Big Data Based Retail Marketing

The vast majority of people think in stereotypes: the sky is blue, the summer is hot, the winter is cold, brunettes are smarter than blondes, men do not cry. Marketing campaigns of the previous decade, with a rare exception, were teeming with these, not always appropriate, cliches. Now, this approach is too primitive to get consumer feedback. The marketing of today is not an intuitive solution but a mathematical analysis. Brand promotion is centered somewhere on the intersection between high technology and data that the average person would never pay attention to. The age of data-driven marketing – what is it?

First, learn how to forecast the weather for tomorrow

The industry may try to resist it, but using data is the only logical way to develop marketing. This is a way to create the right brand communication with consumers. Data-driven marketing involves predicting customer behavior to improve your targeting performance. This approach is based on analyzing big data and using insights that you should notice everywhere. Separating the right insights out of the flow of information is important for setting up retargeting, dynamic advertising creatives, and targeted email campaigns. In retail, big data-based marketing has a significant impact on increasing ROI:

  • Attracting the most relevant leads based on knowledge of customer needs.
  • Media buying becomes more effective by eliminating false hypotheses using the machine method.
  • Audience segmentation becomes automatic.
  • Cross-channel messaging remains consistent and unified across all customer access points.

Expanding loyalty borders

Big data helps the retailer develop more customer sensitivity and get as much information as possible. A 2018 Forrester survey found that 44% of B2C marketers use big data and analytics to improve their responsiveness and implement a successful strategy. This leads to a 36% increase in business income. In this way, marketers are expanding the boundaries of customers’ loyalty and improving their lives. One example of successful data engagement in marketing strategy is cookie analysis. These files collect information about customer browsing activity, generating personalized data. For example, by collecting and analyzing cookies, you can find that women are more likely to respond to online campaigns, use coupons, participate in loyalty programs and online offers.

Not the data, but the solution

Today, the challenge is not just where to get the data, but how to use it for your business. You have to clearly visualize the way the availability of data can affect a particular business. Data is a step towards the decision, but there is also an intermediate link between these two steps: insights. It is important to remember that obtaining relevant data is necessary but not sufficient by itself. The differentiator is how you generate ideas that will improve your decision making. So before you start using big data you have to ask yourself: what do we expect from it? Where should it lead us?

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