Marketing Analytics


Marketing Analytics

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.

In other words, predictive analytics analyzes patterns based on historical and transactional data that can be processed further for identifying future risks and opportunities.

Why Marketing Analytics?

With the rise of Big Data and Artificial Intelligence, marketers have more powerful analytics tools at their disposal than ever before. Data-backed customer insights can be used to enhance marketing efforts at every stage of the funnel, and one of the most effective tactics is using predictive marketing analytics.

Technical Video

When to use Marketing Analytics?

A b2b marketer uses several strategies and methodologies to manage their campaign, content, products as well as prospects and services.

But what if the marketer wants to know more?

What if the marketer wants to not only understand but also predict the ROI across marketing channels and business units?

That’s where our product marketing analytics in iSmile technologies comes into action. Our product covers two major industries namely the banking industry and telecom industry.

Our product will optimize the rate of return by using analytical methods to find patterns in complex and diverse data, and generate business insights that are available to marketers for decision making.

How Marketing Analytics works?

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.

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