Products are the lifeblood of every retail business, yet I’d argue that marketers have missed the opportunity to leverage product attributes (such as color, size, style, fit, etc.) as a way to create a more personalized eCommerce experience for their customers. This really isn’t the fault of the marketer. The systems they use daily — their ESPs, for example — don’t provide a way to track product attributes easily, so it’s terribly challenging to create targeted audiences for a set of products.
In my previous predictions for retail, I declared that this will be the year of the product since the key to delivering a great personalized experience starts with the product, not with the customer. To get personalization in retail right, marketers need to ask questions like: Which customers would be most likely to purchase a given product? It’s something that leaders like Netflix and Stitch Fix figured out early on (not to mention executed well), and an area where we can expect to see a lot more buzz in the coming months.
What it Means to Really Know Your Products
Typically, the marketing team knows about its customers — how much they’ve purchased, when they’ve purchased, the messaging that drives customer response. Meanwhile, the merchant team has a deep knowledge of products, from margins to inventory to sell-through. The struggle has been to combine customer and product data that drives better marketing while also providing merchants with feedback about product selection. For example:
- How do customers interact with products on your site and even in stores?
- What products do customers typically pair together?
- Which products are typically sold on markdown?
- Which products sell well when highlighted in an email to customers that marketers believe have an affinity for that product?
The product knowledge that merchant teams have is only so good on its own. It becomes truly powerful when you can pair it with a deep understanding of products and their attributes.
And that brings us back to a focus on the product — retailers need to start investing in deepening their product knowledge and using that knowledge to improve everything from marketing and beyond.
A Case Study in Deep Product Knowledge
Stitch Fix is one of a handful of leaders that has demonstrated the power of deep product knowledge. In fact, their bet is that the future of apparel retailing lies in understanding product attributes, matching that knowledge with a knowledge of customer behavior, layering on data science to understand what drives each individual customer and then using humans to make recommendations.
It seems like a lot, but Stitch Fix has made it scalable, and that set up makes sophisticated yet organic personalization possible. Essentially, it allows Stitch Fix to create a highly individualized experience from the top down (including what communications customers receive or don’t receive as well as the products and offers featured in those messages) in a way that makes sense to customers. It also makes the idea of blasting out marketing messages seem ridiculous, since Stitch Fix has a strong understanding of its customers’ preferences.
And Stitch Fix isn’t the only one who’s gotten this product knowledge right. Consider Spotify and Netflix. They might not be retailers, but they do have products in the form of movies, TV shows and music, and they’ve taken a very similar approach to understanding product attributes and customer behavior and pairing the two together.
Of course these are examples of companies with massive data science teams that have built their own technology to make this possible, but that doesn’t mean this type of model is unattainable without those resources. Quite the contrary, retailers like Teleflora and Discount School Supply have made significant headway in this area by investing in third party technology that can bridge this gap.
For example, let’s say you want to run a campaign promoting a specific line of shirts or pants. Instead of targeting all of your customers or even past purchasers, you can combine your product and customer knowledge to create a more personalized experience. Specifically, you can time the send of the email based on when customers tend to make their next purchase and include dynamic product recommendations that populate based on customers’ product affinities.
What Does a Deep Knowledge of Your Products Make Possible?
Of course just because something works for another retailer doesn’t mean it will work for your needs, but the fact remains that having a deep product knowledge makes it possible to tackle several of the efforts that top retailer’s wishlists. For instance, if you had a deep knowledge of your products and could pair that knowledge with customer behavior, you could do things like:
1) Improve Your Recommendation Engine
At its core, personalization in retail is all about matching customers to the products that excite them. Today, that effort centers largely around making personalized product recommendations within emails and advertisements across channels. But for many retailers, these recommendations are somewhat arbitrary. They might be based on similar items (e.g. showing someone who bought shirts more shirts) or pairs (e.g. showing socks to someone who bought shoes).
However, a deep product knowledge can make a significant difference when it comes to these recommendations. When you combine a deep knowledge of product attributes with insight on customer behaviors and overlay the right data science, you can make a wider variety of recommendations and those recommendations will be far smarter. In terms of variety, you can make recommendations based on factors like common co-view, co-cart and co-purchase combinations, popularity in certain regions, specific color or size preferences… the possibilities are really endless.
2) Personalize Your Email Mix
A strong recommendation engine is a solid start when it comes to delivering a great personalized experience, but it’s only the beginning. The most sophisticated marketing teams don’t just think about personalization as getting recommendations right within an individual email (though that in and of itself is a win). Instead, they look at the bigger picture and they view that email as one piece of a larger customer journey that they can also personalize.
When it comes to personalizing your email mix, you need to think about both what emails you do send to customers and what emails you don’t send them (and from there you can personalize what’s inside of those emails). It’s a lot to keep track of, but understanding how customers interact with your products can go a long way to help inform that mix. For example, if your merchandising team asks you to promote a line of vests, instead of sending that email to everyone, you might only send it to customers who have demonstrated an interest in vests.
3) Empower Store Associates
As we move toward a world in which digital and physical are blurring, it becomes increasingly important to have a seamless experience as consumers move between online and offline retail. As a result, you need to ensure your store experience is as personalized as your eCommerce experience — and that requires empowered store associates.
In turn, empowered store associates require easily accessible data on both products and customers so that they can react quickly and intelligently when it comes to answering questions and helping customers shop. With the right combination of deep product and customer data at their fingertips, store associates can become something like the old fashioned shop keepers (or even the small boutique owners of today) who know exactly which products each customer wants.
4) Get the Product/Store Mix Right
We might be moving toward a world where online and offline blend together, but the fact remains that customers are different in every market. When it comes to eCommerce, your market is the world, but in the realm of physical retail, you still only have a subset of the market, and in many cases geography can play a role in what customers want.
Given the impact of location on product interest, the more you can understand your products and how they sell through in different areas (whether that’s in physical stores or even online by looking at where eCommerce customers are located), the better you can make strategic decisions about your product/store mix.
5) Build a Faster Supply Chain
Fast fashion has put a squeeze on the apparel space by shortening supply chains. Since the rise of fast fashion, it’s no longer acceptable for retailers to start showing cold weather looks in August. In apparel and beyond, the new imperative for retailers is to stay in the here and now, and the key to doing so lies in getting access to real-time data.
As Retail Dive puts it: “Immediate feedback and speed to market have become critical to keeping a constantly fresh cycle of products on the virtual and physical shelves… Retailers and brands these days are falling over each other to collect as much data as possible with the hope that the more they know about the consumer, the more engagement and brand loyalty they’ll build.” It’s the same story once again, with the ability to layer data about consumer engagement on top of a deep knowledge about product attributes what separates the winners from the rest of the pack.
Are You Ready for the Year of the Product?
Leaders like Stitch Fix, Netflix and Spotify have set the tone when it comes to what you can do with a deep knowledge of your products and they’ve made clear that having that product knowledge can unlock a lot of what marketers have wanted to do for years when it comes to personalization in retail.
Best of all, retailers like Teleflora and Discount School Supply are already making headway in this area and proving that you don’t need an army of data scientists at your beck and call to make all of this possible. But you do need the right technology on your side. Are you prepared?
To learn more about what the Discount School Supply marketing team is doing, click here.