Scenario Planning – Generates a warm feeling but little value

Forecasting the future – in any discipline but especially in the relatively fast moving world of telecoms – is always important. With massive investments in new technologies and networks and with payback periods that can extend over decades or more, making the right bet is critical. As mentioned in a previous posting, we are now seeing the results of some companies having made poor bets, for example Nortel’s assumption that 4G and WiMAX would arrive more quickly than they have.

Many do try to forecast the future of wireless but the trend in the last decade has been to move from particular predictions to the use of scenarios. This can be seen in any of the forecasts from major entities such as the World Wireless Research Forum (WWRF), European Commission publications, outputs from regulators and a host of research papers.

Scenarios, on the face of it, represent a very sensible approach to forecasting. There are some variables that appear just too uncertain, such as whether mobile TV will take off. Better then to model a range of scenarios often representing extreme cases. This would work well if all the scenarios, or almost all, pointed to a similar outcome. For example, if 4G was needed under all reasonable scenarios then the analysis would have demonstrated strongly that its emergence was near-certain. But in the communications sector this never happens. Instead, the “status quo” scenario shows that no new networks or technologies are needed while the “wireless data explodes” scenario shows that networks will need a ten-fold increase in capacity or more. Effectively, scenarios demonstrate that in order to make a bet on the future you have to pick a particular scenario. Sadly, the forecasters who use scenarios never do. They simply present their 2×2 matrix and assume that their work is done.

There is another kind of forecaster which I term the “hockey stick forecaster”. These tend to be analysts and consultants who produce reports about particular services, such as location based services, providing predictions of revenues over the coming five years. They always look similar – slow growth for the next couple of years, rapidly accelerating in future years. Their predictions almost always prove optimistic so when they revisit them every year or two they slide the “hockey stick” a few years to the right and republish.

Time and experience has shown that neither of these approaches generates much value – indeed if anything they tend to lead companies like Nortel into bankruptcy. We need an alternative – experts with a considerable experience of the industry and great insight who can provide an unbiased and carefully thought through analysis of the future and some way for the industry to coalesce around their views.


2 Responses

  1. This is a very important issue, thanks for putting it out there. The problem you raise with scenarios is huge, and real, but little spoken of in the foresight industry. So how to get more than warmth from scenarios? First the tool must be used for the right problems, and this, for brevity (there is lots to say) comes down to correctly identifying the level of uncertainty [See Courtney: 2020 Foresight] in the real pending situation faced by the company. The other part is to correctly distinguish between future-influencing and future-aligning scenario methods, and use aligning methods for the types of situations you discuss above – for this distinction see the discussion in my book, Future Savvy, and I’m very happy to follow up with more detail. regards, Adam

  2. Adam,

    Thanks for this and for the pointer to the book. I’ve read it and can thoroughly recommend it to anyone involved in forecasting. It has much useful insight as well as explaining different types of forecast and the ways to use them. It offers pragmatic advice and appropriately cautions against excessively optimistic predictions.

    It would appear that key to using scenarios in this case is to avoid extreme input cases that aren’t really realistic, or to assess the outputs and discount those scenarios that obviously can’t happen. For example, a scenario that predicts that cellular operators would need, say, five times as much spectrum, could generally be discounted on the basis that this is highly unlikely to happen.

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