February 25, 2019 by Mark Brownlee

Three ways predictive analytics can help you reach your marketing goals

Shaping marketing strategy has always been, in part, about trying to predict the future.

Which channels are likely to yield us the most customers?

What time period would be the best bet for achieving our goals?

Which tactics are going to do the most to engage our audience?

That remains the case today.

The difference?

There is more data than ever before – and the associated tools for making sense of it – available that can help you to take the guesswork out of your predictions.

Predictive analytics has changed a lot of decision-making practices as it’s become more popular in the last few years.

Now, it’s doing the same with marketing.

Here are three ways predictive analytics in marketing can help you design campaigns that do a better job of reaching your audience.

Provide a better audience experience

Netflix became the most popular streaming service in the world today because of its unparalleled level of choice, ease of use and (relative) affordability.

But one of the main reasons it’s managed to stay that way?

Predictive analytics.

The Netflix algorithm has proven adept at predicting what content its users are most likely to engage with based on the movies, TV shows and documentaries they’ve watched in the past.

That means viewers are more likely to find a new show to watch once their last show is done, which makes them more likely to keep renewing their membership month after month.

A photo from the Netflix film Roma.

The Netflix film Roma received several Academy Award nominations.

The utility for marketers in other areas may differ depending on their goal, but the principle is the same: Using predictive analytics to keep audiences engaged.

For example: Movie theatres have developed sophisticated algorithms that they can tell what sorts of snacks you like based on how you use a customer rewards program. This allows them to serve you ads and offers that are more likely to entice you to the theatre.

These are both examples from the consumer world.

But the idea can be used in almost any context to better serve the needs of your audience.

A member-based non-profit, for example, may want to use predictive analytics to see which sorts of content are most likely to be of use to which audience members.

That way it can increase the value it provides to those members and make it more likely they’ll continue to support the organization.

Knowing the audience you’re trying to reach

“Ordinary” analytics is a great tool for knowing more about the audience you’re already reaching.

A huge benefit of predictive analytics, though, is in telling you more about the audience you’re trying to reach.

Many e-commerce retailers, for example, are able to use predictive analytics models to optimize their marketing campaigns based on when audience members are most likely to make a purchase.

It can also be used to identify the audiences that are most likely to engage with your content.

Let’s say in the past you’ve targeted your ads to women aged 55-64. A predictive analytics model, by contrast, might find that women aged 35-44 are far more likely to interact with your brand.

The same idea can be put to use for almost anything: The right advertising channels, the right audience segment, the right campaign timing.

Predictive analytics takes the guesswork out of getting to know who you need to reach to be successful.

Make better business decisions

Predictive analytics has great utility at the tactical level.

But arguably an even bigger benefit is in developing and refining strategy.

Consider the issue of planning for an upcoming quarter. In the past this process might involve quickly looking at revenue totals for this quarter for each of the past five years and then making an estimate based on past performance.

The problem with this approach is that it doesn’t account for the actual factors that drive revenue up or down.

Maybe a strong economy helped push revenue up during one particular quarter. Maybe a period during which it was difficult to hire pushed it down during another.

Predictive analytics helps to take the guesswork out of this exercise. It accounts for all the factors that contribute to a business metric moving one way or another and helps you to plan accordingly.

An organization could make use of more accurate revenue predictions to make better decisions for a given year. Or predictions about staff turnover could alert an organization to when it will likely need to hire new employees.

Predictive modelling is also useful for helping to provide the answers to many “what-if” scenarios.

For example: A company may want to determine how much a 25 per cent increase to the marketing budget might do for sales, or how a 10 per cent increase in head count would improve productivity.

The implications for using predictive modelling to make better business decisions are nearly endless.

Conclusion

Predictive analytics is a fairly new solution to an age-old problem for organizations.

How will you make use of it to better your organization?

Mark Brownlee is a Digital Marketing Strategist in Ottawa, Canada.

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