Today, big data gives companies across industries the ability to accurately predict customers' next steps and future behavior.
For the retail industry, which leverages point-of-sale transaction-level data, store size, demographics, social media metrics, competitive intelligence and many other data points, this can have a significant impact on their bottom line. For instance, in 2017, retailers added $1.14 trillion in value through data-driven decision making.
This technology, dubbed predictive analytics, is already helping retailers determine the future needs of their customers.
Predictive analytics is predicated on the assumptions of social physics, the theory that human behavior can be anticipated using transactional and interactional data. Together they can change the way businesses interact with and anticipate the needs of their customer base.
Using predictive analysis to drive competitive edge
The retail industry is incredibly competitive – hence, one of its biggest challenges is customer retention. By applying predictive analytics to customer data, retailers can harness their user habits and customer preferences to tailor their offerings to providing a personalized experience that can promote customer retention.
In some ways, this technology is already at work. Prominent retailers like Amazon provide customers "recommendations" based on items they have purchased in the past.
Predictive analytics is changing the retail experience in other ways as well. For instance, retailers can identify the customers that will likely abandon their product, so they can suggest additional incentives to these customers to maximize returns. Marketing departments can generate targeted marketing campaigns for those already most likely to make a purchase and then use that info to send coupons only to those customers.
Developments in AI and machine learning are taking these processes a step further by making assumptions, testing data and learning autonomously.
For retailers, these developments can make a significant impact in gaining competitive edge. Writing for Harvard Business Review, AI-researcher Victor Antonio identifies several consequences for retail including price optimization, price forecasting, data-driven upselling and lead scoring. Taken together, these changes can create real value for the retail industry.
In other words, predictive analytics and retail are on a crash course with one another. Retail adoption is inspiring improvements to AI's capabilities even as AI is reshaping the ways retailers pursue client needs and demands.
How can retailers access these services?
For many retailers, implementing AI-powered predictive analytics could be the difference between profitability and obscurity. However, for a small and medium enterprise (SME), cost is likely to be the biggest hindrance to adoption. Most businesses will require $53,000 to integrate AI and predictive analytics into each of their stores. For businesses which lack the financial or personnel resources available to develop large data operations, technology standardization is a natural next step.
Unfortunately, AI appears to be following an all-too-familiar path, with the largest corporations using the tools and SMEs essentially being walled off.
Several companies including Google and Microsoft are building AI platforms that are configurable and usable as a data solution for many different professional use cases. It is estimated that 80% of AI implementation can be completed using off-the-shelf products from these companies. To some extent, this helps democratize access to AI by making it more usable and affordable for more companies.
Of course, relying exclusively on Google and Microsoft alone to fund and proliferate AI would be a mistake. AI is a market like any other – and healthy markets need healthy competition to keep costs competitive and avoid stagnation. This is particularly true for emerging technologies, where innovation and creative thinking are prerequisites.
Moreover, companies like Google have a vested interest in keeping their most cutting-edge innovations for themselves. Thus, market domination by a few big players brings more risks than opportunities. To begin to bridge this gap and reach its growth potential, we need more democratic access to AI.
Leveling the playing field
Thankfully, the decreasing cost of computing power has provided a broader choice for retailers looking for solutions to gain competitive edge. As a result, SMEs which have previously been unable to venture into AI solutions on their own now have more opportunities to enter the AI and predictive analytics markets.
One example is Vue.ai, which targets the highly visual online fashion retail business. Vue offers various AI solutions, but its Vue Commerce tool is the one which fully leverages the power of predictive analytics. It uses image recognition to serve up to each shopper a personalized version of a stores home page based on their past browsing history.
So, if someone was browsing blue dresses, next time they browse the shops home page, the intent is that they will see other blue clothing and dresses in different colors. The tool also operates across additional channels.
Endor is blockchain-based solution, offering access to its "do-it-yourself" predictive analytics platform developed by MIT researchers. The platform offers fast predictions based on specific questions. A retailer could access the tool on demand to ask questions such as "who is likely to buy such-and-such new product?" or "who is most likely to refer their friends and family?"
The creators of Endor pioneered the concept of "social physics" to create the algorithm driving the predictions, which has been used by major enterprise clients, such as Coca Cola and Mastercard. However, the engine also aims to make its resources useful to SMEs by allowing users to sell them the niche, specific data that SMEs need to make the most of predictive analysis.
With the Prism Skylabs solution, bricks-and-mortar retailers can use the data from their existing surveillance cameras to improve store design, merchandising and inventory management. Camera data feeds into an algorithm which can, for example, analyze how customers move around the space to help retailers optimize the layout of their store, ensuring the best chance of making sales. Allegedly, it functions with data from as few as 80 people, making it an ideal tool for SMEs.
These kinds of new and innovative predictive analytics solutions are levelling the playing field for SMEs. This has the dual benefit of democratizing access to AI within retail commerce, while ensuring healthy competition for the tech giants.