Making use of Big Data is a staple of every digital marketer’s playbook, especially those in the retail sector who will use it to measure such things as transaction and customer loyalty. There is a lot of potential to be found in Big Data analytics, but it’s lost when retailers grow too comfortable with a “proven” approach and fail to keep testing and discovering new viable marketing strategies. Ultimately, realizing the limits of Big Data analytics and becoming comfortable with experimentation is the key to making sure your business stays in touch with the demands of its environment and its customers.

Analytics are Impressive, but Incomplete

Current trade promotion optimization (TPO) solutions simply weren’t designed to extract signal from the large volume of noisy data that modern retailers generate, and analytics is still unable to specifically measure the effect of qualitative factors like ad design, layout, and wording on ROI. This means that, for better or worse, the current limitations of our technology mean that Big Data can only give us a partial look at what worked and what did not.

Don’t get me wrong. Data analytics provides marketers with an incredible degree of insight, but we must remember to use it as a tool with defined goals and a good understanding of what it can and cannot accomplish.

For instance, analytics is much better at explaining the past than predicting the future. This means that you can’t predict with certainty the effect that a certain kind of discount, pricing structure, or cross-merchandising tactic will play out: you need to run an experiment. This can be scary, but an unwillingness to try new things could leave your business wading in the shallow end of success, perhaps losing very little but not making much either.

Offer Innovation: A Better Way

As a rule, the best way to experiment in this field is to maximize your exposure to positive (beneficial) risks while minimizing your exposure to negative risk. The best way to do so in the omnichannel age is to experiment with offer innovation, a series of micro-tests that you can use to analyze the small-scale performance of diverse offers and messages across channels. Instead of shifting your entire marketing campaign in one great, lurching motion, you’ll be testing many ideas at once and keeping an eye on any promising developments without risking the bulk of your business. The gamble you’re taking is absorbing little failures (i.e., a particular method falls flat) in hopes that you’ll stumble upon a new, reliable way to connect with a certain kind of customer.

Ironically, realizing the limitations of data analytics will help you make better decisions and make more calculated risks. Don’t be afraid to make small mistakes, especially when there’s a large potential payoff at the end.