Today's digital world is one where virtual aisles can quite literally be re-configured for every single shopper, where offers can be constantly re-shuffled and where online reviews and ratings can dominate product marketing materials.
Even so, finding the right mix of levers to move a product most efficiently for any given customer can be daunting.
The range of merchandising levers and product mix opportunities has never been greater, but there has been no corresponding increase in the sophistication or capabilities of the tools used to measure and optimize them. Web analytics systems are still, almost without exception, confined to page-based views of data with limited or no analysis of merchandising variables.
The limitations of page-based analysis
Perhaps the most striking developments in the growing sophistication of ecommerce sites and the shoppers who visit them, have been the dramatic increase in the importance of product aisle and product search pages. These pages now do most of the heavy lifting when it comes to merchandising and often contain a rich mix of products, customer offers and other purchasing drivers.
The effectiveness of the page is a function of many internal merchandising levers, such as the products actually shown, the order of those products, the offers made and the mix of prices, discounts, presence of ratings and reviews on the page. It's also the mix of call-outs and calls-to-action displayed by products.
None of these are typically captured by the Web analytics tools whose apparent function it is to assist online marketers in making informed decisions.
The trend toward product set marketing
The vast majority of heavy merchandise lifting on ecommerce sites is no longer concentrated on the product detail page. Indeed, almost all the important drivers of consumer choice now come before it on pages at least one level up.
In a search-driven world, the product contents of a facet page may be beyond optimization, except for a small set of high-demand searches. For fixed aisle and category pages, discount and offer pages and a host of other relatively static pages, the actual product mix on the page is critical to the page's success.
The products themselves, however, are only a part of the merchandising equation. The price mixture on the page has its own specific impact, as does the associated product ratings and reviews - both of which are a curse and a blessing to the online seller.
Merchandising analytics explained
These product list pages have an almost daunting array of possible levers to pull. But options are only valuable when you have good methods for choosing among them. As an online retailer, it is imperative to fully understand product set pages, determining:
- Optimal price spread (highest to lowest) for a product set page
- Optimal gap between the highest and the average price of products on the page
- Optimal density of merchandising call-outs on the page, such as discounts, banners and highlights
- Optimal value spread between discounts offered on a product set page
- Optimal density of discounts offered on a product set page
- Optimal position for the largest discount on the product set page
- Relationship between largest absolute and largest relative discount on a product set page
The answers can pave the way for a truly significant improvement in product set merchandising, but how do you answer them? The first step is in the collection of the necessary data to measure and analyze differential performance. That means knowing exactly what a shopper saw when they viewed a product list page.
To do this, you need to create a method for the layout of a page.
You need to capture the areas on a page that contain products, the grid layout of the those areas, the products (SKUs) and their price, discount, offers, rating, number of reviews, type of merchandising call-outs and position within the area. With this data feed, you can to begin answering those merchandising questions.
By focusing on a subset of the key merchandising levers (e.g. density of call-outs or discount increments), it's possible to develop data-driven rules to optimize overall page merchandising performance. Controlled testing on a fixed product set can also produce the data necessary to optimize across multiple merchandising variables. In areas like search, however, controlled testing is generally impossible. Instead, you'll need to analyze large numbers of product list combinations to create merchandising rules that can help drive the search results logic.
So what is the right balance between analysis and testing? There isn't one answer. Your best strategy is to start with a comprehensive analysis that identifies the most important merchandising levers and initial testing strategies. By following up with controlled tests, you can validate the results and further refine your merchandising hypotheses.
For most ecommerce sites, answering even the basic questions enumerated above, provide significant opportunities for site improvement and competitive advantage. Through careful analysis, you may find the best mix of levers to drive optimal merchandising performance on your critical multi-product pages.
Optimization opportunities
Pages with multiple products displayed are the single most important and impactful merchandising pages on the site. The temptation, however, is to treat these pages as if they were product detail pages and simply add more and more merchandising levers to each product. This doesn't work. Unlike product detail pages, adding merchandising levers to the products on a multi-product page are more likely to shift the distribution of product clicks than to drive superior overall performance. Indeed, you may easily be shifting visitors from more to less profitable products. What's more, overuse of merchandising levers can create pages with "wall-to-wall" discounts that can erode brand perception and diminish the effectiveness of your merchandising strategy.
Careful use of analytics can help you understand the optimal density and type of merchandising levers, as well as the optimal mix of products on the page (by price, ratings, etc.). For most ecommerce sites, there is no bigger optimization opportunity.
About the Author: Bringing more than 20 years of experience in decision support, CRM and software development, Gary Angel co-founded Semphonic and is its President and Chief Technology Officer.
