Ever since the early days of Amazon, online retailers have increasingly recognized the selling power of integrating personalized product recommendations into the customer journey. And, for a while, the rules for recommending were pretty basic. If a consumer added to cart or expressed affinity for a particular shirt, the resulting recommendation might be a similar shirt and/or matching pants. If interest was shown for certain book, books by the same author or genre were presented for consideration. 15 years later, recommendation capabilities and data sources have multiplied—along with consumer expectations for relevancy. If you are unsure as to where you stand compared to others in the retail recommendation game or are considering integrating more sophisticated recommendation processes into your online retail experience, these are 10 recommendation “must dos” no retailer should overlook.

1. THE 10 THINGS EVERY ONLINE
RETAILER MUST DO TO MAXIMIZE
PRODUCT RECOMMENDATIONS

2. Ever since the early days of Amazon, online
retailers have increasingly recognized the selling
power of integrating personalized product
recommendations into the customer journey.
And, for a while, the rules for recommending
were pretty basic. If a consumer added to cart
or expressed affinity for a particular shirt, the
resulting recommendation might be a similar shirt
and/or matching pants. If interest was shown
for certain book, books by the same author or
genre were presented for consideration. 15 years
later, recommendation capabilities and data
sources have multiplied—along with consumer
expectations for relevancy.
Today, consumers are completely immersed in
an omni-channel world. Not only do they expect
a personalized experience across all of their
devices, they respond more favorably to retailers
who guide their brand experience in a helpful, yet
unobtrusive way.
Of course, all retailers know by now that making
recommendations that are noticed, valued, and
acted upon takes more than simply matching
styles or suggesting a complimentary product
before checkout. Recommendation technology
has become a pretty crowded space and there’s a
lot of advice out there on recommendation “bests.”
If you are unsure as to where you stand compared
to others in the retail recommendation game or
are considering integrating more sophisticated
recommendation processes into your online retail
experience, these are 10 recommendation “must
dos” no retailer should overlook.
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3. ALWAYS GO ABOVE THE FOLD
kk
Does that mean nothing important
should go below the fold?
No, scrolling, especially on mobile devices is
important to design usability. But, according to
well-respected web usability consultant, Jakob
Nielson, “It’s as if users arrive at a page with a
certain amount of fuel in their tanks. As they drive
down the page, they use up gas, and sooner or
later they run dry.”
How can you be sure your recommendations
always go above the fold with so many devices
and variations in screen size? Integrating
responsive web design into your recommendation
practices will ensure that your recommendations
will be above the fold, every time for every
device—encouraging existing and potential
customers to keep clicking forward.
The placement of recommendations on a site
page is critical both to a shopper’s ability to
discover a product and a retailer’s success
in driving them along a pre-determined path
to conversion. If the phrase “above the fold”
as it applies to web design is new to you, it
simply means the portion of a web page that
is visible in a browser window when the page
first loads. Above the fold is prime real estate
for recommendations not only because the
user is not forced to scroll, but also because it
is where your shoppers’ eyeballs’ instinctively
go. You might be thinking that sounds right, but
how do we know? The proof, just like nearly
everything these days, is found in the data.
Through the use of eye tracking software, web
strategists have confirmed the importance of
above the fold by tracking consumer attention
as they follow content further and further down
the page using gaze plots. What did they find?
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Users spent 80.3 % of their time on webpages above
the fold, and 19.7 % below
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4. A customer who made a recent purchase and
is looking at your return policy has a different
motivation, shopper profile and need than
someone who found their way to your site by
clicking an ad. Each of these is an example of
what is becoming known as a micro-moment.
Cookie-cutter recommendation logic will do little to
optimize how well you drive a single visitor forward
or move the ROI needle very far to the right.
Your recommendation engine is likely getting a
massive amount of customer and product data to
crunch on. The level of success achieved by 1:1
personalization depends on the engine being able
to use this data to create as many logic variations
possible. Varying logic can answer questions like:
kk
- How do you know when to offer a more
expensive item?
- What page should a complementary item
appear on?
- Who is a good candidate for cross selling?
- What product should you offer to this
customer in this moment?
A deep set of logic patterns allows you to not only
customize recommendations to an exact customer,
it enables you to help that customer keep
clicking forward by providing recommendations
in the context of the micro-moment. There are
easily 100 logic patterns available through some
recommendation engines right now including:
- What others are looking at right now
- Complete the look
- Override the recommendations system and
force specific products
- Include recommendations that only have a
margin over x%
- Include or exclude recommendations by brand
Understanding the context of a customer’s
decision path along with sophisticated logic
patterns that can be applied to help drive their
decision is huge to continued recommendation
optimization.
MAKE SENSE IN THE MICRO-MOMENT2
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5. Some brands make the mistake of
recommending to customers only the
inventory the data says they are currently
interested in or would most likely buy. Retailers
with the most successful recommendation
strategies are able to factor both buyer
behavior and their own sales initiatives into
the recommendation equation. For example,
a visitor to an online outdoors store has
indicated their intent to buy various camping
supplies. If the store is on a push to promote
specific brands, they can tag certain supplies
to be recommended first, over others.
