Isn’t it interesting that, for as long as anyone of us can remember, we’ve come to rely on the daily weather forecast to decide what to wear, what to do and where to go tomorrow? Yet, when it comes to forecasting business outcomes, we’re far more sceptical about the way forward.
Every business tries to maintain some type of forward perspective to help guide their strategies (from sales and expense forecasts, through to researching the impact of emerging customer trends and seasonality). So, much like the weather forecasts, we’re using yesterday’s data to understand tomorrow’s likely outcomes.
Traditionally, larger businesses have been better equipped at throwing resources at trying to perfect this type of modelling. SME’s have struggled bringing the appropriate level of expertise and time to ensure they too have the benefit of better information to guide their strategic direction.
In spite of all the ‘paralysis by analysis’ that we endure, we still know there are only two roads that lead to sales growth. More spend from an existing customer, or new sales from new customers. Getting the balance right between the two has been a constant dilemma faced by marketers and business managers everywhere. According to research by Gartner (Gartner for Marketers CMO Spend Survey 2017-2018), marketers are now spending twice as much on reducing customer churn than they are looking to acquire new customers. Yet, understanding customer churn continues to be the one of the most mis-understood elements of the business mix.
In search for answers, there has been a rather massive adoption of customer satisfaction research. Virtually every customer touch point is being researched to gauge levels of satisfaction. Satisfied customer equates to customer retained….right?
But with the growth in satisfaction research comes research fatigue. So how then will you know what the customer is feeling about their experience with your brand?
For those who run businesses with a combination of traditional ‘bricks and mortar’ store based outlets as well as online, it’s often easier to gauge customer satisfaction. That is, you can often see if a customer is not satisfied. However, as customer relationships become almost completely online in nature, it’s far more difficult to understand what the customer is likely to do.
Today, the constant evolution of predictive analytics that use machine learning has brought a range of solutions to the table that are widely available, cost effective and easy to integrate into any sized business.
Much like the weather forecast, it too uses past behaviour as a predictor of future behaviour. Algorithms are designed to learn from the patterns that are inherent in what your customers do. Patterns often not discernable to you through traditional methods of analysis.
I’m sure there wouldn’t be a CEO, CMO or CIO anywhere in the world who wouldn’t want to know when a customer is about to churn and have the opportunity to reach out to them and try to convince them to stay. Predictive analytics does exactly that. It provides you with an informed prediction, based on your own data, that identifies the relative probability of a customer leaving you.
Based on the same Gartner report mentioned above, “out of 13 marketing capabilities, CMO’s allocate the most, 9.2%, of their total marketing expense budget on marketing analytics. Marketing analytics jumps ahead to the No. 1 area of spending compared with last year”.
So next time you’re listening to tomorrow’s weather forecast, remember to think about the role of predictive analytics. You might be heading for stormy weather in your business, but now have a chance to do something proactive about it.
Steve Emanouel is Managing Director at Spyglaz and is based in the Melbourne, Australia office. He is passionate about bringing change management to organizations that are prioritizing the reduction of customer churn as a key objective.
Spyglaz delivers churn management software. We use machine learning algorithms to identify which customers you’re likely to lose before you actually lose them. Our proprietary algorithms analyze your historical customer data and identify macro trends that have historically led to customer loss.