The adoption of machine learning, deep learning and predictive analytics is growing at a fast pace in the retail sector, where many retailers are trying to fit AI strategies into their future budgets to remain competitive and increase their Return-On-Investment. According to a Global Market Insight study, the retail sector will increase its investment in AI to $8 billion by 2024. 

In this article, we are going to highlight the diverse opportunities where retailers use AI and machine learning to increase the ROI of their businesses.

Let’s take it from the very beginning. One of the main fundamentals of a successful organisation is the workforce.

Good personnel add a significant value, but could also be one of the biggest costs

AI is used in workforce optimisation, and studies suggest that personnel can affect the cost of an organisation, amounting to 10–20% of revenue. As a result, accurate workforce planning can generate millions in annual savings. So how can AI help?

Retailers must use several forecasts to predict future workload accurately, this includes:

• Avoiding over-staffing or under-staffing by predicting workload through AI-powered workforce scheduling.
• Estimating in-store replenishment workload, order picking, and order line forecasts.
• Transparentreporting, including a dashboard and financial statistics.
• Programmed shift assignments based on employee performance and given accurate predictions of customer demand and associate availability.

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Personalisation requires data mastery to make an impression

AI algorithms help retailers gain a comprehensive understanding of consumers’ shopping patterns, recommending the best possible promotions based on more robust modelling, machine learning, and business rules. 

There are two dimensions that a retailer considers while harvesting data: 

• What knowledge they have about a customer across different touchpoints or channels.
•  What they know about a shopper based on every unique interaction or purchase.

To optimise personalisation, two main types of data need to be available:

• Product Data— Information related to product categories, subcategories, and products’ characteristics, such as the type, colour, size, price, sales margin…etc.

• Transactional Data— Information related to the transactions of the retailers, based on a historical analysis of what, when, where, and how much a customer bought. 

The power of AI is to examine the product and transactional data, while continuously monitoring internal and external data sources, including weather, location, time-of-day, device type or other environmental factors. Additionally, analysing and noticing capricious or contradictory demand patterns that are not obvious to the human eye.

This leads to 

• Updated models in real-time with additional data feeds that continuously refine the forecast accuracy and optimise trade spend.
• AI optimisation of spend across circulars, displays and personalised offers, maximising the ROI for each tactic—along with an integrated system, of course.
• Retailers move from static to dynamic ad zones, where they track key competitors’ actions to decide on optimised local promotions.

Engaged customers spend more! Give them an outlet to engage 24/7

• 34% of consumers are browsing online every day.
• 47% think it would be awesome to receive real-time offers based on browsing history.

Statistics show that engaged customers represent a 23% premium in terms of share of wallet, profitability, revenue, and relationship growth compared to only 13% for disengaged customers. The more retailers take the time to understand the customer, put effort into engagement and invest in building, the higher the influence over marketing ROI and the bottom line.

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AI in digital and In-store assistance

Chatbots

Instant messaging has the potential to help retailers fill the gaps in the customer journey and earn customers’ trust, but only if the brand is equipped to respond thoroughly and quickly. Humans cannot always meet that demand continuously. The goal of implementing chatbots within businesses is to boost the customer experience and provide personalisation for online and offline retail.

Popular examples

 Sephora 

The Sephora bot does not only help customers find the right products but also teaches them how to use each chosen product. Their AI-enabled feature allows users to try different looks created with the selected makeup items using filters on the camera.

How many times did you enter a store and left as you couldn’t find available assistance and you were in a hurry?

Many customers find it more convenient to choose the assistance of a chatbot rather than a personal assistant from staff while shopping. For example, if someone is late and needs quick assistance, they might simply get out their phone and use the retailer’s chatbot feature. The chatbot API has been integrated with the store's inventory, which includes in-store location data for each product, so the chatbot can tell the customer whether the product is in stock and which aisle to check. As a result, the retailer would avoid losing a customer who is in a hurry.

Kroger EDGE technology 

Kroger started to eliminate paper price tags in their stores and started using smart shelf tags instead. This reduces the cost and encourages sustainability which is a competitive advantage nowadays. This technology also provides video ads, nutritional info, and promotions on the displays while taking into consideration the internal and external factors, influencing the customer’s shopping decisions.

In-store Mood Tracking with Natural Language Processing (NLP)

The most valuable tool for NLP is measuring customer sentiment. The power of AI sentiment analysis is that brands can stay updated on how their customers are feeling and know when to intervene as needed through voice and digital channels. AI acts as a mood detector during the shopping process. This could also be called, “Customer Satisfaction Tracking”. 

Walmart has already introduced a facial recognition feature for NLP. Cameras are installed at each checkout lane and if any customer shows signs of annoyance, a shop representative communicates with them. Mood tracking will definitely help build stronger relationships with customers.

Overstocking and understocking are critical factors that directly affect revenue 

Artificial intelligence helps companies minimise losses while managing inventory using real-time monitoring facilities. AI helps to predict the market trends, forecast the demand for the products based on previous orders, and maintain a balance of demand and supply throughout the supply chain, then take decisions accordingly. 

Stay relevant and outshine your competitors in the market! 

Automated processes, enhanced business insights, and improved customer engagement will result in increased ROI. Intouch.com can help you earn more money and save costs using the smartest in-store AI solutions. Get Intouch!