Personalization is often synonymized with relevance, and relevance is delivered effectively when there is an understanding of the context of a situation. At a very granular level, context as it is related to Commerce Search is derived from understanding who the user is, what the user is communicating, and in what channel the user is communicating their message. It is the ability to understand who an individual is and the context in which they are searching. It is knowing what he or she is searching for, on what device and most importantly how successful their search was. Without this understanding, Search query results often fall flat, lack relevance and disappoint users.
To enhance the user experience, drive clicks, conversion and ultimately customer satisfaction RichRelevance has created a Commerce Search solution that considers context and incorporates three layers of personalization.
RichRelevance Find™ is an innovative new Commerce Search solution that applies personalization at indexing, query time and at the dashboard level so shoppers receive more relevant results – in real-time. Find develops comprehensive affinity models for every product subcategory, product attribute and shopper. Then Find automatically manages how much personalization is applied as shoppers interact to get the most relevant products in real time.
This cautious and finessed type of personalization can only be accomplished by incorporating various levels or layers of personalization at different points in the Search functionality. With 3LP results are highly curated and personalized when appropriate and lightly peppered with personalization when catalog browsing is deemed more suitable.
RichRelevance Find™ delivers unmatched personalization for every Commerce Search experience by deploying 3LP into every query for every shopper, anonymous or recognized.
To learn more about the Three Layers of Personalization and how they enhance the customer experience please read our brief: Find™ The Only Personalized Commerce Search Engine Using Three Layers of Personalization.
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. As holiday shopping is on everyone’s mind, RichRelevance’s very own Kris Kringle, Alex Ciorapaciu, shares some tips and insights on how you can maximize on personalization (and cheer) this holiday season.
In this series of short videos Alex has personally selected some optimization tips to help set you up for success this holiday season.
Videos in this holiday series include:
Boosting Product Attributes
With days dedicated to limited time offers and promotional pricing, consider creating holiday or even ‘deal’ specific rules to ensure promoted products and categories are top of mind across your site. Alex’s step by step guide shows you how to create holiday rules within minutes right in the RichRelevance dashboard.
Customer Preference Center
With Customer Preference Center (CPC) enabled, shoppers can explicitly tell you what products, categories or brands they like and dislike and RichRelevance harnesses that information to influence the weight algorithms place on those attributes or categories. CPC helps make personalization more robust, but we never want to confuse personal purchases with gift purchases. Watch Alex’s video to uncover a quick way to eliminate this concern.
If you don’t have CPC enabled and want to learn more about it please reach out to your account manager discuss setting it up.
Holiday Strategy Messaging
Personalization comes in many forms and in many ways what you say is as important as how you say it. When it comes to recommendation messaging customers say that when brands use their own language they’re 35% more likely to engage with a recommendation placement because it feels more genuine to the brand. Customizing your strategy messaging is easier than ever within the RichRelevance Dashboard, Alex shows you how.
King of the Hill
Ever wondered how RichRelevance algorithms and strategies really work? How they know which strategy to show to which shopper? In Alex’s quick video he explains how King of the Hill eliminates the need to worry about manually manipulating recommendations.
Dynamic Landing Pages
With RichRelevance you can create dynamic landing pages for any segment you dream up. Alex walks through how you can ensure you’re getting the most money from your holiday search engine advertising spend with custom holiday landing pages by creating rules with specific date ranges or referrer URLS.
Ready to unwrap your holiday tips? Watch the video series here.
Last night RichRelevance and Shop Direct were crowned as leaders at The Retail Systems Awards at the Millennium Hotel, in Mayfair, London.
RichRelevance won ‘Online Solution of the Year’ for its personalisation project with innovative retailer, Shop Direct. The winning solution was chosen for the superior online shopping experiences created for very.co.uk customers. Shop Direct were also crowned ‘Retailer of the Year’ for their Personalisation initiatives with RichRelevance.
Now into their 11th year, the Retail Systems Awards recognise technology excellence and innovation within the retail sector. Winning entries come from retailers and technology suppliers who are leading the way in areas such as mobile, online, payments, loyalty and personalisation.
The awards ceremony was attended by retail and technology industry leaders from across the UK. The Retail Systems’ judging panel included Clare Joel, Head of IT, French Connection Group, Nadine Sharara, E-Commerce & CRM Director, Moët Hennessy, Sharon Peters, Programme Manager International Supply Chain, Marks and Spencer and Michelle Stevens, Editor, Retail Systems.
Congratulations to all the other winners of the Retail Systems Awards. A great night was had by all and we’re proud to have been among other industry leaders from across the UK at the awards ceremony in Mayfair, London.
RichRelevance research unveils consumer attitudes towards in-store technology are markedly similar across America, Britain and Europe, except when it comes to facial recognition.
Whilst American and British shoppers welcomed a lot of shopping experiences they were the most creeped out by personalization initiatives involving facial recognition. Out of all the countries surveyed, the French were identified as the most open to in-store technology, with the highest cool ratings against questions.
