Here is a question to kick things off, have you ever searched something on Amazon (The kingpin of the online retail industry) and then decided to not buy it, at the moment. However, everywhere you go online there is a constant reminder through display advertisements by Amazon. How do they do it? The short answer is that they integrate Artificial Intelligence into the very fabric of their retail technology. Machine Learning has completely revolutionized the concept and methods of Market Research. Artificial intelligence can also help you win the battle for productive retail strategy.
This article is going to discuss in detail some of the key areas AI and Machine Learning techniques have affected to reshape the current retail technology.
The need for AI and ML innovations to improve the current Retail Technology
Areas of the retail technology that will use AI based innovations in the near future
We have selected ten top ways Artificial Intelligence is supercharging the retail technology. Let us get into it.
#1 Inventory management
Research shows that improper inventory management (overstock and out-of-stock goods) cost the retail industry 1.1 trillion USD annually. An overstocked inventory needs to be cleared often at slashed prices. On the other hand, an understocked inventory costs a retailer in terms of sales opportunities and dissatisfied customers who are more than viable to switch to a competitor.
Artificial Intelligence techniques can be applied to take reshape a retail brand’s restocking strategy. Retail technology makes use of AI for demand forecasting, inventory planning, and stock replenishment.
AI-based market research makes use of data points such as:
- sales patterns
- user feedback
- promotional efforts
- industry trends
- …and so on
Incorporating AI in their strategic think tank retailers can prevent overstocking products that are less in demand and not likely to sell much and up stocking the products that are in demand. Major retailers across the world have made use of AI-powered market research methods to bolster their retail technology.
H&M has used AI to analyze retail activities and demand forecasting. Morrisons has also incorporated AI in their stock management strategy. The results show that an AI-based analysis can reduce shelf-gaps of retail companies by up to 30 percent.
#2 Pricing Strategy
AI techniques have been increasingly used to determine a pricing strategy by major players in the retail industry. The pricing strategy for an organization depends on two often opposing factors- Internal economics (revenue, margin targets, etc.) of an organization and the consumer price perception (competition, customer loyalty, sales volume, etc).
Retailers use KVI (key value items) to decide if their prices will be decided based on economic factors or customer perception. Almost all retail stores have some standard products, the prices of these products help a customer decide whether a store is expensive or not. These items are termed as KVIs.
Artificial Intelligence can give Key Value Item scores to products based on consumer’s purchasing patterns. Predictive analysis can be used to quantify demand, revenue or profit based on factors like season, special discounts, store location etc.
Established retailers such as e-bay and Krogers make use of AI-based pricing strategies.
#3 Chatbots for customer support
Chatbots have established themselves as a prominent addition to the current retail technology. Chatbots are used by retailers to improve their customer service. This technology is capable of emulating and understanding human interactions to answer customer queries.
Industry leaders have incorporated chatbots to serve very creative purposes. Chatbots are increasingly being used as an easier alternative for filtering by the e-commerce retailers. A chatbot can understand the requirements of a customer and display the desired results.
Brands have also incorporated chatbots as an in-store virtual assistant. Research shows that 97 percent of people use their phones while shopping. A chatbot can help the shopper with suggestions in the real-time.
Eighty percent of global retail brands plan to incorporate this technology to enhance their customer service. Brands like Tommy Hilfiger, H&M, etc already have their chatbots live on their respective platforms.
#4 Product Categorization and Description
Retail technology has evolved with AI incorporation. Nowadays e-commerce websites are capable of automatic product categorization. AI algorithms can identify and categorize products on the basis of the description provided by the seller.
Some organizations also use computer vision and image recognition technology to identify and consequently classify a product based on product images alone. Hence, making it extremely easy for sellers to list their product on such an AI-enabled platform.
An AI-based categorization system can also be used to to enhance the customer experience. AI has enabled e-commerce players to suggest customers relevant categories based on search query.
#5 Retail Shelf Strategy
The most fundamental rule of the retail industry is, “a customer buys, what a customer sees”. Shelf positioning of a product in a store plays a vital role in the retail strategy of a brand. Brands across the globe fight and pay handsomely for prime shelf positions. So, it becomes imperative for brands to monitor their respective shelf presence to get better insights from their sales data.
Research has found that attractive visual merchandising can often compel a consumer to take at the moment buying decisions. Brands can considerably increase their overall sales by investing in visually appealing packaging. Retail shelf monitoring can help brands to figure out if an investment in the visual aesthetics of a product is working or not.