The ability to coordinate customer intent with retail
sales goals also includes using recommendations
to move seasonal inventory. A customer browsing
online from their home in Florida is more likely
to purchase lawn chairs out-of-season than one
living Minnesota. One final example of making
personalized recommendations that optimize
the financial benefits to brands is recommending
higher-priced inventory over products marked
more modestly. Though the customer may still
choose to search inventory by price, other
searches can be set to promote the higher value
products first.
TIE SALES INITIATIVES TO
RECOMMENDATION LOGIC
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6. Recommendations aren’t something you do just once. It’s important that recommendations are present
and relevant from product discovery to conversion. The recommendations given at checkout should
be different than those given when a shopper first arrives. Both sets of recommendations should also
differ from those displayed to a shopper returning to their previously abandoned cart.
RECOMMEND ON EVERY PAGE4
For repeat visitors, recommend products
that they have viewed but not purchased.
Another option is showing them products
relevant to their previously tracked
preferences including brand, color, price,
etc.
For new visitors, show top-selling products.
Though you don’t have any personal data
yet to go on, if your customers like it there’s
a pretty good chance they’ll like it too.
HOME PAGE
Personalized recommendations based on past
search behavior and/or items that have been
recently viewed but not purchased.
People here are most likely your browsers with a
buying intent that is questionable. Showing the
most popular products by category is your best
recommendation bet.
SEARCH RESULTS PAGE CATEGORY PAGE
Here your shoppers are closer to conversion.
More like these and viewed also viewed
encourages customers to add to cart. Upsells of
higher-priced items or upgraded configurations
are also optimal recommendations.
At the final stage, show recommendations
with complementary products to decrease the
likelihood of cart abandonment.
PRODUCT DETAIL PAGE SHOPPING CART PAGE
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7. ADD RECOMMENDATIONS TO EMAIL
kk
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Recommendations are no longer just about the
upsell; they help retailers forge a bond with
consumers by personalizing the entire customer
experience from product discovery to checkout.
Though your website is your primary pallet for
producing action-instilling recommendations,
pairing recommendations with emails is a tactic
that often gets overlooked. Key scenarios that
should prompt personalized recommendations via
email include:
- Cart abandonment
- Browse abandonment
- Purchase follow-up
- Order confirmation
For example, when you send email that says,
Good news! Your order has shipped; take
advantage of a customer’s attention by providing
additional retail recommendations. At first glance,
these efforts might seem like a pushy way to
squeeze as much immediate revenue as you
can out of the consumer. But done right, adding
email recommendations isn’t about how much a
business can take—it’s about continually giving
consumer subtle cues that you know them and
you care.
You may be thinking: We already
use emails to thank customers,
confirm purchases and update them
orders. What’s further personalizing
one email really going to do?
Personalized emails improve
click-through rates by14% and conversionrates by 10%.
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8. Leveraging mcommerce (mobile commerce), as part of your
retail strategy has never been more important, therefore, your
recommendations need to fit well into mobile. With over 10
billion mobile-connected devices in use, representing 80 million
consumers in the US alone, mcommerce is a revenue sweet spot for
retailers. First, let’s consider why it’s so important to make sure your
recommendation operations are mobile ready:
BE MOBILE-FRIENDLY6
75% of shoppers use a
mobile phone inside a physical
store. There is considerable value
in tying the mobile and in-store
experience together.
67% of users are more
likely to buy from a mobile friendly
site. Being mobile-friendly means
the ability to provide the omni-
channel experience today’s retail
customers reward.
52%are less likely to engage
with a business after a bad mobile
experience. Customer retention is highly
dependent on mobile-friendliness.
By 2016,
mobile ad spend with
be 5x the ad share
of 2012. Ad dollars
follow audiences. The
audience you want to
engage today is mobile.
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9. kk Mobile-friendly means having the ability to
leverage responsive design that allows an
easy, intuitive and relevant mobile experience
whether the customer is comparing your
products to a competitor’s, adding items to
a wish list or sending a special message at
checkout.
kk Mobile-friendly recommendations also require
that you can provide consistency access every
digital device a customer uses so their omni-
channel shopping journey is one continuous,
fluid motion.
kk Finally, optimizing recommendations for mobile
relies on having access to data that can give
you revenue-changing insight on customer
behavior across every channel.
Making your recommendations mobile
doesn’t mean simply hoisting up a “we
are mobile” flag or adding those QR
codes consumers like to use for in-store
discounts.
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10. Winning at recommending means delivering the most relevant results.
These relevant results rely on your ability to continually absorb,
interpret and take action on incoming streams of data. And you can
always be better.
DESIGN WITH CUSTOMER
SEGMENTATION IN MIND7
A recommendation system that not only provides relevant results but
includes tools to continually define and refine customer segments
based on data already being collected is another recommendation
must for companies who want to remain competitive. These “bonus”
tools provide segmentation insight for both customers and products.
