Evergage The Future of Product Recommendations€¦ · increase average order values and drive more conversions. In the past, before the data was available for more advanced analysis,

10 Rules for Success

The Future of ProductRecommendations

THE FUTURE OF PRODUCT RECOMMENDATIONS | 3 © 2016 EVERGAGE, INC

The Future of Product Recommendations: 10 Rules For Success

Product recommendations often come to mind when thinking about digital personalization.

Most of us have experienced recommendations through e-commerce sites as consumers, and

they are the most obvious form of personalization that we can see while shopping online.

At the most basic level, recommendations provide a visitor with alternative product

options, typically based on similar products they have viewed or purchased, or what others

have viewed or purchased. When done well, recommendations are used to improve the

shopping experience, promote product discovery, boost engagement (i.e. time on site),

increase average order values and drive more conversions.

In the past, before the data was available for more advanced analysis, recommendations

were often based on hand-picked or personally curated items. E-commerce sites then began

to provide automated recommendations based on items that were typically browsed or bought

together, without consideration for an individual’s particular interests.

Today, the best recommendations leverage behavior and intent

at the individual level, taking into consideration the person’s

browsing patterns, online and in-store purchase history, aggregate

site trends, and a host of other factors.

This eBook explores what the future of product recommendations looks like, guiding you through new trends in recommendations that will determine what it will take to succeed, including the following ten rules:

Use the best data possible

Deliver recommendations in real time

Manage recommendation strategies on your own

Understand the role of content

Employ multiple channels

Leverage your product catalog without a feed

Incorporate multiple, customizable algorithms

A�ect the whole experience

Test, test, test

Make recommendations individually relevant

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The Future of Product Recommendations: 10 Rules For Success

Product recommendations often come to mind when thinking about digital personalization.

Most of us have experienced recommendations through e-commerce sites as consumers, and

they are the most obvious form of personalization that we can see while shopping online.

At the most basic level, recommendations provide a visitor with alternative product

options, typically based on similar products they have viewed or purchased, or what others

have viewed or purchased. When done well, recommendations are used to improve the

shopping experience, promote product discovery, boost engagement (i.e. time on site),

increase average order values and drive more conversions.

In the past, before the data was available for more advanced analysis, recommendations

were often based on hand-picked or personally curated items. E-commerce sites then began

to provide automated recommendations based on items that were typically browsed or bought

together, without consideration for an individual’s particular interests.

Today, the best recommendations leverage behavior and intent

at the individual level, taking into consideration the person’s

browsing patterns, online and in-store purchase history, aggregate

site trends, and a host of other factors.










Test, test, test


01 02

03 04

05 06

07 08

09 10

1. Use the Best Data PossibleIt’s not just about having any data; it’s about having the right data. Most recommendation engines today depend on clicks.

For example, if a shopper views a green shirt and then a blue shirt, the recommendation engine may show the green shirt to

other visitors who also look at the blue shirt. But suppose that shopper actually clicked on the green shirt and then immediately

clicked away, showing that he was not, in fact, interested in the green shirt; and later, he spent several minutes studying a

purple hat.

Recommendation engines of the future will recognize, in this example, that there is no relationship between the blue and green

shirts, and instead will establish a stronger association between the blue shirt and the purple hat. This insight is based on

visitors’ true engagement, a�nities and intent at the product, category, brand, price-range, you-name-it level.

But recommendation engines of the future won’t stop at just using in-depth behavioral data; they will also incorporate additional

customer data whenever possible. This includes data from in-store transactions, CRM and DMP systems, email and mobile

app activity, data from search engines and other referring sources, and on-site search activity. All of this deep behavioral and

attribute data is critical for providing the most relevant recommendations based on the intent of each visitor. In fact, it provides

the necessary foundation for delivering truly relevant 1:1 recommendations.

Even if a visitor clicks on both a hat and a shirt once, he may not necessarily be interested in both equally. If he clicks on the shirt and immediately clicks away, he is clearly not interested in it. His recommendations should reflect that preference.











Test, test, test


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03 04

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purple hat.











2. Make Recommendations Individually Relevant Equally important, recommendation engines of the future will leverage all of the in-depth behavioral data available to generate

recommendations that are personalized to the individual.

Think again about the example of the shirts. In that situation, the recommendation engine operated like many traditional

solutions by building relationships between products (e.g., people who viewed the blue shirt also viewed the green shirt,

or people who bought the blue shirt also bought the purple hat). These types of recommendations are primarily driven by

popularity scores.

