Series: Three Pillars of Search Quality in eCommerce
In the first part of our series, we learned about Search Quality dimensions. We then introduced the Findability metric, and explained the relationship of this metric on search quality. This metric is helpful when considering how well your search engine handles the information retrieval step. Unfortunately, it completely disregards the emotionally important discovery phase. Essential for both eCommerce, as well as retail in general. In order to better grasp this relationship we need to understand how search quality influences discovery and inspiration.
What is the Secret behind the Most Successful high-growth Ecommerce Shops?
If we analyze the success of high-growth shops, three unique areas set them apart from their average counterparts.
What Separates High-Growth Retail Apart from the Rest?
1. Narrative: The store becomes the story
Your visitors are not inspired by the same presentation of trending products every time they land on your site. What’s the use of shopping if a customer already knows what’s going to be offered (merchandised) to them?
Customers are intrigued by visual merchandising which is, in essence, brand storytelling. Done correctly, this will transform a shop into an exciting destination that both inspires, as well as entices shoppers. An effective in-store narrative emotionally sparks customers’ imagination, while leveraging store ambience to transmit the personality of the brand. Perhaps using a “hero” to focus attention on a high-impact collection of bold new items. Or an elaborate holiday display that nudges shoppers toward a purchase.
Shopping is most fun, and rewarding, when it involves a sense of discovery or journey. Shoppers are more likely to return when they see new merchandise related to their tastes, and local or global trends.
2. Visibility: What’s seen is sold (from pure retrieval to inspiration)
Whether in-store or online, visibility encourages retailers to feature items that align with a unique brand narrative. All the while helping shoppers easily and quickly find the items they’re after. The principle of visibility prioritizes which products retailers push the most. Products with a high margin or those exclusive enough to drive loyalty, whether by word of mouth, or social sharing.
Online, the e-commerce information architecture, and sitemap flow, help retailers prominently showcase products most likely to sell. This prevents items from being buried deep in the e-commerce site. Merchandisers use data analytics to know which products are most popular and trending. This influences which items are most prominently displayed. These will be the color palettes, fabrics, and cuts that will wow shoppers all the way to the checkout page.
So why treat search simply as a functional information retrieval tool? Try rethinking it from the perspective of how a shopper might look for something in a brick and mortar scenario.
3. Balance: Bringing buyer’s and seller’s interests together in harmony
In stores and online, successful visual merchandising addresses consumers’ felt needs around things like quality, variety, and sensory appeal. However, deeper emotional aspects like trust are strongly encouraged through online product reviews. These inspire their wants: to feel attractive; to be confident, and hopeful. We can agree that merchandisers’ foremost task, is to attend to merchandise and the associated cues to communicate it properly. It’s necessary to showcase sufficient product variety, while at the same time remaining consistent with the core brand theme. This balancing act requires they strike a happy medium between neither overwhelming nor disengaging their audience.
An example for the sake of clarity:
Imagine you are a leading apparel company with a decently sized product catalog. Everyday, a few hundred customers come to your site and search for “jeans”. Your company offers over 140 different types of jeans, about 40 different jeans jackets and roughly 80 jeans shirts.
Now the big question is: which products deserve the most prominent placement in the search result?
Indeed this is a very common challenge for our customers. And yet all of them struggle addressing it. But why is it so challenging? Mainly because we are facing a multi-dimensional and multi-objective optimisation problem.
- When we receive a query like “jeans”, it is not 100% clear what the user is looking for. Trousers, jackets, shirts, we just don’t know. As a result, we have to make some assumptions. We present different paths for him to discover the desired information, or receive the inspiration she needs. In other words, for the most probable product types “k”, and the given query, we need to identify related products.
- Next we find the most probable set of product-types. Then, we need to determine which products are displayed at the top for each corresponding set of products. Which pairs of jeans, jeans jackets and jeans shirts? Or again in a more formal way: for each product type “k” find the top-”n” products related to this product-type and the given query.
