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January 12, 2017

The digital era has witnessed an immediate embracing of new technologies and applications by consumers, leading to the rise of the digital shopper. Todays fast evolving digital shopper personifies a shift in convenience – from store to finger tip, a want it now and designed for me attitude, comparison of everything at the most granular level, and highly social behavior with ample collaboration.

These shifts in consumer preferences and digital technologies make the traditional paradigms of merchandising such as constrained space, limited assortment, one-a-day price changes and isolated promotions irrelevant. Moving from around 15 thousand mixes, merchants are now dealing with an endless aisle online, making decisions on over 4 million items as compared to about an average 30000 SKUs. Online retailers such as Amazon are waging a price war, with price changes almost 80 million times a day, making it extremely difficult for merchants to keep pace with. To stay relevant, merchants need to leverage the very technologies that are threatening their existence.

A merchants workspace has to be completely transformed, leveraging digital technologies such as Big Data, artificial intelligence, 3D printing and image processing. This will empower them to analyze data from various new sources such as clickstream data, accurate weather data, demographics, shopper sentiment and more. This, in turn, will help retailers derive real time insights and even enable them to automate their decisions based on deep customer insights.

Here are a few areas where digital is transforming merchandising processes:

1. Assortment planning: Assortment planning has moved from rationalizing the long tail to completing the basket – identifying items customers need, but do not purchase with the retailer. This requires analysis of shopper’s baskets across channels, shoppers path to purchase, competitors assortment, and market share. Intelligent machine learning algorithms analyze shoppers behavior and help identify missing opportunities.

2. Item Management: The challenge of increasing the breadth of assortment comes with the increased complexity of that item being found. Last season, many shoppers searching for a Christmas dress at a retailers online store returned with empty baskets since what they were really looking for was a little girls dress in Christmas colors, but the retailer had not tagged these items that way. Online searches are different than in-store ones, and curating the attributes of the item by analysis of search terms, social sentiments, shopper path and cognitive sciences is very critical. Artificial intelligence and graph databases play a crucial role in making items more searchable.

3. Space planning: Digital channels put tremendous pressure on stores, which often account for over 60% of retailers investment in assets , and are now transforming to experience and pick-up centers. Maintaining the right balance on which category gets how much space based on their incremental benefit and opportunity, localized for the market or store requires leveraging Big Data and machine learning techniques to derive insights from weather, traffic, demographics competition, location, proximity and other data. Similarly, visualizing these changes and simulating them in a virtual store using VR technologies can help manufacturers and retailers de-risk investments in expensive store trails and collaborate better.

4. Pricing: The online price war and frequent price changes mandate retailers to move from reactive price changes to pre-emptive price updates, and shift from pricing for product to pricing for the customer. This requires deep understanding of customer price sensitivities, loyalty and an understanding of the competitors price changing patterns. Image recognition technologies and crowd sourcing help provide easy access to competitors prices in store. Furthermore, online bots provide real-time price updates from competitors. By leveraging intelligent prescriptive algorithms, retailers can define strategies preempting competitor prices without losing gross margin, thus maintaining their price image.

5. Promotions: Resonating with the sensitivities of want it now customers and with large number of customers willing to share their location information, promotions have to be location specific, context specific and personalized. This requires in-depth analysis of customer location data, social media footprint, purchase and search patterns, time of the day relevance, channel preferences and other parameters. This mandates the use of Big Data technologies to crunch this data and sophisticated personalization algorithms to target and persuade customers into buying

6. Buying: New fashion every day requires a shorter product lead time from design to store. This greatly impacts how merchants buy- crunched timelines for analyzing trends, testing samples, vendor negotiation and manufacturing. Digital technologies such as 3D sampling, and VR help reduce the iterations between the designer, merchant and the vendor. Similarly social media sentiment analysis and social media image processing help understand fashion trends and identify inspirations.

Digital shopper meets Digital Merchant
From being gut-feel-driven to powered by analytics at the point of action, the digital merchants workspace has to transform to become more intelligent, agile and collaborative. This underpinning strategy alone will enable the digital merchant to cater to the needs of todays digital shopper.

As an intrapreneur, Shilpa is passionate about combining art and science to improve how businesses make decisions. Shilpa has helped leading Fortune 500 companies reimagine their merchandising processes with TCS Optumera™, AI powered retail optimization suite. She has been responsible for conceptualizing, developing, evangelizing, and executing the products and solutions strategy.  


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