Why Predictive Analytics Just Might Save Your Business
When big data first became a trendy talking point among sales and marketing teams, it felt like the answer to marketers’ problems. But now, the reality is that marketers do have access to seemingly limitless amounts of data, big data no longer feels like the easy solution. In fact, many marketers struggle with execution. They have access to tons of meaningful data, but they don’t know what to do with it. The ability to utilize that data to orchestrate and operationalize marketing tactics represents a real challenge for many marketers. Using predictive analytics gives marketers a huge leg up to create and execute comprehensive, multi-channel account-based marketing programs rooted in data. But if you’re the individual charged with leading a predictive analytics initiative, how do you get it right?
Learn To Crawl Before You Walk
The first step in any predictive analytics initiative is securing business buy-in and recognizing the outcomes you want predictive analytics to influence. If that’s in marketing, it may be identifying your next best customer or detect those customers at risk of defection. In sales, it could be increasing the predictability, conversion and flow of marketing qualified leads to sales qualified status. In financial services, it might be finding the anomalies in data that indicate risk and prevention of crime. Whatever the use case, the success of any predictive analytics initiative relies on that driving towards solid business outcomes, and not just treating the implementation like any other technology project.
We recently invited Rachel Bryant from Thomson Reuters, to join us as a guest on the latest episode in our Artificial Intelligence for B2B Marketing podcast. Discussing what it means to be in the infancy stages of beginning to utilize predictive analytics, we consider what it really looks like to crawl, walk, and then run, in terms of truthfully starting to embrace predictive analytics within your organization.
Accuracy Is Everything
It’s true that the accuracy of predictive analytics outcomes depends on the richness of the underlying data. One thing you can’t shortchange on is the quality of your data, as it directly impacts the accuracy of your predictions. Although possible that the largest amount of time and effort you will spend will be dedicated to ensuring your data quality is on point, success will depend on making sure you’re addressing those missing values, standardizing the data and ensuring the data definitions are well understood.
The True Measures of Success
How do you gauge whether your predictive analytics project is successful? It’s easy to design a predictive model that perfectly models past behavior yet fails when used to predict the likelihood of future events. Before beginning any initiative, record any historical measures such like forecast accuracy, conversion rates, churn estimates, etc. When you start making decisions based on predictive analytics, record the results and the instances during which you did not act on those predictions. Having a solid methodology to measure the impact of your program goes a long way towards refining predictive models, allowing you to trust the recommendations they provide.