Next-level targeting, bidding and optimization
Digital activity is typically optimized for driving conversions, but a single action misrepresents marketing return on investment. It’s loyal, repeatedly converting customers that represent real value. We’ll analyze long-term customer value, and reveal the characteristics of customers across a range of value segments.
When we train a machine learning model to give an accurate prediction of lifetime value (LTV), at the moment the customer is first acquired, LTV will then become game-changing for your audience targeting, bidding and optimization.
An LTV modeling project has several stages. We’ll prove the value of the work early on, taking key stakeholders with us. We work with omnichannel customer data when available (made possible by our data integration experience) because it makes for a more complete record of value.
We explore the cost and value of ongoing ad interactions, and we use multiple models to determine whether shorter-term customer value is a good indicator of long-term value.
In deployment we enable models for continuous learning, and our work becomes a lasting asset.
The Croud difference
Your data is only as valuable as your ability to activate it. As an independent advertising business with market-leading expertise, Croud can maximize its value. It means our work delivers real-world effectiveness. It also means that we’re marketing data natives, so we understand the practical application of every feature in your marketing data. We know what we’re looking at, and this is fundamental to data feature engineering. Quite simply, we build effective models for marketing, faster.
Our areas of expertise
Collecting and readying data
We’ll integrate relevant data from disparate systems, then clean and engineer it ready for analysis and modeling.
Analysis and insight
We’ll reveal the LTV profile of your customer base, and contrast LTV against revenue as a measure of campaign performance.
We’ll determine the optimum “lifetime” date range, and train a machine learning model to predict LTV using customer acquisition data.
Deploying and activating
LTV metrics will track automatically into ad and analytics platforms, and the model will be set up for continuous learning.
Testing and proving
Performance measurement is fundamental in developing a machine learning model, but split-testing also proves the effect of the new metrics.
Presentation & training
Insight and business case presentations can build support, and we’re keen to build skills in client teams.