What’s ‘In-Store’ for Data Analytics in the Retail Industry for 2018?

Feb 8, 2018

Marketing Mix Modeling

Retailers are more reliant than ever on business intelligence and data analytics tools to remain profitable in this highly competitive era. The battle for survival is fierce in the retail industry with the looming economic uncertainty and the growing consumer power. Amidst such challenges, the retail industry is still flourishing with a little bit of aid from data analytics. The retail trends in data analytics seem to be constantly evolving as traditional tools and techniques prove to be inadequate. The influx of mobility, artificial intelligence, IoT, and machine learning tools is transforming retail trends. The analytics systems are getting more efficient and accurate; thereby, boosting the numbers in the company’s balance sheet. As a result, a majority of the retailers are using data analytics in every aspect of their retail business to improve operational efficiency and profitability. So, how will data analytics impact retail trends in 2018?

Advancement from Predictive to Prescriptive Analytics

The retail industry thrives on its ability to accurately predict everything, right from retail footfalls and demand to personalizing the customer experience. However, recently, traditional retailers have started feeling the heat from online retailers such as Amazon, who have mastered dynamic pricing. By being able to price differently to different customers at different times, Amazon has been able to generate the maximum amount of profit from each customer. As a result, there is a growing trend for the adoption of prescriptive analytics, which suggests the best course of action for a given situation. Prescriptive analytics can do so by gathering a vast set of data including product availability, customer trends, geolocation, and time.

Contact our analytics experts to know more about how data analytics impacts retail trends, retail analytics, in-store analytics, merchandise analytics, and prescriptive analytics.

Omni-Channel Data Consolidation

With rising big data processing capabilities, retailers have been able to capture data from various sources and multiple channels. Previously, retailers faced the challenge to consolidate such data from multiple channels and perform an accurate analysis. This year, retailers seem to be focused on retail automation, which is why they are hiring data scientists to deploy cutting-edge retail analytics software to consolidate information from multiple channels together to get a holistic overview of their brand’s performance.

Importance of Location and Location Analytics

In a traditional retail setting, knowing where customers spend most of their time and what sections they usually browse through is vital. Although a host of technology such as beacons, thermal imaging, 3D sensors, and video cameras are available to a retailer, it doesn’t provide detailed information to perform analysis. It becomes difficult to identify if the customer is a first time customer, repeat visitor, or loyalty member. The ability to precisely know the customers in detail is driving the need to perform in-store WiFi analytics. Such a platform can give detailed information on a customer to track their behavior. Also, the upcoming retail trends are being affected by location analytics, as retailers can send customized promotions and offers by sending geo-targeted push notifications to mobiles.

Product Assortment Analytics

The product assortment is imperative to the success of in-store conversions and sales. In the past, retailers were highly inefficient as they put in minimum effort on product planning and assortment. As a result, retailers weren’t maintaining optimal levels of inventory due to which their profits took a hit. Consequently, the current retail trends are being dominated by product assortment analytics as retailers focus on understanding the shopping patterns of customers to find correlated products and place them in close proximity. This year, retailers will look forward to being more proactive in their product assortment by implementing big data and retail analytics solutions.

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Data Analytics in Loyalty Programs

Companies have realized that customer loyalty matters to them more than profits. It may seem absurd and philosophical, but in the long run, loyal customers are the most profitable ones. With 94% of the retail sales happening at stores, it clearly suggests that a large number of shoppers are still loyal to traditional stores. In-store data analytics assists retailers in providing customized preferential treatment to customers to increase their loyalty. Data analytics is amongst the hottest retail trends to drive loyalty marketing campaigns this year.

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