Merchandising is both an art and a science. But while some of us only got an A in art and a C (+) in science – and we’re not talking about C-Suite professionals here – there’s nothing to fear from the fusion of AI science with the art of merchandising.
In fact, the two make for a powerful combination that can help retailers to combat today’s core challenges in retail e-commerce, boost revenue, and move inventory out the door faster.
The Core Challenges in Today’s Retail Merchandising Teams
There’s no shortage of challenges a retail merchandising team faces surrounding planning, buying and allocating apparel correctly, but some of the core challenges today include the following:
Today’s retail merchandising teams are wrapped up in the details of assortment planning – buying for different styles, the allocation of different styles, and even sizes and color selection across the physical location and e-commerce site.
So, how does the merchandiser typically go about introducing new products into the assortment? Of course, there are a ton of things to consider, but the opportunity to introduce styles a shopper may not have seen before is an appealing set of circumstances.
Apart from discerning where interest and demand exists in areas that haven’t been covered yet, or trends set to take wave in upcoming seasons, merchandisers also have to look at the bigger picture. This bigger picture covers where there are areas to save money and/or budget, how to balance the breadth of the assortment, and more.
Web and Store Merchandising
With regard to web merchandising, retailers need to focus on new and creative ways to position their site so customers can discover it and actually find what they are looking for. It’s important to take into consideration how products are placed on a site, what promotions will be used, what a listing page might look like for a given category, and which categories should be featured on guided navigation, as well as how they intersect with facets and filters.
Once the products have been acquired, retailers then face the challenge of determining how to move them and actually get them sold. The tools in their toolbox also play a role; how can a retailer make sure that the right products are surfaced up when someone types “jumpsuit” into a search bar?
Or, incorporating uber-popular social media platform TikTok and its focus on fashion trends (ex: cottage core) into the mix; how can a retailer make sure their products are attributed properly so their products are discoverable to customers in the ways they’re talking about? It’s truly a constant battle to stay on top of trends.
Then there’s also the challenge of store merchandising. What should the store look like? Where should the products be placed? What styles should be on promotion? What should the frequency and quantity of restocking look like to keep the overall shelf health good and free from the “Out of Stock” blues. And of course, how can products consistently stay at full price and not have to be marked down at a later date?
Forecasting and Predicting Demand for Products
To create a shopping experience that converts, retailers must dive in a bit deeper to understand how to plan their assortment accordingly. The truth of the matter is inventory that doesn’t match the inclination of the shopper is inventory that doesn’t sell.
After identifying the particular styles to buy, there’s the challenge of figuring out how many units to buy and when they should be bought – i.e., as soon as possible to mitigate future supply chain issues.
All of the above mentioned challenges are really smaller layers within a quite large customer-centric cake where retailers are tasked with understanding the right combination of components in order to predict the future, essentially.
It’s no easy task to try to get ahead and forecast demand and styles. This is why this is a key challenge that needs to be addressed.
Meeting Today’s Core Challenges with Customer Intent Data
One of the best ways to meet today’s core challenges in retail and connect shoppers with what they’re looking for is to infuse customer-centric product attribute data into your existing e-commerce stack.
Lily AI, trusted by global retailers, brands and industry leaders, helps merchandise teams in a number of ways:
If a retailer has ordered too much product that isn’t selling as well, it’s going to have to be marked down eventually. But before that potential becomes a reality, retailers can look back at previous seasons and focus on what may have happened in similar types of products. This can then be used to help base projections for future products and decisions.
Lily AI helps by understanding exactly how these new products are similar to previous products and how they sold relative to others. This is an incredibly useful tool to predict demand and determine how a retailer may want to classify a new product – i.e., good, better, best, etc.
We’re able to automate and improve the process of identifying the proxy products from previous seasons without having to manually depend on a team member who has to lose time looking through a directory of images in order to find the right one that’s similar to the new product. Machine learning takes care of this, and can be a truly useful input to an existing process, making it faster, easier, and more consistent.
One of the best ways for retailers to stay on top of their inventory is to make sure their merchandise is updated with relevant attributes across a variety of styles, especially those based on the latest trends – whether that trend is six months to a year to two years down the road (or runway).
Lily AI is well-equipped to help you quickly understand which product attributes are actually moving merchandise. We have a team of style experts/trend analysts who are not only committed to researching short-term and long-term trends, they also identify the relevant tags that embody each of those styles. They then partner with our data science team to pull and highlight the specific attributes that resonate with each trend.
Our domain experts are spread across three categories: fashion, home, and beauty, and come from large brands and retailers like Nordstrom, West Elm, and even Sephora. They bring their hands-on experience and style expertise to help build and develop our taxonomy, as well as make sure it remains current and fresh to reflect the ongoing cultural changes that can happen in the world of retail e-commerce.
The team not only is able to build an extensive list of styles under each category, but they’re also able to capture other styles or sections that haven’t even been mentioned yet.
Product Attribution Data Taxonomy
Lily has extensive product attribution coverage within our existing data taxonomy and can fully attribute products under a variety of styles that are going to resonate with the consumer and their own language.
What’s great about our taxonomy is that we actually can look forward to upcoming trends and then rapidly build them into our taxonomy. Our platform is designed to take in retailers’ data and attribute products using AI in an automated fashion. What this creates is a much richer way to describe products – not just “crewneck,” or “pink crewneck,” but also incorporating occasion-based and trend-based attributes.
This helps with predicting styles for next season when all of the previous styles from last season are attributed in-depth. When a retailer already has the tags that are already relevant built in, then they can automatically get full coverage within their catalog which can be surfaced up to assist during the buying process.
Our product attribution data is easy to work with, can be incorporated into the tools a retailer uses today, and there is no specific set of products we have to pre-integrate with.