What is predictive analytics and why is it important?

In the 21st century, data and analytics are playing an ever-growing role in the way you strategize your business. No matter the industry, or size of the company, data is impacting the way you operate and how you do marketing.

In the last decade, there has been much hype about Big Data. While it might sound like a buzzword, from a marketing perspective, it is a trend of understanding your customers, their buying cycle, and guiding your content strategy.

Data needs to inform everything a marketing team does. Everything in the modern world is connected. You post on Facebook and Instagram, write a blog, have a website, send emails, answer the phone, and create ads. Each of these interactions creates data and weaves a complicated journey that spans various platforms and channels.

Over 2.5 quintillion bytes of data are created every single day, which is 1.7MB every second for each person on earth. For a digital marketing team to gather, process, and analyze such vast information, they need specialist software. Traditionally, a customer base might be segmented on age or gender, but the current demand is for individual personalization. To tailor content in this way would be impossible, which is where predictive analytics comes in.

What is predictive analytics

Predictive analytics helps marketing teams create a personalized experience for every lead or customer. It goes beyond typical segmentation, which is often based on assumption, by taking data and turning that into appropriate insight and decisions.

Predictive analytics uses data, statistical algorithms, and machine learning methods to identify how likely a future outcome is using historical data. It is not about knowing what a customer has already done, but understanding what they will do next.

evolution of predictive analytics

Source: ngdata.com

It is a myth that predictive analytics applications are only available to tech giants like Amazon, Facebook, and Google. Businesses of all sizes can now use the majority of Big Data tools and technology. Improvements in computing power, cloud processing, data science have driven down costs and opened up several open-source opportunities. There are four primary reasons why predictive analytics is essential now.

  1. A growing volume of data means that businesses have more opportunities than ever before. 
  2. Tools are easy to use. The advent of no-code platforms allows even the least technical of users to explore predictive analytics
  3. The competitive nature of a digital ecosystem means businesses need to find new ways to differentiate themselves in the marketplace. Data is unique to a company and they must treat it as an asset.
  4. Businesses can store and scale their data using cloud solutions like Amazon Web Services (AWS) at low cost without affecting the existing IT infrastructure.

How predictive analytics works

Predictive analytics uses an application of artificial intelligence (AI) known as machine learning. Machine learning can build statistical algorithms in real-time, using massive amounts of data. The algorithms teach computer systems to perform tasks or make decisions, from the experience they gain with that data. When the machine learning models ingest more information, they continue to learn independently.

Predictive analytics

Source: bigdata-madesimple.com

The example above shows how predictive analytics might work in practice. We start with a random assortment of data, and perhaps these are customers buying products. The algorithms sort and clean the data before identifying any patterns. The patterns might be grouping together customers in the same location buying your product.

Predictive analytics will then fill in the blanks. Based on the data from the dark blue shapes, it predicts the likely behave of the other shapes (customers). For example, if customers are buying product A also tend to buy product X, other customers who buy product A are predicted to follow the same path.

Starting with predictive analytics

Predictive analytics needs to start with a clear business objective. The most common goals are to use data to reduce waste, save time, cut costs, improve revenue, or drive conversion. A predictive analytics workflow will look something like below.

Predictive analytics workflow

Source: mathworks.com

Keeping the analytics function in-house is the ideal option, as it helps you retain complete control over your data and technology. However, doing so can take time and resources. You will likely need some data expertise like a Data Scientist, new software, and IT infrastructure upgrades. The faster you can bring an analytics function into the business, the quicker you get a return on investment. Speed is fundamental for providing an excellent customer experience.

There are plenty of vendors who can assist with outsourcing your predictive analytics strategy, which may be the best option when starting out.

The usual concern for those who decide not to invest in predictive analytics is the cost if they do outsource. However, if the strategy is well-planned, and goals are clear, it will not take long to make a return on investment. Here are some of the ways that predictive analytics helps to accelerate marketing activity.

Knowing your customers

One of the primary use cases for predictive analytics is to model customer behavior. One of the most common techniques is known as clustering. Clustering is like an advanced form of segmentation. The critical difference is that instead of the marketer determining the segments for campaigns, the machine drives the decision.

An algorithm will gather and process a large amount of data. The dataset can include everything from disparate systems like customer details, transactions, online and social behaviors, conversations, and more. From the mix of data, the machine learning models will split the customers into X number of clusters, or groups that appear to share similarities.


Source: acquia.com

New customers will fit into the clustering model, and the business can instantly get a view of their likely future behavior based on that of others in the segment. In the example above, if new customers fit the long-term, high-value group, it is worth spending time to nurture them and ensure they are retained in the business.The clustering information can be turned into revenue from automated and targeted campaigns. 

Automating and targeting campaigns

In digital marketing, a common dilemma is working out which customers are the most likely to respond to advertising. Teams will have a budget for pay-per-click, Facebook advertising, and email, and need to stay within that. Once they know their customers, predictive models can help understand which ones are most likely to respond to specific campaigns. 

A standard Facebook campaign might drop an ad to every 25-year old, in the hope some of those will be part of the target audience. With clustering, we can target more granularly, and know the exact type of customer that will engage with advertising. For example, a 25-year old is presented an ad at the time they will respond, with a product that best suits their segment.

Predictive analytics pushes the right campaigns, via the right channel, to the right people, at the right time. Ads, emails, notifications, and other forms of outbound marketing are always sent to those most likely to respond. According to the Aberdeen Group, predictive analytics users are twice as likely to identify high-value customers. 

The cost of marketing reduces, while revenue improves with better targeting.

Recommender systems

One of the most popular recommender systems driven by predictive analytics in the 21st century is Netflix. Subscribers to the streaming giant spend 80% of their viewing time watching shows that are recommended to them, rather than choosing themselves.

Recommender Systems

Source: laptrinhx.com

While there are more sophisticated algorithms in the background, the premise of a recommendation is quite straightforward. If Customer A and Customer B both purchase Product Z, then Customer A also purchases Product X, Customer B will probably like Product X as well. When it comes to Netflix, the shows it recommends to each user come from historical data, and lots of it. It isn’t uncommon to see something like below where visiting an e-commerce site.


Source: Google

The scenario above uses data to predict the likely products a customer will want to add to their cart. A report from Rejoiner talks about the success of the Amazon recommendation system, putting much of the increase in sales down to the integration of predictive analytics.


Data and predictive analytics models are becoming the driving force behind modern-day marketing. As the amount of data and the digital competition continues to grow, the need for such methods will only increase. Businesses must know their customers, automate and target campaigns, and personalize product offerings dynamically through recommendations if they are to achieve a return on investment. Although the technology has been around for some time, the volume of data available, coupled with improving technology is making predictive analytics a transformation strategy.

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