Or in simple words: diversify the result set into multiple result sets. Then, learn to rank them independently.
Now, you may think this is exactly what a search & discovery platform was built for. But unfortunately, 99% of these platforms are designed to work as single-dimension-rank applications. They retrieve documents for a given query, assign weights to the retrieved documents, and finally rank these documents by weight. This dramatically limits your ability to rank the retrieved documents by your own set of, potentially, completely different dimensions. This is the reason most search results for generic terms tend to look messy. Let’s visualize this scenario to clarify what I mean by “messy”.
You will agree, the image on the left-hand side, is pretty difficult for a user to process and understand. Even if the ranking is mathematically correct. The reason for this is simple: the underlying natural grouping of product types is lost to the user.
Diversification of a search for “jeans”
Grouping products based on visual similarity
You may argue that the customer could easily narrow the offering with facets/filters. Data reveals, however, that this is not always the case – even less so on mobile devices. The big conundrum is that you’ve no clue what the customer wants. To be inspired, to be guided in his buying process or just to quickly transact. Additionally, you never know for sure what type of customer you are dealing with. Even with the new, hot, latest and greatest, stuff called “personalization” – that unfortunately fails frequently. Using visual merchandising puts us into conversation with the customer. We ask her to confirm her interests by choosing a “product type”. Yet another reason why diversification is important.
Still not convinced, this is what separates high-growth retail from the rest?
Row-based diversification – explore product catalog
If you have ever tried creating such a page, with a single search request, you know this is almost an impossible task. Not to mention trying to maintain the correct facet counts, product stock values, etc.
However, the approach I am presenting offers so much more. This type of result grouping also solves another well-known problem. The multi-objective optimization ranking problem. Making this approach truly game-changing.
What’s a Multi-Objective Optimization Problem?
Never heard of it? Pretend for a moment you are the customer. This time you’re browsing a site searching for “jeans”. The type you have in mind is something close to trousers. Unaware of all the different types of jeans the shop has to offer, you have to go rogue. This means navigating your way through new territory to the product you are most interested in. Using filters and various search terms for things like color, shape, price, size, fabric, and the like. Keep in mind that you can’t be interested in what you can’t see. At the same time, you may be keeping an eye on the best value for your money.
We now turn the table and pick up from the seller’s perspective. As a seller, you want to present products ranked based on stock, margin, and popularity. If you run a well-oiled machine, you may even throw in some fancy Customer Lifetime Value models.
So, our job is to strike the right balance between the seller’s goals and the customer’s desire. The methodology that attempts to strike such a balance is called the multi-objective optimization problem in ranking.
Let’s use a visualization to illustrate a straightforward solution to the problem, by a diversified result-set grouping.
Row-based ranking diversification
Interested in how this approach could be integrated into your Search & Discovery Platform? Reach out to us @searchHub. Our Beta-Testphase for the Visual-Merchandising open-source module, for our OCSS (Open Commerce Search Stack), begins soon. We hope to use this to soon help deliver more engaging and joyful digital experiences.
High-Street Visual Merchandising Wisdom Come Home to Roost
This is all nothing new, rather it’s simply never found its way into digital retailing. For decades, finding the right diversified set of products to attract window shoppers, paired with the right location, was the undisputed most important skill in classical high street retail. Later, this type of shopping engagement was termed “Visual Merchandising”. The process of closing the gap between what the seller wants to sell and what the customer will buy. And of course, how best to manufacture that desire.
Visual merchandising is one of the most sustainable, as well as differentiating, core assets of the retail industry. Nevertheless, it remains totally underrated.
Still don’t believe in the value of Visual Merchandising? Give me a couple of sentences and one more Chart to validate my assumptions.
Before I present the chart to make you believe, we need to align on some terminology.
Product Exposure Rate (PER): The goal of the product exposure rate is to measure if certain products are under- or over-exposed in our store. The product exposure rate is the “sum of all product views for a given product” divided by “the sum of all product views from all products”.
