Quiet Owl marries holistic digital marketing with advanced data science and measurement strategies. Our process shifts discussions from opinion to utilizing data for growth.
Test, learn, and rapidly implement winning solutions.
Discover the opportunities your competitors are missing.
Align your product data with how consumers search.
Use social testing to discover which content drives growth.
Minimize spend wasted on non-human traffic.
Untangle your data. Discover actionable insights.
Craft a cross-channel strategy to fit your business.
Remedy the bottlenecks in your customer buying journey.
Utilize rapid testing to learn and improve.
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Ready for a proposal? We’re listening.
Tangible and Transparent
Most good growth marketers and growth agencies strive to deliver tangible results. We believe that what we learn on the journey is usually more valuable than the destination. Complete transparency with our clients on everything—opportunities, strategy, weaknesses, processes, obstacles, and results—empowers our clients to learn and grow along side us. We’re in this together, and open honest communication is the bedrock for a great working relationship.
6-Week Money Back Guarantee
Try us for 6 weeks. We’re confident you’ll be thrilled with the results. If you’re not, we’ll refund all agency fees.
“We worked closely with David during 2020, a pivotal year for our company as our business model was changing to DTC. David’s insight and execution on our digital ad spend and overall strategy were instrumental in us achieving a 20X increase in website revenue over the previous year. Working with David was a pleasure, as he is a dedicated professional and a great human being.”
Co-Founder at Breeo | Forbes 30 under 30
Leveraging Automation Without Losing Control
Advertising technologies that replace manual tasks with automation have dramatically increased the efficiency of digital marketers. These technologies can be used for simple tasks like managing bids and budget, or in the case of smart campaigns, leveraging the platform to manage targeting, bids, budget, and even creative.
Sounds great. No question these are powerful tools that give a competitive edge to those who know how to use them. But what are the dangers?
Our formula for deriving exceptional results
We start by listening to the executive team about company history, unique challenges, and upcoming targets. We listen to what analytics data tells us about the business opportunities. We listen to third party research about the industry. Then to the sales and customer service teams, as well as gathering feedback from customers and prospects across social media platforms. Listening first builds the foundation for executing a meaningful strategy.
After much listening, we are positioned to identify bottlenecks and opportunities for growth. Our process provides clients with a prioritized list of opportunities that we’d like the tackle with them.
We’ve agreed on the prioritization of opportunities and it’s time to get creative about developing solutions. Solutions may include new paid campaigns to test, new landing pages or content concepts, as well as improvements to the website user experience.
The only bad test result is one where the test was not run properly. A proposed solution that underperforms in a test is still a win; it provides valuable insights that guide the next round of solving and testing. Of course we also love test results that show improvements and more commonly see this as the outcome in our workflow.
What is measured will determine what we learn. Historically we were quick to jump to concluding metrics of uplift, revenue, or profit. The test either won or lost. The end. With experience we’ve discovered that the metrics in the middle, which show more of the full journey, can be as or more important for understanding how a test performed and where the future opportunities lie.
Within every test is an opportunity to gain a deeper understanding of a client’s consumer and the opportunities for growth. We go beyond the immediate test result. We ask, what happened within the test? How did users interact at each stage? How will this inform future tests? Can the learning be applied to other channels, pages, or flows?