Data collection has skyrocketed in the past decade and continues to even today. According to Statista, the amount of data created, consumed, and stored globally is set to nearly get three times the size it was in 2020. As a result, all competitive businesses understand how important it is to evaluate their data effectively as it’s powering not just BI but also AI.
Artificial Intelligence (AI) is no longer just a buzzword, much less for the retail sector. Ever since the beginning of the pandemic, the digital transformations have been fast-forwarded by several years, and the retail industry is starting to benefit from AI-powered applications. The retailers winning today are doing so because they leveraged AI and put it at the center of their business. They used it as a tool to make smarter data-driven decisions within their entire organization. This is a highly practical use case for what we call Decision Intelligence — the commercial application of AI to expand and drive profit.
What Is Decision Intelligence and Why It Matters
Decision Intelligence is a new paradigm that’s transforming numerous retail brands. It leverages AI to help organizations make more informed business decisions based on all their data, including data from siloed departments and functions. And it can do across vast amounts of complex data quickly, consistently, and accurately. Moreover, the greater the amount of data that gets analyzed, the better the decision will be — something that’s an infinitely lengthy process when done manually. So in that regard, decision intelligence saves you the time to do something more meaningful.
Moreover, Decision Intelligence helps make the right decisions quicker; thus, it drives growth for the company by capitalizing on market fluctuations and changes in consumer behaviour. It’s all the more relevant for retail businesses where Decision Intelligence can leverage data from across the entire value chain to drive better customer engagement and improve acquisition rates, thus making the fulfilment process more efficient.
It’s like having a recommendation engine that’s familiar with your challenges and reacts accordingly. For example, it allows retailers to foresee consumer demand, ensure that the right stock is always available, and run a personalized marketing campaign to force-shift products down the supply chain. In a way, Decision Intelligence sees the entire organization working in sync to achieve a common goal of profitability and growth.
Applications of Decision Intelligence
Besides what’s already mentioned above, decision intelligence has various quantifiable applications in the retail industry.
Reducing Cart Abandonment
About 70% of all online shopping carts are abandoned, leaving businesses and e-commerce stores a huge opportunity. Retailers are increasingly relying on data to understand their shoppers and their preferences. This data helps them accordingly shape their sales, marketing, and customer service experiences. The only reason it hadn’t worked out until now was the sheer volume of data flowing through the age-old analytics platforms.
Decision Intelligence can feed on data from sources like sales, clickstream, and directly from customers. This covers all information, including their contact and demographics, and can help discover anomaly drivers related to all the lost revenue from cart abandonment. It can also help discover the best opportunities for upselling and cross-selling by predicting which segment will prefer a given offer. To a degree, Decision Intelligence puts the key to the most advanced data analysis in the hands of retailers with very limited technical prowess and supports them in bolstering their revenue.
Building More Sustainable Supply Chains
“Stock Vacations” are common in supply chains where products are often moved around warehouses and distribution centres based on their real-time stock. Every time stock is moved, the retailer must pay for extra logistics, not to mention the unnecessarily increased carbon footprint it leaves behind from additional trips.
Retailers are now using Decision Intelligence to optimize their inventories and limit stock movement around distribution centres. They use parameters such as production output, actual and forecasted demand, and processing and transportation costs. This prevents tonnes of CO2 emissions and optimizes operational costs from warehouses to supermarkets.
Optimising Warehouses For Improved Customer Experience
Warehouse logistics are often quite complex and intricate by design. A small decision can impact the delivery timelines of a group of customers. Retail businesses need to find a way to increase product picks from a given warehouse worker in a given timeframe.
While leveraging Decision Intelligence, you need to gather all the relevant information which will be used to create a machine learning solution. This solution can optimally schedule the orders for every pick, thus increasing the number of picks per worker. Not only does it directly improve logistics, but it also has a domino effect throughout the business’s value chain, leading from warehouses to customer experiences.
The Future of Decision Intelligence In The Retail Ecosystem
Artificial Intelligence, the buzzword it is, has influenced the way businesses imagined their operations. However, its more practical version, Decision Intelligence, offers much more tangible results to the users. Today, 78% of businesses investing in data warehouses are yet to realize returns on their investments fully. Decision Intelligence, on the other hand, promises ROI in a much shorter timeframe.
Decision Intelligence took data and went beyond delivering insights to make actionable recommendations. This empowers retailers to make more informed and impactful decisions. All the retailer has to do is to feed the system with as many data points as possible, and it will parse through the data and define the products with as many attributes as possible.
Decision Intelligence is product-neutral and largely flexible, making the capacity for recommendations enormous. Given the retailer identifies the profiles that go into making a product, the system can tell why some products sell more than others. In addition, it has the potential to predict inventory so that companies don’t hold excess stock or face product shortages. To that end, DI has a promising future in retail.
Bio: With 26+ years of experience managing profit and loss, customer advocacy, client relationships, nurturing and growing businesses, Shiva Mathur is heavily involved in Digital Transformation and driving innovation in Retail and CPG Industry. As a customer champion, Shiva has been leading and implementing aggressive strategic plans around Customer Experiences (CX), thereby creating business values which accelerate the transformational outputs in a highly complex IT environment. He works with companies to define digital transformation with enterprises across industries to maximize business potential. Guide clients on their digital journeys to enable them to do business in real-time and help organizations rethink their business models to place humans at the center.
By Shiva Mathur, Vice President, HCL America