Retailers today are in constant search for ways to cope with increasing competition, operational costs, and mounting pressure. As always, most of the strategies towards achieving these are centered on enhancing the consumer experience at different levels. Category duplication is among the immediate concerns which act as a roadblock towards ensuring consumer satisfaction.
FREMONT, CA: It is a challenge for retailers to carry out rationalization of categories without negatively influencing a customer’s perception of options.
Studies have observed that about 17 percent of items within a given category exhibit a duplicative nature in the retail sector. With these many items showing a duplicative tendency, retailers may be unintentionally leading to undue stress and confused shoppers. At times, presence of too many options, in-store or online, empowers customers not to choose.
It is critical to optimize assortment offerings through an understanding of the customer’s perception of choice. However, it is not as easy as it sounds to be. In order to be confident about rationalized decisions, one has to master massive data sets to derive insights and be able to take actions on those insights spontaneously. Achieving these together would mean going beyond conventional retail practices. Many retailers resort to technology, with artificial intelligence (AI) being the most coveted among them. What adds to AI’s aptness is its ability to carry out both the tasks.
AI’s Emergence in the Retail Sector
During the last few years, AI has made interesting advancements across several sectors, and retail has been one of them. Not all retailers have begun to incorporate AI technologies, but a significant part of retailers have. What creates a gap between retailers and adaptation into AI are factors like inaccessibility, proprietary systems, and high costs.
Today, more and more retailers are adapting to this technology. AI in retail is the perfect option to learn about customers' shopping preferences by analyzing vast amounts of data. Through AI, it becomes easy to know what a customer would like and wouldn’t beforehand. As AI produces this information quickly, a retailer can offer discounts or other attractive features on the preferred products by the customer, the retailers can make them buy more and extend superior customer preference.
There is a lot more that latest AI technologies are capable of doing for businesses, such as enhancing inventory turnover, optimizing stocks or predicting future revenue.
Although AI is being increasingly utilized by retailers, the comparatively higher costs associated with these prevent smaller firms from leveraging the technology completely. Only big players who can allocate massive budgets for technology upgrades can look forward to implementing them. However, in the next few years, we can expect to see AI being accessible across all sectors and with different budget through the emergence of new technologies.
As per a study by Global Market Insights, AI in the retail sector is likely to exceed 8 billion USD by the year 2024. As for other applications with respect to machine learning, deep learning technologies, and predictive analysis are being successfully utilized, the retail segment is all set to witness a digital disruption powered by AI.
Dealing with Category Duplication through AI
Customer data is vast, difficult to access, difficult to be consistent and can’t be trusted at the very first instance. In other words, customer data is highly scattered and duplicated across a wide variety of systems. It is often difficult for marketing, sales and customer service operators to derive more insights into this data to arrive at an authoritative and complete view on critical customer information. This is easily achieved through an AI-based approach.
AI can understand the dynamics between products and shoppers. To decide if a product is relevant to be included in a category, an understanding is required on the customer perspectives and not just the sales performance. AI is able to bring such awareness to life. AI, as a technology, leverages consumer loyalty insights for arriving at strategic decisions on probable outcomes of removing a particular product over another.
As part of learning customer behavior, AI understands the priorities and motivations of customers along with information on what products can be complementary to another and also as to what should be bought as a substitute if a particular product is not available.
Also, in assortment optimization in the retail sector, AI goes a step ahead to help evade expensive category cuts. As a result, retailers can move away from conventional ways to arrive at timely, assortment decisions.
An assortment optimization concerning a scheduled cadence fails to be based on real opportunities. Instead, machine learning and AI brings an alignment between a retailer’s categories and real-time market and consumer needs. This way, recommendations are made exactly when changes are needed.
Further ahead, AI can also reveal the forecasted result of eliminating one product over another, thereby presenting potential returns on investments to inform retailers about critical decisions.
Personalized purchase data collected by analyzing customer behavior is shared through highly structured web stores, online chat bots, and intelligent in-store bots.
Until the present, retailers were reliant on annual and sweeping category reviews. The emergence of AI has paved the way for an AI-powered analysis of the available data through which retailers are able to arrive at continuous and bold refinements within their product categories.
Moreover, when assortment decisions are customer-centric and data driven, retailers can benefit from lesser items to manage, enhanced forecast accuracy, and highly predictable demand in the short term and long-term periods.
To conclude, the current retailers are unable to afford taking decisions besides a specific understanding of their customer and their preferences, and shopping behaviors. This is exactly what AI in retail can do. The technology makes it possible and easy for retailers to arrive at sustainable category development, while also making customers satisfied.