’Tis the Season to Optimize

It’s almost the most wonderful time of the year! And we know it’s also retail’s busiest, so we’re here to help you get in the holiday spirit and also make sure you get the most magic (and ROI) out of your personalization platform.

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Decoding Your Recommendations Performance

Product recommendations, also known as “recs,” are a cornerstone to an effective ecommerce merchandising strategy. When fully optimized, recs typically increase retailer revenues by up to 5%.

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Book People Case Study

With one of the largest direct competitors, Amazon, Book People needed a solution to wow customers through creating innovative and personalized experiences, without presenting the customer with too much choice.

Read the case study to learn how Book People implemented both Recommend™ and Engage™ to improve conversion rate and remain competitive.

Download Case Study

Leading Marketplace gives RichRelevance Top Review

I wanted to highlight a particularly interesting case study of RichRelevance customer, PriceMinster. The second most visited ecommerce site and leading marketplace in France, PriceMinister, part of the Rakuten Group, was looking for a personalisation platform to help improve their commercial performance and give them a competitive edge.

PriceMinister turned to RichRelevance in 2012 because they needed a scalable and robust personalisation engine to cope with one of the broadest product catalogues (up to 200 million products) and 15 million visitors per month to their website.

After A/B tests showed a much greater engagement of visitors with the RichRelevance personalised recommendations verses their incumbent provider, plus a 1.5% revenue lift, PriceMinister implemented the RichRelevance Recommend™ solution.

Wanting to ensure they were practicing as much personalisation as possible, PriceMinister went on to utilise the help of one of the RichRelevance personalisation consultants to run optimisation tests in order to further improve performance. As a result they’ve maximised the amount of personalisation opportunities that can occur across their various customer touchpoints (web, email, mobile, etc.).

To find out how PriceMinister got on with their optimisation project and for more results, download the full case study.

Better recommendations are worth $500M

They say you can’t put a price on love, but Netflix CPO, Neil Hunt, recently put a price on the value of better recommendations for his customers, and that figure is half a billion dollars.

In his RecSys 2014 keynote, he said that recommendation systems should not try to be an oracle asking users to “trust me, you’ll love this” but rather advise users that “based on your watching title X, theme Y, actor Z, you’ll probably enjoy this.”

Neil Hunt is pretty spot on, you don’t want to be preached to. The most powerful recommendations are those you inherently trust because they reveal their underlying logic. Many systems leverage black box technology driven by complex algorithms or based on simple rules. Black box applications don’t allow your intuition to complement machine computation and result in irrelevant “You may also like” recommendations, while the basic approach feels forced and overly simplistic.

Combining sophisticated machine learning technology and intuition, leveraged the flexibility of the Relevance Cloud to implement unique selling strategies that directly combat the pitfalls associated with black box and rules-based systems. After implementing RichRelevance recommendations across their site, they noticed that some of the unique qualities about selling and buying wine also make it slightly more complicated to recommend. For example, many people repurchase the same bottles and often purchase in large quantities; upsell/cross-sell strategies tend to recommend popular products since there really is only one product to sell – wine.

With this in mind, began to develop strategies to address the particular nuances of their product set. Starting with a ‘Similar Products’ strategy, they offered recommendations that keyed off attributes unique to wine, like varietal and region preferences. With strategies like these and other personalization efforts, has generated a 15% increase in average order value and $5 revenue per click [View the case study here]. Cam Fortin, Senior Director of Product Development at shares how he implemented his own strategies in this webinar.

When it comes to what works, everyone’s path to personalization is unique. Many vendors offer canned personalization that can be simply turned on. In today’s competitive marketplace, it is imperative that you are empowered to build a relationship with your shopper that is best-suited to your unique business and affords you the ability to test and implement creative personalization tactics. You need to innovate with agility, which is why we brought tools like “Build Your Own Strategy” to retailers like you, eliminating the need to wait on someone else to do the work for you.

Creating the most personalized experience possible requires adherence to one simple principle: respect the shopper. A modern personalization platform should give you the control over your recommendations to deliver experiences that are unique to you and respectful to your shoppers. With an open platform technology, you’re able to analyze and activate your own data in real time to develop more relevant recommendations that drive higher engagement, stronger relationships, and ultimately, revenue.

Contact us at to learn how you can bring human intuition into your recommendation strategies.

Webinar: Build Your Own Recommendations

Are your recommendations as mysterious as a magic 8-ball?

It’s time to rethink your recommendation strategies.

A modern personalization platform should give you control over your strategies to deliver the right upsell and cross-sell opportunities. You should be able to analyze and activate your own data in real time to create relevant recommendations that drive higher engagement and revenue.

Black-box applications don’t afford you the opportunity to use your own data to articulate clear strategies. Instead, you’re stuck with static “You may also like” recommendations that are sterile and lack transparency.

Consider an agile, flexible approach that gives your team the tools to tailor each strategy to reflect your customer data and brand promise. We have put together a webinar on Building Your Own Recommendations to help you learn how.

In this webinar, Cam Fortin, Senior Director of Product Development at, will share how they built their own, highly effective recommendation strategy that resulted in 15% increase in average order value and $5 revenue per click by utilizing the "Build your own strategy" offering within the Relevance Cloud™.

Join us to learn how you can translate your brand promise into tailored customer experiences.


Register Now

Build Your Own Recommendations
Thursday, March 19, 2015

9:00 a.m. PST / 4:00 p.m. GMT


Cam Fortin

Sr. Director of Product Development,

Brad CerenziaAn e-commerce industry veteran, Brad Cerenzia has more than 15 years’ experience as an innovator, designer and engineer working at companies like Amazon and Redfin. Currently, Brad is the Director of Data Innovation at RichRelevance, introducing proofs-of-concept with top retailers eager to establish themselves as market leaders in adapting personalization to such areas as mobile shopping, sales associate on-floor tools, fitting room technology, POS marketing, cross-channel data technologies and assisted personal shopping.

Jolie Katz

Product Marketing Manager, RichRelevance

Jolie KatzJolie Katz is the Product Marketing Manager for the Recommend and Discover products at RichRelevance, creating and delivering go-to-market assets that drive demand and generate pipeline for the business. Prior to RichRelevance, Jolie worked in the Consumer Insights division of the Estee Lauder companies, where she was responsible for analyzing global market and consumer trends to deliver strategic recommendations and insight to a broad range of internal departments. She received her BS in Organization Leadership from the University of Delaware.

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