AI can be adopted for any purpose these days – well almost any. In my previous blog, I mentioned how adoption of AI can help a small or medium sized company to grow faster. In this series I will explain step by step how AI can help you with your retail business even if your sales volume isn’t too high right now and you do not have a bucket load of data available. Please note that I will not be focussing on installing elaborate AI gimmicks to attract more footfall, rather the discussion would be around more practical adoption of AI to increase operational efficiencies and profits, while keeping budget in mind.
Following are some of the areas where latest machine learning algorithms can be harnessed to get a major boost. Please note that I am using the terms “artificial intelligence” (AI) and “machine learning” (ML) interchangeably. They mean the same thing almost always.
Future can be foretold with certainty I guess, well atleast with enough data (How much data is enough data? – I will write on this when I get some time). It is all about finding the patterns and machine learning is all about that. Predictive analysis techniques can be used to calculate how a particular process will trend, which can be sales, revenue or even product cancellations for example. Very crudely put, it is calculating the probability of a certain event or a series of events, and AI is very good at it.
The unholy art of having the right products at the right time, in the right place, at right quantities, and at the right costs is called inventory management. However, in order to have so many things correct, even more parameters are needed – current trend, seasonal changes, festivals, demand, product volume and weight, sales history and many many more. Once the parameters have been identified, you somehow need to make sense of it – what causes what to change, and finally their impact on sales and thus on demand. Currently, most products provide a “rules based approach”, where the most important parameters are selected (on gut feel, most of the times) and hard rules are created around them. These are as mentioned, “hard”, meaning they cannot adapt and they do not have a brain of their own. Luckily, some machine learning techniques are designed just to do fix that. These will be able to factor in hundreds of such parameters and generate models for predicting the right inventory configuration. Moreover, the model will learn over time and will automatically correct its mistakes increasing the accuracy even further over time.
Amazon drives 35% of its revenue from its product recommendations; and 70% of the videos that you watch on YouTube are driven by an AI based recommendation genie – ’nuff said. Now the question is – can I get my business a robust recommendation system? Darn yes! AI can help here by finding patterns in the behaviour of an individual user and that of the entire user base thus, providing a neat personalized recommendation to an individual based on their behaviours and based on the latest trend – all handled by the AI once deployed. It also looks at how your products move in respect to each other and calculates the correct cross-sell and up-sell products.
Being the face of any company, the primary goal of any customer support department is to maximize customer satisfaction while minimizing the operational costs. Chatbots have become very popular these days and have a huge advantage over regular chat applications. These are always available and can scale with traffic. A smart chatbot will be able to resolve many regular questions like timings, order statuses and some can even help your customers make a purchase – like the one we designed. Apart from chatbots, AI can help your customer support team by monitoring the conversations and requests made by the customers and tracking their tickets, improving the turn around time.
Product returns not only causes incremental supply chain costs, but often the item cannot be resold at the original price owing to damage, wear and tear, or obsolescence/devaluation. AI can help here by finding patterns in the returns and by attaching a “fraud score”, warning you when such a purchase is being made. The algorithm keeps on learning as time passes and gains more “experience” and thus becomes more accurate order by order.
This was the first part of this series – a summary of what to expect next. In future posts, I will try to elaborate each of the points mentioned above and will try to throw in some technical mumbo-jumbo as well.
We at Scientist Technologies help our customers adopt AI everyday. Please reach out to me in case you have query around the same. See you next week 🙂