How Predictive Analytics Is Changing Digital Marketing

Today more companies than ever see the value of collecting data to truly understand their target audience. They collect data using artificial intelligence (AI) and machine learning to lay the foundation of every decision they make, whether it taking a new direction in uncertain times or the next tactical move in their marketing strategy that will outwit the competition. Predictive analytics is beginning to boom – and it isn’t hard to see why.

The power of predictive analytics 

Predictive analytics comprises a series of statistical techniques that analyses historical data in order to make predictions regarding the future. Quite often, predictive analytics uses data mining, predictive learning, and machine learning technology. The process relieves companies from the manual work, providing more accurate and beneficial data.

With the predictive analytics market projected to reach approximately $10.95 billion, this new way of collecting data is going nowhere. In fact, it is set to completely change the way companies collect data. Analytics questions and requests will shift from harvesting descriptive data about their products and customers, to pulling predictive insights from the information they collect.  

While relatively new, predictive analytics is widely used in the marketing world. It is already been used in areas such as brand awareness tracking and analyzing post-purchase campaigns. More than 50% of marketers agree that data-driven marketing produces more relevant communications and allows them to be more customer-focused

Why use predictive analytics in marketing?

Predictive analytics is equipped to help companies to improve their marketing performance. One way in which companies are jumping on board is by using data to dig deeper into their target audience. Predictive analytics can be used to determine the best audience segmentation to reach and predict customer behaviors, such as their likelihood to convert on your site. These insights help determine how you should interact with your audience and how you can meet their needs.

Netflix is a great example of successfully using predictive analytics to better understand audience intent. Netflix analyzes the curious actions its users take when streaming (search history, preferred genres, buttons pressed) to provide recommendations on what to watch next. And it worked! Over 75% of viewer activity on Netflix from personalized recommendations.

Predictive analytics can also open up new opportunities for upselling and cross-selling. For example, someone who has bought some yoga equippment might also be interested in yoga clothing or best yoga mats. Basing your actions on an analysis of historical data will enable you to deliver relevant recommendations that have a higher chance of converting

How predictive analytics can be used to transform data 

Brand performance, brand loyalty, customer acquisition, customer retention – all super important areas where predictive analysis can help you grow. However, to obtain that growth, you need to have a clear plan as to how you will use the data. Here are some tips that can help you make the most of this advancement in the field of data analysis.

Start with a clear goal 

In order to use your newly acquired data to make smart decisions, you need to have a clear goal in mind from the offset. Begin by defining what you want to achieve by deploying predictive analytics. For example, an e-learning company looking to drive their revenue during the holiday season might focus more on targeting the parents paying for the service rather than the student using it.

Having a clean goal from the beginning helps you focus on the data you actually need, rather than becoming sidetracked in an area that will not add to your company goal in the end. Use your goals to dictate the types of data you need to collect and the right analytics tool you should choose for your business. 

Break big data into smaller slices

There is such a thing as too much data and it can lead to confusion and missing the mark in regards to your goal. Instead of jumping headfirst into your data, take the time to slice and dice it into different data sets to help you focus on what is important. 

Consider bringing the “chunking strategy” from your studying days into your data analysis. Break your data set into smaller, more manageable units so the information is easier to process and remember. This will better enable you to turn your data into action points.

Integrate predictive analytics with marketing automation

By combining predictive analytics and marketing automation, you can get the insights you need to drive stronger campaigns to take company growth to the next level. Start by using the data from your predictive analytics to discover the audience segment that responds best to your marketing efforts. This data can then be used as part of your next campaign to personalise messages, refine your outreach strategy, and do whatever else is needed to drive more conversions using the right marketing automation tools.

E-commerce brands strive when they combine predictive analytics and marketing automation. In a time where choice is abundant for consumers, customer loyalty is taking a hit. However, if a company analyses the purchase behavior of a repeat customer who has abruptly stopped buying, it is easy to send them follow-up emails along with exclusive offers to win them back. 

Final thoughts

Predictive analytics is becoming more and more important as time goes on. Pretty soon, it will be a must for companies to survive in industries becoming oversaturated. But why wait until then? Implementing predictive analytics into your strategy today will enable you to make stronger marketing decisions and propel you toward the growth that you desire. Don’t let the competition take the first step and move toward this highly beneficial way of dealing with data today.