Use predictive marketing to cut CAC at your PLG B2B startup • TechCrunch

The rise in customer acquisition costs (CAC) is creating quite the dent in marketing budgets, placing marketing teams in a position where they have to do more with less.

When it comes to user acquisition campaigns, a few small fires need to be put out first. Many organizations’ issues stem from major premature decisions that are made based on incomplete data, and this is a problem that weighs more heavily on startups that sell to other businesses than those that sell to consumers.

For starters, B2B startups typically have longer funnels than their counterparts because their offerings often include freemium options and free trials. As a result, these startups don’t see many conversions within the first few weeks of acquiring new subscribers. That’s not to say there won’t be more conversions — B2B startups following a product-led growth model simply need more time.

Ultimately, marketing teams at such B2Bs end up scrambling to make major campaign decisions based on early CAC or return on ad spend (ROAS) metrics that rely on historical averages. They need a little extra help in the form of predictive marketing, of which some elements can easily be done in-house.

To help you better evaluate your campaigns early on, our data science team created an Ad Group Likelihood Simulator.

As the name implies, marketers can use this tool to estimate the likelihood of a campaign’s ability to yield high ROAS over time simply by entering a few numbers.

How to use the simulator

Step 1

Based on your historical campaign data, fill in the quality group classification, which divides your campaigns into quality cluster groups 1-5, where 5 is the best quality (with the highest probability to convert) and 1 is the least favorable (lowest probability to convert).

Naturally, campaigns have a higher probability of belonging to the latter. If you don’t have this data available, ask your BI team to extract it for you by following the instructions below:

Choose the quality cluster group average conversions. Let’s assume you have the history of 500 ad groups and you are interested in conversions that happened within 12 months.

Option 1

Take all of your 500 ad groups and calculate the 10th, 30th, 50th, 70th and 90th percentiles of the 12-month conversion rate. These are the centers of your five cluster groups’ conversion rates.

Option 2