The retail environment is undergoing a significant change with the proliferation of e-commerce, emergence of new digital channels, innovative payment platforms.
By S Swaminathan
The retail environment is undergoing a significant change with the proliferation of e-commerce, emergence of new digital channels, innovative payment platforms, etc.
This is significantly altering consumers’ shopping behaviour. An interesting study found 38% of shoppers mentioning that retailers are learning about their personal behaviour and preferences but are infrequently using them to create personalised experiences. Customer engagement in retail is only as good as how well retailers know their customers and how well they use these insights across channels.
Sameness of experience: The real challenge
Before we look at how retail analytics will help, it is important to look at shopping experiences that consumers go through today.
1. Era of abundance: We are entering an era of abundance and retailers in India will increasingly face this challenge. Be it fashion shopping, value-grocery shopping, specialty retail shopping or single-brand retail shopping, there are abundant choices across a range of merchandise, designs, prices, etc. This poses a real challenge for a differentiated customer experience and loyalty generation.
2. Era of shop now: E-commerce retail has created a new kind of impulse shopping behaviour which categories have never seen before. Planned shopping journeys are increasingly being replaced by the ‘shop now’ purchase behaviour. Consumers discover products with online retail and tend to shop across different categories — both high value and low value products — in real time.
3. Era of fickleness: Customers show fleeting loyalty when relationships are transactional. The ease with which they can switch due to digital platforms and wide availability of online store options, is making it difficult for retailers to retain customers and get a better share of their wallet. Therefore, they just switch when they find better offers, attractive cashbacks, etc. This is also going to get increasingly complex as customers will switch between offline retail and online retail.
4. Era of convenience: As customers in India get more affluent, they seek more enriching shopping experiences; boring shopping chores are also being relegated to online shopping as they are time-starved. They now seek more convenience and ease of ordering for these products. Retailers need to adapt to these convenience-seeking shoppers.
Insights in the context of changing eras
Most often, analytics and insights from retail data are not often looked at with the context of purchase in mind. However, Amazon’s recent decision to venture into offline retail with Amazon Go has pushed the retail industry to scout for a possible counter using retail analytics.
While statistical techniques, methodology and tools are important, understanding retailers’ business context and customers’ purchase context are critical for building deep analytics insights for retailers to make a difference in business outcomes.
The application of segmentation, store-level insights, assortment mix and replenishment strategies need to be overlaid with customised analytical and contextual rule engines, predictive models and solutions to make things count for a retailer.
In the age of customers having a polygamous relationship with brand loyalty, it is important that each customer is treated as a separate entity. However, clustering millions of SKUs and building attribution/lifestyle around what is called as ‘purchase mindsets’ of consumers is possible today. For example, looking at shopping baskets of customers and indexing them against wellness and fitness SKUs/clusters across departments, fine dining and gourmet product clusters, convenience and snacking product clusters, etc.
Using insights, an engagement and lifecycle framework for each cluster of customers can be created for both offline and online retail. In the fashion retail space, tagging the merchandise around fast fashion clusters, emerging fashion clusters, traditional fashion clusters, etc and looking at customers’ shopping basket around these clusters can provide meaningful insights into the customer’s mindset. This ‘mindset’ driven segmentation helps differentiate customers who are early adopters of fashion, late adopters of fashion and laggards.
At the same time, it helps the retailer personalise its new range to these customer fashion-driven mindset clusters leading to higher stickiness with the store and increased loyalty.
New York-based Elie Tahari is an example of how fashion retail is jostling with the problem of plenty. Data generated by the company’s core systems could take as long as two days to analyse and present a format that managers could easily understand and act on.
Real-time visibility into store-level sell-through and inventory data enables optimised replenishment and merchandising practices, while predictive analytics enables highly accurate production planning, lower-cost logistics and more efficient inventory management.
Such data intelligence using retail analytics opens the possibility of taking it to the next level in the future. An example of this is the Badgley Mischka Runway mobile app which provides a unique post-show experience for both on-site and virtual attendees. Consequently, show attendees have the opportunity to provide feedback for invaluable real-time data and analytics that the company can use for future design and production decisions. Similarly, an ‘ethnicity-driven analytics engine’ can be used for better engagement with customers in India. To top it, an engagement framework can be built based on ethnic festivals of a customer, including shopping festivals.
This is the age of the customer. The current challenge, however, for retailers is to build a strong data-
driven ecosystem that is tuned to understand the in-store behaviour of the customer to deliver immersive and experiential shopping.
The author is co-founder and CEO, Hansa Cequity
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