Why Landing Page Optimization is Critical for ROAS

While brands continue to spend on digital ads to attract customers, conversion success needs to go beyond getting more clicks. Today’s consumers are well-informed, digitally savvy and weary—not taking marketing messaging at face value. Converting website visitors into loyal customers takes much more than clever advertising. 

Marketers tend to agree that when visitors make the conscious decision to click on an ad, it’s an important crossroad in the customer journey and an opportunity to engage that shouldn’t be squandered. If the landing page experience doesn’t connect with visitors or the information isn’t relevant to the ad or what they’re looking for, they’re more likely to drop off before completing their goal. 

This is why there is such a strong focus on optimizing landing pages. Improving customer experience (CX) on landing pages has the primary goal of leading visitors further along the customer journey with the aim of increasing conversions. But more critically in the short term, landing page optimization is a way to improve return on ad spend (ROAS).

Increased competition requires getting better at building customer connections

The digital customer experience space is no longer the sole domain of e-commerce retailers or B2C brands. It’s now a playing field where everyone competes for customer loyalty regardless of industry, product and services, or distribution channels. One of the reasons is that digital channels provide more opportunities to connect directly with consumers without limitations of geography or store hours. But with that broader reach comes growing competition as well as many complexities and variables that influence CX. 

Personalization is becoming increasingly important as consumers tire of navigating through endless options to find the solution they’re looking for. Instead they want relevant information immediately served to them when they respond to an ad. 

Creating dedicated landing pages seems the logical solution and it may be manageable with just one or two landing pages, even if it is very time consuming. But try to scale landing page optimization across multiple pages linked to an equally high number of ads, while taking into consideration all the possible variables, and it quickly becomes an impossible manual task. 

It’s not just about coming up with ideas to optimize landing pages. It’s having the ability to test those ideas, and any variation or different combinations to see what resonates with visitors. And then getting answers quickly enough to be able to respond with relevant experiences. 

Building better connections with customers requires understanding their current needs and  preferences and then being able to track how this changes over time. Customers value personalization and are even willing to trade their data for it, but only if it results in better experiences that deliver greater value to them. 

To achieve this level of optimization two critical elements are required: speed and scale. This is not something that can’t be easily achieved through manual processes. Fortunately when technologies such as artificial intelligence (AI) are combined with optimization expertise, complexity becomes an advantage, generating more accurate insights quickly that can be used to build better experiences on landing pages that connect with and convert customers. 

Piecing together the landing page optimization puzzle  

On landing pages, working to understand what makes the most impact on individual visitors and smooth's the customer journey is an important starting point in an optimization strategy. This requires knowing what data is relevant and gives a better indication of intent. Behavioral data can show how visitors are navigating pages, including which elements of a page they click on. It can also highlight possible points of friction, where visitors drop off or get frustrated in their journey. AI-driven experience optimization can help identify relevant data, analyze it in context and produce actionable insights. It makes it easier to scale optimization efforts by bringing together all the pieces of the customer experience puzzle, identifying which ideas and combinations are having the most impact and why.   

The learnings gained from real visitor responses can be used to predict the best experiences to serve the next visitors that come to specific landing pages. An even greater benefit of AI-driven experience optimization is that the optimization dynamically updates, always working to serve the top performing experiences to make the most impact. Because this whole process happens quickly, it brings brands significantly closer to achieving real-time personalization which improves the overall customer experience. But more importantly for brands, it drives an improvement in ROAS. 

A case study: Landing page optimization in action

This case study highlights a common situation being faced in e-commerce today. A brand operating in a highly competitive industry with a common product has an ambitious growth target with a focus on improving ROAS. The question being asked was how much could ROAS be improved by using AI-driven experimentation and personalization for landing page optimization? 

The set-up: A media streaming company wanted to run a promotion on three specific movie titles with the objective of increasing click through rate (CTR) by at least 5%. A simple request but with many different elements to take into consideration. Running the numbers quickly highlights the complexity involved. 

Ad campaigns for three different movie titles would be run across five different social media channels. As part of the landing page optimization, 30 variants across 12 experiments were identified resulting in a total of 34,560 possible combinations. The optimization would run for just over 6 weeks. These requirements already exceeded the capabilities of more traditional testing tools. Would AI-driven experience optimization be up to the task?

The strategy: Taking a different approach, instead of setting up a separate landing page for each title and ad source, everything was consolidated into a single landing page. The primary difference with this approach would be the benefit of maximizing traffic through the landing page while still dynamically optimizing  experiences to align with the various ad sources. Increasing the traffic creates more data and more opportunities to learn from how visitors respond to various ideas. It also provides the opportunity to use those learnings to personalize experiences for visitors. Plus managing the optimization is simplified by the single landing page. 

The score: During the optimization the average real-time conversion campaign lift achieved  was 15%, which was three times more than the optimization goal. Additionally, the top performing combination revealed the potential for a 30% increase in click through rate.

This landing page optimization demonstrates the potential to dramatically improve engagement and customer experience while simultaneously generating lift and achieving key metrics. It’s a prime example of leveraging the capabilities of AI to drive more value for customers and brands within a short time frame. A major benefit is that the learnings from this optimization can be applied to new optimizations as it becomes the baseline to continually adapt and improve digital customer experiences. Instead of achieving a 15% increase in CTR it becomes possible to achieve 30%—which is 6 times more than the original optimization goal. 

With growing competition in the digital space and increasing customer expectations, optimization is all about maximizing opportunities to learn. The companies that adopt this approach will be a step ahead and be far better equipped to continually improve customer experiences. As economic pressures grow, this will be a key factor to maintaining better connections with customers and improving loyalty. Most importantly, it generates the return on ad spend in a very tangible way.