Pricing strategy is shaped by how products are structured, how value is communicated and how customers compare options. In financial services, customers are not evaluating price or fees in isolation. They are weighing them alongside product features, service levels and how the offering compares to alternatives in the market.

Pricing research can include a range of approaches, from qualitative exploration and value assessment to structured quantitative methods such as conjoint analysis and discrete choice modeling. These latter approaches are designed to reflect how customers evaluate complete offerings, not just price, and how they make trade-offs between features, brand and cost in real-world decisions. This article focuses on conjoint and discrete choice modeling as one excellent way to gain robust insights that inform your pricing strategy for financial services products. At the most basic level, conjoint analysis involves presenting consumers with a series of product descriptions at different price points, varying attributes such as features, price and, in some cases, brand. Discrete choice modeling builds on this by structuring those choices to more closely reflect real-world decision-making.

Pricing research begins with clearly defining your product that you want to price

In financial services—including digital products, credit cards, banking products, brokerage accounts, wealth management offers and more—important questions to ask are:

• What are the product features? (e.g., digital access, advice, frequency of advice, consulting, speed, rewards, interest rates, service)
• What other companies offer this product? (e.g., top banks, credit card or payment companies, brokerage or wealth management companies)
• How will pricing be structured? (e.g., asset-based fee, flat fee, subscription, price per product purchase)
• What is the highest and lowest price point? (e.g., free to the highest possible price)

Answering these questions ensures that price is evaluated in the context of a complete offering. This step also requires confirming that your offering is clearly understood by customers. It is very important that each of these is defined in a clear and simple way so that when someone is reading the description, they understand quickly what is being included in the product. For example, in wealth management, terms like “guidance” and “advice” can mean different things to different customers and affect how they perceive value. Establishing a shared understanding of your product ensures that pricing is evaluated in the context of a clearly defined offering.

Trade-offs are measured through structured choice 

Once your offering is clearly defined, a discrete choice (DCM) exercise is used to measure how customers make trade-offs. Respondents are shown sets of offerings that vary across attributes/features and price, and are asked to choose between offerings repeatedly. This produces data on how each product attribute impacts their preferences and how price and brand interacts with other attributes.

The output includes:

• The relative importance of different attributes/features
• The relationship between price and attributes/features
• The combinations of attributes/features that increase likelihood of choice, which help inform potential configuration of products or packages
• Expected demand under different configurations and prices

For example, in wealth management, pricing research leveraging conjoint analysis and discrete choice modeling can be used to evaluate fees and fee structures alongside various advisory offerings. Offerings may vary in advisor access, level of planning support and fee structure, and more. This method allows you to measure how customers trade off price against advice-related attributes.

The graphic below is a hypothetical illustration of how client demand declines as fees increase, highlighting strong price sensitivity.

As another example, in financial subscriptions or packages, this pricing research method can be used to evaluate how offerings are valued at different price points and the role brand plays in shaping that value. In B2B payments, the same approach can be applied to assess transaction pricing alongside platform capabilities for B2B payments solutions. Offerings may differ in payment speed, supported payment methods, integration capabilities and pricing structure. Results may show, for example, that larger companies are less sensitive to fees when a product delivers workflow efficiency and system integration that saves time and offsets those costs.

Identifying potential new markets

Pricing sensitivity and perceptions of value can vary across customers. Latent class analysis and related techniques can group customers based on how they make decisions, rather than only on demographics. This supports the development of pricing structures and strategy that reflect variation in the market and can be used to identify new target markets.

Outputs are tied to product decisions and GTM strategy

Pricing research that leverages discrete choice informs decisions beyond price. Results can be directly applied to product positioning, messaging and targeting as part of broader go-to-market strategy.

Typical applications of this type of research include:

• Organizing features into tiers or bundles
• Determining which features belong in which bundles at different price points
• Estimating how pricing affects preferences across market segments
• Identifying where changes to an offering improve potential market share

Outputs from this type of pricing research are typically delivered as scenario simulations, allowing you to test alternative product configurations and estimate resulting demand, revenue and segment-level uptake. The example below illustrates how a discrete choice (DCM) simulator translates differences in brand, access, service level, asset minimums and advisory fees into estimated share of preference by asset segment, highlighting how preferences shift in response to changes in offering design and fees.

Application across financial services

Pricing research that leverages discrete choice modeling can be applied across banking, wealth management, payments, lending and many digital financial products. While product structures differ, customers consistently evaluate complete offerings and make tradeoffs. Pricing research that reflects these trade-offs helps ensure strategies are aligned with real customer behavior and competitive dynamics. At Logica, we design pricing research that reflects real customer trade-offs and translates directly into product, pricing and go-to-market decisions.