Retail Analytics Trends: 2017 and Beyond

retail analytics trends

According to MarketsandMarkets, the global retail analytics market will likely more than double in size during 2015-2020, totaling about $5.1 billion at the end of the forecast period. The adoption of analytics solutions is increasing as more enterprises worldwide are realizing significant returns from using BI and analytics platforms and services.

Of late, many retailers seem to be jumping on the AI bandwagon to improve their marketing efforts. Sailthru, a marketing technology company, published a study that included a survey with more than 200 retail marketers. Over the course of 2017, these retailers plan to leverage AI to expand their mobile and social media marketing strategies aside from improving customer journeys.

As per a Gartner study, AI solutions employed by retail companies might autonomously manage as high as 85% of all customer interactions. Though this figure should probably be consumed with a pinch of salt, nobody can really deny the growing applications of AI and machine learning in the retail space. With that said, let us discuss six major retail analytics trends that are impacting the industry and will continue to gain prominence in the coming years.

Adoption of Machine Vision and Deep Learning Algorithms

AI has seen quite a lot of progress in terms of deep learning over the years. Deep learning algorithms are what will make the benefits of AI more apparent and tangible in 2017 and the years following.

For instance, retail giant Amazon opened its first Amazon Go store in Seattle late last year. The plan for Amazon Go had apparently been in the works for the last couple of years. The goal was to leverage technologies, such as computer vision, deep learning algorithms, and sensor fusion, to improve shopping experiences. People can buy groceries from the Amazon Go store without having to wait in lines or go through the checkout process. The company calls this innovation ‘just walk out’ technology, which is basically its key selling point.

However, as per a Morgan Stanley report that included a consumer survey, only 30% of the people deemed quick checkout important; 50% said they cared more about prices, and 70% favored a convenient store location over anything else. Nonetheless, the grocery market in the US is valued at $770 billion, and Amazon Go has the potential to see tremendous growth in the future. We can expect similar applications of machine vision and deep learning in the near future.

Use of Micro-segmentation for Better Decision-making

While segmentation has been one of the core mechanisms retailers leveraged for a long time, they are likely to leverage more advanced methods to facilitate business strategies and increase customer satisfaction. Segmentation helps with functions such as pricing, promotions, inventory management, and product assortment. It uses data mining and engineering to create meaningful groups at both product and store levels. The technique can consider several parameters such as:

  • Store performance
  • Product performance
  • Product attributes
  • Store attributes
  • Customer segments or demographic data

Retailers can develop and measure multiple what-if scenarios for various clusters. This process helps identify optimal clusters that are more likely to improve planning, decision-making, and execution.

Analytics platforms with advanced clustering and other capabilities are becoming more productized. This means retailers won’t necessarily need their respective managed analytics providers for addressing various platform requirements. These platforms are scalable, flexible, and straightforward in terms of usability.

Omnichannel Data Integration and Spend Optimization

More retailers worldwide are realizing the significance of omnichannel data integration solutions as they are highly scalable and robust. These solutions are important for retailers looking to expand their reach across more geographical locations in terms of both brand awareness and sales.

According to a study by Accenture and the Alibaba Group, global cross-border e-commerce sales will likely reach $1 trillion by 2020. This figure indicates growth of a whopping 335% with respect to the figure in 2014. Another report from International Post Corporation revealed that 63% of cross-border shoppers buy internationally at least once in a month. Given the growing trends of cross-border shopping and overall e-commerce sales, retailers, regardless of their size, must focus on global expansion to hold their own in the highly competitive market.

In this scenario, omnichannel data integration solutions give a competitive advantage to retailers because they integrate seamlessly with online marketplaces. Retailers would likely optimize investments across channels and media simultaneously and provide a consistent customer experience across offline and online channels.

As for omnichannel spend management, attribution modeling is among the key solutions. Retailers tend to spend a lot of money on marketing strategies. But overall, there seems to be less clarity about which strategies are working and to what extent. In this scenario, attribution modeling helps retailers understand how to optimize their marketing spend based on how customers reach and navigate through their sites.

To elaborate, a customer can reach a retail website through several ways:

  • Direct URL: Customer enters the site URL for access. This can be influenced by various offline programs like advertisements on TV, print, etc.
  • Organic search: Customer searches for a keyword and clicks on a result to reach the site.
  • Paid advertising: Customer clicks on a pay-per-click (PPC) link.
  • Referrals: Customer clicks on a link on another site to reach the retail site.
  • Social media: Customer clicks on a site link posted on various social media platforms like Facebook and Twitter.

Now, attribution models determine the effectiveness of each channel and every customer touch point through the conversion process. These analytics insights help retailers target customers more effectively to improve sales.

