Each and every customer wants to buy something from their comfort place, and that is made possible through the internet. The digitally savvy consumer has changed the face of retail, and brands find themselves striving to adapt to this transformation. So in this blog, we plan to discuss top retail trends which will make retailers top in their competitive market.
The traditional brick-and-mortar model of retail has been under siege by digitally-enabled online and mobile channels. However, simply adding digital channels may not help. Deeper analysis reveals that a brand’s long-term survival entails moving from a single or multi-channel approach to an omnichannel one. This is because consumers expect seamless brand presence and consistent experiences across devices and channels. While digital technologies are at the core of this transformation, their successful execution requires thoughtful planning and cross-collaboration across various retail functions. A careful analysis of the trends and consumer behaviors driving better transformation. That analysis can help retailers maintain their focus and achieve tangible benefits.
For decades, traditional analytics have worked perfectly fine for the data-driven retail industry. However, Artificial Intelligence (AI) and Machine Learning (ML) have introduced an entirely new level of data processing which leads to deeper business insights. Data scientists could open a new world of possibilities to business owners. For example, they are extracting anomalies and correlations from hundreds of Artificial Intelligence/Machine Learning models. According to a report by McKinsey, companies that adopted AI in at least one business function — like marketing and sales or human resources — saw an average revenue increase of 66% in 2019. That’s a 3% increase from 2018.
So, we are in 2022 and Artificial Intelligence solutions still have plenty of room to grow. So here we are presenting the top 5 trends that are going to be changing the face of the Retail industry.
Today, every company has vast amounts of data about its users and products. But all these data are kept in a storage system without making use of it. The predictive aspect of AI is incredibly useful in the era of data, and probably the most developed of AI trends. Instead of storing vast amounts of data and having more work on your analysts to make sense of it, AI can take over. The collected data can fit into such categories as Behavior, Action, and Product. Companies can harness big data for problem-solving and creating solutions to help address business challenges.
Predictive analytics solutions can increase sales, revamp customer experience, and facilitate attracting new customers. Predictive analytics help retailers analyze masses of historical data on user-product interactions. Using them, it’s easy to see how customers respond to your products, spot customer churn, and find out your business strengths and weaknesses. Getting the analytical insights, a retailer makes the shopping experience cater to a specific audience and exact users. If the service or products have better improvement, customer loyalty and more outstanding sales are guaranteed.
What if we say that each one of your customers is seeing only what they have liked in past, what they bought most frequently, which products they are visiting most frequently. This not only improves the customer experience but also improves the sales as showing what they liked tends to attract more customers to buy them again.
Rich datasets can breathe life into recommender systems. A prominent player in the E-commerce industry is Amazon. It has been using recommender systems for a very long time to attract customers and suggest products to them. Another industry giant Netflix saves $1 billion yearly thanks to AI-powered systems and delivers 75% of content to users through targeted recommendations.
Providing accurate results to search query is one of the basic needs of an e-commerce website. And sometimes, customers come up with the proper words for their product, but they may not get proper results. There is a saying, “An image is worth a thousand words” and that’s exactly what every retail company can offer in their product catalog. Along with word-based queries, they can introduce something called “visual search” which takes an image from the user and finds similar product images from the index of products.
Visual Search not only looks for basic similar products it also looks for other attributes. For example, color, shape, pattern, and many image representations to find the most similar products to uploaded images. This provides a convenient way for customers to purchase without typing and putting hundreds of filters. So it’s high time companies start using image-based search along with traditional word-based search.
Today, a chatbot is everywhere. Whatever website you visit, you will find a chatbot pops up on your screen to ask few details of your, your requirements, etc. This is where a chatbot becomes useful; it helps automate routine tasks. Examples include booking an appointment, answering basic questions, taking your details for further inquiry, etc. This helps in engaging its customers 24×7 as your customers don’t have to wait for a real human to answer their questions. A basic chatbot can be helpful in automating daily routine tasks, whereas a conversational AI chatbot can handle more difficult tasks. For example, handling customer queries, guiding them through solutions, talking to them about company products, etc.
Instead of having a decision tree-type chatbot, a conversational AI chatbot can handle complex queries and provide faster replies. With the help of Natural Language Understanding (NLU), every chatbot can understand every customer. That means, sentiment and intent of each query raised by customers. And helps the company to identify what their customers are looking for. These advanced chatbots are built in such a way that they can understand human’s natural language and provide solutions with ease. For example, providing support for misplaced delivery, refund-related queries, product-related queries like when they will arrive, what their specifications are, etc.
Brand monitoring is another one of the retail trends that will Monitor your brand value and its reputation is the key in stay competitive. You want to monitor your company’s progress, how customers feel about products, your products’ price suiting customer requirements, etc. To answer these questions you will need to have a very large marketing team who will analyze and study the social media data, your products reviews and then draw conclusions for those. This is not only time-consuming but also not scalable. As you grow that data increases and with increasing data the number of resources for the analysis will also increase which is not a good sign.
That is where AI comes with its capability to read and understand large volumes of data and provide necessary conclusions. That means with very no or little no effort and it’s easily scalable too. AI will help you estimate the scale of customer satisfaction by detecting and analyzing positive, neutral, and negative reviews online. It is fantastic if your brand receives only words of praise. But even if you offer top services, competitors may attack your brand by posting and sharing fake unfavorable reviews. By automating searching and detecting brand mentions in a fake context, you can manage the good reputation of your brand. Running a marketing campaign, you can employ AI to assess the results. Algorithms can monitor brand exposure during mass events and collect relevant data to help you understand whether your investments have paid off or not.
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