But recommendations of the future take a leap forward in relevance because they understand not only that product

relationship, but also the surrounding context. In other words, recommendations should take into account each individual’s

detailed actions, engagement, intent and a�nities as it relates to product category, price, brand, size, color, style, tags and

keywords.

So, in the shirt example, while there may be a strong relationship between the blue shirt and the purple hat in terms of overall

popularity, a recommendation engine of the future would also consider personal preferences. One shopper could be shown a

purple hat of a preferred brand while another sees a pair of pants instead — because he has never shown an interest in hats.


Recommendations of the future consider the detailed actions of the individual, as well as the context of the products. This allows them to be individually relevant to each shopper.





purple hat.
















popularity scores.




keywords.






3. Deliver Recommendations in Real TimeWhile it’s important to understand the intent of individual shoppers, their needs and interests are constantly evolving.

What interested a shopper yesterday may not be what he wants today. Though historic interests and preferences are

hugely important for understanding an individual, they do not always encapsulate someone’s current needs or preferences.

It is important to have all of the data about your visitors available immediately. This real-time information allows your

recommendation engine to adapt to accommodate a customer’s needs at the precise moment recommendations are to be

presented. This does not mean recommendations should ignore all previous behavior, rather they need to combine current and

past session behaviors to determine an individual’s intent in the moment.

For example, if a shopper visits your home improvement site and researches gardening supplies, but comes back a week later

to look at lighting, it doesn’t make sense to base all of her recommendations on gardening. Her needs this time have changed,

and the recommendation engine should allow you to incorporate her intent in real time to show her only what is relevant to her

now, not what was relevant to her last week.


Even if a visitor viewed gardening supplies last time she visited your site, that doesn’t mean she is interested in gardening supplies this time.

Recommendation engines of the future allow you to incorporate real-time changes in intent to focus on lighting solutions instead.






popularity scores.




keywords.




















4. Leverage Your Product Catalog without a FeedYour product catalog also needs to be up-to-date at all times. Most recommendation engines retrieve product details from an

API catalog integration. In this case, after a lengthy integration process (often several months or more), the recommendation

engine will generally only sync with the catalog once every 24 hours.

Although a catalog feed option should always be available, progressive recommendation engines can operate with or without

such a feed – with real-time data updates pulled right from the site.

The primary benefits here are speed and accuracy. Cutting-edge recommendation engines will immediately account for out-of-

stock inventory in their algorithms to avoid recommending those products to visitors. This immediacy removes the possibility

that a shopper could be recommended an out-of-stock item, which would create a poor experience. Likewise, new additions

to the catalog will be included in recommendations immediately. On all sites this is important, and on certain types of sites, like

those that o�er flash sales, it is essential.


AVAILABLE INVENTORY

SHADOW CATALOG

Recommendation engines of the future allow the marketer to leverage a “shadow catalog” pulled directly from the website (rather than an API feed) to ensure that recommendations always factor in real-time inventory changes.


























AVAILABLE INVENTORY

SHADOW CATALOG


5. Manage Recommendation Strategies on Your OwnTraditional recommendations have relied on vendors that o�er “black box” algorithms, also known as “trust us, we know better

than you” algorithms. These vendors often claim that their proprietary algorithms can increase conversions by a whopping

20-30%, but there is no way for you to prove it by testing against a control or by analyzing attribution results. They’ll show a

report that says their recommendations added an incremental $X million to your bottom line, but you know for a fact that your

bottom line did not grow by that amount!

The only way to prove the e�ectiveness of recommendations is to be as transparent as possible about the algorithms that

power them, allowing marketers, who know their products and customers best, to build, deploy, test and iterate di�erent

recommendation strategies. A di�erent recommendation algorithm may be better suited for first-time visitors versus loyal

customers on the home page. Likewise, a di�erent algorithm may be more e�ective on a PDP for someone who came to

that page deep-linked from a Google search, versus a browser who has been on the site for 15 minutes and has shown

strong interest in that category. Each of these situations have nuances that a recommendation strategy needs to take into

consideration.

Marketers need to be able to understand the algorithm, test it prior to deploying it live on the site, see the true click or view

attribution results, and continuously iterate and improve.


Recommendations of the Future

“Black Box” Algorithms

Many recommendation engines are black boxes that don’t let you build and test your own algorithms. Recommendation engines of the future are transparent to give you complete control.