CUSTOMER SEGMENTATION EXAMPLES:
• Your best customers by spend using any customized time frame
• Customers who have bought things only after seeing a certain
number of products
• Customers who saw a certain number of products but did not buy
• Customers who showed the highest intent, yet left items in their cart
PRODUCT SEGMENTATION EXAMPLES:
• Top selling products over a specific period of time
• The products that customers have viewed first before they bought
something else
• A list of products that were viewed but never purchased
• Products left behind in the shopping cart over a specific time period
• Products people tend to consider together
The most versatile of these tools allow marketers can create just
about any behavior-based product or customer segment that they
want. And just how can you use these newly created segments to
your advantage?
• Send special offers to customers within a given segment.
• Remarket to your customers who have been looking but not buying.
• Make way for new inventories by clearing out the old.
• Increase stock on new items that are showing a lot of initial sales
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11. Don’t let yourself fall into the category of retailers
who fine-tune their recommendations and then
leave them on autopilot. Though this fine-tuning
may result in (for example) a 5% increase in
revenue, you’ll never know what you might be
leaving on the table unless you continually test
the validity of your original assumptions.
DON’T SET IT AND FORGET IT8
Product recommendation engines are a powerful
marketing tool, but its merchandising potential
often goes unrecognized because marketers don’t
take advantage of its capabilities in the beginning
and fail to check up on their recommendation
process regularly to ensure optimal performance.
Retailers should A/B test and tweak their recommendations every few months. Testing is a very easy
(but often forgotten) way to optimize product recommendations and increase a retailer’s average order
value (AOV). The title is an important area in the recommendation section and should also be tested to
increase AOV. For example:
SUGGESTED TITLES TO TEST:
80%people who viewed above
product also viewed these
80%people who
viewed above product also
viewed these
80%people who
bought above product also
bought these
80%people who
viewed above product bought
these
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12. Following the Amazon example, your homepage is a place where significant recommendation gains
can be made. Instead of showing the same home page to everyone who walks through a retailer’s
digital doors, the homepage is a prime place to imprint consumer mindset with personalization cues.
Depending on the customer and their most recent behavior, your homepage recommendation blocks
could include:
ADD MULTIPLE RECOMMENDATIONS
ACROSS YOUR HOMEPAGE
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Best sellers this week
Items still in your card
People who are like you also liked these products
Based on your recent purchases, we think you will also like
Why is the homepage such important real estate for recommendations? Often someone coming to
your homepage is a new visitor you know nothing about. They may spend time on your page, leave
and then return. It is this returning customer you are appealing to with recommendations that silently
communicate, welcome back, we saved your place!
Many recommendation engines are able to continually use customer intelligence to ensure the
homepage always keeps up with a customer’s place in their buying journey and can enjoy a fresh
homepage experience every time. This includes the ability to alter page elements, alternate banners,
show them items complimentary to what they left in cart or recommendations based on previous
searches.
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13. CAPITALIZE ON THE OMNI-CHANNEL10
The most optimized recommendations accurately recognize customers wherever they are, according
to what digital device they are on and what behaviors they have previously taken across channels. The
ability to effectively execute on the omni-channel relies on a retailer’s capacity to create such a unified
experience.
Omni-channel marketing is the key to getting “micro-moments” right. Recommendation engines today
exist that help retailers determine and deliver, without a doubt, the right experience and message for
each of these potentially revenue-impacting micro-moments. Such recommendation engines help
retailers:
• Create single-view user profiles that consolidate data from multiple channels and devices in addition
to offline information to enable marketing to deliver more value to each person
• Take the most relevant action or deliver the most relevant message in real-time
• Manage adaptive content efficiently
WHAT VALUE CAN DOING RIGHT BY THE OMNI-CHANNEL REALLY BRING
RETAILERS?
• Omni-channel personalization can increase sales conversion by as much as 70%
• Companies with extremely strong omni-channel customer engagement retain on average 89% of their
customers, compared to 33% for companies with weak omni-channel customer engagement
• 56% of consumers say they would be more inclined to use a retailer if it offered a good personalized
experience
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14. There’s nothing simple about online retail strategy anymore. Consumers demand personalization at
every turn and reward retailers who can execute a flawless omni-channel experience. Though the data
to do it is out there, retailers must continually equip themselves with tools that can quickly and efficiently
leverage customer behavior and profile information to draw customers nearer.
Even these 10 must dos to maximize product recommendations will eventually be revised and added
to along with the ebb and flow of new ideas, increasing data and emerging consumer expectations.
Whether you are a retailer just getting onboard or have been riding the personalization trend for
awhile, what is most important is using all of the tools at your disposal right now to optimize product
recommendations, draw more value from 1:1 relationships and increase recommendation revenue
potential.
IF YOU’D LIKE TO LEARN MORE ABOUT MAXIMIZING PRODUCT RECOMMENDATIONS:
REQUEST A DEMO
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