This much I know: Why machine learning will be the next big disruptor in B2B
Management information solutions might have been built for humans, but the truth is that despite the analysis capabilities of such software, none of it really solves the inherent problem: what should we really be doing with it all?
Most people are just too busy to analyse and understand their data, regardless of how good the visualisation tools are. This typically leads to decisions being made based around gut feel rather than the information itself. It could be argued that this human weakness is down to management (often data analysts don’t have the skills or time to do it properly). But rarely are resource and skill shortages addressed.
For the last five years I’ve been closely following advanced analytics and the use of machine learning to analyse data. And this is now coming of age. Some of you will know that this capability isn’t anything new; 20 years ago I first saw Clementine and thought my career was over, but this time it’s different.
With the rise in cloud computing, the vast quantity of data companies are creating and a recognised impact on buyers’ influence, this generation of machine learning is producing more results and its entry price makes it affordable to nearly every company.
For humans to analyse data they need to simplify it into different dimensions. A machine, on the other hand, can quickly analyse and compare thousands of variables across thousands of dimensions until it finds something that correlates to an outcome. It can consider far more data than a human and, crucially, find genuine correlations rather being prejudiced about why things happen. This enables the machine to forecast and predict future outcomes with a high degree of accuracy.
Fortunately, I don’t believe it’s all over for humans quite yet – the action that companies take and the way it’s executed is still very much a human decision, and often requires creative and emotional intelligence, which machines don’t currently have.
Machines are great at the data lifting, sifting, filtering and analysis – this is the legwork that can turn insight into action, and extracting this ‘actionable insight’ is what machines are now very good at.
The first commercial sector to really benefit from this technology was consumer sales and marketing, where social signals and data are continually analysed by machines to make the most appropriate customer offers. Personalised adverts on the internet and product recommendations are other common examples.
In B2B businesses it’s working well too. Millions of pounds have been identified in up-sell opportunities that had previously been ignored. But it’s usually prospect profiling where machine learning and predictive work best. Prospect profiling for telemarketing carried out by the machine typically has a three times better conversion rates when compared to the best human-selected list.
With the previous track record of human performance in this area, it’s inevitable that machines will soon begin to take over this function. The question now is: how long will it be before the default is a machine over a human? My guess is that within five years machines will become widely used, and humans will be left to pick up the more emotional and creative functions.
So the final question is: ‘Can you really afford to ignore machine learning?’