Opportunity
One of our ADU builder clients approached us about implementing a configurator tool on their website. To date, the main call to action on their site was to either call or fill out a form to inquire about an ADU. After completing the form, the prospect would then be given the opportunity to book a site visit — the primary goal of our marketing activities and the conversion we optimize campaigns toward.
The configurator tool would allow users to envision ADU models and customize them by selecting features such as roof pitch and exterior colors. Once satisfied with their selections, the prospect could enter their information and receive an email with a link to their model for safekeeping. At that point, they would also become a lead in the client’s system, just like a traditional form fill.
The theory was that the configurator could provide an improved user experience and increase conversions throughout the funnel. Our job was to test this theory.
Pains
As a SaaS tool, the required implementation of the configurator meant that we would lose a significant amount of important data. The implementation came with several limitations, including:
- Inability to track channel source: Because it was implemented via an iFrame, much of the reporting and attribution we rely on was broken or disconnected (for example, determining which channel a site visitor originated from). This, in turn, skewed data across all marketing activities, including PPC campaigns on Google and Meta ads.
- Inability to customize form options: The form within the configurator did not include standard fields we typically track, such as address, and at launch the provider was unable to customize these.
- Inability to block spam: While it is unclear how well spam tactics could navigate the configurator, we were unable to apply the necessary spam protections that are standard on our native website forms.
- Inability to redirect upon submission: The most significant limitation was that, after form submission, we could not redirect prospects to a thank-you page prompting them to book a site visit. As a result, the first opportunity to prompt booking came only in a follow-up email.
Despite these challenges, we still needed to find a way to evaluate the configurator’s impact on conversion rates throughout the funnel.
Approach
In order to reach a conclusion, we outlined three key questions we needed to answer:
- What were the on-site conversion rates (from site visit to information capture) before and after the implementation of the configurator?
- Do leads generated via the configurator convert differently throughout the funnel compared to leads from the standard process (form fills and phone calls)? Most importantly, what is the conversion rate to site visit?
- Are increases or decreases in these measurements offset by one another? For example, could a lower on-site conversion rate be justified if leads convert at a much higher rate later in the funnel — or vice versa?
Additionally, throughout the test we wanted to continue evaluating channel performance (paid ads, Meta ads, organic, etc.), even though the configurator stripped away much of the attribution data.
Implementation
When we first launched the configurator, we took a conservative approach by using it on model pages as a secondary CTA. However, traction was low, and reaching a statistically significant sample size would have taken too long. Since the tool’s monthly subscription was not cheap, we decided to accelerate the process by adding it as a dual CTA. Prospects then saw both options — using the configurator or contacting the company — presented equally.
Because much of our data was stripped due to the iFrame embed implementation, we had to do a bit of creative problem-solving to capture the insights we needed.
Answering Our Conversion Questions
We were able to track whether a lead came in via the configurator or through the native lead flow (form fills and phone calls). This allowed us to measure several key things, including:
- On-site conversion rates before and after implementing the configurator
- Conversion rates through the funnel for configurator vs. non-configurator leads
Answering Our Channel Performance Question
We could track which channel users came from when they clicked into the configurator embed. However, while we could measure how many people submitted their information via the configurator, we could not identify their specific channels. To address this, we applied an average conversion rate from configurator click to configurator submission, allowing us to estimate channel performance. While not perfect, this approach gave us enough visibility to monitor campaign performance during a test period in which much of our data was otherwise stripped away.
Results
The results were overwhelmingly unfavorable to the addition of the configurator:
- Traffic cannibalization: An overwhelming majority of site traffic was absorbed by the configurator.
- Slight lift in lead capture: Having the configurator on the site did improve site-visit-to-lead conversion rate by 22% with 95% certainty.
- Severe drop in lead quality: However, this improvement was negated by the fact that leads captured via the configurator converted to site visits 560% worse (with 100% certainty) than standard leads.
- Break-even analysis: To offset the quality drop, the configurator would need to deliver 5.6× as many leads (a 560% increase in volume)—just to break even in total value.
Takeaways
We still believe configurators can be a great tool and may work well for other businesses—especially where the value proposition is less dependent on a personal, custom, or luxury sales experience. For this specific client, however, the configurator did not create the ideal customer journey needed to sell the product.
Tests like this highlight the importance of choosing a marketing partner who looks beyond vanity metrics like total lead count and instead digs into the data to identify what will truly move your business forward.