Retail Is Increasingly Adopting AI to Tailor to Consumer Needs and Boost Revenue

By Nikolay Savin

Retail Is Increasingly Adopting AI to Tailor to Consumer Needs and Boost Revenue 1

The agility of a company is the key to enticing customers and thus increasing revenue. The majority, or 70%, of successful retail businesses use innovations to react to the market demands quickly and cater to the wants of consumers who are increasingly expecting personalized offers.

Being flexible does not equal just the speed of repricing. It means setting the right prices for the right products at the right moment based not only on the data about competitors’ pricing and promotional activities but also on the data about seasonality and customer browsing behavior. And that’s where AI takes the floor.

According to a recent study by Deloitte, machine learning (ML) applications, which are a primary element of artificial intelligence (AI), will spread widely, but won’t become ubiquitous, in the next five years. Such increase in use will happen thanks to the AI solutions growing more affordable to retail enterprises, says the report.

Using ML to analyze historical, competitive and consumer data, and benefiting from its recommendations in price and demand forecasting, ranging and delivery optimisation, and analyzing consumer behavior gives and will continue giving retailers a competitive edge. The study states that those businesses which are not harnessing the potential of AI in their data analysis yet are losing the market to those who are. And the gap will keep growing making it difficult for laggards to catch up.

What Business Tasks Does AI Solve?

The AI algorithm learns the same way humans do. It uses every transaction, every bit of bad and good experiences, every piece of knowledge about the effects of pricing and promotional decisions as the foundation for learning. It works fast, never gets tired and never forgets anything. The algorithm uses all the available knowledge to establish non-linear connections between any number of pre-set parameters and factors, including the current business goals and needs, and makes predictions and recommendations based on objective data rather than on the expertise of a particular manager.

Naturally, retailers look for a way to sell as many products as possible at the highest price. Such an approach begs for the question: what exactly the optimal price is which would allow for generating the biggest revenue and prevent customers from choosing competitors. In other words, how can retailers maximize sales without losing revenue? AI can answer this and other questions:

  1. How to increase prices without decreasing the number of transactions? AI factors in the demand elasticity and suggests the optimal price which ensures the highest margin possible while securing the number of sold products.
  2. How to factor in customer behavior? Among other parameters, AI takes into account changes in customer expectations, as well as seasonality and promotional activities. Thus, the suggested price improves customer experience and increases customer loyalty.
  3. How to forecast the effect of pricing decisions? Machine learning solutions offer precise models of potential market conditions, which allows for understanding the outcomes of every decision.  
  4. How to get rid of margin-killing promos? AI calculates the effect of various promotional activities on sales and suggests the optimal scenario without engaging managers.
  5. How to benefit from the whole experience which your business has paid for with money? The algorithm analyses all the available historical, customer and competitive data much faster and more accurately than humans.

How Does AI Benefit Businesses?

A recent market study states that AI algorithms increase revenue by 1-5%, push margins by 2-10%, and boost customer LTV by 20%. Also, they can decrease discount approvals by 80%.  

The effect of the AI adoption in pricing depends on several factors, including the retailer’s position in the market, their initial performance indices, as well as the efficiency and speed of decision-making, though.

Competera has tested the effectiveness of its ML-powered price optimisation solution as part of a one-month pilot with a UK-based retailer. As a result, the company has seen the number of transactions grow by 22,3%, while its revenue surged by as much as 13,9%.

What Is the Potential of AI in Pricing?

ML applications are a perfect solution for retail enterprises operating on dynamic markets through a variety of selling channels and requiring repricing for thousands of products weekly or even daily, as well as for those who have exhausted all the other ways to increase operational efficiency through optimisation.

To benefit from an AI-powered solution, retailers need to ensure they have two essential elements of price optimisation:

  1. Data. ML applications require well-structured data stored in one place and a unified format spanning at least three years.
  2. A flexible team. AI introduction calls for the shift in team roles as the solution undertakes routine tasks and allows managers to switch to high-level activities, including but not limited to negotiations with vendors, crafting a winning pricing strategy, fine-tuning strategies based on the recommendations provided by the solution.

Conclusion

Consumers are becoming more demanding and expect personalized offers based on their online behavior. Retailers who look for the ways to enticing buyers and thus selling as many products as possible at the optimal price are facing the need to analyze the ever-growing amounts of consumer, historical and competitive data to be able to set the right prices.

AI has been actively penetrating retail as it can process massive amounts of data, which are unmanageable for retail teams, and offer data-based recommendations, forecast demand and delivery.

Before anything else, AI adoption requires two basic elements: the abundance of well-structured data and a team capable of embracing the technology and changes in roles.

As ML-powered applications are becoming more and more commonplace, those companies which are not benefiting from them yet risk being deprived of their market share, as they won’t be able to compete with precise machine-generated price and promo recommendations based on objective data.

About the author
Nikolay Savin, Head of Product at Competera, build the price optimization solution for e-commerce and brick & mortar retail to achieve better results through the merge of data, machine learning, and retail best practices.

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