Product Net Profit Margin (PNPM): With this metric, we try to find the products with the highest Net Profit Margin. Please be aware: it’s sensible to include all product related costs in your calculation. Customer Acquisition Costs, cost of Product returns, etc. The Product Net Profit Margin is the “Product Revenue” minus “All Product Costs” divided by the “Product Revenue”.
Now that we have established some common ground, let’s continue calculating these metrics for all active products you sell. We will then visualize them in a graph.
Product Exposure Rate vs. Product Net Profit Margin
The data above represents a random sample of 10,000 products from our customers. It may look a bit different for your product data, but the overall tendency should be similar. Please reach out to me if this is not the case! According to the graph it seems that the products with high PER (Product Exposure Rate) tend to have a significantly lower PNPM (Product Net Profit Margin).
We were able to spot the following two reasons as the most important for this behaviour:
Two Reasons for Significantly Low Product Net Profit Margin
- Higher Customer Acquisition Costs for trending products mainly because of competition. Because of this you may even spot several products with a negative PNPM.
- Another reason is the natural tendency for low priced products to dominate the trending items. This type of over-exposure encourages high-value visitors, to purchase cheaper trending products with a lower PNPM. Customers to whom you would expect to sell higher margin products under normal circumstances.
I simply can’t over-emphasize how crucial digital merchandising is for a successful and sustainable eCommerce business. This is the secret weapon for engaging your shoppers and guiding them towards making a purchase. To take full advantage of the breadth of your product catalog, you must diversify and segment. Done intelligently, shoppers are more likely to buy from you. Not only that, they’ll also enjoy engaging with, and handing over their hard-earned money to your digital store. For retailers, this means a significant increase in conversions, higher AOV, higher margins, and more loyal customers.
Initially, I was going to close this post right after describing how this problem can be solved, conceptually. However, I would have missed an essential, if not the most important part of the story.
Yes, we all know that we live in a data-driven world. Believe me, we get it. At searchHub, we process billions of data points every day to help our customers understand their users at scale. But in the end, data alone won’t make you successful. Unless, of course, you are in the fortunate position of having a data monopoly.
To be more concrete: data will/can help you spot or detect patterns and/or anomalies. It will also help you scale your operations more efficiently. But there are many areas where data can’t help. Especially when faced with sparse and biased data. In retail this is the kind of situation we are essentially dealing with 80% of the time. All digital Retailers, of which I am aware, with a product catalog greater than 10,000 SKUs, face the product exposure bias. This means, only 50-65% of the 10.000 SKUs will ever be seen (exposed) by their users. The rest remain hidden somewhere in the endless digital aisle. Not only does this cost money, it also means a lot of missed potential revenue. Simply put: you can’t judge the value of a product that has never been seen. Perhaps it could have been the Top-Seller you were always looking for were it only given the chance to shine?
Keep in mind that retailers offer a service to their customers. Only two things make customers loyal to a service.
What makes loyal customers?
- deliver a superior experience
- be the only one to offer a unique type of service
Being the one that “also” offers the same type of service won’t help to differentiate.
I’m one hundred percent sure that today’s successful retail & commerce players are the ones that:
- Grasp the importance of connecting brand and commerce
- Comprehend how shoppers behave
- Learn their data inside and out
- Develop an eye for the visual
- Connect visual experiences to business goals
- Predict what shoppers will search for
- Understand the customer journey and how to optimize for it
- Think differently when it comes to personalizing for customers
- Realize it’s about the consumer, not the device or channel
I can imagine many eCommerce Managers might feel overwhelmed by the thought of delivering an eCommerce experience that sets their store apart. I admit, it’s a challenge connecting all those insights and capabilities practically. And while we’re not going to minimize the effort involved, we have identified an area that will elevate your digital merchandising to new levels and truly differentiate you from the competition.