Customer growth Acquisition costs (CAC) are taking a toll on marketing budgets, putting 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. The problem for many organizations stems from making key decisions based on incomplete information, a problem that weighs more heavily on startups that sell to other businesses than those that sell to consumers.
For starters, B2B startups have longer funnels than their counterparts because their offerings often include freemium options and free trials. Because of this, these startups don’t see many conversions in the first few weeks of getting new subscribers. That doesn’t mean there won’t be more conversions – B2B startups following a product-led growth model simply need more time.
Ultimately, marketing teams in such B2Bs are scrambling to make major campaign decisions based on historical CAC or return on ad spend (ROAS) metrics based on historical averages. They need a little more help in the form of predictive marketing, some elements of which can easily be done at home.
To help you evaluate your campaigns early, our data science team created an ad team opportunities mockup.
Marketers can use this tool to estimate the likelihood of a campaign producing high ROAS over time by entering a few numbers.
As the name suggests, marketers can use this tool to estimate the likelihood of a campaign generating higher ROAS over time by entering a few numbers.
How to use the simulator
Step 1
Based on your historical campaign data, complete the Quality Cluster Classification, which divides your campaigns into Quality Cluster Groups 1-5, with 5 being the best quality (highest likelihood to convert) and 1 being the least favorable (lowest likelihood to convert).
Naturally, campaigns are more likely to be the latter. If you don’t have this information, ask your BI team to generate it for you by following the instructions below.
Select Quality Cluster Group Average Conversions. Let’s say you have a history of 500 ad groups and you’re interested in conversions over a 12-month period.
Option 1
Take all 500 of your ad groups and calculate the 10th, 30th, 50th, 70th, and 90th percentiles of the 12-month conversion rate. These are the centers of the conversion rates of the five cluster groups.
Option 2
