8 Big Retail Data Analytics Trends For 2018

- 8th Jan 2018

In spite of the retail apocalypse with numerous U.S retail stores shutting down, 2017 ended on a high note with a massive surge in holiday sales. Keeping innovation at the forefront, the retail industry went through a significant transformation led by digitization in 2017. Retail analytics, powered by ‘big data’ played a crucial role in this digital transformation and revolutionized the way retailers operate.

The past year’s retail technology trends prove that retailers can utilize data analytics to overcome the e-commerce challenge. By understanding customer behavior using data insights from different channels, they can effectively execute marketing campaigns and personalize the shopping experience for today’s highly demanding and informed customers.

Furthermore, data analytics is now being applied to every aspect of the retail business – from optimizing shelves, to improving loyalty, to predicting sales and more. Enter 2018, retailers should now look at improvising this strategy to further overcome challenges and adapt to the changing market. For retailers that are planning to leverage data analytics, here are 8 trends that we think will help them progress further this year.

1. In-Store Analytics To Become More Precise With WiFi

Most retailers today understand the need to capture in-store analytics and have invested in various analytics platforms based on beacons, video cameras, thermal imaging, 3D sensors etc. However, the data that is being captured today via these platforms is an aggregated data that does not always turn into actionable business insights.

The customers walking into the store still remain unknown. Retailers cannot differentiate whether a customer walking in through the door is a first time visitor, repeat visitor or a loyalty member. Therefore, in-store customer behavior and personas are never captured. The ability to know Who, Where and When will make in-store analytics more precise and accurate. A smart in-store WiFi analytics platform can provide such precision. Using such a platform, retailers can identify each customer by asking them to opt in via captive portal using their name, email, phone or loyalty number. According to a new market research report by MarketsandMarkets, the WiFi analytics market is expected to grow from USD 2.94 Billion in 2017 to USD 10.72 Billion by 2022 with retail having the largest market share. In 2018, we think WiFi analytics adoption by retail industry will increase as they recognize the need for more accurate in-store analytics.

2. Retail Will Advance From Predictive To Prescriptive Analytics

Predictive analytics has become a norm in retail and is being used for everything from forecasting demand & footfalls to personalizing customer experience. However, pricing remains the biggest challenge for retailers competing with the likes of Amazon. Equipped with big data, they can now even overcome this challenge using prescriptive analytics. Prescriptive analytics provides a best course of action for a given situation. By analyzing different types of data such as customer trends, product availability, resources, time and geo location, retailers can optimize profit margins to capitalize any available opportunities. With increasing pressure from e-commerce brands, we think prescriptive analytics could be very useful tool for retailers in 2018.

3. Data From Omni-Channels Will Get Consolidated

Retailers are capturing various types of customer, loyalty and sales data from different sources and multiple channels. With the number of channels increasing significantly, managing and analyzing this data has turned into a challenge. Retailers have been hiring data scientists to analyze and manage this data. With increased focus on automation this year, we think more retailers will deploy cutting-edge retail analytics softwares to bring all that information together to get a holistic view of their brand’s performance.

4. Product Assortment Analytics Will Help Improve Sales

Product assortment bears a significant amount of impact of in-store conversions and sales. In the past, many retailers who have either neglected or poorly planned their product assortment have seen devastating results on their sales. Reviewing shopping patterns to understand correlated products that are bought together can help retailers optimize their product assortment to maximize sales. Now equipped with in-store analytics, retailers can integrate in-store customer behavioral data with purchase history from POS to discover shopping patterns. Looking ahead, data analytics will help retailers to be more cautious and proactive with product assortment in 2018.


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5. Data Analytics Will Rejuvenate Loyalty Programs

Although e-commerce sales are skyrocketing, 94% of retail sales still happening at stores, clearly suggesting that a large number of shoppers still prefer retail stores over e-commerce. So if you think of it, price discounts are not driving the customer loyalty — customer experience is. Loyal customers typically seek preferential treatment and in-store analytics technologies are enabling retailers to do that. By understanding in-store customer behavior, brands are focusing on personalizing experiences to drive customer loyalty. Data analytics will drive most of the loyalty marketing campaigns in 2018.

6. Store Operations Will Be Optimized Using Data Analytics

When it comes to running a profitable retail business, appropriate optimization of store operations is one of the challenges. Figuring out the ample number of associates required for customer assistance can be difficult due to various shopping trends based on day of the week, seasons, events, holidays, etc. In 2018, in-store analytics data will play a crucial role in managing store operations by enabling retailers to optimize the staff based on various scenarios and historical data.

7. Data Sharing Between Retailers And Suppliers

Data sharing between retailers and suppliers will be a key to making profits in 2018. In the past, data sharing couldn’t get rationalized due to lack of adequate technology which made this process more time consuming and difficult. With access to various cloud tools and big data technologies, data sharing between retailers and suppliers will get streamlined. Having ability to forecast demands and understanding shopping patterns will help both retailers and suppliers manage purchasing/delivery schedules, improve efficiency and reduce costs.

8. Data Analytics Bringing Dynamic Pricing to Retail Stores

With Prescriptive analytics and ability to understand customer personas and purchasing patterns, physical retailers can now take a leap at offering ‘Dynamic Pricing’ which has been Amazon’s biggest advantage over physical stores. This trend is expected to hit the ground running in 2018 with the availability of lower priced Electronic shelf labels (ESLs) and NFC tags that enable physical stores to frequently update product prices based on shopping trends and other data.


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