The importance of retail analytics
The pandemic disruption challenged online and offline retailers on various fronts. Despite the decline in footfall due to restrictions, data indicates shoppers’ preference for an in-store shopping experience.
A PwC survey confirms almost 40% of consumers visit a physical store at least once a week to make purchases. While 65% of shoppers opted for in-store shopping to avoid delivery fees, over 60% chose it to get the items immediately. 61% of consumers prefer in-store shopping because they like to feel or try the products.
While it is not the apocalyptic scenario predicted for brick and mortar retailers, transformation in the experiences is here to stay. 62 percent of Baby Boomers and 58% of Gen Zers1 who prefer in-store shopping would like the conveniences of online as well.
Retailers can make the most of this demand and shift the tide in their favor with retail analytics. Pure-play online players like Alibaba or Amazon owe their success to retail analytics. It helped them understand their customers to come up with hyper-personalization strategies. Physical stores can level up too by implementing data-driven strategies. After all, they have one very distinct advantage over pure-play retailers – physical availability.
No wonder the conversion rate for brick and mortar retailers2 is higher (almost 13 percent) than that of online retailers, which are around 3 percent.
Physical stores are here to stay. Retail analytics paves the way.
There is an abundance of data in the Retail sector across consumer preferences, inventory usage patterns, transaction records, and more. Collating and studying the relevant data with the appropriate Data Analytic Tools is the difference between gaining insight and getting lost in dense details of the business.
For example, Starbucks, the coffee chain giant, used data analytics to empower human connections, enhance in-store experiences, and induce transparency in the supply chain. Analytics helped it brew exciting menus, offers, and addictive coffee for its more than 100 million weekly customers.
Yet, most retailers cannot attain the data maturity required to stay ahead.
While 25% of retailers are leading in terms of their data maturity, over 50% are still struggling to prioritize investment in data capabilities and find the right people on their path to digital transformation.
The in-store experience is the leverage. In-store analytics can make a difference.
Modern retailers are using various methods to track the movement of customers once they step into the store.
Innovations like smart mannequins that analyze faces to determine age, gender, race, and the time spent by shoppers at the stores help brands enhance the in-store experience. Smart carts with location beacons and sensors collect customer data on the store sections visited, time spent, products selected but discarded prior to purchase, and more.
In-store analytics helps:
- Differentiate between shoppers and consumers and how they behave from the entrance to the exit
- Identify products that fly off the shelves and those that don’t
- Prevent theft and shoplifting
- Evaluate the effectiveness of store displays and employee actions that motivate purchases
- Efficiently assign staff resources.
The Chinese eCommerce giant Alibaba’s retail store Hema allows customers to scan barcodes through an app to get product information and pay for their groceries. This provides insights into shopper preferences on products not purchased. The analytics enable them to create personalized shopping experiences, devise loyalty programs and targeted promotional campaigns.
Analytics is key to optimizing inventory and supply chain logistics
The one thing that matters most for every business is maximizing sales.
Retail analytics helps retailers:
- Manage inventory and achieve assortment optimization
- Ensure demand fulfillment by stocking the right product at the right place
- Align merchandising decisions with customer expectations and performance of product categories
- Assess new products for their incremental financial contribution and the value they offer to customers
- Ensure smart delisting to replace slow-moving items with those in demand
- Enable optimal space utilization based on the popularity of products and their contribution towards overall profitability
Nordstroms’ incident offers important lessons for Retailers. Beyond its premium stores, they relied heavily on Nordstrom Rack discount stores to overcome the pandemic-induced lull. The company added more products at lower price points while adjusting its assortment. Low inventory levels in premium brands across key categories led to understocking, creating a gap in merchandise availability.
Shares plunged by about 23 percent, and CEO Erik Nordstrom was quick to confess, ” We brought some lower price product in categories that we’ve heard from customers is not what they [want].”
While it is good to look into ‘what could go right’ with data analytics, it is essential to prepare for things that could go wrong effectively.
Assortment analytics should be supported by inventory analytics to optimize the supply chain.
The French retailer Carrefour uses AI-powered predictive analytics to optimize inventory management across warehouses, stores, and websites. It helps the company predict demand, refine supply orders, reduce stock outages, and avoid overstocking.
Demand fulfillment should be effectively balanced against the cost of excess inventory to achieve profitable outcomes.
BWG, the company behind Spar in Ireland, is all set to roll out an AI-based predictive stock ordering solution. With a €6.5 million investment in smart technology, it will use it to anticipate customer behavior across its 1,000-plus stores.
Successful retailers rely on:
Descriptive analytics – It gives them insights about the performance of their business actions to tweak marketing campaigns and determine response rates, conversion rates, and costs per lead. It is more effective when used along with web analytics
Diagnostic analytics – It looks at past performance too but it studies the relationship between variables and outcomes. It helps retailers understand ‘why’ they got those outcomes to decide what they can do in the future.
Predictive analytics – It helps retailers analyze shoppers’ behavior based on insights obtained from diagnostic analytics. It enables them to forecast trends and stock accordingly.
Prescriptive analytics – It helps retailers make incremental adjustments to match steps with changing sentiments, demand, and supply shocks. Recommendations come in real-time and changes can be made immediately. It’s how airlines adjust their ticket prices.
The Spanish retailer Zara exemplifies what retailers can achieve with retail analytics.
It manages a tight supply chain with real-time updates on SKU-level inventory data. It gives customers what they want while keeping the ones that lack the pull away.
Zara obtains qualitative feedback from sales employees to understand customer sentiment. It initially orders in small batches and increases the inventory only when the designs get a satisfactory response in store.
In contrast to its competitors like H&M which creates 80% of the designs ahead of the season, Zara designs only 15-25% ahead of the season and more than 50% mid-season depending on what becomes popular. Its quick refresh cycles create a sense of scarcity that further increases the demand for its designs.
As Masoud Golsorkhi, the editor of Tank, a London magazine puts it, “With Zara, you know that if you don’t buy it, right then and there, within 11 days the entire stock will change. You buy it now or never. And because the prices are so low, you buy it now.”
Tools for data-driven retail
Data generated, captured, copied, and consumed globally has increased by a whopping 5,000% from 2010 to 2020.
There’s a lot of raw data out there that can offer a wealth of insights to study customer needs and deliver delightful experiences. All you need is a robust data analytics tool.
Embark on a retail analytics journey with Trigent
The right data tools can help build a suitable data ecosystem. Our extensive suite of data analytics tools can help you align data investments to desired business outcomes.