As depicted in the infographic, all the nations surveyed agree that being able to scan products on their mobile device in-store to read product reviews is the coolest trend. 79% of Americans, 76% of French, 73% of Germans and 62% of British, all think this capability is ‘cool’.
However, differences appear when facial recognition is concerned with the French being the only nation surveyed to think it’s cool to be identified as a high value shopper via facial recognition technologies (62%) verses all the other nations surveyed in agreement it’s quite creepy – US 67%, UK 75% and Germany 43% creepy.
While 40% of British and Americans are keen on digital screens in dressing rooms, the survey found the French and Germans are even more enthusiastic with over 65% of French and 61% of Germans thinking it’s cool. Likewise interactive mirrors which model outfits were considered 42% cool in the UK, verses 63% cool in France and 58% cool for Germany.
Even where shoppers were creeped out by certain initiatives in-store, the French were less ‘creeped’ out than their British, American and German counterparts. For example where 75% of British, 64% of American and 48% of Germans would not like to be greeted by a sales person who identify them via their mobile phone or app as they enter the store, only 36% of French are turned off by this idea.
It’ll be interesting to see how attitudes change in the forthcoming year in particular with virtual reality technologies becoming more widely adopted by retailers to enhance shopping experiences in-store.
Find out more about the Creepy/Cool survey here.
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%. That’s a substantial contribution when extrapolated across annual sales in the hundreds of millions or even billions of dollars. However, one thing I’ve noticed while navigating the world of ecommerce personalization is that the word “performance” is often misused in the context of recs. The reality is that there are multiple meanings to this word, and for each meaning, there is a discrete method for assessing its relationship with your shoppers and your revenue. So, let’s decode your recs performance.
At RichRelevance, we group performance into three analytical categories: value, engagement, and cohort analyses. While each type is useful, each tells us vastly different things. Misappropriating the type, like confusing an engagement analysis for a value analysis, can lead to disastrous business decisions. I’m here to protect you from making such mistakes.
First, let me summarize the three types of analysis:
- Value Analysis: this indicates the incremental revenue impact of recs, and is usually the report of most interest to retailers. The key metrics are typically revenue per session (RPS), conversion, average order value (AOV), and the lift contribution is determined by running an A/B or multivariate test that compares key performance indicators (KPIs) of populations based on their exposure to recs.
- Engagement Analysis: this quantifies shopper utilization of recs, and can be a signal for relevance. The key metrics are recs sales and clickthrough rate (CTR). Recs sales represents the sale of items clicked on in recommendations.
- Cohort Analysis: this profiles the spend patterns of shoppers that choose to use recommendations vs. those that do not—comparing the RPS, conversion, and AOV of shoppers based on their engagement with recs.
Most retailers stumble with using engagement or cohort analyses interchangeably as indicators of value. I get it; these reports are readily available, whereas conducting a true value analysis requires an A/B test which can take weeks and expose the retailer to undue opportunity costs. However, the reality is that these reports don’t convey the incremental benefit of recommendations, and here’s why:
Disassociating Engagement and Value Analyses
An engagement analysis tells us how much shoppers use recs and, to some extent, indicates relevance. If the recommendations are random, shoppers won’t click or buy from the retailer. That said, striving to maximize recs sales or CTR is equal to saying that recommendations are the most important piece of content on your site—and we all know that they’re not. What’s most important is getting shoppers to convert with the highest level of spend. Recs merely support that objective.
In fact, certain kinds of recs engagement can have a neutral or negative impact on your business. It’s the responsibility of the recommendation technology and how it’s configured to mitigate these instances. To shed more light on the matter, here are three events you should be aware of that explain why value is not a function of recs engagement or sales:
- Overlap: Sale of items that would have happened anyway, even in the absence of recommendations. Frankly, the vast majority of sales that happen through recommendations aren’t incremental, so indiscriminately increasing recs sales does not guarantee more cash in your coffers.
- Cannibalization: Reduction in AOV due to the presence of recs. If recommendations cause a shopper to buy a cheaper SKU than they otherwise would have purchased, that takes money out your pocket as a retailer.
- Shopper Distraction: Reduction in conversion due to recs. If recommendations are optimized to attract as many clicks as possible, they can bait shoppers to click in perpetuity until they become fatigued and leave your site without converting. No one wants this.
And of course, we have copious amounts of data to demonstrate that maximizing recs engagement has a detrimental impact on revenue. We’ve plotted recs CTR and recs sales against RPS lift for a multitude of RichRelevance A/B tests, and the resulting scatterplot shows no positive correlation between engagement and incremental revenue. In fact, the data suggests that at extreme levels of recs engagement, RPS lift can be severely compromised. For more details, view this interesting TED Talk on the “paradox of choice”, which implies that oversaturating a shopping experience with product options and baiting shoppers into excessive exploration can result in non-conversion.
Quite simply, it’s not about maximizing recs engagement; it’s about driving the right level of engagement that minimizes the aforementioned events and maximizes your per session revenue.
