Data Analytics changing the way of Retail Vertical

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Traditional marketing has taken a backseat with the advent of technology, and companies target consumers by means of digital and virtual marketing. Whatever we do on the internet remains out there somewhere, and companies track and analyse this information for understanding customer behaviour and preferences, latest trends, and insights. All this helps them engage customers on a more personal level. This digital footprint that we leave behind on such a large scale is a part of Data Analytics.

Marketing is changing; you will now find words like predictive modelling and advanced analytics being used to target customers more effectively. Data Analytics helps here by offering comprehensive information on your brand’s perception in the market. It offers a great deal of data about your targeted consumer base and how they are interacting with your brand. The advent of Data Science has turned into a revolution of its own, and is ushering in a trend in the retail market, which rakes in close to $3 billion for the US economy alone. It is transforming retail in a massive way.

Here are some examples of the Data Analytics use case for retail vertical

1. Consumer behaviour analytics -A comprehensive view: Today, customers have more media to interact with companies – social media, e-Commerce, mobile, email, and more. Companies use techniques like Hadoop analytics to understand what customers expect and how to interact with them most effectively using new marketing methods. Here, Data Analytics helps by obtaining customer browsing history and behaviour, product preferences, transaction data, browsing history, and more. It is even helpful in compartmentalizing unstructured data streams such as traffic to specific channels, for analyzing consumer behaviour.

2. Predictive analysis and Targeted promotions for boosting conversions: An effective marketing campaign is one that gets maximum conversions at the optimum cost. When a company manages to achieve its marketing goals without splurging resources, it is called good marketing. To run an effective marketing campaign, increase customer acquisition for optimal costs, a company needs to understand their customers. Analyzing customer behaviour is key here. Predictive analysis uses algorithms to collect data from various sources. Based on this data, marketers can predict their consumers’ behaviour and preferences, and even any upcoming trends. Targeted campaigns using predictive analysis is the most effective way to increase conversion rates

3. Measuring brand sentiment: A brand sentiment analysis helps companies figure out how customers feel about their brand. It is important for companies that their targeted customer base perceives a brand as intended. Data analytics helps retailers measure brand sentiment by analysing consumer behaviour on social media platforms. This analysis is helpful for retailers to create further campaigns and other marketing strategies

4. Optimizing E-commerce search result in real-time: Optimizing e-Commerce helps retailers and marketers understand customer preferences in terms on products. They can thus stack specific products in particular amounts at particular locations. Customers today look for a hassle-free and quick shopping experience. Data Analytics helps marketers and retailers analyze customer needs and expectations in order to help companies serve them better.

In a nutshell, Data Analytics is truly revolutionizing the retail vertical in particular and marketing as a whole. It is offering new insight into customer preferences and behaviour, allowing companies and marketers to create more personal and engaging experiences to boost engagements and conversions.

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