Next Generation In Retail Technology – How Analytics and Digital Will Drive Next-Generation Retail Merchandising
The real-time customer experience (CX) is happening, with or without you. To keep up retailers must increase their reach and impact by moving toward innovative, customer-focused technologies like IoT networking, artificial intelligence, and 5G deployment for higher bandwidth and faster input. This is all to keep pace with customer experience (CX) in real-time or as near real-time as possible. A large part of keeping pace with customers is through tracking and understanding the behavior of your customers via analytics. Now more than ever that data is crucial for businesses to provide a better customer experience
In addition to the need to provide better CX, other aspects of Retail are being impacted like the merchandising function and shorter product life cycles. Established brick and mortar expansion pathways are drying up, and newer models of growth, such as online localized assortment, and expansion into global markets, are more aggressive than ever. This polarized the need for businesses to understand their customers in relation to their products, services, and B2C relationships.
Users are constantly on the go and need systems that adapt to their face-paced lifestyles. This means merchants must deliver a more interconnected shopping experience to better suit the customer. This can be accomplished by quickly getting the right products to customers at precisely the right time. Merchants are reorganizing via vertical integration to have cross-functional departments and third-party partnerships working together to expedite supply chain systems. These changes have enabled merchants to become nimble and more adaptive to consumer trends while they pursue the objective of offering a seamless, Omnichannel customer experience.
Businesses can no longer afford to have decisions to come from instinct. Analytics are helping to inform and open new areas of growth. What was successful in the past is now a poor predictor of the future. Retailers are employing technologies that can make forward-looking predictions. Prescriptive analytics is no longer considered advanced but are quickly becoming the “table stakes” just to stay in the game.
As we continue to learn about our customers, we provide ourselves a unique opportunity to grow our strategies into a more forward-thinking initiative with prescriptive analytics. We can take steps to predict the outcome of customer behavior and journeys centered around specific products or services, thus giving a business the advantage of developing multiple new avenues for any business process, application, and even sales strategies in an effort to accelerate the results or desired outcomes.
Retailers Are Personalizing the Shopping Experience
A recent Forrester Research Report revealed some interesting insights. “Consumers are willing to share data with brands –
63% of US consumers say they’d share more information with a company that offers a great experience.”
“91% of consumers are more likely to shop with brands who recognize, remember, and provide relevant offers and recommendations.”
Customers like to feel that their shopping experience is being tailored to their needs. It’s not a surprise that nine of ten people prefer relevant recommendations for products that might interest them.
Another interesting data point is that according to research from the SDP Group, “37% of shoppers came back when they clicked on a personalized recommendation during their first, compared to only 19% of shoppers who didn’t.”
How Will Artificial Intelligence Impact Retail?
AI is the computing powerhouse that is driving the personalized shopping experience revolution. There are exciting initiatives impacting retail because of artificial intelligence and machine learning.
1. Dynamic Price Adjusting
AI is great at is sorting through tremendous amounts of data to uncover trends and predict outcomes. Computers utilize analytics to enable faster and more automated price adjustments by accelerating calculations. Combining Price Optimization with Demand
Forecasting and AI the outcome is an accumulation of intelligence that keeps improving over time.
Artificial Intelligence can rapidly analyze historical sales data, factor in previous promotions and sales figures for similar product categories. The compiled data is used to predict the optimal price point for a product. This is a dynamic process and prices can be tweaked and adjusted as new information becomes available.
Price optimization is not limited to physical products either. The hospitality industry uses it as well. For example, Airbnb automatically adjusts prices upward when demand is high and the airline industry has been AI-driven pricing for some time.
2. Just-in-Time Manufacturing
Inventory has some downsides. It needs to be warehoused and it might end up as excess inventory. Just-in-Time (JIT) manufacturing is a tactic that optimizes and improves processes and timing to impact the bottom line – building products for immediately anticipated demand instead of for inventory.
JIT manufacturing results in smaller production runs. Smaller runs reduce costs, which can free up capital for other uses.
Artificial Intelligence analyzes dozens of data points in order to make predictions about when to start a production run. Machine learning and AI can learn and adjust so that Just-In-Time manufacturing predictions improve over time.
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