Retailers are using big data for better marketing

Durjoy-Patranabish-Blueocean-Market-IntelligenceToday, the customers’ expectations are growing by leaps and bounds and the credit goes to the technology that has given ample choices to them. Retailers are leaving no stone unturned to provide better shopping experience by adapting to analytical tools to catch up with the changing expectations of the consumers. Durjoy Patranabish, Senior Vice President, Blueocean Market Intelligence divulged Dataquest about the role of analytics in retail sector. 

How retailers are using big data analytics to drive real business value?
The idea of data creating business value is not new; however, the effective use of data is
becoming the basis of competition. Retailers are using big data analytics to make variety of intelligent decisions to help delight customers and increase sales.

These decisions range from assessing the market, targeting the right segment, forecasting demand to product planning, and localizing promotions. Advanced analytics
solutions such as inventory analysis, price point optimization, market basket analysis, cross-sell/ up-sell analytics, real-time sales analytics, etc, can be achieved using
techniques like clustering, segmentation, and forecasting. Retailers have now realized the importance of big data and are using it to draw useful insights and managing the customer journey.

How advanced clustering techniques can be used to predict better purchasing behaviors in targeted marketing campaigns?
Advanced clustering techniques can be used to group customers based on their historical purchase behavior, providing retailers with a better definition of customer segmentation on the basis of similar purchases. The resulting clusters can be used to characterize different customer groups, which enable retailers to advertise and offer promotions to these targeted groups. In addition to characterization, clustering allows retailers to predict the buying patterns of new customers based on the profiles generated. Advanced clustering techniques can build a 3D-model of the clusters based on key business metrics,

such as orders placed, frequency of orders, items ordered or variation in prices. This business relevance makes it easier for decision makers to identify the problematic clusters that force the retailers to use more resources to attain a targeted outcome. They can then focus their marketing and operational efforts on the right clusters to enable optimum utilization of resources.

What trends are boosting big data analytics space?

Some of the trends in the analytics space are:


„„1. The need for an integrated, scalable, and distributed data store as a single repository will give rise to the growth of data lakes. This will also increase the need for data governance.
„„2. Cloud-based big data analytics solutions are expected to grow three times more quickly than spending on on-premises solutions.
„„3. Deep learning which combines machine learning and artificial intelligence to uncover relationships and patterns within various data sources without needing specific models or programming instructions will emerge
4. „„ The explosion of data coming from the Internet of Things will accelerate real-time and streaming analytics, requiring data scientists to sift through data in search of repeatable patterns that can be developed into event processing models
„„5. Analytics industry will become data agnostic, primarily having analytics solutions focused around people and machine rather than on structured and unstructured data
6. „„ Data will become an asset which organizations can monetize by selling or providing value added content.

What are your views on ‘Big Data for Better Marketing’. How retailers can use analytics tools to be ahead of their competitors?

Whether it is to provide a smarter shopping experience that influences the purchase decisions of customers to drive additional revenue, or to deliver tailor made relevant real-time offers to customers, big data offers a lot of opportunities for retailers to stay ahead of the competition.


Personalized Shopping Experience: Data can be analyzed to create detailed customer profiles that can be used for micro-segmentation and offer a personalized shopping experience. A 360 degrees customer view will inform retailers how to best contact their customers and recommend products to them based on their liking and shopping pattern.
Sentiment analysis can tell retailers how customers perceive their actions, commercials, and products they have on offer. The analysis of what is being said online will provide retailers with additional insights into what customers are really looking for and it will enable retailers to optimize their assortments to local needs and wishes.
Demand Forecast: Retailers can predict future demand using various data sets such as web browsing patterns, buying patterns, enterprise data, social media sentiment, weather data, news and event information, etc, to predict the next hot items in coming seasons. Using this information, retailers can stock up and deliver the right products
and the right amount to the right channels and regions. An accurate demand forecast will not only help retailers to optimize their inventory and improve just-in-time delivery but
also optimize in-store staffing, thus bringing down the cost.
Innovative Optimization: Customer demand, competitor activity, and relevant news & events can be used to create models that automatically synchronize pricing with inventory levels, demand and the competition. Big data can also enable retailers to optimize floor plans and find revenue optimization possibilities.

Source: DataQuest

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