RetailMeNot Browser Extension Growth

Product Strategy   •    Research    •    Design

The Big Picture

RetailMeNot’s mission is to make everyday life more affordable to consumers by making savings meaningful, effortless, and on their terms. 

Deal Finder is the company’s browser extension. It searches for the best coupon codes and cashback offers, automatically applying them for shoppers at checkout. Competitors include Honey, Rakuten, WikiBuy, and Shoptagr in addition to a handful of others.

Design Process

Qualitative and quantitative research


Brainstorming, sketching, design explorations


Prototype and test


UI specifications and analytics

The Challenge

Though the extension, originally called RetailMeNot Genie ™, was launched in 2018, the acquisition of new customers has been weak. Competitors have grown much quicker and have more features.

Much of this is because RetailMeNot has not heavily invested in advertising and promotion for the extension. Competitors, on the other hand, have consistently outspent the company, utilizing radio, television, and digital media to promote their own extensions.


We conducted a national remote research study with 12 participants over two days. Participants were required to have at least one savings extension (the company’s or one of it’s competitors) to take part in the study. I designed the study, and we enlisted a third-party research partner to conduct the moderated usability sessions.



Most participants—even long-time customers—did not know the company had a browser extension.



All participants had difficulty locating information about the browser extension on the company’s website.



When participants did finally see information about the extension, they were confused about what it was and what to expect when they clicked the CTA.

They’ve done a pretty bad job marketing it if I’ve never come across it – it shouldn’t be this hard.

Preliminary Data


Total Installs


Avg. Monthly Installs


Monthly Uninstall Rate

Problem Statement

How might we improve discoverability and awareness of our browser extension, driving greater installs with limited investment in marketing?

Product Strategy

I used the Digital Experience Optimization (DXO) tools I created to understand and identify: 

  • All the elements of the experience
  • What data, analytics and research methods are available to us
  • Details of specific experiments we will run and how we plan to iterate

The Solution

After reviewing the research, inspecting the data and working through the DXO strategy, I worked with the product manager to determine a project plan. Together, we ranked our ideas based upon projected installs with the help of a member of our data science team.

Exit Modal

Previously users had no way of knowing that we have a browser extension. There were few placements within our website that promoted the extension. By using an exit modal, we increased awareness about our extension when users show intent to leave. The result was a 2.5X increase in monthly installs.

Offer Modal

The exit modal worked well for users that showed intent to bounce, but what about users that engaged with store page content?  I designed a placement in our offer modal to manage that scenario. This approach lets users get the code they are seeking, allows RetailMeNot to drop our cookie so that we can get attribution driving the sale, and promotes the value of Deal Finder. This method increased monthly installs by 1X.

Store Page Banner Messaging

Research showed that the previous store page banner copy did not clearly communicate that we were promoting our extension or the value it provides. Starting simple and focusing on the messaging, we tested four variations of improved copy. The “Stop Copying Codes!..” messaging was the clear winner, consistently driving 2X more installs than other variants. All variants outperformed the existing copy. The improved messaging led to 0.5X more installs per month.

The Outcome

My approach to quickly validating and testing small improvements proved successful. Driven by our initial research and data, we were able to build well-formed hypotheses which could then be prioritized based on projected impact.


Total Installs


Avg. Monthly Installs


Monthly Uninstall Rate