Impact of AI on the Retail Industry

Artificial Intelligence (AI) is a term we have known for a long time. However, this word has been buzzing more often than ever. The word immediately brings the image of an elegant glass robot to our minds. We also know that such conceptions are insufficient to comprehend what AI really is. In the course of this blog, we will see plenty of examples that seek to encompass the essence of AI. However, the topic at hand is not just AI but the impact of AI on the retail industry. The objective here is to shed light on how AI can and has already started to impact how retail brands and businesses conduct business with loud and visible results.

Personalisation

Personalisation has been a buzzing term for quite some time now. However, the entry of Artificial Intelligence (AI) changes the dynamics of personalisation. AI is capable of analysing more complex data and providing far more insightful information for far better personalisation efforts than how it is simply possible with human-made programs. The thing with AI is that it can learn and improve itself. These capabilities can be applied not just for sending personalised recommendations but also to improve the shopping journey of customers. Let us explain this with an example riding on potential AI capabilities.

We have seen how retail and eCommerce brands email or show us product recommendations (which sometimes also reach our social media feeds). Most of these recommendations are futile unless there is something extraordinary in the offers. AI can transform the entire game of personalisation in retail and eCommerce. AI holds the potential to help retailers learn beyond what customers have already explored within their website/app. AI is capable of analysing data on multiple parameters to derive new customer insights and analytics. For example, AI can learn by picking up what is not searched by customers. Among other things, it means either a customer does not require that product or is buying from some other place. Even that could be narrowed down further if more data is provided to reconcile with trends in similar conditions.

AI does not mean preying on the websites of competitors. The internal data alone is sufficient to provide a galore of new insights for improved personalisation efforts.

Demand and Trend Analysis

AI has a far bigger role to play in demand and trend analysis. We are no longer talking about static statistical models but ones that can learn and improve analytical output with the help of AI. This will significantly help retailers in making more informed and reliable decisions on market behaviour. AI can process massive volumes of data from diverse sources to arrive at more reliable correlations between different market variables to better predict demand patterns and trends. The best part is that it can help spot new product and market opportunities.

For example, not all departmental stores can make the best out of the festive season sales. One of the main reasons for this is that they have little information to do it. They rely on their legacy systems and human judgement which often are constraining in forecasting future demand with detail and precision. The use of AI-based analytics can bridge this gap. AI can process data from different internal and external sources across different timelines, apply learning, and present conclusions that are backed up by evidence. When a departmental store has access to such analytics, it can make its festival-specific purchase decisions with a higher level of awareness and confidence.

Process Automation

Although process automation in changing forms arrived a long time ago, the application of AI is changing how and to what extent business processes can be automated.

In traditional forms, retail process automation platforms are straightforward programs. They do what the codes tell them to do without the element of so-called ‘intelligence’. AI brings in the components of learning and improvisation which is why it is regarded as ‘intelligent’. This enables the incorporation of more dynamic adjustments to how automation works. AI enhances the capabilities of automation. This kind of automation is no longer confined to the idea of automating bulky and mundane tasks or reducing human errors and mistakes. It goes far beyond that.

Chatbots and virtual assistants powered by AI can be used as an example here. These programs can learn and improve their response mechanism. It can figure out quicker ways to resolve queries. For instance, if queries on delivery are marked as resolved by customers when a particular set of information is provided to them, the chatbot can be trained to present that information and it will shorten the query resolution process. In this case, AI is not only carrying out a process but is also trained to improve it.

Inventory Management

Companies around the world are now using or considering the usage of AI in inventory management. Done right, AI can help a company improve its inventory management in multiple ways. A few of them are discussed next.

We have already discussed how AI can help in demand forecasting. With more accurate and timely demand projections, businesses can avoid undesirable situations like overstocking, under-stocking, or even running out of stock. All one needs to understand is that instead of relying on human judgement or simple projection software, AI does the same job for us by analysing more information and deriving more complex interrelationships between hosts of variables.

Another advantage provided by AI is that it can help in optimising warehouse space. For example, it can help classify inventory based on their movement. This enables the stacking of goods based on frequency of requirement. Goods can be rearranged accordingly. The application of such tactics allows businesses to make the best use of the available space in their warehouses.

AI also eases the re-ordering decisions. After demand forecasts are available, warehouse and purchase managers also need to determine the ROQ levels of the existing inventory. Even if it is a routine purchase, ROQ levels still have to be ascertained. Then there is also the quotient of ROLs. The involvement of all these elements makes reorder decisions challenging for the human mind. AI can be trained to comprehend such complex equations towards facilitating decision-making.

Customer Orientation

We know how AI can significantly improve customer support mechanisms like chatbots. However, in the bigger picture, we need to see how AI can help businesses become customer-oriented. It is one of the best compliments you may want to hear as a retailer. AI holds tremendous potential in helping businesses adopt customer orientation. For example, in market research, AI can help derive deeper insights into the needs and expectations of the target segments. It can be trained to analyse demand patterns and predict emerging gaps in markets. The awareness of such gaps can prove to be a big competitive advantage. These gaps could be in products or services or any other element in the value chain or the customer shopping journey. Say that a retail departmental store figures out that sustainability is an emerging concern among customers. The store can capitalise on this insight and carry out some rebranding exercises to communicate the same to customers and the public in general in the given market. Customer orientation need not always be transactional in nature; it is more centred on the concept of value. Thinking of such applied benefits of AI carries transformational capabilities.

Use of AI in IoT applications

Think of AI as a way of thinking. Wherever it is applicable and you apply it, it can enhance the capabilities of that system. IoT (Internet of Things) is one such system. There are two elements in the word IoT – internet and things. Here, things refer to all kinds of devices and assets that can be connected to the internet. IoT is the networking of these devices and assets over the internet. When IoT and AI come together, we are talking about an intelligent network of devices that share and process data and derive meaningful information and insights for improving the quality of the ecosystem to where it is applied.

Today, IoT is already being used in many areas of retail like inventory management, managing smart shelves, asset tracking, personalisation, and customer experience in retail. A simple example of the use of AI in IoT is predictive repair and maintenance. In this particular case, AI-IoT changes how retail businesses dealing in electronic and electrical appliances can extend their value propositions. AI-powered IoT solutions can predict requirements for repair and maintenance or purchase of utilities more accurately and relevant to customers. It is an opportunity for retailers to keep their brand connections active with customers.

The thing with AI is that it can learn and improve itself. These capabilities can be applied not just for recommending products but also to improve the shopping journey of customers. AI has a far bigger role to play in demand and trend analysis as well. The application of AI is changing how and to what extent business processes can be automated. Companies around the world are now using or considering the usage of AI in inventory management. Considering the bigger picture, AI can help businesses become customer-oriented in more profound ways. Something to watch out for will be the integration of AI and IoT in retailing.

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