Modern Retail

How Machine Learning Boosts Retail Pricing

Retailers with optimal prices entice customers, eat up the market and earn more. AI-powered price optimization helps businesses craft winning pricing strategies and increase revenue in the highly intensive UK market.

It is getting more and more challenging to compete in the retail market. Customers are becoming more demanding and expect to buy at the optimal price, the data which retailers need to analyze is growing in amount, while the market itself is becoming increasingly dynamic, which calls for the non-stop fine-tuning of prices.

Price helps shoppers to decide whether or not to buy something, and which retailer to choose. As retail is growing more digital, customers are empowered with multiple tools to check prices offered by a plethora of retailers before making a purchase. Thus, the business with optimal prices wins.

The right price perception is the key to the hearts of customers. Machine learning (ML) price optimization algorithms are the tool to help retailers persuade clients that their prices are the lowest on the web.

According to a recent study by Deloitte, ML applications will become more affordable, powerful and commonplace in the next five years. Some retailers have already unleashed the power of neural networks to perform better: 70% of successful retail businesses benefit from innovations to cater to the needs of customers.

What are the advantages of retail price optimization software?

According to a recent Gartner report, that AI-powered price optimization tools increase revenue by 1-5%, boost margins by 2-10%, and push customer LTV by 20%. Also, they reduce discount approvals by 80%.  

At the same time, the effect of the AI adoption in pricing is different for various businesses. It depends on many factors: the retailer’s position in the market, their performance before AI, as well as the efficiency and speed of decision-making.

A UK-based online retailer Find Me a Gift wanted to increase the number of transactions without losing margin. Competera’s ML-fueled price optimization solution helped the business boost revenue by 13.9%, increase sales and margin by 22.3% and 0.5% respectively during a one-month pilot.

What Makes AI So Efficient?

The algorithm processes massive amounts of data, learns from everything — successful and failed experiments — which the business has paid for with money, and provides pricing recommendations with predictable outcomes, which are possible to repeat. The system knows which products need repricing, what kind of repricing and when to reach the goals set by the business.

The self-learning algorithm considers all non-linear interconnections between all the products in question and pre-set parameters, or pricing and non-pricing parameters, to recommend the optimal price: the price elasticity, the effect of price changes of a product group or an SKU, a grace period, seasonality and customer browsing behavior. Also, it takes into account everything which retail managers consider when suggesting prices based on their gut feeling: the reserve price, the stock, the average market price, as well as the role and positioning of the SKU.

ML algorithms are efficient when it comes to promos. AI models the dependence of sales on promos, evaluates how various promotional scenarios influence sales and offers the optimal promo scenario without engaging managers. Such an approach allows retailers to eliminate unnecessary promos which destroy margins.

The data is what makes ML so efficient. Before adopting AI, retail businesses need to collect macroeconomic, historical, sales and Google Analytics data in a unified format spanning 1-2 years.

Conclusion

Machine learning algorithms help retail businesses build the right price perception by analyzing enormous amounts of data and considering all the non-linear interconnections between products and multiple pricing and non-pricing parameters. Armed with the optimal prices, retailers entice customers and win the market.

Nikolay Savin, Head of Product at Competera. Combining 8 years of experience in supporting technology businesses and entrepreneurship in Europe on their effort in Silicon Valley with building a product for retail revenue growth Nikolay is passionate about sharing stories on technologies and innovations for retailers to help them grow.