The idea of electricity fascinated people centuries before. Phenomena such as lightning were observed but not even partially understood. Visionaries such as Benjamin Franklin, Alessandro Volta, André-Marie Ampère, Michael Faraday were curious enough to unlock the phenomena and potential of electricity. Today, we cannot imagine living in a world without it as it is such an essential part of our everyday lives.
Andrew Ng suggests that artificial intelligence (AI) is the new electricity; he has the evidence to validate this statement. When the smartphone unlocks itself in the morning by seeing your face, it’s AI. All the recommendations that pop up on your browser or your mobile phone are AI-based. When you tell Alexa to close the light before falling asleep, AI strikes again.
We want to look at how the retail sector can benefit from AI-based technologies. The companies that will deliver faster, greater, and smoother experiences to their customers will most certainly win. But how can AI contribute to the retail sector?
There are already many AI-driven technology solutions used to deliver a personalized experience for customers, helping retailers reduce costs and become increasingly customer-centric. They can predict shopper behavior and optimize the entire supply chain from inventory management and forecasting to workplace planning. All these segments are pivotal in helping retailers to increase their market share. Let’s start discussing some of the retail market statistics and trends.
The retail sector statistics indicate that AI gives retailers a valuable add-on to make smarter business and product decisions. AI has brought positive disruption to the vertical by improving operational efficiency, and the forecasts predict that the retail sector will increase more than 30% CAGR between 2021 and 2027. Also, industry growth is driven by AI-based technologies to develop innovative applications for improving retail customer experience.
Here are seven statistics for the retail market that you should consider:
Let’s begin by stating the most significant AI benefits for retail as defined by leading industry experts.
The above benefits show a huge potential for implementing AI in future solutions for retailers. Just think of improving business operations to increasing their edge over the competition or catering to customer-specific wishes. Over time end-customers will turn to turn to retailers using AI-based technology solutions.
AI may seem new and trendy, but scholars have been writing about AI as a concept for the past 50 years. The show stopper for AI algorithms to bring value on a large scale was the lack of computer power. That power has increased tremendously, and on top of it, the software libraries also evolved, making AI accessible and valuable.
Our world is digitally interconnected. Tweets, posts, all data travel around the globe in seconds or less. In the past, consumers relied on print to learn more about a product before deciding to buy. Now magazines are more for showcasing, and the buying decision is made online. With just one click, a consumer can find helpful information online on any product.
So, let’s go from the theories and forecasts to examples of AI in the retail world.
Brand monitoring is one of the most interesting areas where AI can be applied in retail. Imagine that you can find real-time how your brand is perceived in a certain location or country and by a particular age category. Would you use this information to improve your marketing strategy? Using Sentiment Analysis, a technique of Natural Language Processing (NLP), which is one of the most popular branches of AI, the emotions in the text can be identified. Brand or product reviews can be categorized as positive, neutral, or negative. The way this is achieved by applying human-defined rules for labeling the polarity of certain words and add them in a vocabulary and counting the frequency of them across the input text to be analyzed to deduce the sentiment. You can also use Machine Learning (ML), another popular branch of AI, by applying classification algorithms trained with labeled pieces of text to learn to classify the new pieces of text. By combining these two in a hybrid approach, more accurate classification is expected.
The outcome of brand monitoring can help you to identify trends, make predictions based on these trends, and adopt a revised market strategy. In the most critical moments, you can avoid damage to your reputation and quickly respond to customers after a bad review. Companies can do a speedy turnaround like Expedia Canada, a travel company from -you already guess where- did with an almost unanimously rejected “escape winter” commercial they launched for the 2014 winter holidays. Social media, blogs, and forums quickly flooded with negative reactions, because of the screeching violin as background music. As Expedia Canada registered solid metrics, they could act fast and resolve the matter.
Chatbots are another interesting application for AI in Retail. Imagine you can provide 24-7 support to your customers, in multiple languages and offering personalized services and all these by just making use of a chatbot. Will it reduce your costs? Would it solve a logistic problem? Could this be a step forward in gaining your customers’ loyalty?
Using Natural Language Processing and Machine Learning you could implement a Chatbot that can find the meaning of the sentences, generate answers but also getting better and better with each learning iteration.
Imagine that the product you have ordered online was lost before it was delivered to you, or it was in a bad shape when you received it. You want your money back and you are ready to request a refund. You are skeptical as you have probably been in this situation before. So, you are expecting many hurdles along the way, and the chance that you will buy from this company again is relatively low. Yet, you still want to claim your refund, so you visit the website of that company for the last time. Finding the refund section, you are surprised that a chatbot pops ups, and you describe what happened, in natural language, and that was it. No more headaches, problem solved. And you like the service and ease of use, so you are most likely to buy from that company again. This is an example of how to retain customer loyalty even when the customer might have good reasons to shop elsewhere. Amazon knew this and they implemented it.
On the inventory demand forecasting side, there is a lot of potential by applying ML algorithms to store data on historic and current product-related trends, customer preferences, or sales location. Having the right product, at the right time, in the right store, at the right quantity is essential. Too many backorders or products that stay too long in-stock can reduce cost-efficiency. H&M puts AI forecasting at the heart of its supply chain sustainability. H&M Group calls their AI Amplified Intelligence — the collaboration between machines and humans, science and art, data and gut feeling.
We would regard the evaluation of the technical developments, external environment, market, and competitors as the starting point for retail software companies. The outcome of the evaluation would allow retail software companies to identify the direction of the technological advancements within the vertical and how quickly they can respond to these challenges. Then you can identify the right AI techniques that you can use that your clients need to maintain or improve their position in the market.
It is clear that AI has the potential to fundamentally disrupt the retail market through the creation of innovative new solutions and new business models. Without a doubt, the pandemic has accelerated the adoption of AI innovation in retail. Now we have to ensure that AI innovations will continue to offer benefits after the pandemic, so our advice to retailer software companies is to create meaningful shopper experiences with the help of AI technologies.
How are your VMS customers forecasting their demands? Would you see benefits in offering them AI-based solutions? Want to find out more, then get in touch with us.