- RETAIL SEGMENT: Luxury Specialty Department Store / Luxury Apparel Outlet
- PRODUCT: Discover
- OPPORTUNITY: Increase conversion on the category page
- SOLUTION: Personalize the sort order of products displayed on category and sub-category pages
- RESULTS: 2 – 5% increase in website revenue as compared to nonpersonalized category page
Opportunity: Increase conversion on the category page
On average, 33% of online shoppers visit a category or sub-category page when they visit a retailer’s online store.1 Currently, almost all retailers sort their category product results the same way for each and every visitor. Whether it’s a shopper’s first visit or the 100th return visit of a loyal customer, the category product results displayed are sorted exactly the same. While the in-store merchandiser’s goal is to appeal to as many as possible, online shopping has the potential to be an individualized experience. Retailers have the opportunity both to curate a personalized experience for a recognized customer to find a product quickly as well as to increase shopper engagement. This engagement drives loyalty, which in turn drives increased sales—and allows the retailer to provide better customer service by improving personalization across all their channels and touch-points.
Solution: Personalize the sort order of products
Discover™ is a real-time retail application that brings the most relevant items for an individual customer to the top of the list—helping shoppers quickly find the products they seek. With Discover, product lists on the category and subcategory pages are pre-sorted on page load to best match each customer’s historical and current shopper behavior.
For a recognized shopper, the personalization algorithm analyzes the individual’s historical views, clicks, purchases, and searches (based on inputs such as categories, products, and brands) to personalize the order in which available products are presented. If this recognized shopper looked at specific products or brands on the retailer’s website in the current session as well as in past sessions before visiting the category page, the algorithm takes into account this real-time behavior as it generates the product listing that’s the best match. For an unrecognized shopper, products are sorted based on a combination of global attributes such as top viewed, clicked, and purchased items in that category.
Both the attributes and their associated weightings can be configured to a retailer’s specific needs, and then automatically optimized using machine learning and multivariate testing. For example, if brand is critical to the retailer’s customers, brand can be added as an attribute, and weighted more heavily than other personalization attributes. Examples of personalization attributes include products, categories, and brands the shopper has viewed, clicked, and purchased in the past and/or during the current visit.
Results across diverse businesses: 2 – 5% increase in website revenue
Discover was launched on a luxury apparel retailer’s website and delivered a consistent 2 to 5% overall increase in website revenue. It receives C-suite visibility and its continued strong performance has since shifted the retailer’s business strategy and prioritization; they have accelerated its deployment to their other online retail stores.
Discover was also launched on the retailer’s outlet and exceeded performance expectations with a 5%+ average increase in website revenue. Within a couple of weeks, the retailer had 100% of the outlet site traffic enabled for Discover.
Presenting shoppers with a concise listing of the right products quickly is paramount. How long it takes shoppers to see relevant recommendations can mean the difference between conversion and an abandoned cart.
What is the most expedient way to help shoppers find what they seek? By satisfying the customer’s agenda through algorithm-driven personalization. The outcome is dynamically generated, personalized product lists—clearly relevant to that specific individual—built on a customer’s past and current shopping behaviors.
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- RETAIL SEGMENT: Household appliances, multimedia, bedding, custom kitchens
- PRODUCT: Recommend™ & Engage™
- CHALLENGE: A multinational electrical retailing company was looking for the ability to create impacting personalized experiences at every customer interaction across multiple sales channels. However with a large and complex product portfolio, the company needed an automated solution to exceed their customers’ experience.
- RESULTS: 5% revenue lift from Recommend
- RETAIL SEGMENT: Home and Garden Improvement
- PRODUCT: Recommend™
- CHALLENGE: Homebase sought to optimize its website experience for customers— taking stock updates, trends or personalization into account—while gaining visibility on impact for any implemented initiatives. They also sought to change the manual process for updating related items on its product pages.
