increase in customers Cost of Acquisition (CAC) has slashed marketing budgets, putting marketing teams in a position to do more with less.
When it comes to user acquisition campaigns, a few small fires need to be put out first. This is a problem that weighs more on startups that sell to other businesses than on startups that sell to consumers.
First of all, B2B startups often have a longer funnel than their counterparts, as they often offer freemium options or free trials. As a result, these startups don’t see many conversions within the first few weeks of getting new subscribers. I’m not saying there won’t be more conversions — his B2B startup, which follows a product-driven growth model, simply needs more time.
Ultimately, such B2B marketing teams struggle to make key campaign decisions based on initial CAC and return on ad spend (ROAS) metrics that rely on historical averages. increase. They need a little extra help in the form of predictive marketing, but some elements of that can easily be done in-house.
To help you better assess your campaigns early on, our data science team created an Ad Group Likelihood Simulator.
Marketers can use this tool to estimate the likelihood that a campaign will generate high ROAS over time by simply entering a few numbers.
As the name suggests, marketers can use this tool to estimate the likelihood that a campaign will generate high ROAS over time by simply entering a few numbers.
How to use the simulator
step 1
Enter quality group classifications based on historical campaign data. This will classify your campaigns into quality cluster groups 1-5. where 5 is the best quality (most likely to convert) and 1 is the worst (least likely to convert). conversion).
Naturally, your campaign is more likely to belong to the latter. If this data is not available, please follow the steps below to request an extraction from his BI team.
Select the average conversion for the quality cluster group. Let’s say you have a history of 500 ad groups and are interested in conversions that occurred within the last 12 months.
Option 1
Take all 500 ad groups and calculate the 10th, 30th, 50th, 70th, and 90th percentiles of conversion rates over a 12-month period. These are the centers of conversion rates for the five cluster groups.
Option 2