Predictive Lifetime Value (pLTV) is a machine-learning-derived value assigned to a newly acquired customer that estimates his/her full lifetime value to the business.

pLTV solves one of the most elusive questions in acquisition marketing, i.e., How much should I pay to acquire this customer profitably?

Our initial use case is for value-based bidding (target ROAS) in Google Ads.

pLTV is built from a variety of first-party data sources, including but not limited to:

• Zero-party data that customers supply along the path to signup

• In-product behavioral data

• Customer payment data

• Geographic, demographic and firmographic data

• Recurring revenue business models (e.g. SaaS or subscription eCommerce).

• Data aggregated in a data warehouse like Google BigQuery, Snowflake, or Amazon RedShift.

• Historic LTV analysis that can serve as a baseline for pLTV modeling.

• Healthy ad spend in Google Ads (minimum $25,000 per month).

• Relatively large number of conversions recorded monthly in Google Ads (minimum 100 conversions per month).

• Willingness to provide ETL Engineering internal support to our team for a limited time.

• Willingness to allow our team to access anonymized, non-PII customer data for the purpose of modeling pLTV.

• Alignment across Sales and Marketing teams, with a common interest in measuring success on the basis of LTV/CAC.

• Reasonable patience. This solution takes a few weeks to build and import into Google Ads, followed by 4-5 conversion cycles before you can judge its effect. Though, it’s worth the wait.

pLTV trains Google Ads to optimize for total profit rather than volume of conversions. Over time, while you should expect higher levels of total value and profit, it may come at the expense of near-term conversion volume.

Our fees are comprised of 3 parts:

• Monthly data infrastructure fee to ensure continuous updating and refreshing of pLTV machine learning models by our data science team.

• Performance-based fee - calculated as a percentage of the ad spend directed towards pLTV-driven campaigns.

• (Optional) Agency consultancy fee to train the agency team on importing pLTV conversion events, building and launching A/B testing campaigns, and reporting on success.

Yes.

Predictive Lifetime Value (pLTV) is a machine learning algorithm that predicts a new customer’s lifetime value upfront, at the time of acquisition. Today, it’s a solution for improving Google Ads, though more platforms will be added soon.

By feeding Google Ads reliable pLTVs, you can move from cost-based bidding to profit-based bidding in Google. For client businesses that count on recurring revenue, this solves an age-old question in acquisition marketing -- what should I pay today to acquire this customer?

pLTV is built from a variety of first-party data sources, including but not limited to:

• Zero-party data that customers supply along the path to signup

• In-product behavioral data

• Customer payment data

• Geographic, demographic and firmographic data

The clients who are ready for pLTV ideally will have:

• Recurring revenue business models (e.g. SaaS or subscription eCommerce).

• Data aggregated in a data warehouse like Google BigQuery, Snowflake, or Amazon RedShift.

• Historic LTV analysis that can serve as a baseline for pLTV modeling.

• Healthy ad spend in Google Ads (minimum $25,000 per month).

• Relatively large number of conversions recorded monthly in Google Ads (minimum 100 conversions per month).

• Willingness to provide ETL Engineering internal support to our team for a limited time.

• Willingness to allow our team to access anonymized, non-PII customer data for the purpose of modeling pLTV.

• Alignment across Sales and Marketing teams, with a common interest in measuring success on the basis of LTV/CAC.

• Reasonable patience. This solution takes a few weeks to build and import into Google Ads, followed by 4-5 conversion cycles before you can judge its effect. Though, it’s worth the wait.

pLTV trains Google to optimize for overall profitability over cost-based volume. Over time, while you should expect to see higher levels of total value and profit, it may come at the expense of near-term conversion volume.

Our fees are comprised of 3 parts:

• Data infrastructure fee of ~1K/month - to ensure continuous development, testing and improvement of pLTV machine learning models by our data science team.

• Agency consultancy fee of 100$/h - a one-time fee to train your agency team (10 people included) on importing pLTVs properly from the data warehouse, and setting and monitoring up proper testing campaigns.

• Performance-based fee - calculated as 1-2% of the ad spend that’s committed to pLTV-driven campaigns (the pLTV budget being 10% of the whole ad spend).

Yes.