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.
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)?
You spend a lot of time optimizing your website to streamline the shopper’s path through the retail funnel—making sure they see value at each point along the way. Once you navigate the shopper to the right product, it’s important that you take the necessary steps to seal the deal, and get the shopper across the finish line.
The cart page is a pivotal area for securing that conversion. When done well, you’ll get the sale, and then potentially entice the shopper to buy more items. When done poorly, you’ll disrupt conversion by distracting the shopper from the transaction you’ve fought so hard to get.
And so we ask: How smart is your cart?
At RichRelevance, we’re sensitive to the cart page experience, and work closely with our retail partners to implement solutions deep in the funnel that will grow cart values without compromising conversion. Here are three things you can do with product recommendations to substantially enhance your cart page experience:
1. Get out of the way
Securing conversion is our primary objective on the cart page. If we can get the shopper to buy more stuff and grow the order value, that’s phenomenal! But, it shouldn’t compromise completing the sale. The location of recommendations must reflect this prioritization and not interfere with the shopper consuming information critical to the buying decision.
As such, recommendations must be on the periphery of core content such as the cart summary, checkout call-to-action, promotional code inputs, and other key messaging and functionality. This usually means slotting a vertical placement in the right margin or a horizontal placement underneath the main content area. Otherwise, if more prominently placed, you’re baiting customers to continue shopping, which can be extremely disruptive to conversion.
2. Recommend complementary, non-competitive products
When you finally get the shopper to the cart page, it’s critical not to challenge their decision to buy. If they add a TV to their shopping bag, they’ve demonstrated a degree of commitment that we shouldn’t impede by recommending another TV upon landing on the cart page. Instead of being helpful, that would be frustrating to the shopper, and elicitreconsideration at a point when a shopper should be firm about their core purchase
On the cart page, it’s imperative to use cross-sell recommendations that display products most often purchased with the seed item. However, since these kinds of recommendations rely on purchase behavior at the individual product level, something that happens much less frequently than browse activity, , it can be challenging for behavioral recommendations systems to always deliver intuitive cross-sell recommendations across a retailer’s entire catalog.
As an example, if a specific TV model has only been purchased 50 times in recent history, that’s probably not enough transactions to reliably identify four or five logical products that are commonly purchased with it. You can overcome this dilemma, in 2 ways:
I. Incorporate point-of-sale (POS) data in recommendations. If you have a brick-and-mortar presence, you probably have more offline POS transactions than online sales. Incorporating those in your online recommendations will provide a wealth of data from which to identify logical product associations.
II. Employ rule-based recommendations. Create a set of advanced merchandising rules that governs what is recommended in cross-sell situations. For each category of products, define what categories you want represented in each recommendation slot—and then let the engine source products based on whatever brand, attribute or compatibility-matching requirements you might have configured.
Ok, so now you have well-placement recommendations and you’ve optimized your cross-sell assortment across your entire catalog. What’s left?
3. Optimize your recommendations layout
Once you’ve gotten the shopper to the cart page, create inertia that pushes them through the checkout process rather than casting them out to higher parts of the funnel. You can do this by presenting a recommendation layout that facilitates exploring a product and adding it to cart without leaving the current page. Implement ‘quick view’ functionality on recommendations that allows a shopper to access product information, configuration options (e.g., size/color), and add-to-cart capabilities with a single click. Without this functionality, you’re forcing shoppers to leave the cart page to explore recommended products, and they may never come back.
Your cart page is sacred as it’s the gateway to more cash in your coffers. It’s imperative that the page experience drive shoppers to transact rather than pull them into a dangerous loop of product reconsideration. These are merely a small sample of tactics RichRelevance has deployed and validated using rigorous A/B and multivariate testing. We strongly encourage you to consider these optimizations for your retail site. They’ll make your cart smarter, protect your conversions, and grow your order values.
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.
Like it. Love it. Prefer it: The Customer Preference Center Makes Shopping Personal
Speak the Language
Deemed by Forbes as ‘‘The Year of the Millennial Customer,’ 2015 continues to see upward trends in social interaction and mobile engagement. In fact, millennial customers are expected to spend $200 billion by 2017, making them the largest global customer segment, with the largest share of wallet. Fortunately, millennials speak a fairly straightforward social language made up largely of likes and dislikes to indicate their interests and preferences. Providing this information across social networks with the click of a button has become second nature, yet the vast majority of this rich data goes unused. Today, there remains a very obvious gap between when a user provides these explicit social signals (consider how many likes you’ve gotten on a recent Instagram post) and how this data is leveraged to improve the customer experience.
But what if it was possible to create a shopping experience that not only mimicked the social experience, but also enabled you to enhance the individual customer’s experience with data collected from their own preferences?
Now you can. With the integration of the Customer Preference Center, available through the RichRelevance Recommend™ solution, you can easily capture individual tastes and preferences and put them to use by intelligently curating shoppers’ experiences on your site, mobile, email, in-store or contact centers in real time. Empowering shoppers to affect the products, brands and categories they see as they’re shopping brings a sense of 1:1 customer service not often found in the digital shopping experience.
Making it Personal is Simple
The Customer Preference Center offers benefits to shoppers and retailers across all channels. Using a flexible API, customer preferences are directly embedded as like/dislike/favorite buttons on any page, or as a survey that collects preferences on categories, brands and products. As shoppers engage and input preferences, their product assortments automatically update as they shop. Behind the scenes, the personalization system collects this invaluable data to deliver more relevant recommendations across all channels. The Customer Preference Center retains customer profile information in the Relevance Cloud’s User Profile Service so that no matter which channel a customer shops, collected data can be utilized to make the experience more personal, ultimately creating a happier and more loyal customer.
A leading apparel e-commerce company in Europe that generates over $155 million in annual revenue integrated Customer Preference Center technology across product pages and has seen 50% of users provided preferences in one month’s time. Those users interacting with preferences are significantly more engaged and spend 3.5x more than the ones that did not interact.
As a shopper, I can’t imagine anything more thrilling (or more dangerous for my wallet) than always being able to easily find things I liked from my preferred retailer.
Preferences Your Way
What does it look like? The possibilities with Customer Preference Center are endless, and customizable based on where and when the retailer chooses to engage customers. Preference can reside within existing recommendations or be as cutting edge as an integrated swipeable shopping experience (think Tinder).
Here’s are some ideas of how Customer Preference Center can work:
The Customer Preference Center enables retailers to avoid and/or supplement complicated social integrations by offering familiar social features across retail sites and mobile experiences. With flexible tools at your fingertips, customizing every shopper interaction has never been easier.
Forty-five years ago, Burger King introduced the concept of personalization with “Have It Your Way” and consumers have never looked back from the expectation of a personalized, relevant experience with key brands. Think of the Customer Preference Center as a digital innovation on “Have it Your Way,” taking its cue from the popularity of social media to share likes and preferences.
Contact us today to get started!
TTS Group has teamed up with RichRelevance to make online shopping easier and quicker for parents and teachers stocking up on school supplies. The latter’s Recommend technology is being used to tailor shopping journeys using real-time consumer behaviour. We have seen huge success since partnering with RichRelevance. In the first month of implementation, we saw a significant increase in sales on products that were personally recommended to our customers, says Jon Vasey, head of e-commerce at TTS Group. Children grow up quickly and the products that our customers purchase may change significantly from month to month. Using RichRelevance’s Recommend solution, we can target product recommendations for different age groups that match our customers™ immediate needs.