Leader Spotlight: Bruno Estrella of Webflow on how predictive UA helped strengthen growth operations

Well here we are, at the cusp of 2023, and times are still pretty tough for marketing and growth teams as they are still expected to do more, with limited marketing budgets. Thankfully, predictive AI is here to help teams maximize each marketing channel’s performance by improving ad spend efficiency and targeting the most valuable customers. In turn, that leads to exponentially greater scalability, and profitability—thereby also deepening the pockets of growth teams. 

We talked about this at length in a recent webinar, in partnership with the World Forum Disrupt, and it was about how LTV-based user acquisition can help B2B businesses grow in a profitable way. You can read about the full webinar by clicking here.

The distinguished lineup of speakers behind top PLGs was what really made this webinar shine:

In previous posts of our “Leader Spotlight,” we focused on the insights offered by Fabien David of Notion; and Chris Cunningham of ClickUp. For the final webinar-installment, we would like to shine light on the outstanding Bruno Estrella, the Senior Growth Manager of Webflow—the next generation cloud-based CMS and web publishing system. 

About Bruno Estrella

Bruno spent his entire career rocking senior growth marketing positions, and managed to work a slew of wonders for Webflow. Before leading their user acquisition team, Bruno scaled Webflow’s paid marketing motion, built the experimentation motion for user acquisition, validated Webflow’s sales-led opportunity, and initiated Webflow’s product-led SEO strategy. Outstanding!

Rundown of Bruno’s insights

Challenges experienced by Webflow

Webflow has always been a PLG. 

Bruno shared that from a UA perspective for PLGs, it is pretty easy to grow top of funnel, but striking the balance between top of funnel and bottom of the funnel, while allowing the team to move fast is the real challenge, especially for young companies with long sales cycles. Because for any premium product, it takes time to understand what works and what does and that's when predicted models, when it comes to LTV, help teams move fast and have stronger operations that end up truly striving for growth. In many ways, it’s a constant challenge for growth teams in PLGs, and continues to be theirs as well. However, tactics and models that stem from LTV-optimized predictive modeling helps PLGs move faster. 

Webflow’s main KPIs

Webflow mostly operated as a bootstrap company for a long time. They have been super financially responsible since when the company was like 50 people and he joined. As such, LTV was always the metric they paid attention to. 

One thing that they started to pay a lot of attention to is that LTV can take a lot of time, as it can take two or three years, so they also paid attention to the payback period.

He pointed out that this is also particularly important for an early startup because you probably don't have a lot of data to even calculate LTV, and you need to reinvest on your marketing channels as soon as possible and the ball spinning faster, and focus on payback.

They mostly paid attention to the average revenue per account that users will give, and then calculate the expansion rate that they got, and see how they get the payback from those channels, and also paying attention to what that means for their CAC to LTV ratio.

Current decision-making process, managing budgets

They have a metric that they feel is good. It's a predictive model of qualified signups with a high likelihood of becoming a big customer. 

On a daily basis, they pay attention to the cost per acquisition, and LTV ratios for the sign ups. Bruno pointed out that they can’t wait a month to determine whether a campaign was successful, when there are early signals that can be acted on, which is why they opted for building predictive models.

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Barriers to LTV adoption for most companies

Bruno insists that most marketing teams just don't know how to work with data, and don't know how to work with data scientists. So having that expertise in building them is foundational. It’s important to first learn to work with data scientists and with the engineers, and then calculate LTV models. Then you can go to the next phase of segmentation and put that into channels.

Efforts that showed greatest value

The main benefit Webflow saw in the past six months was actually from a higher level, in terms of how to structure the team In a way that they can leverage them. For example data and engineering, and what sort of skills they need to bring on to leverage different techniques.

How you can achieve results similar to Webflow

If you’re a data-driven B2B PLG company of a similar size that is ready to use predictive AI to up the ante on your growth, scalability, and profitability, there’s no time like the present to get on it!