- RESULTS: Following the implementation of RichRelevance Recommend, Homebase:
- Averaged one additional item to the shopper’s cart
- Increased the average order value by 30%
- Attributed nearly 10% of online revenue to a Recommend influence (over 12 months)
“RichRelevance have been a pleasure to work with, providing very helpful and timely support through our implementation as well as advice and guidance since going live. We are now looking to leverage their consultancy services to see what further benefits they can provide.” – Todd Coughlan, Commercial Optimization Executive, Homebase.
Homebase is a leading home and garden retailer selling over 38,000 products for the home and garden. In addition to more than 265 large, out-of-town stores throughout the United Kingdom and Republic of Ireland, it has a growing internet offering at www.homebase.co.uk.
Confronted with a manual process to update related items on product pages, Homebase sought a personalization solution that would not only relieve pressure on internal resources, but would also help them improve the customer experience.
In June 2014, Homebase partnered with RichRelevance to implement the Recommend™ product recommendations solution.
Not only does Recommend automate personalization functionality, saving significant internal time and resources, but also the recommendations drive revenue and relevance to consumers through its relevance modelling. The ensemble modelling leverages over 125 algorithms, which compete for the opportunity to respond to each shopper request, taking into account user details and the context of the request. This advanced algorithm gives shoppers the right recommendations and helps drive sales and basket size. Recommend also gave Homebase the ability to analyze performance at a granular and top-line level as well as perform intelligent tests to further optimize performance across the mobile, application and desktop devices.
In addition to the revenue driving capabilities, Homebase really liked Recommend for its intuitive, self-learning ability to constantly test and optimize for the best recommendation, based on shopper interaction. This helped Homebase cut resource costs by freeing up internal resources to be more strategic, rather than tactical in their daily responsibilities.
“RichRelevance provided a complete recommendations engine for us which we felt was the best choice for a sustainable solution moving forward,” said Paul Canavan, Head of Digital Programme & Operations at Homebase.
Since implementing Recommend in October 2014 across category, item, cart and add-to-cart pages, Homebase has seen on average a 30% increase in cart sizes for those customers who interact with product recommendations. Plus, customers who engage with the Recommend product are proven to add (on average) one additional item to their cart.
Paul Canavan added: “We’ve seen particularly good results since the addition of the mobile and tablet channels, with the ‘add to cart’ placement demonstrating really good uplift. It’s not always about basket size; sometimes it’s just as important to recommend similar products that the customer may prefer. The use of RichRelevance technology is a great way of highlighting the product range we have.”
In addition to the increase in basket size and average order value, following the implementation of RichRelevance Recommend, over the last 12 months nearly 10% of online revenue could be attributed to a Recommend influence.
Because Homebase is so happy with the success of Recommend, the company has decided to invest in a resource to fully manage the Recommend product. The resource allocation demonstrates how the company understands it can further increase its positive performance by trialing new placement locations and merchandising strategies, which are included as part of the Recommend product. This resource will also focus on optimizing the system by testing and learning using the data provided through RichRelevance, as well as producing new metrics and key performance indicators (KPI) for the business to understand the purpose and performance of the tool.
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- RETAIL SEGMENT: Apparel
- SOLUTION: Build™ & Recommend™
- CHALLENGE: An international retail company sought to create a customer journey that is consistent, seamless and positive across online and offline channels. .
- RESULTS: Project X shoppers (compared to shoppers who did not interact with X)
- 133% increase in AOV
- Increase in one item per checkout basket
- Reduced returns by 2%
- Improved customer experience: 84% “excellent” rating Personalized Digital Receipts
- Open rates in excess of 70%
- Click-through rates of 8%
“Transforming sales associates into indispensable shopping assistants recasts tomorrow’s physical shopping experience into one that is as adaptive, flexible and innovative as the online environment.” — I.T. and Ecommerce Director International retail company.
In today’s digital world, the retail store continues to offer critical opportunities for omnichannel retailers to level the playing field with the likes of Amazon. Omnichannel retailers are in a unique position to integrate the best practices and learnings derived from the online experience into the store environment, with mobile device integration that not only converts sales associates into personal assistants with minimal training, but also unlocks access to real-time product, sales and inventory information that can directly inform the shopper. Further, retailers can extend customer engagement beyond the store visit—by delivering highly targeted recommendations and offers on digital receipts.