Karna-AI has a product that is capable of in-depth retail shelf analysis called ShelfWatch. Click the button below to schedule a free demo and learn more.
#6 Improving the way customer satisfaction is tracked
A satisfied customer is the most important asset to a brand because such consumers are not only loyal to the brand but also generate referral business for the brand. Naturally, brands spend a fortune to track whether or not their customers are satisfied by the service they receive. One of the most popular metrics to track a brand’s overall health in terms of satisfied customers is NPS or Net Promoter Score.
Net Promoter Score is a simple survey that seeks a numbered rating from the respondent. Based on this rating a customer is identified as a promoter, passive or detractor. Effective NPS surveys are often coupled with an optional open-ended question. The open-ended feedback left by a respondent takes into consideration the “why” behind the NPS.
In theory, this looks perfect, but in practice, it is not. The analysis of open-ended feedback can become cumbersome. Let us do some basic maths. Suppose your survey generates 10,000 open-ended responses with each response of 2 sentences and each sentence having 10 words. That means 20,000 sentences to be analyzed and give you a more detailed view almost 200,000 words. That is a lot of work!
Using NLP and Machine Learning techniques ParallelDots created the ultimate tool to assist with feedback analysis. This tool is called SmartReader. SmartReader is really unique because not only can it mine tens of thousands of open-ended customer responses for valuable insights such as sentiment, emotion, and keywords. This tool can also identify themes that your data revolves around. The tool learns from your data and in a way is tailor-made to suit your needs and produce highly accurate results. This D-I-Y AI-based SaaS tool works its magic directly on your Excel Spreadsheet making it very easy to use.
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#7 Transforming eye-tracking coding to generate efficient gaze maps
Retail technology has always focused on understanding a buyer’s mindset. One of the most innovative methods to understand what the customer is thinking is to track their eye movements to analyze what catches their attention.
Eye tracking traditionally is considered to be a difficult process requiring a lot of resources. Now, with the assistance of AI and computer vision eye tracking coding has become easier than ever.
Karna-AI has devised a state-of-the-art eye tracking coding technology called SmartGaze. SmartGaze completely eliminates the need for cumbersome manual coding methodology. Using this technology a gaze map can be generated for offline (in-store) as well as online retailers.
Click the button below to schedule a free demo and learn more about SmartGaze.
#8 AI based smart advertisements
Advertisements can be considered as the backbone of the retail industry. Years ago advertisements were primarily shown in a comparatively untargeted manner through mass mediums such as TV, radio, print media, etc. However, with the advent of the Internet and social networking sites, smart advertisements are quickly replacing traditional advertisements.
AI can track your search queries on online retail sites and show you ultra-specific ads which serve as a reminder of your interest in a retailers product. Hence, increasing the chances of a sale. Online retailers also make use of AI to create recommendations that are personalized based on your search queries.
Different ads appeal to different audiences. AI can understand your behavior online and show you ads based on it. Smart-Ads are a brilliant new addition to the retail technology space.
#9 Visual Product Search
Retail technology has incorporated AI to enhance its search facilities. Online retailers have introduced a visual search feature to improve their customer experience. Computer vision analyzes an images shape, size, color, etc to identify the product and give recommendations.
American Eagle an up and coming online retail brand has taken the visual search to the next level. Not only does American Eagle provide similar products to your search but also provides pairing recommendations.
#10 Automated Observational Research
One of the oldest and most effective
Observational research is based on data points identified while interacting with the product. For example, while conducting observational research on a cigarette, the data points can be the number of puffs, the duration between consecutive puffs, etc. The efficiency of observational research depends upon the number of data points taken into consideration.
Manual observational research is difficult to undertake because to identify minute data points, very close observation is required which makes the consumer conscious and they start behaving differently. Also, carrying observational research is tiresome for humans and very prone to human-induced errors.
Karna-AI has made a brilliant addition, called Perceptron, to the current retail technology by completely automating the process of observational research. Perceptron is capable of identifying thousands of very minute data points without making errors.
Click the button below to schedule a free demo and learn more.
By now we have seen the ways AI has impacted the Retail Landscape. The objective of market research has never been different: to develop an understanding of consumer. What has changed, though, are the paths that take us there, drastically shorter and cheaper.
Do you want to be innovative in your retail strategy?, book your free demo of Karna AI’s products.