Consumers have more choices than ever for buying furniture and hardware. That said, buying a wall hanging is different than shopping for clothing. Why? A customer may be more aware of their preferences for color or fit. Homeware goods, however, are purchased less frequently, and buyers may not be as aware of what’s out there. With 76 percent of consumers stating they expect companies to understand their preferences, meeting expectations, even when consumers themselves don’t know what they want, can make or break a brand’s success. So, let’s explore how three homeware brands are leveraging data successfully to meet (and exceed) consumer expectations. Plus, we’ll share tips on how your brand can do the same.
Houzz started out as a couple looking for home remodel referrals. Today, Houzz represents a community of 40 million home improvement professionals who help consumers simplify planning for all types of home renovation.
The brand attributes its success to customer-centricity. According Houzz co-founder, Alan Cohen, the brand increased conversions on their site to data and emerging technology. Just last year, the company added a visualization tool so customers could view approximately 1M products as 3-D images to better understand the items in context, add notes, and share with others. Cohen said for the 2 million people that used the tool, those who did were 11x more likely to make a purchase than users that didn’t. It’s data points such as these that help the brand drive deeper personalization. Cohen highlighted one more example, stating that while 38% of consumers who landed on a barstool product page bought the item itself, at least 62% bought one of the recommended products listed below.
In short, Houzz centralizes customer data to ensure its recommendation engines have the info necessary to make good suggestions: consumer style preferences, what kind of photos customers are liking and saving, and products similar users have viewed. To learn more about how recommendation engine algorithms actually work, check out our blog on AI in retail.
Home Depot has used data to create “One Home Depot”—a strategy designed to improve its e-commerce capabilities and connect the store’s online and offline experience. The brand’s dedication to an omnichannel strategy make sense, especially considering 60% of sales were influenced by digital visits in 2017 and online sales have grown by about $1 billion per year for the past four years.
According to Home Depot CMO, Kevin Hoffman, the clear connection between online and offline sales led to a push for integrating data sources to ensure 360 customer insights. But understanding your customer doesn’t mean you’re done. Home Depot hopes to create even more personalized customer experiences by:
- The improved use of weather-triggered ads, including messages relevant to local weather conditions that could impact business professionals
- Leveraging geo-fencing to highlight inventory in local stores and send ads to customers in a specific radius the store with relevant products recommendations and discounts
- Adding digital navigation and robust product information in-app for customers shopping in the store to more easily make a purchase
- Fun fact: After implementing digital navigation, Home Depot’s customer service scores in the category of ‘easy to find’ improved by 30%.
Tip: Like Home Depot, use data to fill in the gaps of your multichannel strategy. It’s about more than price and product relevancy, it’s about making the entire experience—no matter the channel, device or location—seamless. By investing in technology that leverages geo-location (our data orchestration software, DataSwitch, can do this!) the Home Depot saw immediate increases in customer satisfaction and sales.
Since its inception, Wayfair has set a high standard for customer experience. Wayfair’s director of product, Matt Zisow, shared how big data impacts everything from the company’s web presence, to how it sources products, to its business decisions.
But collecting data doesn’t just magically create perfect customer experiences. It starts with breaking down team silos to ensure everyone has access to the same information. As Zisow put it, “…We know that data democracy is critical to our success. We don’t want to limit the power of big data to insights that just a few people have access to. Whether you’re in marketing, logistics or engineering, the ability to see and manipulate data is what keeps our organization continuously innovating.”
At Wayfair, a customer can take a picture of a chair they like at a friend’s house, and then upload that photo to the company’s website to search for (and buy) similar looking items. By adding this feature, Wayfair saw an increase in repeat customers year-over-year: “Not only do we use the data to find answers, we use it to find new questions that we hadn’t even thought of asking as well. By continuously analyzing the entire feedback loop, we use data to keep working toward that goal of improving the customer experience in ways that are significant and measurable.” This example highlights how the brand is using data to improve CX, strengthen its visual search capabilities, and discover new revenue streams.
How Lineate Can Help
We understand how leveraging audience data more efficiently can create amazing retail experiences. In fact, Lineate has decades of experience building solutions for retailers looking to centralize their data and leverage custom machine-learning algorithms. Plus, our data orchestration platform, DataSwitch, helps retailers merge data from disparate systems for 360° customer views, deeper audience targeting, and centralized campaign management. To see how data orchestration can help identify the best way to improve your customer experience, schedule a personalized DataSwitch demo today.