Wayfair: Building Consumer Relationships in an Online World

Businesses use various analytics to establish consumer profiles, understand consumer behaviors, and encourage engagement.  Brick and mortar analytics may vary in comparison to online retailers, but the overall goal remains the same – increased sales and satisfied customers. 

GOAL

Wayfair is an online retailer of home goods.  The company was founded as a customer-oriented company, a difficult task for an online only retailer.  In order to properly follow through with this concept the company needed to create a relationship with the consumer through online methods. 

APPROACH

Using various web analytic tools and optimization tools, the company successfully found a way to properly profile Wayfair consumers while encouraging engagement and developing relationships. 

WAYFAIR

History

Wayfair is one of the largest online retailer of home goods.  The company was founded in 2002 by Niraj Shah and Steve Conine under the name CSN.  Rather than concentrating on one particular item, Shah and Conine saw a need for an online retailer that sold lots of little things (Wehrum, 2012). The duo decided the best way to reach numerous markets was to create 200 niche markets, each with its own website. The websites ranged from hotplates.com to allswivelbarstoolscom.  The thought process was driven by consumer website searches.  If someone wanted a barstool that swiveled and entered this into the search engine, chances were the allswivelbarstools.com would appear (Wehrum, 2012).

“CSN Store’s growth was a testimony to the power of web analytics, target marketing, and near perfect execution” (Wehrum, 2012).  From an early start, Shah and Conine understood the importance of analytics.  In the early years, these analytics were limited but the duo found a way to use the data to create more efficient search options.

In 2011, the company decided to merge all of the niche markets into one large retail site, Wayfair.com.  Ultimately, the company wanted to provide the consumer with a single website that was easily to navigate and make purchase. Along with new consumers and sales, Wayfair.com created large amount of user data to be analyzed to create a better consumer profile.

Data Analyzing and Optimization

Wayfair is a growing company that takes pride in its online presence.  Just as a brick and mortar store would observe consumer behavior, Wayfair finds ways to analyze and observe online behaviors.  The company looks to various analytic tools to provide consumer profiles, possible web improvements, and conversion rates. 

In 2008, Wayfair enlisted the help of SiteSpect, a web optimizing solutions company to pinpoint website concerns and user behavior.  SiteSpect provided technology that allowed Wayfair to use non-intrusive multivariate testing to measure conversion optimization.  This technology was used to test and analyze consumer behavior during the purchase process to increase conversion rates.  The funnel analysis provided insight into which aspects of the purchase process presented obstacles for the consumer (SiteSpect, 2008). 

Wayfair also used the technology to acquire information from consumers on site content changes.  By using this technology, Wayfair increased their conversion rate and addressed issues within the site content to better the visitor’s experience.

Recently, Wayfair announced record sales results for 2013, with sales in excess of $1 billion.  The increase in sales can be accredited to numerous factors, including web analytics (Moore, 2014).  Many of the tools, content, and interactive features were created from analyzing consumer behavior. 

Data analytics can be used to reinforce search engine optimization techniques, improve customer engagement, measure marketing success, and ultimately offer suggestions on ways to deliver a more responsive service to meet the demands of the consumers (Bowden, 2014).  Businesses should use analytics to understand consumer preferences, obtain consumer behavioral impressions, and adjust to the needs.   Wayfair is an example for businesses who may be looking to create stronger customer relationships through data mining. 

SUGGESTIONS

Wayfair’s website design has been created with personalization in mind.  The company could use location data or previous purchases to provide a personalized shopping experience.  The site itself is clean and easily navigated with two call to actions which encourage engagement.  The call to actions can also be used to track visitor actions and follow through by tracking events.

The website provides numerous items which are categorized by use, furniture collection, room use, and brand.  Tracking consumer involvement with specific brands could prove beneficial to the company in evaluating consumer trends and possible brand associations.  The company encourages relationship building with a loyalty program, which can be found in small print at the top of the page.  This loyalty program could be used more effectively if it were located in a more visible location on the website.  This would also be a great way to acquire personal data that could be combined with web analytics to provide a more precise consumer profile. 

In my opinion, the company has successfully created a website that encourages engagement.  With bright colors, beautiful décor and easy navigation, the website appears as an interactive catalog. 

CONCLUSION

Wayfair is a great example of a company that has learned to use website analytics to create a loyal consumer relationship. Wayfair’s success can be attributed to understanding specific consumer needs, personalizing features, and providing an easy to use website (online storefront), all of which are used to establish consumer relationships both for brick and mortar and online stores.