Advanced Merchandising Analytics for Retail


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If you’re an online merchandiser, how many ways to move product do you really have? You have price, of course. There’s no doubt that price is the single most potent mechanism for controlling sales. By adjusting price, you can cripple sales or send products rushing out the door. Sadly, however, you rarely have any real freedom around price. Between your cost-of-goods and your competitors prices, there is often but tiny band within which you have any real flexibility. And, of course, every cent you take off a product’s price is one cent less for the bottom line.

Product obviously matters too. We don’t all have the luxury of selling iPhones, and if we did, we’d all be competing against each other anyway. It’s the same with brand.

From a merchandisers perspective, Product and Brand are essentially exogenous variables – someone else’s responsibility.

What merchandisers need are levers within their control that can be used to improve sales.

In my old direct marketing days, there were three types of levers: offer, targeting and creative. Offer was always the most important. Targeting was next in line. Creative was last.

It’s the same on the Web.

The quality of your offer – be it price, product, or incentives like free shipping or no sales tax – is by far the most important merchandising lever you have. These are also the most expensive levers you have.

Targeting – the quality of audience you message to and the specificity of your message to that audience – is next. Targeting on the Web can be problematic, but it’s still a powerful lever and one with significant room for improvement at most online shops.

But to the merchandiser, the most common set of levers and the most easily adjusted are around creative. You can’t expect these levers to have an impact equal to Offer and Targeting. On the other hand, where they have impact, they have it without cost. Improved creative has no bottom-line cost and sacrifices no audience opportunity.

So what are the merchandising levers around creative? Product description and imagery obviously top the list of possible candidates. Unfortunately, from an analytics perspective, they can be almost impossible to measure and optimize. Which drives us one more rung down the food-chain to the types of merchandising levers that surround most products in the online world. These include call-outs of discounts, product ratings and reviews, visual highlights of offers, warnings about low-stock or inventory (only 3 seats left!), and other visual tricks-of-the-trade designed to steer consumers to specific products or improve conversion rates.

A few years back, most of these techniques were confined to product detail pages. That’s changed. Most eCommerce sites these days expect to do most of their merchandising work before a consumer ever gets down to the detail page. With the increasing use of faceted search, hover-overs, and direct to cart from list pages, it’s become quite common to purchase products without ever viewing product detail pages.

Not surprisingly, merchandising levers have ventured up to these product list pages as well. They are part of the “creative” attached to each product. And when that product is assembled onto a list or aisle page, they come right along with.

Which is where things start to get interesting.

On a product detail page, it should be quite easy to measure the impact of adding merchandising levers like discount highlights, high product ratings, and free delivery banners:

Merchandising Blog1

It should even be pretty easy to measure whether the addition of multiple levers has an incremental effect or whether and when there are diminishing returns. Of course, to do that you’ll have to capture the presence and nature of these levers in your Web analytics tool. That’s not something that’s always or even often done, but at least on Product Detail Pages it can and should be done.

On the other hand, nothing is simple or straightforward when you move the same analysis up one level to Product List, Aisle or Category Pages.

Merchandising Blog2

On a page like this, we see that that almost every single product as at least one merchandising call-out and some have two or three.

When pages are multi-product, the analysis of merchandising levers is far more complex. We know, intuitively, that listing discounts for every product is going to work less well when the products are all shown together than when taken individually.

In other words, there’s a kind of Laffer curve when it comes to merchandising levers on product list pages.


Some number of merchandising call-outs will optimize page conversion just as some tax rate will maximize total revenue collected. With taxes, the only two numbers you can be certain won’t create a maxima are 0% and 100%. Any other combination might be a maxima depending on the exact shape of the curve (and, of course, economists still hotly debate the actual shape – not the existence – of this curve).

It’s the same for merchandising levers. Some number between none and every possible lever will yield a page maximum. Your job is to find that point by defining the curve.

In other words, the basic measurement of success changes when it comes to a Product List page. Merchandising levers will have two distinct and measurable impacts. The first impact will be on the distribution of success across products on the page. You expect a merchandising lever to shift the distribution of success by emphasizing one product. Naturally, this will come at the expense of other products. So the second measure of success is the overall success of the page in converting to any product (and the overall success of the page in converting to total revenue or profit).

In addition, the product list introduces an element of direct comparison. Is one discount smaller than another? It can make a difference in the effectiveness of each when the two are placed side by side. This comparative element is particularly important for Product Ratings and Number of Reviews. There may be virtually no difference in Product Detail impact between a product with 25 ratings and 4.4 score and a product with 230 ratings and a 4.5 score. But put the two side-by-side in a product list, and the impact may be considerable.

With direct comparison comes another complicating factor: the distance between two products. In a product list, pages that are side-by-side will have a strong merchandising relationship that products that are at opposite ends of the page.

And the fact of distance and comparability create a whole new set of implicit merchandising variables; these are levers that don’t exist on product detail pages but can be vitally important on multi-product pages. For example, price on a product detail page is just price. But put the Price of Product A next to the price of Product B and you’ve created an inherent comparison between the two. So product list pages have implicit merchandising variables like the price difference between the most expensive and least expensive products displayed on the page. These levers matter.

This can get tremendously complex. When you consider that a typical product might have between 0-6 merchandising levers and that you’re trying to ascertain the relationship between the presence, density and position between all of those factors PLUS the implicit merchandising levers like price range, the possible permutations on a typical product page might number in the millions.

So instead of trying to solve for every possible page, your best bet is to refine a set of merchandising rules similar to the Laffer curve. You want to understand what types of call-out work best together (with the least cannibalization). You want to understand the optimum density for each type of call-out. And you want to understand the optimums for implicit merchandising levers like price range.

With these rules, you can construct merchandising rules for product set pages that are likely to be, if not optimum, at least superior.

Of course, to create these rules takes analysis and to do analysis takes data. And therein lies the rub. Because most eCommerce sites simply don’t collect ANY merchandising-lever information on product list pages. So there’s a pretty good chance that you don’t have ANY real opportunities for analysis.

In my next post, I’ll explain why that’s so and why Semphonic partnered with Cloudmeter on white paper about merchandising analytics.

[In the meantime, I hope you’ll register for our webinar in early September]

Republished with author's permission from original post.

Gary Angel
Gary is the CEO of Digital Mortar. DM is the leading platform for in-store customer journey analytics. It provides near real-time reporting and analysis of how stores performed including full in-store funnel analysis, segmented customer journey analysis, staff evaluation and optimization, and compliance reporting. Prior to founding Digital Mortar, Gary led Ernst & Young's Digital Analytics practice. His previous company, Semphonic, was acquired by EY in 2013.


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