Philippe van Mastrigt

Integrate Mather Price Modeling with Advantage

In acquisition and retention strategies, pricing policies have become a key element for publishers. Offering the right price at the right time to the right customer, while ensuring the best possible profitability, has increasingly been applied to conversion strategies as well as to renewal and retention offers. 

One consultancy company has unique expertise in this field: Mather Economics. Founded by Matt Lindsay, Mather Economics describes itself as:

“Mather helps develop fact-based strategies with our clients, and we help implement those strategies with analytics-as-a-service offerings that augment our client’s capabilities. Our recommendations are supported by our knowledge of best practices, benchmarks, and the latest thought leadership.”

Our companies often cross paths as we serve similar markets—whether at conferences like INMA and WAN IFA or at our customers' sites. We invited them to our 2020 user group meeting where they presented our clients with a compelling story of real-world analytics and concrete findings. But above all, we had the opportunity to create an integration between our Advantage platform and their data analytics models.

Price Elasticity, Strategy, and Profitability

Mather focuses on a key criterion of any pricing policy: its elasticity. In other words, how volumes (acquisitions, attrition, etc.) behave over time when price rises or falls. This elasticity varies greatly between customer segments, or in different economic contexts (periods of inflation, periods of declining interest in information, etc.). This elasticity has an impact on volumes, and it is through the customer's lifetime value (i.e., its profitability over its lifetime) that decisions can be made.

Typically, Customer Lifetime Value (CLV) is calculated according to the following formula:

CLV = [(Average Revenue Per Unit – Cash Cost Per User) * Expected Lifetime] – Cost Per Gross Add

In the early 90’s and 00’s, CLV was a critical metric I tracked in my role at Yves Rocher and then at Bayard Presse. The formula is robust, but the interactions are complex and can vary widely based on the specific markets being engaged. Additionally, revenue is often generated from a multiple-model approach from subscriptions to traditional sales to advertising to affiliations (and beyond!). The costs can be variable as well as most acquisition costs are only valid for given volume ranges. In short, while the formula is generically sound, it is complex and unique to each organization and market!

Let's take an example of a price increase decision

If I increase my subscription price, how much will my attrition increase? In certain segments (loyal customers) and in certain contexts, attrition will be low (up to a certain increase, of course). The price increase then increases the CLV. In other segments, attrition will be very high, and price increases can destroy profitability. This destruction is not confined to subscription margins, but also affects advertising and diversification revenues. 

Customer segments are one dimension, but there are also contexts (political, economic, etc.) and product variations. For example, Liesbeth Nizet, Mather's Managing Director for Europe, analyzed pricing policy in the light of the post-Covid situation and the return of inflation, at a Wan Ifra workshop in Paris in 2023. She also analyzed the product dimension with a specific focus on paper. Other parameters also exist, such as communication (impact of the message to convert or retain a customer), customer personalization, newsletter communication etc.

This knowledge is what Mather offers—combining robust analytics with a veritable treasure trove of hard-earned industry knowledge and insights. This the key to why we recommend Mather Economics to our clients—there are thousands of analytics engines, but without real market insights you are left with uncertain data decisions.

But how do you industrialize it on a day-to-day basis, offering the right price at the right time to the right customer? That's where Advantage's powerful capabilities come into play, as illustrated below. 

From study to action: integration between the Mather model and Advantage

The Mather model is regularly fed with a set of customers and their subscription data by Advantage’s data export mechanisms. For each subscription, the Mather model identifies the optimal pricing policy (attrition risk/sales optimization) and associates it with an identifier. This Mather identifier is associated with what Advantage calls a renewal chain. A renewal chain is a sequence of offers (and therefore prices) that the subscription follows when it renews. For example, a channel may be 6 months at a discounted price, then the following months at the reference price.

To put customers on the new chain (when a change is suggested), Mather feeds those subscriptions back to Advantage with the chain transactions that are automatically processed with Advantage’s transaction upload framework. When Advantage processes the subscriptions, they are moved to the new chains, following the optimized pricing policies recommended by Mather, without any manual intervention!

This integration is designed to catch people before their next renewal. Advantage supports virtually any subscription cadence (daily, weekly, monthly, etc.), so Mather considers the term length variables as well. For example, weekly subscribers may be more sensitive to price increases than monthly subscribers. Once the integration is set up, these variables and rules are built into the process and automatically handled. Our clients consult with Mather as well to adjust the rules over time as conditions change, markets evolve, and our client’s strategy shifts.

In this integration, while Mather’s strengths lie in its analytics decision-making, Advantage's strength lies in its openness—not only to interact with other applications but also to offer operational implementation of our customers' strategies.

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