Strand Boosts Revenue by 16.5% with Personalized Recommendations from AWS Partner Obviyo

Many ecommerce companies believe that adding personalization to their online stores is too complex and requires vast technical resources. In reality, the opposite is true. Certainly, building a solution in-house can be time consuming and can cost tens of thousands of dollars in data cleaning, code updates, and system engineering. Instead, businesses like Australian retail company Strand can use Obviyo Recommend, a simple-to-deploy product recommendation app for Shopify Plus storefronts powered by Amazon Personalize, to deliver a unique online shopping experience and drive new revenue growth. In 4 weeks, Strand saw a 16.5 percent lift in total sales and is continuing to optimize the solution to further personalize the entire buying journey.

Strand has been a reliable presence in the lives of thousands of people since 1927. Founded under the name Strandbags, the retailer sells handbags, wallets, backpacks, and other personal accessories. Strand’s mission is to elevate all life’s journeys, from running errands to planning a trip around the world. With an online shopping presence through Shopify and around 300 physical stores across Australia and New Zealand, Strand helps customers find the right product to complete a look, whether the goal is fashion or functionality.

Throughout its nearly 100-year history, Strand has recognized that every customer is different. In the world of ecommerce, a one-size-fits-all experience can harm both customer-lifetime value and revenue.

With a goal to pair delightful in-store shopping experiences with an equally effective online buying journey, Strand wanted to serve product recommendations individually curated for each visitor. It turned to Amazon Web Services (AWS) and adopted a dynamic personalized recommendation solution from AWS Partner Obviyo.

With artificial intelligence (AI) and machine learning (ML) capabilities, customers can use relevant data to make personalized product recommendations, which in turn increases user engagement and drives a measurable revenue lift. In fact, a McKinsey study shows that direct-to-consumer companies can achieve up to a 25 percent increase in revenue with personalization. To tap into the potential of ML, Strand sought a simple-to-deploy solution that could quickly test various personalization capabilities while scaling across diverse business lines and customer needs.

Founded in 2021, Obviyo is the creator of Obviyo Recommend, a product recommendation and personalization platform powered by Amazon Personalize, which uses ML to deliver omnichannel personalization. Through research, Obviyo has found that 94 percent of all visitors to its clients’ ecommerce sites are unauthenticated customers with unknown needs. Conventional product recommenders based on sales history often miss the mark because they are only relevant to returning customers and a small percentage of unknown customers. Obviyo, through its integration with Amazon Personalize, uses visitor actions as a proxy for buying signals to generate recommendations, helping the remaining 94 percent of unauthenticated visitors discover products that they want to buy. This helps companies like Strand increase visitor engagement and unlock revenue growth from existing traffic.

To help retailers better serve these visitors, Obviyo Recommend provides an extensive library of Growth Bots, which are ready-to-use intelligent micro-applications that blend branded content with personalized product recommendations. These bots harness user activity data as actionable insights at every touch point, helping shoppers find products of interest while improving upsell and cross-sell opportunities.

As an AWS Partner, Obviyo is uniquely positioned to help Strand take full advantage of all that AWS has to offer. Obviyo Recommend automates recommendation engine integration and deployment, reducing the dependency on IT resources while bypassing the cost and complexity of traditional system integration. As a result, Strand can take advantage of a range of powerful ML technologies without a large technical team.

“The Obviyo growth marketing solution powered by Amazon Personalize is a super powerful combination,” says James McGill, head of ecommerce trade and merchandising at Strand. “It empowered us to stage personalized buying experiences on our Australian and New Zealand site, but more importantly, it empowered our teams to experiment and innovate on how to optimally serve our customers while enhancing our brand image.”

Using self-learning ML modeling powered by Amazon Personalize, Strand’s recommendation solution analyzes shopping behaviors and automatically serves relevant products to the user. Now, Strand quickly predicts and makes personalized recommendations based on visitor actions and context in near real time. After the initial implementation of Obviyo Recommend, Strand saw compelling results: Obviyo contributed to 16.5 percent of the company’s total sales within 4 weeks of implementation.

After successfully launching Obviyo Recommend, Strand’s next step was to increase the percentage of visitors engaging with recommended items, which could ultimately lead to revenue growth. To achieve this goal, Strand deployed more of Obviyo’s Growth Bots, which use Amazon Personalize to learn about customer preferences, make accurate product predictions, get deeper insights from data, and improve the overall shopping experience.

Rather than simply increasing the number of recommendation blocks on its homepage, Strand used Obviyo’s testing and optimization capabilities to experiment with advanced personalization strategies.

In one experiment, Strand hypothesized that deeper engagement could be achieved if personalized recommendations were activated once a visitor interacted with particular products rather than just showing recommendations on the homepage before any engagement. To test this hypothesis, instead of immediately sending a visitor to a new page after clicking on a homepage image, Obviyo created stealth recommendations that activated a pop-up with personalized content. The visitor can then click on a product of interest, click on a “Show More” link to explore the entire collection, or close the pop-up and continue exploring the homepage.

Through the stealth recommendation experiment, Strand increased conversions among desktop users by 6.18 percent and 13.44 percent for mobile shoppers. These results demonstrated that it is better to avoid sending visitors to the next webpage until they first discover a product of interest.

The following are aggregate store-wide results for Strand that demonstrate the direct measurable impact of personalized product recommendations on key performance indicators.

Time Interval: 30 days

Revenue impacted represents personalization-assisted sales where a visitor engaged with (clicked on) at least one recommended product.

Conversion lift is calculated based on the difference in conversion rate between the cohort of visitors who engaged with personalized recommendations and those who did not.

RPV difference measures the relevancy of personalized recommendations by comparing results between two cohorts: visitors who engaged with recommendations and those who did not.

Time onsite difference is calculated by comparing the time spent on the site by two cohorts: visitors who engaged with recommendations and those who did not.

Obviyo Recommend has influenced 24.5 percent of Strand’s revenue. The retailer also saw a 4.98 percent increase in conversions, as well as a 217.35 percent increase in time spent on its websites.

With personalized product recommendations powered by Amazon Personalize and Obviyo, Strand is not only selling more products—it is enhancing the customer experience. Strand engages consumers on a deeper level with individualized experiences across channels, including websites, ads, and marketing emails. This case study demonstrates the value of a personalization methodology that uses near-real-time visitor context and shopping behavior as predictors of online visitor product needs and preferences when compared with having historical data alone.

This is a modern approach to how ecommerce companies can use personalized recommendations to increase brand loyalty and sales. And when products are presented in a visually effective way, personalized recommendations increase brand loyalty and drive a measurable lift in revenue.