What Is Predictive Marketing Analytics?

Before we explain what predictive analytics is, here are some facts about just how big Big Data is:

• Over 2.5 quintillion bytes of data are created every single day
• By 2020, it’s estimated that 1.7 MB of data will be created every second for every person on earth.

Think of it this way: using available data for planning, designing and deploying a marketing campaign is like having a superhero cape that almost guarantees better results.

Predictive marketing analytics is a branch of advanced analytics that harnesses all that big data to predict future events or results. It integrates various techniques from data mining, statistics, modeling, machine learning and artificial intelligence to process and analyze various data sets for the purpose of developing predictions.

The steps in the predictive analytics process are:

• Defining outcomes: Determine which business questions you want the data to answer, like “How many of my products is a repeat customer likely to buy in the next 12 months?”
• Data collection: Have a plan for which data you need, how you plan to collect it, and the best ways to organize it.
• Data analysis: Inspect data for useful information and form conclusions about your customers.
• Statistics: Test the conclusions.
• Modeling: Create predictions about your customer’s future behavior.
• Deployment: Utilize the data to inform marketing strategies and implement tactics.
• Model monitoring: Track and report on the effectiveness of predictive data-driven campaigns.

Why are predictive analytics so important?

To most businessmen out there who are not that familiar with the aspect itself, answering this question is extremely important. After all, why is the aspect of predictive analytics so important? For this, we need to take a good look at the last decade and how marketers have seemingly shifted from an outbound focus to a dual focus of both outbound as well as inbound.

This is mainly due to a literal explosion of data, thanks to the wide proliferation of various digital channels, an upsurge in the use of mobiles as well as the overall rise of eCommerce and social media. For all those of you thinking that this indicates the end of tough days for most marketers, please do bear in mind that there are many additional challenges to be dealt with.

The challenges are dealing with an unprecedentedly huge volume of both structured and unstructured data as well as a plethora of design marketing initiatives that are dependent on other aspects. These other aspects such as triggers and events are not within the control of any marketer.

How to create a predictive analytics model ?

The first step up the ladder of success is to focus on taking a thoroughly structured approach towards creating a detailed predictive analytics model. There are a few proper ways to go about doing this:-

• Firstly, make sure that you thoroughly define your goals: For this, you need to ask yourself the main question “What exactly are you trying to solve?”. Then only can you thoroughly get down to defining your goals.
• Quantifying the overall success of the task: Make sure that you identify the sources of data, clean up the data and map it across different plans.
• Building initial predictive models and testing them out: Testing the models out is essential if you are to find out whether they are working properly and providing accurate information.
• Lastly, make sure that all of the steps are actionable, accurate as well as insightful: You need to understand the basic fact of the matter, being that predictive analytics is a science by itself. This automatically means that there will be a fair amount of recalibrating to do from your end.

The main benefits of predictive analytics

The main benefits of a predictive analytics model is something that everyone needs to keep in mind:-

1. The targeted profiling of customers:

Predictive analytics does indeed help in mapping out the different journeys and identities of various customers across the board. This, in turn, gives marketers a much better understanding of how customers are likely to respond to a particular marketing activity.

2. The optimization of customer intelligence:

We can plan our marketing initiatives much better if we have a basic and more intimate understanding of our customer base in general. This can be achieved by pairing predictive analytics with customer data.

3. The ability to create marketing campaigns that are impactful and measurable:

On the whole, predictive analytics mainly look for micro patterns in data. Hence, personalized campaigns can be much more impactful as well as measurable.

4. The optimization of loyalty:

Let’s face it — the time one invests in acquiring a new customer is much higher than simply retaining the existing ones. Once you identify those customers that display a certain attrition risk, you will be able to interact with them and take the proper measures to prevent the loss of customers.

5. The identification of various growth opportunities as well as new trends:

In this particular aspect, there is little doubt of the fact that predictive analytics is extremely helpful. Additionally, if one happens to have a better idea of the pain points of the industry, industries can tweak their particular service/product to suit the requirements of their customers.

6. High-quality lead generation:

Predictive analytics also enables the marketers to gauge the propensity of the customers to buy, with a much greater level of accuracy. This will enable the marketing team of the company to provide high quality leads to the sales team.

How to use predictive analytics for your next digital marketing campaign?

Having spoken about the benefits of predictive analytics, let’s take a look at the next step — how to implement it into your digital marketing strategy in the best possible way:-

1. The improvement of your customers segmentation analysis:

The process of segregating people based on needs can now be applied in extremely innovative ways. This can be done by compiling more demographic data or even channeling most of your focus on psychographic data to gain a better insight into the motivations and interests of your customers.

2. Creating more precisely targeted customer personas:

With customer personas, you need to ask yourself an extremely important question — In order to yield the highest conversion rates, how, where and when should I target some of the most profitable segments? Knowing the answer will lead you to score better leads as well as get a better customer experience on the whole.

3. Smoothening your market automation process:

This is a sure-shot way to help you convert new leads into customers. This can only be done once you demonstrate that you understand them and their needs. In this regard, you really need to have a special attention for detail.

4. Reducing churn:

Consider this — It ends up costing a good five times more to acquire a new customer than retaining an existing one. Hence your focus needs to be reducing churn. In this regard, the specific trends of churn-risk customers and various marketing campaigns can be used to target the cause of those risks.

Predictive analytics in the future:

It is difficult to say with absolute certainty, but one can talk about the state of predictive analytics in the present. Nowadays, we not only have self-learning technology, but also the ability to focus on a one-to-one interaction with our customers from any part of the world. Surely one cannot say that things were this easy a couple of years ago.

On that note, all the aforementioned aspects combined with the fact that technology can process a humongous amount of data in this day and age, are all set to create a game-changing future. Surely it will not be too much of a stretch to imagine that most aspects will be automated in the future due to the ever-growing potential of technology.

Conclusion:

At this point in times, it’s fair to say that marketers on the whole are at a tipping point with regard to understanding just how much traditional database marketing and analytics have changed over the past few years. People are beginning to realize just how much data can be processed by technology as well.

However, one of the main drawbacks is that we are still limited to thinking about the future keeping the current marketing capabilities in mind. In a good two to three decades, what the nature of jobs and marketing will be is anyone’s guess.

However, one thing is for sure. The companies that don’t end up using predictive analysis will find it very difficult or almost impossible to efficiently compete with other companies that do.

Additionally, there is also the fact that one of the most biggest challenges with regard to employing predictive analysis in marketing, will be honing the ability to test out various actions and subsequently analyse their implications on both revenue and sales. In the future, this is exactly where the concept of cognitive computing or machine learning come into the fray.

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