How to use AI in retail? Find speed and impact
These are turbulent times for the Flemish retail scene: every month, another shop or retail outlet seems to be teetering on the brink of bankruptcy or reorganisation. We see the retail sector at a tipping point, where old established values are giving way to companies that can inject new DNA into their organisation.
Meaningful use of AI is undeniably one of the cornerstones of a future-proof retailer. But what exactly does that mean? Because AI is often discussed from a technical angle, it is not always clear how the technology can make a meaningful difference for retail.
"Just walk out"
In 2020, Amazon launched the first ‘cashierless stores’ with much fanfare: customers could grab what they needed, and AI ensured that their credit card was charged after leaving the shop. Barely four years later, Amazon quietly decided to discontinue this project. Behind the scenes, much of the work appeared to be outsourced to people checking camera images. Almost everything could be automated, but the final details were difficult to perfect. A staff-free store seems to be a utopia (or dystopia?) for now.
It is just one of many examples that highlight how important it is to think carefully about the elements in your business where technology really adds value and where it does not make sense.
AI is a lever, not a replacement
Generative AI, like ChatGPT, makes a difference today especially for team members who use the technology to speed up their tasks. Some examples include:
- People with a lot of product knowledge, but no experience in Excel, can create complex formulas.
- People who never worked with databases are now writing complex SQL queries.
- A marketing email that used to take half a day is now completed flawlessly in half an hour.
Use AI for core processes
Retailers should focus on core decisions that have the biggest impact. Consider, for example, putting together collections, a task that involves numerous variables:
- What is the impact of a warm winter?
- How many products do we offer within a category?
- How do we monitor the procurement budget?
- Can we send enough stock to each location?
- What products can be reordered during the season?
This complexity often leads to simple rules of thumb, leaving retailers at risk when the market changes. Erosion starts slowly, but once a tipping point is reached, the situation can quickly become irreversible.
“ The greatest added value can often be found in processes where many small decisions have to be made quickly, or in complex situations where a good compromise has to be found. ”
In which processes does AI really add value? For speed and complex puzzles
The greatest added value is achieved in processes where:
- many small decisions have to be made quickly,
- or in complex situations where a good compromise must be found.
Some examples:
- Pricing: Prices are often set quickly and without review. With AI, you can better predict willingness to pay and margins, and set better prices. Of course, human oversight remains essential to ensure that the pricing strategy matches the organisation’s objectives.
- Returns: High return rates undermine the profitability of online sales. AI can help predict which products and customers are problematic, so you can take timely actions for these.
- Hyperpersonalisation: Marketing automation is evolving into systems that analyse individual transactions to make tailored offers. The concept ‘a segment of one’ thus becomes a reality.
- Discounts: Discounts have a big effect on consumer behaviour, but generic approaches cost retailers money. AI can more accurately predict the impact of discounts on margin and customer value, significantly increasing the return per euro of discount.
Conclusion
AI is not a magic solution to the challenges facing the retail sector, but it can be a powerful lever if deployed sensibly.
The key word here is evolution, not revolution. Acquired knowledge should not be thrown out of the window. Above all, think about how to make your organisation stronger with new technological capabilities. The answer will be different for every retailer.
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Louis-Philippe Kerkhove
As the Founder and CTO of Crunch Analytics, I am deeply invested in the intersection of business strategy and data-driven decision-making. I lead a diverse team of professionals, including data scientists, business strategists, data engineers, and statisticians. Our mission at Crunch is to leverage the power of data to help businesses navigate complex challenges and implement solutions.
In addition to leading Crunch Analytics, I serve as a Professor of Operations Management at Ghent University, where I teach supply chain management and conduct research in artificial intelligence, operations research, and business strategy. I spearhead courses on data-driven retailing for the university's lifelong learning tracks, part of a larger initiative to promote evidence-based decision-making in retail. I've also authored a book on data-driven retailing, underscoring my commitment to practical data applications in business. More information on this project can be found at datadrivenretailing.com.
Throughout my career, I have been driven by a relentless pursuit of knowledge, a commitment to practical application, and a dedication to fostering growth in others. I am a firm believer in the power of curiosity, the importance of scepticism, and the value of innovative thinking.
Whether in the boardroom or the classroom, my goal remains the same: to use data and analytics to drive informed decision-making and create meaningful change.
Expertise:
- AI, Forecasting, Optimization, Simulation / Digital Twins, LLM
- Retail-specific applications
- Supply chain and operational research
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