Artificial Intelligence Can Help Retailers Understand the Consumer

consumer brands and retailers struggle to fully understand the ever-changing customer needs. That is why you will most likely find XL sizes in your favorite fashion store and no M sizes. That is why you need to spend hours looking for the style you saw on Instagram, and haven’t found it yet. That is why the dead inventory cost to American fashion retailers is estimated at $ 50 billion. This is one reason America produced 16 million tons of textile waste in 2014.

Also Read: What Artificial Intelligence Can Do For Retail Industry?

This is not due to a lack of any purpose or effort in the industry; Instead, it is difficult to understand customers at scale. Categorizing customers with broad brush definitions of age, gender, and income is not only effective in terms of diverse and ever-changing consumer preferences, and retailers now have to look at pretty much all market segments — even for single people. Consumers tend to be more up-to-date than merchants define them, and this is coupled with a lot more experimentation and disruption in the market.

In order to create and sell the “next big thing” in such a dynamic environment, designers, buyers, and merchants must use their own creativity, but with unprecedented granularity, consider how consumer preferences are changing and the different design, merchandising and marketing options. Perform. This is where AI and automation come in.

Also Read: Artificial Intelligence Solutions For Retail Industry

For example, consider a fashion retail buyer. She is responsible for the financial success of her chosen merchandise at any given season, but it is impossible for her to evaluate any design performance or best promotional interventions that apply to the season 12 months before the target season. Over time she has little visibility into how consumer preferences in stores are changing and how competitive products work in the market.

Incorporate a natural language text of millions of online customer reviews and an AI-based system that can analyze all the products on the market to capture key relationships between location-specific customer sentiment and product features. For example, how do customers respond to color block pullovers in Buffalo, Kansas, and what characteristic is the lack of customer sentiment for her color block pullovers as opposed to competing for color block pullovers. Such information for products on her market and for new planned products can help improve her assortment, pricing and markdown, and marketing plans.

Similarly, consider a sales manager for a yogurt brand. With a system that can analyze cross-brand sales of food products nationwide to produce high-quality estimates of demand for the spinach artichoke flavored yogurt the company produces, the sales manager can then discuss product contacts and planograms with retailers. Such negotiations often fail when there is no such capability.

In fact, according to a recent study, more than 1,900 retail and consumer products leaders, the adoption of intelligent automation in the retail and consumer products industries today is estimated to be over 80 percent in three years from 40 percent of companies to the balloon.

Our team in India has partnered with the Metropolis team to bring such first-hand, AI-based capabilities to industry platform, heavy market, external and client datasets. The new capabilities use AI and automation to fuse these structured and unstructured datasets around semantic, visual, and location contexts and uncover insights into customer preferences hidden in this fused data. These insights enable consumer brands and retailers to make wise choices about product design, inventory planning, demand forecasting and product assortment in line with dynamic consumer preferences.

There are three layers to the platform, each with deep industry content:

The data layer

1. Up-to-date market data, location-specific signs of consumer preferences, product landscape, and brand/retailer behavior. Some examples of datasets we monitor are online customer reviews and comments, point-of-sale data and product images. Analysis of these large data sets can provide companies with insights into how consumers’ preferences change across brands, retailers, cultures, and regions — city or neighborhood.
2. Hyper-local third-party data is constantly updated, with neighborhood-level codes of demographic, weather forecasts and external factors affecting consumer behavior such as history, local events, and visitors.
   Private company data that contains information about the retailer’s own stores, products, merchandising, promotions, and sales history. This data is treated with high security and privacy guarantees.
3. As seen in Supply Chain Management 2018, it is essential to include such multiple data sets, Supply Chain Management 2018: In Customer Service, Retail Systems Research, December 2018, where 60–70 percent of respondents consider “very valuable” sentiment, trade area data and past Without considering the demand for new data such as promotions.

Knowledge layer

Retail-industry specific digital vocabulary — Knowledge graphs that capture industry information in the form of entities, attributes, and relationships. This layer allows the data in the data layer to be understood and analyzed by AI systems in a standard and meaningful way, generating insights for end-users. For example, consider a fashion classification that captures different fashion terms and their relationships (eg “peplum” is a kind of “top”) or grocery ontology that captures different food types, ingredients, flavors, and type hierarchies.

Industry intelligence layer

1. A variety of AI algorithms and models that can detect and interpret signals buried in data, turn them into insights and recommendations that are revealed in a meaningful and authentic way through the dashboard and API. These insights and recommendations help business leaders, product designers, marketers, and other company customers understand and optimize their target customer behaviors and preferences. Shopper-facing solutions for personalization and cognitive assistance can also make use of these APIs for enhanced customer engagement during sales. These algorithms leverage multimodal AI, descriptive AI and the latest AI techniques in forecasting and tune them to understand industry-specific knowledge and concepts.
2. Multimodal AI combines visual perception and natural language processing to extract insights from multiple modes of data. For example, it identifies the fashion items in the image and relates to the reviews as well as expressions of customer opinion; Or the semantic similarity pattern that makes the consumer understand that apple juice is more similar to lemon juice than the apple, but is closer to apple juice in terms of taste and ingredients.
3. Explainable AI explains why a model produces a specific product for a given input. As AI models become more complex, it becomes nearly impossible for non-data scientists to understand their behavior, making it difficult for them to rely on predictions from models.