Other parts of this series:
The future has a habit of catching up with us.
And sometimes, just sometimes, predictions do turn out to be reliable.
Back in March 2020, when September seemed like an impossibly long time away, experts predicted that’s when South Africa would hit peak infections. And indeed, as I write this, the new case numbers have stopped growing, just as the models predicted.
So now seems like a good time to make some predictions about business, bearing in mind (as Danish politician Karl Kristian Steincke quoted in his 1948 memoir) It’s difficult to make predictions, especially about the future.
Looking at companies which thrived after the last two recessions, evidence seems to suggest that those that balanced cost management and growth outperformed their competitors in the aftermath.
And if this pandemic has taught us anything at all, it’s the value of moving quickly. Early on, countries which acted even days earlier than others saved many thousands of lives.
Modeling a pandemic starts off with educated guesses, but the models are constantly being fed data in as close to real time as they can get it.
That’s a big lesson for business.
The pandemic has thrown everybody’s forecasts out the window. All that trend data that we based our projections on turned out to be as useful as a chocolate teapot. I’m sure there are still people with toilet paper in their cupboards from March 2020. Who could have predicted that?
Maybe we can take a lesson from epidemiologists and inject some real-time data into our projections. The truck drivers at one large snack food company scan out the products they’re delivering — but they also scan in the stale products that the store is returning. The company uses that real-time data to analyse what is selling at a store level, and it plans its daily production accordingly.
When our own data sources aren’t helping, some companies have turned to social listening or third-party data. We were working with a wireless carrier. Half of its customers were small and medium-sized enterprises (SMEs) which it serviced through its network of retail stores. That stopped working completely when the lockdown hit, so the business turned to social listening and third-party data to generate leads.
Another thing companies can do is to reset their leading indicators. I met a man once who went into the real-estate business. His idea was to sign up sellers before the competitors did — even before the sellers had told anybody they were selling. His strategy was to hang out in the local paint shop and chat to the customers who were buying paint. He reasoned that they may be sprucing up their houses in preparation for putting them on the market in a few months’ time.
And it worked! He said he won several deals that way. One of our customers, a benefits and insurance provider, is using AI to scan job boards, credit reporting services and social media to spot its high-risk SME customers. That way the insurance provider can be proactive about business support and customer retention.
Have you had success with real-time or near-real-time data collection? Maybe you’re thinking of using networked sensors to collect data? Or maybe you’ve spotted a counter-intuitive leading indicator? I’m always looking for success stories and lessons learned. I’d love to hear from you. Drop me a line at email@example.com or leave a comment.
If you would like to read the research, here’s a link to it.