We are at the beginning of a significant journey to bring digital capabilities to the in-store environment and vice versa. Personalization should be a large component of this journey. Over the last couple of years, some retailers have deployed interesting omnichannel applications. Examples are those that allow consumers to check inventory or locate items in stores (Best Buy and Target respectively) or manage loyalty (Guess and Sephora). Also, some vendors have launched mobile shopping applications (like Shopkick, RetailMeNot or ShopSavvy). But still, very few of these initiatives are truly personalized.
Yet, the use cases and technology for personalization in omnichannel have never been more viable, and the imperative to get started more apparent.
Omnichannel personalization can be done at two levels. The first consists of pulling together consumer data from different channels to create a comprehensive profile of each consumer (omnichannel data). The second consists of deploying personalization across multiple channels (omnichannel touchpoints).
When these two levels are combined, the following is possible:
- Personalizing a single channel using omnichannel data
- Personalizing multiple touchpoints using single channel data
- Personalizing multiple touchpoints using omnichannel data
One example of personalizing a single channel using omnichannel data is what one of our customers in the upscale department store category did. By merging online intent data with store POS data and applying it to onsite recommendations, they were able to drive a 2.1% lift in website sales (compared to recommendations that utilized only online intent data).
One example of personalizing multiple touchpoints using single channel data is what Monsoon Accessorize, a UK high street apparel leader is doing. They use online intent data to personalize their onsite, in-store and mobile channels. The store solution integrates RichRelevance, Micros and YESPay to allow in-store sales associates to locate and sell a product anywhere in the UK supply chain and show shoppers an extended and personalized product range on their tablets.
Stores utilizing the solution see a 133% improvement in AOV, an increase in one item per check out basket, a 2% reduction in returns and improved customer experience ratings.
Delivering this type of access to rapid innovation is the reason we built our infrastructure and SOA (service-oriented architecture) platform for API-based personalization services. We call this platform Build™ and it provides our retail customers the intent data and functionality needed to bring offline data (like POS) from stores and call centers to the digital experience. This includes real-time data ingestion and export, model import and building, consumer profiles, product and content co-occurrence, segmentation and consumer ID matching. With this functionality, retailers can quickly explore, experiment and build personalization capabilities using an iterative cloud-based approach that doesn’t require data scientists or large investments in infrastructure.
This ability to rapidly innovate to build superior customer experiences through personalization is not limited to omnichannel retailers, it applies to pureplay etailers as well.
In the US, long-time customer Wine.com was able to test an age-old theory on the impact of recommending similar products based on the number of common attributes. Using Build, it developed and implemented a custom personalization algorithm that leveraged the intersection of multiple attributes and measured its performance against the existing set of pre-built RichRelevance algorithms. Not only was the speed with which Wine.com moved from concept to deployment to production unprecedented, the new “similar products” algorithm became one of Wine.com’s best performing strategies, generating about $5 per click.
Our expectation is that Build will become the personalization platform of choice not only for our retail customers, but also for many consumer-facing applications developed by multiple vendors in the space. Build has a light footprint, rich functionality, and is a super performing platform that can sit underneath Hybris, Demandware, Responsys, Exact Target, SLI Systems, HookLogic, Olapic, LivePerson, CrowdTwist and other applications to enable omnichannel personalization via APIs and services.
So make today your Day 1 and commit to omnichannel personalization. Analyze your current and/or future omnichannel applications, determine what different data sources can be used for personalization, decide how many channels you want to enable and then leverage Build to create a superior omnichannel customer experience. We’re here to partner with you each step of the way.
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, Wine.com 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, Wine.com 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, Wine.com 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 Wine.com 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 personalize@richrelevance.com to learn how you can bring human intuition into your recommendation strategies.
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 Wine.com, 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.
WEBINAR DETAILS
Build Your Own Recommendations
Thursday, March 19, 2015
9:00 a.m. PST / 4:00 p.m. GMT
ABOUT THE PRESENTERS
Cam Fortin
Sr. Director of Product Development, Wine.com
An 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 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.
Digital technologies are not only redefining the way customers shop; they are redefining retail business models. More and more retailers have started to leverage cloud APIs to innovate and build exceptional customer experiences through personalization. No longer a buzzword, rapid innovation has become a competitive advantage for many companies.
This webinar will explore how the recently introduced Build™ platform is helping retailers accelerate innovation using API-based services as building locks to personalize every customer interaction.
Today, I’m super excited to announce the launch of the Relevance Cloud™– what we at RichRelevance believe to be the most comprehensive personalization solution for retail today. The Relevance Cloud is a re-imagining of all RichRelevance products with new features and more simple ways to access, use and implement each of RichRelevance’s products.
Our Discover, Engage, Recommend and Build products empower retailers to deliver and innovate brand-centric customer experiences that span the customer lifecycle across all channels. Each of these products is powered by the Personalization Graph—a unified customer view which aggregates key data on customer behavior, content, context and products. The Personalization Graph is the next generation customer view. Powered by Big Data technologies, it’s flexible, streaming and scalable. Unlike the rigid CRM-centric view of the customer, the Personalization Graph sees each consumer as a fluid, ever-changing and increasingly complex stream of events and touchpoints, reflecting the reality of today’s consumers.
Here are the parts of the Relevance Cloud:
Especially exciting to me is the debut of our Build API-based services (I am an engineer after all! ☺). The opening up of our SOA and platform are key to unlocking the next revolution in personalization. Personalization is not simply an application (e.g., the market-leading Recommend product recommendations) but a capability which must be deeply integrated into the fabric of every customer-facing moment, incorporating the essence of each retailer’s unique customer strategy. Data science as a skill set continues to mature within retailers, and with Build, we provide an open, flexible architecture for our retailers’ data science teams to create entirely new customer experiences.
Case in point: one of our long-time customers, Wine.com employed a “bring your own algorithm” approach to test and deploy a “similar products” recommendation strategy, incorporating their unique knowledge of their specific assortment. It ended up driving $5 per click—becoming one of their best strategies in terms of revenue per click and increasing overall orders and revenue, not to mention dazzling their customers by putting forward their industry specific expertise.
RichRelevance’s Relevance Cloud manages all the heavy lifting (fault-tolerant infrastructure, the operationalization of data, etc.) so that our customers can focus on what they do best: designing differentiated experiences that speak to shoppers 1:1, ensuring that personalization delivers a significant market advantage. I hope you are excited as I am about this next generation of personalization!