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Marketing Manager
In this episode of the tgndata podcast, Alex welcomes back pricing expert Krzysztof Szyszkiewicz to explore one of the most powerful — yet often overlooked — sources of pricing intelligence: internal data.
Many companies invest heavily in external tools and market research but ignore the goldmine of information already inside their organization. Sales history, cost structures, marketing analytics, and customer behavior data can all provide critical insights for building smarter and more profitable pricing strategies.
This discussion dives into how companies can analyze internal data to make better pricing decisions, improve margins, and develop a structured pricing process.
Internal data is often the most valuable starting point for a pricing strategy.
In this episode, Alex and Krzysztof discuss:
The three main categories of internal pricing data
How historical sales data reveals pricing opportunities
Why discounting is frequently misunderstood
The role of cost analysis and margin layers
How companies can use customer behavior to optimize pricing
Why price elasticity is often misused
How to ensure data accuracy and reliability
The conversation highlights that data-driven pricing doesn’t have to start with complex algorithms. Companies can achieve powerful results simply by analyzing the information they already have.
The Three Types of Internal Data for Pricing Decisions
According to Chris, companies should focus on three primary internal data sources:
Financial and P&L Data
This includes:
Financial data is critical because it reveals patterns in:
Often, companies can generate fast ROI by simply optimizing discount strategies based on this data.
Marketing Data
Marketing analytics help companies understand customer demand signals.
Important sources include:
By comparing:
Companies can determine whether pricing is preventing conversions or leaving margin opportunities on the table.
Customer Behavior and Usage Data
Usage data shows how customers interact with products.
Examples include:
This type of data can help identify:
Historical data reveals how customers actually responded to previous pricing decisions.
Chris highlights three key areas where historical analysis is especially valuable.
Certain products naturally experience seasonal demand patterns.
Example:
Sunscreen demand peaks in summer
Seasonal goods require dynamic pricing adjustments
Companies that ignore seasonality often miss opportunities to increase margins during peak demand periods.
Discounts are extremely common in eCommerce, but they often destroy margin if not analyzed carefully.
Chris explains a simple example:
Product price: €1,000
Cost of goods: 60%
Margin: 40%
If the price is reduced by 15%, the company must increase sales by approximately 60% just to maintain the same margin.
Without historical data, companies often run promotions that reduce profitability rather than increase it.
Some products act as entry points for customers.
These products:
Are frequently the first item added to a cart
Attract new customers
Influence long-term purchasing behavior
Companies often price these products more competitively to encourage initial conversion and future customer value.
Price elasticity measures how demand changes when price changes.
In theory, it is a powerful pricing tool.
However, Chris explains why it is frequently misapplied in real business environments.
Elasticity calculations often fail because they ignore key factors such as:
Competitor pricing
Marketing spend
Market trends
inventory levels
Without these variables, elasticity models can lead to incorrect pricing conclusions.
For smaller companies, simpler analyses often provide better insights with far less complexity.
One powerful analysis discussed in the episode is Revenue per Visit.
This method combines:
Website traffic data
Sales data
Pricing data
It helps identify two important scenarios.
This usually means the price may be too high.
Customers are interested in the product but are not converting.
This suggests the product could support a higher price, or marketing investment should increase.
This simple analysis can quickly reveal pricing opportunities across product portfolios.
Krzysztof introduces a simple framework for identifying price-sensitive products.
Products are classified based on:
Category importance
Subcategory importance
Product importance
This creates a structure like:
AAA products → highly important items (very price sensitive)
CCC products → long-tail products (ideal for pricing experiments)
Companies should begin pricing experiments on less critical products to reduce risk.
Cost analysis ensures pricing decisions are financially sustainable.
Chris recommends analyzing three margin levels.
Margin 1
Revenue – Cost of goods sold (COGS)
Margin 2
Revenue – COGS – Shipping costs
Margin 3
Revenue – COGS – Shipping – Marketing costs
The third margin layer is particularly important because it reflects true profitability after marketing expenses.
Many companies mistakenly increase prices while ignoring how marketing costs change.
Internal data alone can provide a powerful foundation for pricing strategy.
While external data such as competitor pricing and market research can enhance insights, internal analysis already enables companies to:
Identify pricing opportunities
Run pricing experiments
Improve margins
According to Chris, companies using internal data effectively are already ahead of most competitors.
In the next episode of the tgndata podcast, Alex and Krzysztof will explore external data and competitive pricing intelligence, including:
Competitor pricing analysis
Market trends
Competitive positioning
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