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Predictive analytics: The missing piece in a single customer view

Jun 07, 2018 Maros Gardon 5 min read

How would you live your life if you possess the ability to foresee the future?
Would it help you to live a better and more fulfilling life?

The answer is probably different for each and every individual.  Some of you may say that fortune telling is taking away all the surprises life can bring, others may oppose, they would live a happy life avoiding all the bad choices they have made.

This idea may seem distant and far-fetched, but the truth is that magical abilities once available only to prophets and magicians are now accessible to anyone thanks to predictive analytics.

Predictive Analytics is not something new. It has already been around for some time but is gaining more attraction only now. Why?

There are 2 prerequisites for predictions to deliver a meaningful value to businesses.

The first, there is a large amount of data.

A few years ago, there was simply not enough data about customer behavior, but the situation has rapidly changed since then.

People are leaving data everywhere.

We are connected to the internet more than ever before. If we are not using our laptops, we are browsing the internet on our cell phones. We are hanging with our friends on social media, purchasing various products, reading content, ordering food, etc. The majority of our daily life has moved to the virtual area.

And as we are leaving footprints when accessing a store during a rainy day, there are digital footprints we leave when accessing an e-shop, regardless of the weather.

These footprints represent digital evidence, while predictive analytics models can be considered as witty detectives able to find connections between these seemingly unrelated footprints and predict our future steps.

The second prerequisite is computational power.

Predictions need a fast and powerful infrastructure. Again, something which was not available up until now. With the advent of big data analytics and cognitive computing, predictive analytics has obtained the computational power required to deliver meaningful insights.

We have the data and the computational power, but what comes to your mind if you hear the word predictive analytics.

Unless you are an experienced analyst or a data scientist, the primary connotation is something like “I do not want to deep dive into this“. The good news is that with the advancement in technology, predictive analytics is no longer the privilege of data scientists but is accessible to ordinary people.

Platforms like Exponea enable anyone to create advanced predictive models and power their marketing campaigns with insights about expected future behavior of certain customer segments.

Insights into customer behavior are something that resonates with the world of digital marketing. Smart businesses understand the value of seeing people behind data and take all measures to collect and analyze as much data as possible.

Modern e-commerce is shifting from a price-based competition to an experience-centric fight for customers. Price is not the differentiating factor anymore,  but customer experience is making the difference.

Personalized experiences in the context of predictive analytics are about delivering the right content to highly engaged customers just the moment they are considering whether they will perform a certain action or not. In other words, predictive analytics enables you to nudge the subconscious mind of your customers where the decision-making process occurs.

We are witnessing a new era when predictive analytics is transforming the world of  digital marketing into predictive marketing. Predicting the future steps of your customers creates a whole new set of possibilities.

Let’s have a look at a few examples of predictive analytics in practice

Campaigns efficiency optimization – predictions allow you to find segments of customers with a high probability of successful conversion. Once these segments are identified, you can use predictive analytics to determine which communication channels have the best response rate for each individual customer (emails, banners, SMS). Using these insights, you can target customers more precisely and effectively.

Increased revenue –  the fact is that 20 percent of your customers are bringing your business 80 percent of the total revenue. Up until now, you have been able to perform RFM analysis and explore high-value customers and target them with special offers. Now imagine the impact of your campaigns if you can predict which customers will buy from you in the next 2 weeks, months, etc.

Better brand perception – the outcome of targeting only customers with an increased propensity to purchase is that customers with lower engagement may appreciate  the decreased frequency of your newsletter or other marketing communication. As a result, optimization of marketing campaigns may lead to increased loyalty. For example, in the context of email campaigns, predictive analytics allows you to send fewer emails with a higher impact.

Reduced unsubscribe rate
The same applies to the unsubscribe rate, if you reduce the number of emails sent, you are decreasing chances that your customers will get upset and unsubscribe from receiving your marketing communication.

Thanks to predictive analytics, businesses now possess data about customer history, present, and future. With predictive scoring, you can now enrich your customer profiles with predictive data (probability of purchase, churn, visit, email open).

With the right marketing tool, you can easily power your campaigns with predictive insights stored in a single customer view and create a higher level of personalized experiences for your customers.

In practice it works like this, you run a prediction model on your customer base. For example, a probability of purchase in the next 30 days.  As a result, you will obtain a different number for each of your customers which represents the probability of purchase. This data is added to customer profiles and can be used in your marketing campaigns.

For example, you can create a segment of customers with the highest probability to purchase and target them with personalized offers (product recommendations, discounts, etc.) through their preferred channel.

While predictive marketing is still a relatively new area,  there are no doubts that it has a considerable potential for the future. According to Predictive Analytics-Global Market Outlook (2017-2023), this market is expected to grow from $3.89 billion in 2016 to  $14.95 billion by 2023.

Based on this data, it is reasonable to expect that by 2023, the majority of e-commerce businesses will use predictions to some extent.  Although this train is running fast, it may be better to jump in now, as there are still some places to sit, rather than wait until it is safe to enter but completely full and slowly moving.

If adopted properly, predictive analytics can help you shift your marketing campaigns from targeting random customers into smart campaigns powered by predictive customer intelligence.

This will enable you to use your resources more effectively and target only customer segments with an elevated potential of conversion regardless of whatever your goal is (purchase, email open, etc.).

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