, the termination of a phone call or something else.
This is where CEP comes in – monitoring business events of interest, looking for patterns which indicate the optimum time and context to send an offer has arrived and then dispatching the message to the customer within the relevant intervention window.
This type of real-time response to customer interactions is becoming more commonplace and, in a mobile world, something we’re coming to demand. Expect more of it.