Five Critical Data Use Cases for Optimizing Pricing and Packaging

In today’s highly competitive SaaS landscape, using data to optimize pricing is mission critical. With the right analytics and clearly defined use cases, companies can leverage customer and market insights to set prices and optimize packaging to accelerate customer acquisition, minimize churn, drive revenue growth through cross-sell and upsell, and ultimately enhance profitability. In this Executive Insights, L.E.K. Consulting explores five critical pricing and packaging strategies that make the best use of an organization’s available data.

Understanding customer willingness to pay

Robust data is essential for accurately assessing customer willingness to pay. Transaction data, usage patterns, and CRM data provide indicators of what customers are currently paying (and not willing to pay) and where opportunities for optimization exist. Voice-of-the-customer data (gathered either qualitatively through current/potential customer interviews or quantitatively through online surveys) delivers direct price sensitivity feedback from customers (see Figure 1). 

Armed with cleaned, complete and quality data, companies can leverage analytics approaches such as: 

• Conjoint analysis: Unveils underlying customer value perceptions by determining feature and price trade-off patterns 

• Van Westendorp pricing model: Identifies optimal price points through analysis of customer responses regarding differing pricing thresholds 

• Gabor-Granger method: Determines optimal pricing by assessing customer response data at specific price points 

Customer surveys can be used to directly gather detailed price-sensitivity data, which can help fill gaps in internal, transaction-level datasets. Qualitative customer conversations add additional color to the rationale behind price thresholds.

It is important to watch for signs that pricing may need reevaluation, such as the following key signals:

• Confusion from customers or sales teams around pricing tiers and packages indicates overly complex pricing. Surveys and conversations can identify pain points in the structure.

• Low upsell/cross-sell rates and high churn point to misalignment between pricing and customer willingness to pay. Directly gathering price sensitivity data can identify appropriate pricing thresholds.

• High levels of unstructured discounting suggest pricing is not optimally aligned to customer value. Surveys can segment customers and quantify value metrics to inform a more structured discounting approach.

• Improper customer segmentation leads to some customers paying below willingness to pay while others churn from pricing above willingness to pay. Customer research can identify segmentation gaps and price sensitivity differences between archetypes.

Continuously gathering pricing feedback is best practice, as the above signals can evolve over time as customer needs change. Direct customer input via surveys and conversations provides actionable data to update pricing for better alignment with customer value.

Leveraging transaction data to test and optimize pricing

A/B testing is a powerful tool, not just for optimizing pricing but also for reassessing pricing models, package design, onboarding ramps, upgrade journeys and more. The key to effective A/B tests is to appropriately tailor tests and ensure clear objectives.

For example, a company could run a short, two-week A/B test with a small price discount to measure the impact on new customer sign-ups. Or it could test a change in pricing model — for example, from per-seat pricing to consumption-based pricing — for a couple of quarters across a segment of renewing subscribers to evaluate changes in account expansion over time.

Whether quick, iterative tests or longer experiments are suitable depends on deal volume and sales cycle length. High volume and short sales cycles enable rapid testing. Low volume and long cycles (and more complex pricing evolutions) require more thoughtful, structured experiments.

In any pricing test, it is critical to:

• Clearly define hypotheses and objectives

• Choose appropriate test duration

• Identify and track relevant metrics rigorously

• Draw statistically valid conclusions

Establishing clear key performance indicator (KPI) baselines is crucial when testing pricing strategies. Relevant KPIs may include new customer sign-ups, average order value, net revenue retention, etc. By comparing metrics before and after pricing adjustments to baselines, it is possible to quantify the impact of A/B testing and dynamically optimize pricing levels. Utilizing A/B testing and leveraging transaction analytics enables data-driven pricing strategies tuned to evolving customer needs.

Building a real-time pricing optimization engine

If an organization has sufficient transaction volume and variability in pricing, it may be possible to leverage predictive analytics and machine learning to optimize pricing in real time. These engines are fueled by rich, transaction-level data. In building a real-time pricing optimization engine, several key considerations will influence design:

• Leverage machine learning algorithms to analyze pricing data and detect historical patterns and insights. Prioritize algorithms capable of managing large, complex datasets that will continuously learn and improve.

• Focus on predictive modeling to identify how different pricing decisions could impact KPIs such as unit sales, average revenue per unit, profitability, etc. This allows the engine to recommend optimal prices.

• Plan to regularly refresh training data to keep algorithms up to date on the latest market conditions.

