How can your marketing department use predictive analytics?

Time travel

The chance to see into the future is an opportunity that not many businesses are likely to turn down. Unfortunately, without any time-traveling DeLoreans, crystal balls or incredible discovery by a quantum physicist this isn’t likely to happen any time soon.

The closest marketing departments have for the time being are predictive analytics – a technique that is much older than you might think, with origins that go back as far as the 1940s. True, things have grown more sophisticated than the predictive modeling conducted by governments during WWII, but the basics are essentially the same: predicting the probability of a future outcome based upon historic data.

Today, using data to calculate trends and behavior patterns is used by many industries, such as risk management for financial companies, the travel industry for ticket pricing, and of course marketing departments.

According to a recent article from The Telegraph, the insurance sector has even gone so far as to develop a ‘death clock’ – an algorithm designed to predict life expectancy in order to calculate premiums.

On a more cheerful note, here are just a few applications for predictive analytics that can drive marketing and sales activities:

Lower acquisition costs: predictive analytics help marketers identify their best prospects with the highest likelihood of converting. Marketers can then direct their efforts and tailor campaigns to specifically target and attract them, minimizing wasted marketing budgets.

Identify loyal customers: propensity-modeling looks at past transactional behavior alongside customer attributes, transactional and operational history, to predict the likelihood of a customer to continue purchasing your products and services.

For example, the Money Mapping B2B propensity analysis from Blue Sheep utilizes purchase history, economic forecasting, SIC codes and unique business profiles to identify top customers and under-performing customers that can then be matched back to all future prospects and leads.

Determine up-sell and cross-sell opportunities: continuing to target customers with products and services best suited to their needs will keep them coming back, and predictive analytics can look deep into past transactional patterns to reveal what to target them with in the future.

Local corner shop owners in the past were highly effective at recommending products based on what they knew about you, your family, your weekly purchases etc. Applying this to millions of customers is somewhat more difficult however when trying to determine cross-sell and up-sell potential. Watch this video to find out more.

Increase the success of outbound efforts: Before the use of predictive analytics, marketers would often use a ‘spray and pray’ approach to campaigns that (unsurprisingly) saw low engagement and yielded poor results. Predictive marketing assesses the profiles of prospects to determine who is most likely to convert and become a customer, improving the efficiency of future campaigns.

Predict churn: After going to the expense of acquiring customers, nobody likes to lose them. A data-driven predictive churn model will identify the customers you are most at risk of losing (as you have not been meeting their needs) and can give insight into the actions you can take to retain them. Reducing churn can have dramatic effects on the profitability of an organization.

Predict lifetime customer value: this projects what a new customer is likely to spend with your organization over the course of their relationship with you. This can help marketing establish how they should market to each customer with future campaigns and retention efforts.

Predictive lead scoring: This methodology applies a perceived value of a prospect to the organization. Previously, this was based on flawed assumptions (for example, high scores would be applied to somebody that visits a ‘pricing’ page or has downloaded two items of content). More recently however, predictive lead scoring uses analytics to mine the behaviors of converting customers to then build a profile to predict how likely it is that a new prospect will also convert - or not.

Pipeline forecasting: Using a combination of external data and internal transactional data, pipeline forecasting looks to determine which of your deals are most likely to close and how much revenue will be booked, along with anticipating any risks that could jeopardize deals from closing.

Predictive marketing may not help you see into the future but it does provide marketers with tools to develop a deeper understanding of their customers, refine their efforts and improve profitability. Arguably not as cool as a time-traveling car but being on the path to business success isn’t a bad future to look forward to. 

Watch this video to find out how to make more informed marketing decisions:

Topics: Article