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Predictive Analytics and How it Connects to Marketing Success


Data is king. The saying is cliché but in the world of marketing, it is true. Data is becoming the driver for everything from market research to marketing efforts and companies that leverage the power of technology in their marketing efforts are seeing better results. Enter AI and things change dramatically. Companies that use predictive analytics crunch huge amounts of data and use it to predict customer behavior. Below, we will look at what predictive analytics is and what it has to do with your company’s marketing efforts.

Predictive Analysis: What Is It?

Predictive analysis is the use of AI technologies, statistical algorithms and data to make predictions about the future. When you have access to vast amounts of user data, you want to break it down so you can see trends and be better prepared for the future. Predictive analysis can do that for you, and it is being leveraged by companies from all over the world.

When doing predictive analysis, companies usually follow these steps:

They define their outcomes – this lets them tune their algorithms so they see what actions they can take to see better outcomes

Collecting data – Companies have to know how and where to get the data they need as well as how to organize it into actionable recommendations

Data analysis – The collected data has to be analyzed so that useful information can be extracted from it. The information can then be used to make conclusions about your customers

Testing the conclusion

Making predictions about the future actions of your customers

Using the data to form and implement marketing ideas and tactics

Tracking the effectiveness of your predictions and recommendations and reporting them back to the relevant people

Taking Advantage of Predictive Marketing

With so much data available, companies can do the following to better analyze it:

Unified marketing measurement – This means unifying all data into a single unit for easier analysis.

Marketing analytics software – This requires the integration of several measurement models and all the data companies have.

AI and Machine learning – AI and machine learning have a huge role to play and are two more tools marketers can use in their predictive analysis efforts. These tools can also give real-time data which gives marketers who use them a massive advantage over those who do not. The good thing for marketers is that these tools will continue to improve which will make marketing easier and a lot more accurate.

Diving Deeper

Companies can use predictive analysis to:

Optimize marketing campaigns – Predictive analysis provides hypotheses that companies can test to see if they are right or wrong. As companies refine their methods, they learn to use only the useful data. This data can be used to segment customers to see which marketing campaigns work best and where. With predictive analysis, companies can then use the data collected from these campaigns to either double their efforts where they succeed or make changes where they do not.

Predict customer behavior – Using data, businesses can follow their customers through the buying process. They are then able to predict when a customer will convert and when they will leave their website or store. Using this same data, companies can see whether people dropped off because the experience was too confusing or if their journey ended prematurely. Predictive analysis can also be used to identify high-value customers, which helps businesses tell them of the best custom offers for them.

Personalize experiences – Once you have your customer models, you can create personalized products and content geared towards those model customers. When you target the right customer at the right time, you will see a better return on investment.

Report the results – Predictive analysis can be used in conjunction with other business analytics methods to come up with all sorts of data. This data can then be reported directly so that a business or company can make the necessary changes as soon as possible.

See what would happen in certain scenarios. For example, what would happen to the sales of a brand if another brand’s products go out of stock? Who would buy the second brand in these circumstances?

Win repeat business – Predictive analysis that informs marketing decisions can be a very important tool in winning a business repeat business. Because business marketing budgets are shrinking, a business must target the right customers to make them repeat customers and therefore see the best return on investment.

Know which customers to prioritize – Businesses need to prioritize customers based on certain factors like if they are likely to buy high-end goods or become repeat customers.

Tools Used in Predictive Analysis

There are two classes of tools used in predictive marketing:

Enterprise-class solutions – These are provided by companies like IBM, Oracle, and SAP.

Small-vendor solutions – These are provided by companies such as GoodData, Marketo, Tableau, and others

Predictive Analytics Is Growing

Predictive analytics is part of online advertising, it is growing and it is here to stay. If you run an online shop today, you know all about practices like:

Cross-selling (selling similar products to the one in a customer’s shopping bag)

Predicting what a customer might need

Predicting what products customers are likely to buy together

Because of the progression of computing power and the falling in prices of computing and storage data centers, predictive analytics will become much more important to business people. You can click here to learn more.

Online shopping exceeded two trillion dollars in 2017 and every business and marketer wants a share of that money. In the new model, businesses have to:

Have a marketing team with a thorough understanding of the online purchasing process.

Have predictive analysis tools that lead to a better understanding of customers. This allows for better allocation of resources, including marketing efforts

Use tools, or have other ways, to collect and analyze user data, using this analysis to come up with marketing strategies

Use the conclusions to give customers a better experience

Understand the importance of reaching out after a sale has been made

Implementing Predictive Analytics

Predictive analytics takes the guesswork out of your marketing efforts by giving you working models that work if implemented correctly. Before implementing the strategies, however, you have to take a snapshot of where you are in your business and the market so that it can serve as a point of comparison in the future.

After understanding the metrics and the data, the modeling phase is the next step. When modeling, companies could:

Predict customer behavior on their predicted lifetime value, probability of engagement, converting, buying and churning.

Segment customers based on more than one variable at the same time. This way, companies can target specific demographics or people who display certain behaviors.

Use past variables to recommend new products and services. Some of these past variables include buying behaviors.

In-house or Outsource?

When businesses start thinking of deploying predictive marketing tools, they often ask themselves if they should deploy their own infrastructure or rent some servers when they need them.

Deploying these predictive analysis solutions is not that complicated. That said, it should not be done in house. This is because it is easier to let someone else do it for you instead of deploying a whole technology stack on your own. There are lots of solutions for those who need these services. Companies that offer these tools serve both big and small companies to ensure every business has access to them.

The Future of Predictive Analysis

Data will keep driving online businesses and the decisions these businesses make. These businesses will continue to collect consumer data by the petabytes. All this data has to be analyzed so that it can be used to predict trends and inform business decisions. The importance of predictive analytics will continue to increase as more and more businesses join the digital space and marketing budgets get tighter.

As computing power increases, it will become easier and faster for businesses to make decisions based on set models. This will make businesses better at targeting their customers, but it will also make them very annoying.

Remember the feeling you get when you see a highly targeted ad that leads you to think that something or someone is listening to you? Well, businesses are following you around the web, collecting and cataloging everything you do. If you see an ad that looks like it was made just for you, it probably was. That is how good predictive analysis has become.

The scary thing is that it will continue getting better and the ads we see are likely to increase as companies increasingly need our attention.


Predictive analytics is here to stay. With the amount of data companies collect online, it is no surprise that they need these tools to analyze it all. The rise of predictive analytics is also being driven by the fact that advertising budgets are falling and companies need to see better returns on investment. As computers get more powerful, companies can expect to be able to make better predictions about their customers so that they can target them much better.

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