Disassociating Cohort and Value Analyses
A cohort analysis tells us the RPS, conversion and AOV of shoppers that choose to use recs versus those that do not. Most often the shoppers that choose to use recommendations have substantially higher key performance indicators (KPIs), which can lead one to presume an extreme level of benefit from recs. That said, please consider that although recommendations do generate substantial revenue lift when properly optimized, a cohort analysis does very little to quantify that impact.
In the business-to-consumer world, shoppers that utilize recommendations tend to have higher spend propensities, so it’s not uncommon for recs users to have 2x the RPS of non-recs users. Intuitively, this makes sense. When dealing with household consumers, willingness to engage with your catalog is a primer for purchase intent. In the business-to-business (B2B) world, the inverse is true. B2B buyers have predictable requisition lists that they rarely deviate from. Consider an office manager that’s restocking his company’s office supplies or an IT manager that’s purchasing specific laptop models for her business. These professionals know exactly what they want, order in bulk, and do not use recommendations.
So, these non-recs users have high RPS, conversion, and AOV. Conversely, the recs users represent those shoppers that are less committed—those that may or may not know what they want, and if they do convert, it’s with much less spend. For sites like Staples, Office Depot, or Dell, these are most usually your household consumers, and a cohort analysis would report much lower KPIs all around. Therefore, due to a self-selection bias, whether a cohort analysis shows higher or lower KPIs for recs users is independent of the value the recs technology actually delivers. Rather, it’s indicative of the types of users that decide to use recommendations.
Are recs impacting your best shoppers? Do recs help monetize individuals least likely to convert, or do they have broad resonance across all levels of spend in your shopper base?
So, there you have it—your performance analyses decoded to help you learn a bunch more about your recs and answer these questions: Who is using recs (Cohort Analysis)? How much are they using it (Engagement Analysis)? What incremental value does this utilization provide (Value Analysis)?
Take a moment and think about how you interact with your mobile device, how do you research or make purchases on your mobile phone? You’ll quickly realize that your path to purchase most often begins with a keyword search, which often is vastly different than how you navigate a dot-com site or your local brick and mortar retailer. Infact, 56% of all retail searches occur on mobile devices. Mobile access has changed the way we as consumers interact with the world and it has significantly changed the path to purchase. Mobile was once used for showrooming purposes, sending retailers into a tailspin about how to ensure shoppers didn’t browse in store and convert elsewhere. Today, our challenge is different, mobile devices and behaviors have evolved, they’re more intelligent and more capable to handle a complete shopping journey. While showrooming and browsing behaviors still occur, the completion of the purchase on the mobile device is accounting for more and more of a retailer’s revenue. According to Internet Retailer, mobile commerce is growing at 3 times the rate of US ecommerce overall, accounting for more than $104 billion in 2015.
Today’s challenge is focused on how retailers ensure that customers are finding what they’re looking for on mobile devices so that they ultimately convert. This begs the question, how well is your site search performing? Are your customers finding what they seek? Is your site search delivering a customer centric and mobile considerate experience? What metrics are you using to determine it’s success or failure?
The traditional and dated way of measuring site performance (and commerce search within) by Revenue Per Session (RPS) and conversion were valid when ecommerce was simply just a dot-com site, which is not the case for today’s world. Today, it is essential to measure the performance of each individual feature that leads to a purchase/conversion. In the case of site search, success is defined by the relevance of the results delivered by a search engine to an individual user.
Search is innately unique in the respect that it is the single place in digital shopping journeys where users communicate with technology in their own words and expect a comprehensive response or set of responses. By default, search demands 1:1 communication, and is one of the most impactful places to influence and enhance the customer experience. Search is often a retailer’s first impression, especially on mobile, it is a critical opportunity to succeed by providing relevant and individualized results. Commerce Search accelerates product discovery and drives consumer conversion by delivering the most relevant results for each unique search query. The accuracy of the responses delivered by search is measured by what is known as “Findability”.
Findability a term first coined by Professor Michael Hendron, whose research indicates that nearly a third of e-commerce shoppers use site search, and 90% of buyers will probably use it. Yet these same sought after buyers only find what they seek in half of all site searches.
Since when did the retail industry accept a success rate of just 50%?
Given that search is a feature most often chosen by a determined and informed consumer who knows exactly what he/she wants, especially in ecommerce, it is essential to measure how successful the actual search experience is for that individual. This is why Findability must be a focal point of consideration when analyzing the performance of site search. Findability tells merchants whether consumers find what they are looking for and if they are ultimately pleased with those items. The conversion of these shoppers can only come if they have in fact located the items they desire. It is a cause and effect relationship, and a relationship that if treated right will thrive. When consumers trust that their searches will yield results they’re satisfied with, their search frequency and dependency simultaneously increases.
Are you measuring your site search properly? Are your customers finding what they’re looking for? Perhaps the metrics you’re currently using to determine performance are not providing you with the whole story. To get deeper insight into how to enhance your site search performance and learn more about Findability please visit https://richrelevance.com/relevance-cloud/find/ to stay in the know about our newest product launch Find™, the next generation of personalized search for an omnichannel world.