How Predictive Analytics Can Improve Your Marketing Strategy

At its most basic, predictive analytics experts looks at data to forecast the future, telling companies what they can expect based on what’s already happened. Truthfully, though, the process is much more involved than it might seem. On top of historical data, it also considers why certain things happened on top of what happened.

Predictive analytics can be carried out via human analysis or software, but most often a combination of both approaches is used. It led to massive strides in marketing in 2018, and there are a number of ways that it can help with your marketing.

Use Predictive Analytics to Model Customer Behaviors

Retail companies like Amazon use predictive modeling to determine what customers will do in the future. With propensity models, companies can determine what customers will do before they do it. Insights include how likely a customer is to engage with the company, unsubscribe from a newsletter or make a purchase. Potential lifetime value can also be determined.

Another model is collaborative filtering, which determines which ads, products and services specific customers will respond best to. A good example of this is Netflix, which uses this type of modeling to suggest movies and shows a viewer will be interested in.

Reduce Churn With Targeted Marketing Efforts

Predictive analytics can tell you when a customer is about to churn, which means they’re going to discontinue being a customer. There are a number of ways marketing departments can reduce churn:

  • When onboarding customers, provide them with all of the information they’ll need to use the product or service. You want to make it as easy as possible for them to use what they’ve signed up for or purchased. If the customer doesn’t get any value out of it, they won’t continue to buy.
  • Send messages to customers who have signed up for a service but aren’t using all of the features available to them. It’s a gentle nudge to get the customer using the service, and it may also explain concepts that the customer doesn’t even know about.
  • Offer a discount or promotion that caters specifically to where the customer is in the customer journey. For example, if a customer’s contract is about to end, the most useful discount would be one that reduces the cost of renewing the contract for another year. On the other hand, a customer that has just made a purchase but has complained that the features they need aren’t available need a different sort of offer. They may respond to a free one-month subscription as you unroll the features they’re looking for.
  • While you don’t want to lose any customers, you may have a hard time connecting with each and every customer who is about to churn. Instead of stretching yourself too thin, focus your efforts on your highest-value customers. If you can keep your most profitable customers from leaving, it won’t be as difficult to part with the others if you have to.

When used to reduce churn, predictive analytics can help practically any industry. It can turn big data into actionable insights, empowering decision-making and enabling business owners to anticipate trends, determine customer needs, and gauge the effectiveness of marketing campaigns, ultimately reducing lost business. In retail, specific events and sales can be promoted during times when churn has been historically high as a way to keep customers. In finance, customers can learn about new options for investing if their current investments aren’t seeing enough of a return.

Plan Content Around Search Spikes

Predictive analytics experts can extract several years of search data to determine when people will be searching for a specific term. You may be able to focus so much that you know the exact dates when people will be searching for the term. You can then start to plan your marketing strategy. Moreover, by using social media data to predict future trends, you’ll be able to determine where certain content will perform best and where you don’t need to post it.

For example, let’s say you predict that search for “email marketing” will spike on September 1 of this year. You might budget to spend more on advertising during that time and have blog posts and social media content scheduled to post on that date. You can also start a PR push early so that your content or information will show up in media publications on or around that date.

A cornerstone of predictive analytics is regression analysis. This is how analysts figure out correlations between what happened and why it happened. Analysts can then gauge these different factors, assess their relationships to each other through modeling systems like Venn diagrams, and determine how much each variable impacted the behavior and the likelihood of it being repeated in the future.

While predictive analytics is highly valuable for companies, it still takes a lot of insight, trial and error to get right — and even then, outcomes may surprise you. Marketing efforts should be varied, tested and changed as needed until the right combination is discovered. The need for experts in this field is growing, making it an excellent profession for those who love both marketing and technology.