• Build in flexibility to adjust for new products, evolving segmentation, competitive actions and other factors necessitating pricing evolutions.

• Establish pricing guardrails and validation checks before auto-implementing recommended price changes. This provides oversight and control, at least during an initial testing phase.

• Link pricing engine data inputs to CRM and enterprise resource planning systems to access more comprehensive customer and transactional data.

Building a customized real-time pricing engine can deliver significant revenue upside through automated, optimized pricing. While this does require investment in robust data pipelines and analytics expertise, the potential benefits make it a strategic option for companies with sufficient data and resources. With the right approach, a pricing engine can be implemented in a phased manner to control costs while still driving incremental gains.

Leveraging data for subscription optimization

For subscription-based SaaS businesses, optimizing pricing and packaging is crucial to drive loyalty and monetization from your existing customer base. By analyzing historical subscription data and patterns, companies can categorize customers into distinct archetypes that exhibit different buying behaviors and can predict when customers either are at risk of churning or are exhibiting strong potential for upsell and cross-sell. This allows for tailored interventions that can be embedded into employee workflows, with effective feedback mechanisms to ensure the model continues to learn and improve.

For example, declining feature usage, increasing support tickets or failed payment attempts can signal churn risk. Conversely, heavy usage of premium features, account expansion to additional users and executive engagement can indicate upsell readiness.

Identifying these signals allows businesses to proactively mitigate churn risks and encourage increased spending. To enable robust analytics, breaking down data silos and unifying subscription data across platforms is critical. This provides a single source of truth to model subscriber behavior, forecast trends, and inform subscription optimization strategies with greater confidence (see Figure 2).

Knowing your subscribers and predicting their needs is now a key competitive advantage. Companies that can harness this capability through state-of-the-art analytics will win subscriber retention and maximize lifetime value. As important as it is to acquire new customers, monetizing the customers you already have is ultimately more effective and efficient. Read more from L.E.K. here or listen here.

Driving sales efficiency and reducing revenue leakage

Lack of discounting oversight can lead to inefficient sales negotiations and unnecessary revenue leakage from excessive price discounts. By analyzing customer transaction data, companies can identify improper discounting patterns and quantify related revenue losses. This enables the development of a proper strategy, guidance and governance to optimize the discounting process.

When analyzing discretionary discounts, key data analysis approaches include:

• Comparing pricing to market benchmarks to understand competitive dynamics and customer discount expectations

• Plotting contract size vs. discounts to visualize inconsistent discretionary discounting patterns

• Evaluating variance in sales rep discounting to identify misalignments in rep incentives

• Evaluating realized price targets, volume, contract terms and discretionary discounts

• Quantifying revenue leakage through outputs like price waterfalls and discretionary discount schedules

Analyzing the data is the first step. To fully stem revenue leakage, companies then need to translate those insights into better strategies, tools and processes:

• Discounting strategy: Develop a standardized approach to discounts that aligns with the company’s growth and profitability goals. Define guidelines on appropriate discount levels and situations through tools such as price waterfalls and discount schedules.

• Sales incentives: Structure sales rep incentives to encourage discounting behavior aligned with the overall discounting strategy.

• Sales guidance: Equip sales teams to adhere to the strategy through training and configuring pricing in configure price quote systems. Create playbooks that outline discounting guardrails.

• Governance: Establish oversight on discounts through deal desk reviews and escalation policies for any exceptions. While governance should not be overly burdensome, some control is needed to enforce discipline.

Pricing and discounting optimization is an ongoing journey that requires a data-driven culture. By continuously analyzing transaction patterns, implementing structured discounting strategies and refining approaches through real-world results, companies can maximize sales efficiency and revenue retention over the long term.

Conclusion

Robust data analysis capabilities allow companies to understand customer willingness to pay, tailor pricing tests, build optimization engines, predict subscriber needs and identify revenue leakage opportunities. However, deriving insights is only the first step. Organizations must also develop strategies, tools and processes to translate analytic findings into improved monetization.

L.E.K. helps companies at any stage implement the robust analytics and data strategies needed to continuously optimize their pricing. By partnering with us, organizations can achieve pricing excellence to maximize customer lifetime value and drive long-term success.

For more information, please contact us at technology@lekinsights.com.

L.E.K. Consulting is a registered trademark of L.E.K. Consulting LLC. All other products and brands mentioned in this document are properties of their respective owners. © 2023 L.E.K. Consulting LLC