How to Accelerate Retail Growth with Business Analytics

With the advent of e-commerce, store retailers have seen a considerable reduction in the footfall. Many tech pundits went as far as to say that physical stores would be shut down eventually, but the reality wasn’t as drastic. Physical stores continue to and will always be around as long as they are able to offer a delightful shopping experience for customers. The key to success is to be as digitally empowered as possible because customers are going digital now. If retailers can channel the large amount of data and signals that shoppers generate around their stores into meaningful experiences, it is a win-win situation for all. Data analytics and business intelligence is a mainstream weapon for retailers to derive meaningful insights that can help accelerate their growth. From tracking customer behavior inside the store to anticipating rush hour sales, data analytics platforms are re-inventing the art of selling in-store.

In a nutshell,

By using hardware like an iBeacon or tracking enabled stickers, or even by leveraging on the CCTV infrastructure, a retailer can collect a huge volume of data that can be helpful for the business. The raw data recorded includes the customer traffic data, the customer heat map of the store, customer events, etc. When this data is properly channeled to a data analytics tool, the insights generated can help the retailer to update their sales strategies. Business Intelligence plays a crucial role in the automation and augmentation of the retail process. For example, the following are few insights that a Business Intelligence system can generate for a retailer,

⊕ Which section of the store had the most customer traffic

⊕ Which product is most/least checked out by customers

⊕ The high-level purchase behavior of customers

⊕ What combination offers can attract more customers

⊕ Best product placement options to sell more

⊕ and much more

The possibilities are limitless when you have access to intelligent insights generated from the data your business already has. Let’s see in detail, how all customer data can be captured from a retail store and how to generate valuable insights from it.  

Using business intelligence in the retail industry to identify shoppers interest

By tracking the movement of shoppers in a store, a data analytics engine can identify their interest levels for different products on display. A customer is likely to spend more time near a  product they are most interested in; especially in high-value markets like auto dealerships or home appliances. By calculating the time spent near the products, cross verifying this data from their purchase history as well as data obtained from social media, the system can generate an interest level for each product for each customer. Additionally, the seller can see the overall traffic distribution within the store. These insights can help the seller to make more meaningful decisions regarding product placements, resource allocations and much more.

If you consider the online shopping market then the recommendation engines divert customers’ attention to items they are unlikely to discover on their own. It also helps the retailer forecast demand and makes supply decisions based on that. Amazon has one of the best recommendation engines; it drives 55 percent of its sales. Its algorithms not only analyze your own behavior but also analyzes the behavior of other customers who are similar to you. For example, if you are searching for a birthday gift then the recommendation engine will look at your purchasing behavior and also the behavior of other people who are looking for similar gifts. Using Business intelligence in the retail industry can take the guesswork of a human sales assistant out of the equation and facilitate highly customized recommendations that are more appealing to the customer.

AI-assisted In-Store Concierges

Active salespeople are one of the most important factors that ensure the smooth working of in-store sales activities. They give relevant information to the customers in the store to help them find what they are looking for. It is very difficult to find the right kind of person for the job and takes a lot of time to train new people. Most of the customers prefer to engage with digital sales concierge rather than dealing with a real person.

AI conversational tools are interactive applications which automate communication tasks. These applications can successfully offload a significant amount of work from salespeople. The customers in the stores can interact with the AI tools to seek information about the products they want. It delivers quick and meaningful information to the customer which in turn enhances the user experience for the customer. The system has face recognition technology to identify usual customers so that it can recommend new offers and suggestions. This system is built with robust Natural Language Processing (NLP) and Natural Language Understanding (NLU) algorithms to facilitate life-like interactions with the user. It also has speech-to-text and text-to-speech engines so that the system can provide instant answers to the inquiries customers may have when examining products. The system helps the management to retain regular customers by sending personalized messages as well.

Incorporating business intelligence in the retail industry for Resource Planning

Resource allocation and planning is the process of assigning and managing assets of an organization in a manner that supports the organization’s strategic goals. It includes managing assets like hardware to make the best use of intangible assets such as human capital. In addition to that, it balances competing for needs and priorities. Moreover, it determines the most effective course of action to maximize the effective use of limited resources. And thereby, gain the best return on investment.

Business Intelligence tools will consider different factors like the capacity of a particular resource before mapping the consignment to the ideal resource for the planning, routing, and scheduling of resources. AI-based on a genetic algorithm, which has the capacity to select the resources judiciously, creates a balance between over-burdening and idle time. It has the flexibility to address complex issues, as there are instances when the number of activities, resource types and execution modes increases in a resource allocation problem. It can also minimize the cost that arises from over-allocation of resource, everyday resource fluctuations and exceeding of project deadlines. A system based on genetic algorithm is the ideal solution for optimizing problems with constraints.

Intelligent Sales Insights

One of the major issues that exist in any retail industry is the information gap between different employees occupying different sales positions. Slow-moving products in a retail shop should be given extra attention. So special sales strategies should be implemented to sell them more. The information gap between the salespersons at times is a hindrance for this. At present, the stores are not able to create relevant personalized offers and deals in real-time. Proper technology is not used to track customer interests while they are inside the store. This is the reason why many stores are missing selling opportunities on a large scale.

With the help of business intelligence in the retail industry,  real-time updating of stocks and its profit margin to the salespersons. This helps the salesperson to know about the margins of a commodity without consulting a higher authority. The salesperson can give offers based on that data. It also gives the salesperson and the manager real-time heat map of users around different products in a shop. This makes it easy to assign salespersons to attend a specific product section as per the user’s need. It studies the customers buying pattern and real-time customer interests and gives that data to the salesperson. The salesperson can suggest products to the user which are less moving, the customer might like it and buy. This can increase the sales of those non-moving products without spending a lot on marketing.

Real-Time Video Analytics

Real-time video analytics can be used to improve safety, customer service and compliance with employee procedures. Video analytics adds an extra layer of safety when it is integrated with security. For example, when an employee swipes their ID card to exit the building, the access management system sends a message to the integrated video surveillance system to zoom the closest camera to that exit door. The system will take note of any other employee who exits the door at the same time without swiping his security ID card. Real-time video analytics enables the identification of shopping patterns and customer behavior.  Video analytics could be used to analyze real-time surveillance images. It could be used to alert the management of activities that require priority. It analyzes the movements of the customers and detect potential shoplifters and alerts the security officials. During a time of major threat when the shopkeeper is held at gunpoint, for example, the AI detects the threat and informs law enforcement about the situation, the location of the threat and sends surveillance footages.

Use of business intelligence in the retail industry can create a customer experience that is far more well-rounded and personal. It can revolutionize the retail industry. This industry has sat on customer data for too long. And hence it deserves a chance to make use of this data for developing the business.