DATA COLLECTION

Definition

Pricing data collection refers to all the processes involved in gathering the information needed to make pricing decisions: competitor prices, internal sales data, inventory levels, purchase costs, product attributes, customer behavior, and external indicators. Without structured data collection, pricing relies on intuition. With reliable and continuous data collection, it becomes a measurable and manageable lever. There are multiple sources (ERP systems, point-of-sale data, web scraping, market panels), and integrating them is a topic in itself.

Why it's important

• Gathering the raw data: the foundation of any pricing analysis—without reliable, up-to-date data, models produce inaccurate recommendations.
• Identify weak signals (competitor activity, sales anomalies, imminent stockouts) that require a quick decision.
• Enable retrospective measurement of the impact of pricing decisions, thereby closing the learning loop.

A concrete example

An omnichannel retailer is implementing a data collection platform that aggregates data from four sources: in-store sales (updated daily via the ERP system), online sales (in real time), competitor price data (from 10 retailers via daily web scraping), and purchase costs (updated monthly via supplier EDI). Before the platform was implemented, this data was stored in separate silos and was manually consolidated each month by a pricing analyst. After the platform went live, the consolidation time was reduced from 4 days to less than an hour.

How to measure/use it

Structuring a pricing data collection process requires mapping out needs (what data is needed for which decisions), identifying sources (internal and external), defining a collection frequency tailored to each source, and implementing a quality assurance layer (anomaly detection, handling missing data). Pricing analytics tools natively integrate the main data sources via standard connectors (ERP, BI, PIM) and web scraping APIs. Data governance is an issue that must be addressed in parallel.

Common Mistakes

• Collecting too much data with no defined purpose: data lakes without governance end up as unusable quagmires.
• Underinvesting in quality: Even a 10% error rate in a database of competitor prices is enough to undermine confidence in all analyses.
• Neglecting frequency: A monthly competitor analysis is no longer of any value in a market that changes daily.

Learn more

• Research & Data: Price Tracking and Web Scraping to Streamline Competitive Data Collection.
• Solutions: Pricing Analytics, which centralizes and validates data from all your sources.
• Consulting: Operational Pricing Consulting to structure data governance for pricing.
• Resources: See our pricing FAQ for data quality standards related to pricing.

Mini FAQ

How often is trash collected?

It depends on the sources and how the data is used. Internal sales: daily. Competitor prices: daily for e-commerce, weekly for in-store sales. Purchase costs: with each contractual transaction. External panels: monthly or quarterly.

Do we need to collect everything in real time?

No. Real-time data comes at a cost (both technical and financial) and does not always add value. For most pricing decisions, data that is 24 hours old is sufficient. Real-time data is primarily justified in competitive e-commerce environments where KPIs are key.

How should missing data be handled?

Three approaches: exclude the relevant references (safe but limiting), assign a default value (to be used with caution), or use AI estimation models that infer the missing value based on other attributes.