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A plain-English guide to using past data to make better marketing decisions, without the jargon. By Kellie Lee Stewart, Founder.
Predictive analytics uses your past data to make an informed guess about what customers are likely to do next. In marketing, that can mean scoring leads, timing campaigns and spotting customers who may drift away. For most small businesses, clean tracking and sensible human judgement matter far more than complex models.
Marketing has always involved educated guesses. Predictive analytics is a way of making those guesses better by looking at what has actually happened before. It sounds technical, but the underlying idea is straightforward, and some of it is useful even for small businesses.
Predictive analytics uses historical information, such as website visits, enquiries, purchases, email opens and ad clicks, to estimate what is likely to happen next. Which enquiries are most likely to become customers? Which customers may not come back? Which message is most likely to prompt a response?
It is worth being clear that these are estimates rather than certainties. A prediction is a reasonable guide, not a promise, and it is only as good as the data behind it.
If you receive a lot of enquiries, it helps to know which ones deserve the quickest attention. Scoring leads by factors such as the service they asked about, where they came from and how they behaved on your site lets your team focus on the people most likely to buy.
Instead of sending the same message to everyone, you can tailor emails and ads to what people have shown an interest in. Someone who has read three articles about one service probably does not need an introduction to your other services.
Signs such as fewer visits, unopened emails or longer gaps between orders can suggest that someone is losing interest. Spotting this early gives you a chance to get in touch with a helpful message before the relationship fades.
Looking at which channels have brought genuine enquiries in the past helps you decide where to put your next pound or dollar. This is a good reminder that budgets should follow results rather than habit.
Many businesses have busy and quiet seasons. Looking back at previous years helps you plan campaigns, stock and staffing before demand arrives rather than after.
Before any prediction is worth trusting, the groundwork has to be sound. We would look at the following first:
For many small businesses, getting these four things right will improve results more than any sophisticated model.
Insights are most useful when they lead to experiments. Use what you learn to decide what to test, such as a different headline, a new offer or a revised follow-up email, and then check what happened. Feed the results back in so your next decision is better informed. Over time this steady loop produces improvements that last.
You do not need a data science team. Start by confirming that your tracking is accurate, then look at where your best enquiries come from. Our paid audit reviews exactly that for your ad accounts, and our Google Ads management uses what the data tells us to keep improving results. If you would like to know more about how AI is changing advertising generally, we have also written about how AI is changing PPC.
FAQ
It means using your past data to estimate what customers are likely to do next, such as which enquiries are likely to buy or who may stop engaging.
Not in a complex form. Accurate tracking and a regular look at which channels bring real enquiries gives most small businesses the biggest benefit.
It gives informed estimates, not guarantees. Accuracy depends on the quality and amount of your data and on how often you review it.
Check that your calls, forms and bookings are tracked correctly and counted once. Good data is the foundation for every later decision.
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