Jan 11 | 4 minutes read |

Predictive Analytics: Futuristic Fortune Telling or Possible Praxis?

Optimise, tailor and predict. As online competition thickens, customers place tough demands on you as a digital marketer. We’ll explain why predictive analytics might be the solution to some of your marketing problems in 2018, and answer the question: is it really that complex?

Stay Two Steps Ahead of Your Customer

Our marketing automation evangelist, Wilhelm Sahlberg, predicts in the following post that predictive analytics will pick up momentum during 2018.

Today’s customers demand a flawless and seamless customer experience. At the same time, marketers are in a constant hunt for solutions that optimise campaigns and maximise budgets. Predictive analytics places itself as the saviour in the crossroads.

Why?

With the help of predictive analytics, you can foresee how your customers (or a specific customer) are likely to act and react in the future. Additionally, it can help you understand and predict what your customers need in order to boost your business.

In other words: predictive analytics is the art to stay two steps ahead of your customer. If fine-tuned, it acts as a fortune ball where you as a marketer can collect the right data, draw the right conclusions and reach the right insights in order to make better and smarter decisions.

Want to evolve your decision-making process? Talk to our experts and find out how we can help you.

From Mathematicians to Marketers

Even though it might seem like you need to hire a full armada of data scientist and purchase expensive systems, predictive analytics doesn’t really have to be extraordinarily complicated to make an impact on your revenues.

Admittedly, predictive analytics isn’t the new kid on the block. But as the competition in the digital arena intensifies, marketers focus on the marvelous insights mined from customer data and computers work harder, better, faster, stronger: predictive analytics is quickly becoming the belle of the digital-marketing ball.

Consequently, predictive analytics has partly moved from a domain devoted to mathematicians and statisticians to a strategy used by companies big and small.

5 Quick Examples of Predictive Analytics in Action

Like all marketing strategies and technologies, predictive analytics can be either complex or simple. If your data-driven journey hasn’t come into full fruition yet, you can kick-start with small (yet revenue boosting) means.

As a digital marketer, you’re aware of the mammoth amount of data that’s produced and stored on a daily basis. However, clear-set goals are always the key to success in all marketing activities and strategies guided by data. Without it, you’ll be buried and blindsided in heaps of customer data, rather than enriched with golden insights.

So, how can you apply a layer of predictive analytics to your marketing activities and overall strategy? Just as with marketing automation and data-driven marketing in general, you need to learn how to walk before you try to run. Here are five examples:

1. An easy way to kick-start is to take a simple peek at your calendar and map out at what weeks, days and hours your website activity and finalised purchases are at their highest and lowest. That way, you’ll be able to tailor your offers, events and campaigns accordingly.

2. Predictive analytics can be used as a way to find out when, how and in which channel you should communicate with what customers. Additionally, it can help you analyse what kind of content you need to push in order activate and boost a specific segment or customer through the sales journey.

3. Furthermore, it can help you to decipher and predict the optimal steps in each customer journey and feed your marketing automation machine with content sent out to the right person, though the right channel, at the right time.

4. Product recommendations can be dramatically improved with the help of predictive analytics. By analysing the customer’s browsing history and purchase history, you’ll be able to predict what products the customer might like or need in the future.

5. A fifth example is to predict when the customer needs is likely to return for a purchase. For example: if you sell ink cartridges, you can calculate how long it takes for the the average (or particular) customer to use up the last droplets of ink. With this method, you'll reach your customer with relevant messages and (hopefully) impress with your level of proactivity.

Predictive analytics can be used at an advanced or simple level. Regardless, when you decide to leverage the power of predictive analytics in your digital marketing scheme, you'll notice a boost in your business in both knowledge and revenues.

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