Telco Pricing & Portfolio Optimization: Balancing ARPU, Churn, and Growth

• Stop treating elasticity as “the answer” when portfolio and promos move around it.

  Build decisions that survive price changes, promotion changes, and offer architecture changes at the same time. 

• Choose the KPIs you’re actually willing to trade off and then design the offer around that choice.

  Most organisations optimise one or two KPIs and unintentionally degrade the rest. 

• Reduce portfolio complexity without narrowing customer choice.

  Identify which features truly drive value and which are just noise that makes decisions slower and outcomes worse. 

• Move from segmented averages to individual-level offer logic.

  Hyper-customisation only works when you can predict how different customers react based on what they’ve done before. 

• Treat “single source of truth” as a harmonisation problem, not a storage problem.

  The hard part is combining inputs (elasticities, studies, transactional data) into decisions that stay coherent over time.

Watch the session if you’re trying to make pricing, promotions, and portfolio decisions that remain coherent when customer behaviour, competitors, and internal priorities shift at the same time.

Meet the Speakers

Mario Koenigsfeld VP Revenue at Buynomics

Mario is VP of Revenue at Buynomics, where he leads and scales global commercial teams. With a background at Simon-Kucher and over 9 years of experience in pricing, sales strategy, and revenue optimization, he combines analytical rigor with hands-on leadership to build high-performing teams and drive sustainable growth across industries.

Session Highlights

What data is non-negotiable to model customer behaviour accurately?
Transactional data plus product/tariff information are the foundation: what customers bought, how much they bought, and what attributes were in those products. Everything else can add accuracy, but you can’t bypass that baseline. 

What additional inputs improve the model beyond transactions and product attributes?
If you have them, you can incorporate promotional information, visibility/placement of tariffs on the site, and competitor moves and portfolios. The impact depends on what data sources exist in your organization and how consistently they’re captured. 

How do you separate true price sensitivity from “elasticity” that’s really cannibalization or competitor reaction?
The core is modelling willingness to pay for product attributes, so you can see what portion of choice is driven by value from features versus reaction to price changes. That attribute-level view lets you interpret switching as portfolio mechanics rather than assuming it’s pure price sensitivity. 

Where do organizations hit the ceiling with elasticity models in practice?
Elasticity is often too static: conclusions can swing dramatically as the market state changes, producing conflicting guidance (raise prices one week, cut them the next). That makes it hard to follow a coherent logic in portfolio design over time. 

Why isn’t segmentation “granular enough” once portfolios get complex?
Segmentation is a step in the right direction, but scaling it means multiplying segments, options, and decisions until compute and operational complexity become constraints. At that point, the organization needs a more advanced way to evaluate many offer combinations across many customer groups. 

How should leaders think about promotion structure trade-offs over the contract period?
A key trade-off is short, deep discounts versus longer, lighter incentives. For example 50% off for six months versus 10% off for twelve and how that changes renewal and churn outcomes. The point isn’t the “right” answer; it’s that you need a way to evaluate how price sensitivity and perceived product value interact over time.