A growth in the use of wireless services has long been seen by the wireless industry as the way to generate revenue growth as subscriber numbers plateau. However, there is a long history of very poor prediction of service success – WAP, location based services and data have for many years drastically underperformed almost all targets, while, as is often mentioned, SMS has been an unexpected success. Wireless analysts have often predict a “hockey-stick” uptake for almost all the services they consider and as the services have languished, they continually shift the inflection point in the hockey-stick curve to the right, despite the increasing evidence that the service is not finding success. Yet, occasionally, a much delayed hockey-stick growth does occur – the sudden rapid increase in 3G data “dongles” is such a case.
Why is it, after all the experience that we now have of launching new services, that we are still so poor at forecasting their success? Predominantly this is because services are based on a very complex inter-related “eco-system” that includes manufacturers, operators, other service providers and importantly the early adopters and advocates amongst the end users. For example, for location based services manufacturers need to build easy-to-use GPS location systems within the handset, operators need to provide a framework for location based services, entities like mapping companies need to produce appropriate offerings, Google needs to provide location-enabled search and early adopters and key influencers need to be enthusiastic about the service in order to convince others to adopt it. The relationships between all these players are complex with some positive and some negative feedback loops. Models of such situations show dramatically different outcomes can be achieved with relatively little change in inputs and “tipping points” are often observed. The complexity is not helped by the tendency of those in industry to look optimistically at the services they are working on, and for analysts to prefer reports with more positive than negative outcomes.
The existence of tipping points – values of particular input variables at which the predictions of the model suddenly shifts from no growth to the hockey-stick – makes it almost impossible to predict accurately the success of such services. The chances are that most will be predicted to be successful for many years during which they will languish and then suddenly, for reasons that may not even be apparent, or appear of little relevance, they will take off rapidly. All we can do is learn from the models as to what behaviours would most likely result in success. But actually we know this already – to be successful all elements of the service launch must be near-perfect. The technology must work, the service must be easy to use, the pricing must be attractive and the marketing must attract the right early-adopters who must be deeply impressed. If any one element is not quite right it could be enough to prevent the service succeeding. That much is common sense. The difficultly, as always, is getting all the companies to work together in a way that is competitive but collaborative, embraces standards but allows competitive differentiation. This is very hard – the incentives on individual organisations are rarely such that they work together well. What tends to happen is that individual elements slowly get solved and when the last one falls into place the service takes off. This may be what happened with data – the last element being the service pricing.
The bottom line is that accurately predicting service uptake will remain almost impossible unless all entities work together on delivering the service. And history suggests that this is unlikely.