In 2017, India’s leading fashion retailer, Shoppers Stop embarked on a digital transformation journey to deliver enhanced, personalised and engaging online and offline experience for customers. It collaborated with Cisco to implement the Connected Mobile Experience (CMX) capabilities across 80 stores in the country.
With CMX Shoppers Stop gained a single view dashboard of connected customers in the store, insights into customer behavior analytics, location based services and analytics for effective in-store marketing and in-store tracking, among others.
The amount of data available today is greater than ever, as more and more devices are connected to our digital environment either directly or via sensors. Making use of this data, several retailers are trying to bridge the gap between digital and physical shopping experiences.
Many have already started implementing data analytics across procurement, supply chain, sales and marketing, store operations, and customer management.
History of personalised experiences
Until the early 1990s, retail was largely unorganised and relied on a strong personal connect with the consumer. The local kirana shops had their loyal customer base, and this consumer strategy worked wonders!
After the introduction of the organised retail sector through departmental and multi-brand stores in metropolitan cities, a new world of possibilities opened up for the sector. It was the decade of economic reforms, driving increasing incomes and rapidly changing lifestyles.
By the end of the decade, global brands came into the picture and the neighbourhood shop gave way to big brand launches. By 2006, organised retail spread far and wide in Indian markets, and data awareness became crucial.
Data is the new oil, and with each passing year, the size of data has only grown. It can be credited to the fast adoption of data analytics, leading to more focused business insights to strategise growth.
A recent PWC survey maintains that almost 90% of their retail respondents have been dependent on the analytics of this data for growth strategies.
Hyper-Personalisation is the key
Gone are the days when retailers and brands could manage with generic marketing campaigns. Today, Retail customers expect personalized communications and products tailored to their individual preferences.
According to a 2017 study, only 22% of shoppers are satisfied with the current level of personalization from brands, which means that there is need for more effort from brands!
Retailers are taking the help of Hyper-personalization to take personalization to another level. Hyper-personalization blends in behavioral and real-time data a brand can extract from its customers. For example – a shopper surfs through a website for cleaning supplies and simultaneously the website recommends similar products based on their specific search history.
For this process to work, brand requires a deep understanding of its own products and customers, then devise a customized marketing strategy.
Also, adopting the right technology and infrastructure to support hyper-personalization techniques used to store customer information across online and offline channels is crucial. This enables shoppers to have a seamless shopping experience, regardless of whether they’re shopping online or in-person.
Whichever approach a brand takes, implementing hyper-personalization techniques is the way of the future as more companies are opting out of traditional methods.
According to a Salesforce study, 51% of consumers expect that companies will anticipate their needs and make relevant suggestions before they even make contact.
Amazon has years of experience when it comes to personalization and through its massive inventory and different membership options, it has taken full advantage of the way shoppers interact with its website. For example: A customer searches its database for a pair of headphones, and when they click on the product, the interface automatically recognizes the search and a “Frequently bought together” section will appear on the page.
Amazon also creates a personalized homepage for each of its customers based on factors like their historical shopping habits, wishlist, and shopping cart. Anticipating the needs of its customer makes it easier to find what they’re looking for and discover new products.
To do this, Amazon uses analytics to gather this data. The company uses both historical and real-time data to gain a deep understanding of its customers, thereby increasing customer satisfaction with hyper-personalized marketing techniques.
Starbucks has long allowed customers to personalize its menu of products. But to take it one step further, Starbucks adopted a real-time personalization engine that produces individualized offers for their customers based on their previous behavior and preferences. The brand primarily pulls this data from their loyalty app, which it introduced back in 2011. The data helps the brand understand the needs and habits of each individual customer, and with this knowledge, Starbucks sends customers personalized emails with deals and updates relevant to them.
Customers are willing to share their data via the app because they’re aware that continuous usage directly leads to tasty rewards for customers and more data for Starbucks. This kind of win-win situation is a direct result of the quality of its products and recognizing what its customers want.
A successful retail analytics strategy
These are some areas where retailers can apply analytics:
Dramatic increase in online research, comparison shopping and bargain hunting has disrupted the traditional customer engagement paradigm for retailers. Using analytics to attract and retain customers has become a retail imperative.
Analytics can be used to identify under-served groups of customers that have the likelihood to spend more. It can also help to find the customers who have the propensity to increase spending.
Pricing and profitability
Pricing is a powerful factor that can improve both top of the line and bottom line financial performance. In a period of declining customer spending, pricing has become a target source for retailers to grow organically. Pricing too high will end up in loss in market share, while lowering the price too much can cost margin.
With advanced analytics, retailers can improve the way they analyse, set and deliver pricing in a sustainable manner. Once adopted correctly, pricing solutions can expect an immediate margin performance improvement of 2-4% and a sales growth of 1-2%.
Supply chain efficiency
As supply chains are growing in size and complexity, retailers have the opportunity to utilise analytics to manage inventory, reduce transportation cost, and increase collaboration with customers, merchants, marketing and suppliers.
Risk and fraud detection
Fraud is another pertinent issue with retailers that can be resolved using analytics. With analytics, retailers can identify unusual patterns and to help manage risk within organisations.
Workforce development and management
Retailers can use analytics to increase workforce productivity and use of associates with regard to cost, customer shopping patterns, locations, and identify performance.
The way forward
Analytics, powered by data engineering and data science, has brought retail customer-connect to a new level. The boom in data collation points and the ease of availing extremely evolved analytics, has provided the ultimate weapon to retailers — the ability to cater exclusively to each customer.
(Edited by Anu Choudary)