In two omnichannel initiatives, an international retail company has partnered with RichRelevance to create an in-store customer journey that blends seamlessly and consistently into a positive customer experience across all channels.
From personal assistant to personal shopper
Leveraging an integrated technology solution developed by Oracle, YESPay and RichRelevance, “Project X” enables this retailer’s sales associates to locate and sell a product anywhere in the UK supply chain, by scanning or searching product names or descriptions using in-store mobile point of sale (POS) tablets and payment devices.
Using Project X, sales associates can perform a number of activities, including:
- Showing shoppers an extended and personalized product range
- Locating stock, whether in the same shop, another store or e-commerce distribution center
- Using mobile POS to order products in store for home delivery and complete POS transactions within the same basket
- Busting or minimizing queues
The solution calls upon a RichRelevance API (application programming interface) to access and deliver recommendations, putting the wisdom of online learnings into the hands of customer-facing sales assistants. On the tablet, the item page or “no search results” pages display RichRelevance product recommendations, enabling the sales associate to offer alternative products or to cross-sell relevant items. Recommendations are derived from online behavior, as well as structured (non-behavioral) product affinities that enable merchandisers to build outfit rules that incorporate cross-sell items.
In addition, if a shopper is identified by sales associates at the beginning of the transaction, the customer journey can be modified to deliver more personalized recommendations, with the goal of driving higher sales in stores and offering a more comprehensive in-store experience to the customer.
Project X has already delivered significant improvements in customer experience metrics such as AOV, return rate, basket size, queue busting, etc (see sidebar).
Subsequent phases will incorporate store sales data into online data.
From the business perspective, in-store staff gain a clear view of stock levels across the business, and are empowered to go the extra mile for customer queries, transforming even novice sales associates into expert personal shoppers. Because sales are made and recognized on an individual store basis, staff are also appropriately incentivized.
Optimizing customer engagement post-transaction
Partnering with RichRelevance and eReceipts, this retailer also incorporates
omnichannel data from both its in-store and online customers in emailed
receipts, from across its 321 UK stores.
The receipts leverage over 125 machine learning algorithms from RichRelevance in conjunction with its real-time decisioning engine to present highly relevant personalized recommendations and offers on digital receipts, based on a variety of shopper inputs such as online behavior, items in basket, etc.
The I.T. and Ecommerce Director notes that “While targeted offers have been commonplace for online customers, the development of our multichannel emailed receipts solution marks a significant industry first and ensures our customers will benefit from the most relevant offers and product recommendations.”
The eReceipts’ technology also augments loyalty card data by identifying customers at checkout and linking them to their transactions, which is of critical importance to augmenting customer profiles, since up to 80% of this retailer’s transactions originate in-store.
Conclusion
The battle for the future of the retail will be won or lost in the store. Notes the I.T. and Ecommerce Director, “Transforming sales associates into indispensable shopping assistants recasts tomorrow’s physical shopping experience into one that is as adaptive, flexible and innovative as the online environment. Similarly, extending customer engagement beyond the store with highly relevant recommendations and offers fosters loyalty—both online and offline.”
For this retailer, both initiatives have reaped improvements in key customer experience metrics and encourage higher lifetime customer value from today’s omnichannel shopper.
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- RETAIL SEGMENT: Food & Beverage
- SOLUTION: Recommend™
- CHALLENGE: Wine.com wanted to quickly develop, test and measure innovative new recommendation strategies that leveraged realtime access to customer data— without having to build out the I.T. architecture required to do so.
- RESULTS: Using RichRelevance Recommend™, Wine.com tested a “similar products” strategy that drove $5 per click— becoming one of their best strategies in terms of revenue per click.
“We now have the ability to come up with algorithms on our own, create new placements and implement strategies immediately.” — Cam Fortin Sr. Director of Product Development Wine.com