Jul 26, 2019


Min Read


Divya Kandwal

Why you should be using Predictive Analytics for Market Research

Predictive analytics

NumrSense AI

Churn Analysis

Text Analytics

There seems to be a lot of debate around what Predictive Analytics is and whether it is just another fad or an indispensable Market Research tool.

Use of Predictive Analytics in Marketing

Suffice to say, it’s not an obscure, esoteric concept meant to perplex marketers. This article aims to breakdown and explain what Predictive analytics is, how it is done and why it can be a pivotal instrument, and what is the use of Use of Predictive Analytics in Marketing.

Predictive Analytics encompasses a wide variety of statistical techniques that analyses historical and current data to make predictions about the future. It is carried out using Data Mining, Data Modelling and Machine Learning. Essentially, Historical facts/data are fed into an algorithm that considers the key trends and patterns, based on which a Model is developed. This Model is then applied to the current data sets to predict what will happen in the future.

predictive analytics formula

Therefore, better data (quality and quantity) will lead to a better model which in turn will result in higher accuracy of the predictions. In a nutshell, Predictive Analytics answers the question of what is most likely to happen in the future depending on what is happening now and what has happened in the past.

Predictive Analytics is not new. The practice of Predictive Analytics is just as old as civilisation itself. The ancient people calculated tides and solar eclipses by observing and collecting relevant data and then extrapolating it to foretell various natural events. This is an early example of PA. Weather forecasting is another example.

But why all the fuss now? According to Google Trends, the interest in Predictive Analytics has been rising steadily over the last decade.

predictive analytics interest

But, it was only with the advent of Machine Learning that Predictive Analytics started gaining mainstream popularity.

What is Machine Learning?

The genesis of Machine Learning in academia is very old. Machine Learning developed out of the pursuit for Artificial Intelligence.

Arthur Samuel (1959) coined the term ‘Machine Learning’ and described it as “the science of getting computers to learn without being explicitly programmed.”

Machine Learning is different than other analysis because unlike others, it improves over time by itself.

Broadly, it is of two types. Supervised and Unsupervised.

  1. Supervised Machine Learning– is when labelled data is used to train the model. An example of this is Social Media Analysis. You start by screening tweets and tagging (labelling) them as good or bad. Then, you create a model out of this to train the machine. From then onwards, the machine tags the tweets by itself with reasonable accuracy.

  2. Unsupervised Machine Learning– is when no labelled or tagged data set is fed to the machine. An example of this is how Google automatically classifies news on its News Feed.

Machine learning can be used with incredible effectiveness in-

  • Healthcare- For instance, to figure out if a tumour is malignant or not
  • Fraud detection in Finance
  • Customer Churn in e-commerce

So, to sum up, the “machine” eventually learns how to read more and more data, and automatically improves the model by using the techniques mentioned above.

Why Predictive Analytics over other forms of analysis?

Dr. Steven in his books titled Predictive Analytics, Data Mining and Big Data Myths, Misconceptions and Methods explains some of the benefits of Predictive Analytics over human analysis.

These are-

  1. Speed– Predictive Analytics is a lot faster than traditional analytics
  2. Accuracy-Predictive models generally make better and more accurate forecasts than their human counterparts
  3. Consistency– A predictive model will always generate the same predictions when presented with the same data. This is not the case with human decision makers.

Predictive Analytics, when incorporated in Market Research offers a colossal jump in what marketers are able to do. With the help of Predictive Analytics, they can-

  • Predict the ever changing market trends and act on a decision before the Market settles
  • Take decisions based on facts and figures (rather than merely instinct) with a stronger sense of confidence
  • Eliminate human variability and guesswork out of their decisions when the same kinds of problems present themselves

How can Predictive Analytics be used in Market Research Analysis?

Predictive Analytics is an exemplary tool for market research and marketing. It can be used to research, examine and analyse various facets of marketing, including survey results. Some of these are-

1. Customer Churn

Customer Satisfaction surveys have been done for over half a century. Initially, they were done mostly in person, door to door, then telephonic and now, majority of the surveys are online. The internet allows marketers to reach a wide and expansive audience, which has led to a huge influx of data.

NPS and C-Sat surveys are generally thought of as Feedback surveys and therefore can be used extremely well for predicting ‘Customer Churn’. With Predictive Analytics, you can train the machine data sets to predict churn. By comparing historic data to current data, one can predict the loyalty of a customer.

For instance, imagine that you’re a telecom provider. You have collected the survey responses of a customer, Mr. X. After 6 months, you go back to your database to check if Mr. X is still a customer. If he isn’t, you train the database to acknowledge that he isn’t a customer any longer. You do this over a hundred records.

After that, the machine can learn that if this is how a person is answering a survey, there is an x percent chance that that customer will churn out. The Predictive Analytics model in a C-sat environment is extremely powerful because with the help of survey responses, one can understand the customer’s emotions.

Based on this and some human factor data, one can get a highly predictable churn number. So, now when another person answers your survey in a way similar to MR. X, you can immediately get an urgent notification that this person has a very high chance of churning out and thereby, take appropriate action. Predictive Analytics makes this possible.

2. Text Analytics

Another important application of Predictive Analytics is that it makes Text Analytics and Driver Analytics attainable. We are not used to analysing a large volume of open-end text using a machine algorithm. Usually, it is read by humans and then coded.

However, with Predictive Analytics, it is now possible to train machines to analyse text across millions of comments and then, make extremely accurate predictions about future customer behaviour.

3. Driver Analysis

Driver Analysis, on the other hand refers to an analysis that identifies what are the levers that one can pull to make sure that a customer is happy. Traditionally it was an analysis that happened occasionally and for very specific elements but with Machine Learning, it is possible to do an analysis of multiple, inter-related, and inter-connected factors.

For instance, a telecom company trying to figure out what is of most significance- network quality, customer support or their interaction and how this affects churn. Predictive Analytics can help predict how the drivers are constantly moving is real time, as the environment changes and the days go by.

4. Ad Test/ Recall Value

Predictive Analytics can be used exceedingly well to appraise whether an ad will work or not. Ad agencies test hundreds of ads before they launch one in the market.

Ideally, what a company should do is, take the ad data, store it in a warehouse and after the ad has gone public, put in the figures about whether the ad worked or not.

And if it did work, how did it reflect on the sales figure. If this is done diligently over a span of time, one should be able to get to a predictability score that indicates the percentage of ‘enjoyability’ that an ad must have for it to work. These numbers can then be fed into an ongoing learning algorithm to predict the kinds of ads that will work well for a specific category. Predictive Analytics makes this achievable.

Market Research on its own offers enormous value but usually lacks the ability to predict the changes in customer behaviour at the earliest stage since it relies on a restricted sample size of customers. With Predictive Analytics, it is possible to comprehensively consider and assess all customer data and arrive at a much more accurate conclusion.

Now, more than ever before the customers are skeptical, discerning with a plethora of choices available to them. Predictive Analytics is an instrument to not just grow your customer base but to retain pre-existing customers as well. Using Predictive Analytics models, marketers can “reverse engineer” customer experience.

Market research has always been a cost center where money is spent to evaluate whether a product or a change will work or not. With Predictive Analytics, market research can become the life blood for making day to day strategic and tactical decisions.

It can help a business take proactive action by predicting the behaviour of the customers based on historical data, thereby impacting the bottom line significantly.

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