AVAILABLE INVENTORY

SHADOW CATALOG













consideration.







6. Incorporate Multiple, Customized Algorithms To promote di�erent product o�erings, it’s common for recommendation engines to use multiple algorithms in a single area of

a web page. For instance, one algorithm will be used to promote items purchased with the product being viewed, while another

algorithm may be tailored to a shopper’s interests.


For example, if two di�erent shoppers view

a cocktail dress, they will see the same

recommendations for shoes that have been

viewed with the dress, as well as di�erent shoes

based on what each shopper has viewed before.

SHOPPER PRODUCT

TRADITIONALLY...

Both shoppers are recommended shoes that go with the dress as well as shoes that align with their browsing history. This results in disjointed product recommendations that may not be the most relevant options for each shopper.

ALGORITHM 1 ( Purchased Together )












consideration.
















SHOPPER PRODUCT

TRADITIONALLY...



A better approach in this situation is to combine multiple algorithms into a single strategy (or recommendation “recipe”)

to produce a more cohesive set of recommendations that work for each individual. With a single “recipe,” marketers can

recommend items that are both typically purchased together but also appeal to individual visitors (based on their preferred

price points, brands, styles, etc.).

In the same example, these two shoppers will see completely di�erent recommendations customized to their unique

preferences.

Ultimately, combining multiple factors into a single algorithm creates more relevant recommendations that are more likely to

generate engagement and incremental purchases.


SHOPPER PRODUCT JUST LIKE ME KNOWN PREFERENCES

$$

BETTER APPROACH...

The visitor from Florida — with a lower price point preference — sees less expensive pumps (for her warmer environment), while the visitor from New York —who prefers a higher price point — sees more expensive boots (for her colder climate).

$$$$










SHOPPER PRODUCT

TRADITIONALLY...








preferences.





$$

BETTER APPROACH...


$$$$


7. Understand the Role of ContentMany e-commerce companies invest in content for their websites, such as blogs, articles and how-to guides. And while we

traditionally think of recommendations as being centered around products, the recommendation engines of the future will be

equally versed in content assets as well.

This means that the content consumed on your site can fuel your product recommendations, and the products viewed on your

site can fuel your content recommendations. For example, if a visitor reads multiple articles about growing and caring for lilacs

on a garden supply e-commerce site, his preferences for lilacs should be incorporated into the product recommendations he

sees.

In the other direction, if a travel site visitor is researching flights to London, the site should recommend articles on activities to

do in London. By reading up on things to do in London, she may be more likely to book the trip!

In the future, the most successful experiences will require a combination of both products and content to capture a shopper’s

attention and provide a completely relevant and engaging experience. And we do mean the whole experience!

A visitor reading a blog post about growing lilacs can be recommended lilac-related products.

A visitor researching flights to London can be recommended London-related content.






preferences.





$$

BETTER APPROACH...


$$$$








sees.








8. A�ect the Whole ExperienceIn the future, marketers shouldn’t need to limit their recommendations to their homepages or a predetermined slot on product

detail pages. Marketers should be able to place recommendations anywhere on their sites, test the e�ectiveness of these

placements to determine what works best for them, and even drive the full site experience. Want to add recommendations to a

small area of your homepage or a larger area of your product detail page? No problem. Want to add them to the checkout page

– before or after purchase? Go for it. Want to only show recommendations after someone has been on your site for a certain

amount of time, or as they appear to be leaving? Done.

Marketers should be able to try di�erent formats and layouts too, including stacking images on the left or right side of the

screen, placing them in a grid under the main product images on the product detail page or in a carousel (or a series of

carousels) on their homepages or product category pages.

But that’s not all. Recommendation engines of the future should allow marketers to power the search box itself with

recommendations. As visitors type, it should not only autocomplete, but also make suggestions of the most relevant search

results for that individual based on everything discussed above. The visitor can skip a whole step and just go straight

to the right product for her. Alternatively, if she goes to a search results page, that whole page should be sorted using

recommendation algorithms so the results are ordered in the most relevant way for that individual.

Essentially, the whole e-commerce experience can be driven algorithmically so that the very layout, navigation,

category/brand selection, and views are individualized.

Marketers shouldn’t need to limit the format and placement of their recommendations. They should be able to test them anywhere and everywhere to personalize the entire shopping experience.








sees.


