Making marketing decisions can get even more complex when we consider an omnichannel set up wherein offline spends are also to be considered for the same. Nonetheless, analytics sure facilitates the overall decision-making process.

Enhanced Data and Advanced Analytics through IoT

The use of powerful data from Wi-Fi, beacons, and RFID tags is helping retailers improve a wide range of operations, whether it is tracking items across the entire supply chain cycle or managing inventory. The IoT data generated from these technologies helps determine customer behavior, create effective in-store marketing strategies, and improve planograms.

For example, RFID data streams help retailers determine the sales performance and popularity of products based on their movement in the stores. They help identify products that are selling out and eliminate slow-moving items. Therefore, these streams shed light on both customer preferences and inventory needs.

Wi-Fi sensors in retail stores can interact with customers’ Wi-Fi enabled mobile devices and generate data related to:

  • Popular areas within the store
  • The sequence of customer movement in the store
  • The time spent by a customer at a particular store
  • Repeat visits by customers

Retailers can, therefore, optimize store layout, enhance merchandising, assess product performance, and improve customer experience.

Beacons, which are sensors that work on Bluetooth low energy, facilitate proximity marketing strategies. They are capable of connecting with customers’ Bluetooth-enabled devices. Beacons send targeted messages, including promotions, discounts, coupons, to customers based on where exactly they are located in a store. Similar to Wi-Fi sensors, beacons shed light on customer movement and dwell time. Also, analyzing the data from beacons helps retailers measure the success rates of advertisements and optimize marketing strategies accordingly. Another benefit is, of course, improved customer experience and loyalty.

IoT sensors also help retailers manage and optimize power and utility consumption based on temperature controls, lighting management, etc.

Dynamic Pricing to Increase Sales

Most people today tend to do a fair bit of online shopping. It’s simple, quick, and convenient after all. For online retailers, however, things aren’t all that rosy. They face tough competition and have fickle-minded customers to cater to. Online shoppers have a lot of retailers to choose from. And the one thing they consider primarily while making buying decisions is, of course, the price. This is where dynamic pricing, one of retailers’ crucial retail analytics solutions, comes into the scene.

Dynamic pricing has been a go-to methodology to push retail sales, especially in intensely competitive segments like electronics. Retailers consider a lot of internal and external factors while designing a dynamic pricing model. Internal factors include supply, sales goals, margins, etc. External factors are traffic, conversion rate, popularity of the products, etc. Retailers often use a combination of these factors to optimize their pricing models.

Retailers use data from analytics models, such as price elasticity and ensemble models, to increase or decrease product prices based on market situations. Companies like Amazon usually opt for the price cutting model, wherein they sell the main product at a reduced price and offer related products at a margin. The idea is to compromise a little on the profits rather than market share. Other companies tend to go with the profit-driven price increase strategy.

With just a few clicks, customers can easily compare the prices of a product on various sites to find the best deal. Retailers need to stay updated about their competitors’ price changes in real time to offer the best price. This is the crux of dynamic pricing. Other industry verticals where companies use dynamic pricing strategies include taxi services like Uber and airlines.

Improved Assortment Management for Better Customer Experience

Many major retailers have begun to improve assortment planning by leveraging analytics insights. Retailers are using predictive and prescriptive analytics to improve merchandising – sort which product where and when within the store. This exercise improves customer experience and retention significantly.

For instance, Walmart sorts its products with respect to demand patterns in the local community. This means that the assortment varies from one store to the other based on customer needs at the respective localities. Walmart also uses in-store and online transactional data to provide convenient payment and delivery options to customers. Customers can even ‘Scan and Go’ and receive invoices via e-mail or pay using Walmart Pay.

Aside from historical transactions, retailers consider many more factors, such as weather, local events data, and demographics, while procuring data for analysis. Using a combination of internal and external data has become an imperative because it helps businesses predict customer demand for specific product categories. Such analyses enable retailers to manage their inventory better such that they never run out of stock.

These are probably just a few of the key trends. As the retail industry continues to struggle with competitiveness and face myriad cost and growth challenges, innovation in analytics seems to be the most critical pillar for staying ahead in the marketplace.

BRIDGEi2i Case Study: Customer Analytics

A leading Fortune 50 global IT company wanted to analyze customers’ purchase behavior to optimize digital marketing spend.

BRIDGEi2i helped the company’s marketing team:

  • Determine the segments for targeting their banner campaigns based on the browsing and purchase behavior of customers and prospects
  • Optimize the marketing spend and performance of digital banner ads

For all the hows, read the complete BRIDGEi2i customer analytics case study.