ML
Sales & Marketing
PLTV Calculation for CAC Optimization
Problem
Digital product operators seek ways to reduce CAC.
Solution
We've built an ML algorithm that predicts LTV for users, acquired through different channels and adjusts the target CA spend for each channel. This can also be paired with an attribution mechanism, that identifies the user's engagement (and corresponding costs) in earlier stages of the sales funnel.
Results:
The implementation of these models has yielded significant results:
- Improved prediction accuracy of potential customer conversions by 35%, leading to a more focused and cost-effective marketing strategy.
- Enhanced resource allocation efficiency within the sales team, with a 25% increase in lead conversion rates.
- A cumulative 20% uptick in revenue through dynamic pricing and effective upselling strategies.
- More engaging and relevant content production, leading to higher student satisfaction and course completion rates.
- Increased customer retention by 15% and a notable improvement in Lifetime Value through targeted engagement initiatives.
Conclusion:
The success of our machine learning solutions in transforming their business operations demonstrates our commitment to advancing educational technology and supporting our clients in achieving their business goals. We continue to work closely with them to innovate and refine strategies, ensuring sustained growth and success.