9. Employ Multiple Channels While we have been discussing the power and relevance of 1:1 personalized experiences driven by algorithmic

recommendations, we have primarily used examples related to web experiences. But of course, this power can and should be

applied everywhere: web, on-site search, mobile web, email, mobile app and in-store.

For example, marketers can harness everything they know about shoppers – including behaviors, search history and more –

to deliver real-time personalized recommendations inside their email campaigns. The era of batch-and-blast generic emails is

over. You should be able to send out one email that provides a unique experience for each recipient. Think again of the clothing

example from the beginning. In this example, one person would receive an email with an assortment of hats, some purple.

Another would see the blue shirt and a few similar to it. The other person would see green shirts and a promotion for a coupon.

Best of all, if inventory levels or prices change, or a customer purchases an item or shows interest in a di�erent set of products,

those recommendations inside the email should be updated in real time so the email is always relevant and up-to-date for each

recipient, every time it is opened.

It is particularly important for recommendations on mobile websites and in mobile apps to be highly relevant as well. Why?

Because you have very little real estate, and people are willing to give you even less time than on the web. Recommendation

engines of the future put exactly the right category, brand and product in front of the visitor, sorted in real time for

maximum relevance.

SHOP THE NEWEST TRENDS

RECOMMENDED FOR YOU

Recommendations aren’t just limited to your website. Everything you know about your shoppers can be incorporated across email, mobile app, on-site search, mobile web and in-store.


































maximum relevance.


RECOMMENDED FOR YOU



10. Test, Test, TestLike all website improvements, the only way to ensure that a recommendation strategy is working is to regularly test it.

Recommendation engines of the future allow marketers to test not just whether recommendations are having an impact on any

of their key KPIs, but also whether one algorithm is the better choice over another for all visitors, or even a specific segment.

Marketers should also be able to easily test recommendation formats, sizes and placements and iterate on those through easy-

to-use A/B and multivariate testing and dashboard reporting. The same is true for recommendations delivered through on-site

search and email communications.

Essentially, every aspect of recommendations from the algorithm, to the format and the channel, should be tested and

tweaked on an ongoing basis by the marketer — without the need to call for help from the vendor or IT.

Don’t Settle for Sub-Par Recommendations When thinking about your product recommendations, remember that you don’t need to settle for anything that doesn’t meet

your standards. You need recommendations that leverage all of the in-depth data available and speak to each individual visitor

in the most relevant way. You need to be able to plan, execute, test and optimize your recommendations on your own, without

a “black box” from a vendor or any delays from IT. And because recommendations in the future won’t be limited by traditional

formats, placements or channels, you should be free to try out these new approaches on your own to find what works best for

your company.

Here’s a little secret, by the way. The future of product recommendations is already here. So why would you settle for anything

less?

SHOP SIMILAR STYLES COMPLETE THE LOOK

On a PDP for a pair of boots, is it more e�ective to show recommendations for similar styles, or recommendations from other categories to “complete the look”? Is a product carousel more e�ective than no carousel? Three products on the page versus four? All these options should be tested to find the optimal choice.
















maximum relevance.


RECOMMENDED FOR YOU
















your company.


less?




About Evergage Evergage’s real-time personalization and customer data platform (CDP) delivers

The Power of 1, enabling companies to transform the dream of 1-to-1 engagement, across

channels, into reality. Combining in-depth behavioral analytics and advanced machine

learning with data from your existing sources, Evergage provides the one platform you

need to build a single, comprehensive view of each one of your customers and prospects

and activate that data to deliver maximally relevant, individualized experiences – “in the

moment,” across touchpoints and at scale.

Contact Evergage at 888-310-0589 to speak to an expert about your needs today!















your company.


less?




About Evergage Evergage’s real-time personalization and customer data platform (CDP) delivers

The Power of 1, enabling companies to transform the dream of 1-to-1 engagement, across

channels, into reality. Combining in-depth behavioral analytics and advanced machine

learning with data from your existing sources, Evergage provides the one platform you

need to build a single, comprehensive view of each one of your customers and prospects

and activate that data to deliver maximally relevant, individualized experiences – “in the

moment,” across touchpoints and at scale.

Contact Evergage at 888-310-0589 to speak to an expert about your needs today!

Documents

Evergage The Future of Product Recommendations€¦ · increase average order values and drive more conversions. In the past, before the data was available for more advanced analysis,