Marketing- The way legacy processes perceived it has come along a long way. Today it is all about the advancements in data and how data-driven enterprises talk data in a bid to attract more potential buyers. Data and Analytics are the two buzzwords that have been thrown around marketing for quite some time, but what do marketers assess from these two hefty words unless they are trained to handle the digital disruption that big data brings to even marketing?

As discussed, marketing is slowly changing, driven by data it is the way businesses leverage on multi-data sources to connect at a personal level with the customers. Talk of personalised and more curated offerings suited to their unique needs. Marketing analytics integrates processes and technologies which let marketers evaluate the success of their marketing initiatives by weighing tangible performance metrics like social media likes and followers and so on. The most commonly used metrics for marketing analytics include ROI, marketing attribution and the overall marketing effectiveness. In short marketing, analytics tells exactly anytime how the products and services are performing vying for customer loyalty.

Building a Case for Marketing Analytics Success

Marketing Analytics is a comparatively new term, and marketing managers are still trying to make a way how to deploy data for customer segmentation, targeting and eventual placement of the product or the services in the customer life cycle.

Here are a few steps that will mean to be helpful-

• Data Assessment

Market Analytics professionals need to know the category of data they are working with, whether it is structured or unstructured or live or static. The USP of marketing data being its high levels of dynamism makes assessment more complex and highly indispensable to steer the future course of action.

• Data Pipelines

Data needs to be prepared for analytics and even future analysis by citizen data scientists who may be the marketing analytics professionals themselves. Data pipelines involve building clean and model ready data for marketing chores.

• Data Modelling

What is data without analytics and modelling? This step essentially involves marketing analysts to build and train models that predict customer demands and preferences. Data Modelling step is highly critical for building marketing blueprints.

• Data Visualization

This step involves presentation of the earlier steps to the C-Suite especially the Chief Marketing Technologists who are the decision-makers of an enterprise.

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Why should Enterprises Invest in Marketing Analytics Data Literacy?
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