Merchandising Evolution And The Future Of Merchandising

Jasper Zeelenberg
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“Just like Netflix knows which movies to recommend local viewers everywhere, brands can recommend relevant assortments to local consumers everywhere. Using data and platform technology.

On a platform, brands and retailers can respond to local retail demand by dynamically adjusting local retail assortments and adjusting the flow of merchandise to retail, while respecting agreements and constraints of space, lead times, pack-sizes, and minimum order quantities.

This reduces the retailer’s dependency on a long-term forecast, which in turn leads to fewer shortages and more sales. With less stock. Everybody wins with platform retailing. Not least our planet.”

- Platform Retailing: No Push, No Pull, Just Flow

The role of the general store in the future of merchandising

Changes in consumer behaviour and expectation have led to a big shift in the role of merchandising in fashion retail. A digital-first strategy is now a vital survival trait for any brand that intends on remaining relevant in a world where shopping will become increasingly digital.

In this new world, retail spaces need to offer something special, and the digital and physical worlds are expected to dovetail perfectly.

Merchandising has come a long way since the time when ‘The Store’ was simply where everything was kept, and ‘successful merchandising’ consisted of ensuring all items were accessible – or available on request.

Today the role of merchandising is ever more complex, needing to maintain profitability in a market that is unpredictable and with wafer-thin margins. Merchandise planning now seems like a near-impossible task.

The future of merchandising is one where we must respond rapidly to the market with the right product offerings in the right place at the right time. How is this possible?

The promise of artificial intelligence in retail

future of merchandising

Artificial Intelligence is often cited as a wonderful solution that will make all our headaches disappear. But is it? It is important to understand that the AI being marketed today is not the same as the AI featured in the movies – it is not self-aware, and has its limitations.

In the simplest sense, AI consists of a collection of algorithms that decides on specific outputs based on available information. These algorithms can be very complex, with ‘machine learning’ that enables new capabilities by recognising patterns across astonishingly large and complex data sets. 

If an algorithm is smart enough, it can even self-evolve and become a better tool. The more data is made available to the AI, the more powerful and actionable the output. 

‘Big Data’ is what makes an algorithm powerful; it can analyse huge data sets and find patterns that escape human attention.

Although the Butterfly Effect is used to describe the inscrutable mess of chaotic data, using AI is akin to seeing the butterfly flap its wings and knowing exactly where the tsunami will hit. This has big implications for the future of merchandising, and smart algorithms are already being employed to make merchandising more effective.

How is AI being used in merchandising?

Smart, algorithmic tools are in use across multiple industries; you will have come across them in your daily life.

If you have ever had ‘similar products’ suggested to you on Amazon or eBay, or if you have had Netflix make recommendations for movies, then you have been weighed and measured by an algorithm that has figured out exactly the kind of thing you like.

For fashion retail, there is a much bigger range of possibilities for AI.

  • Tools like Google Lens enable searches based on visually similar images; this kind of functionality could be used to find visually similar fashion items, or identify companion products that are frequently bought with visually similar items.
  • Location-based data can inform future trends that might be affected by weather, national holidays, or sporting events.
  • Best prices can be determined based on demand and trend signals.
  • Physical stores can replenish or update selections based on local online interest.
  • Opportunities for promotions, bundles, collections, and other merchandising planning can be automatically suggested by smart algorithms.
  • Products can be clustered together based on shared use/occasion, or frequency with which they are bought together.
  • Replenishment can be automated and reordering can reflect real demand.

Will AI replace merchandisers?

No. The smart algorithms that make up an AI are a tool – they can accomplish a lot, but they still need a skilled merchandiser to point them in the right direction. To begin with, these tools can take a lot of time while they are in the ‘learning’ phase, and it might seem counterproductive to spend hours feeding data and tweaking the algorithms just for it to tell you that ‘the sunscreen should be shown next to the swimsuits’.

However, once it is fully operational, an AI will be able to respond to real-time trends and anticipate demands that are beyond human capability.

In this sense, it is like being able to use night-vision goggles – it extends our senses, but it doesn’t replace the role of merchandising entirely.  Rather, the role of merchandisers becomes more specialised and strategic, as you have to decide which data is fed into the algorithm and what kinds of outputs you want to come out of it.

Instead of developing custom AIs, many retailers make the decision to use a platform. Platform retailing enables brands to coordinate their processes with the power of AI, but using a ready-made platform that is already developed for their exact needs. The advantages of this are that it plugs right into existing systems and that it is always being developed with new capabilities.

What is the role of AI in the future of merchandising?

Smart algorithms have a clear, demonstrable use in online sales but they are equally valuable in core retail operations too.

Demand management is one key area where AI is able to aggregate patterns in buying behaviour to predict demand on a granular level.

Being able to convert this insight into automated alerts or decisions can ensure that high-demand products remain in stock, especially in those locations or channels which the AI has identified as important.

Automated inventory management is another huge benefit of AI in retail because it can allocate stock to those store clusters where the highest demand is going to occur, reallocate from stores with lower demand, and keep items moving through the supply chain as a smooth and continuous flow.

Markdown and re-pricing can both be automated by different kinds of algorithms, based on competitor price, and early waning-demand signals that are apparent to the algorithm but invisible to the human eye. Overstock is avoided, and the best possible price is attained every time.

Employee training can accommodate local insights about specific customer demographics and needs for specific locations.

This can lead to adjusting how the staff address customers in-store, which questions they ask to engage with them and to understand what particular concerns they might have. It can even provide insights that help you hire the right staff.

Store layout can be optimised using AI-derived insights about customer priorities and habits in a local area, placing products in the right place, at the right height, and with the right offer to entice and make a sale.

Special value for the ethical supply chain

future of merchandising

Consumers are ever-more focused on ethical and sustainable fashion, but it can be hard to have transparency on this. A significant role for AI will be for conscious consumers who have special demands, because they will appreciate the targeted marketing that is possible using this technology.

The future of forecasting

There is no gentle way to put this: forecasting is dead and has been for a while. The old tools used to approximately gauge demand are no longer ‘fit for purpose’ in the modern, digital-first, retail landscape.

It is simply not possible to predict with 100% accuracy how many of any items will be needed without a significant buffer of overstock or a degree of understock.

Instead, algorithms are able to deliver much better demand-sensing accuracy but in a shorter timescale. This is a better tool, but the supply chain needs to be able to respond quickly enough to the demand, meaning supply lines need to be shorter and more responsive too.

Best practices to prepare your organisation for the future?

  • It is essential that your company immediately adopts and maintains a digital-first strategy with targeted investment in platform retailing, digital talent, and an integrated online and omnichannel retail model.
  • Every digital touchpoint with the customer is an opportunity to gather data – this is the lifeblood of your digital-first strategy. Make sure you take this opportunity.
  • Data also needs to flow freely through the supply chain, among all partners and stakeholders. Connect the dots by creating a custom solution or using a platform retailing solution.

Advances in smart technology and AI have already transformed many industries, and it is just getting started with fashion retail. Not all brands will see the importance of investing in this area – they will disappear.

Those retailers that make the required investment will live to see AI transform their industry with better processes and a closer relationship with the consumer we serve.

Jasper Zeelenberg
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