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
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 e-commerce 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 e-commerce
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
For most e-commerce 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.
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 e-commerce sites, there is no bigger
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.