Pricing Strategies
Protect your profit margins with a complete view of the market. Execute competitive adjustments based on price and promotion monitoring, with data that arrives structured, dated and ready for the model that makes the decision.
What our clients say
Real results from companies using our platform
"By automating competitor price tracking, we were able to react 80% faster to market variations."
Camila R.
Pricing Director, Retail
"Dynamic promotion monitoring saved our corporate margins; it is the fundamental tool of our commercial team."
David L.
Financial Manager
Key features
Everything you need to succeed
Coverage of products and stores tailored to you
We work on the list of products and stores you define. We can start with a small subset and scale as the use case grows. Starting narrow is usually the better call: it lets you verify the data answers the question you had before committing to full coverage.
Matching between your products and competitors'
We define the matching key together (code, normalized title, attributes) so the price comparison is valid. This avoids comparing against non-equivalent products. When a universal code exists we lead with it; when it does not, we build the key from brand, model and the attributes that actually distinguish one variant from another.
Configurable frequency for your case
Some pricing decisions need daily updates; others work fine with a weekly or monthly run. We define the optimal frequency in the first meeting. Categories with frequent promotions justify several captures a day; a strategic positioning analysis rarely needs more than one weekly run.
Delivery ready for your pricing tool
CSV, Excel, JSON, via API, or direct import to your database or pricing tool. We define the schema at the start so nobody on your side has to reshape a file before using it. The columns stay stable across deliveries, which is what lets you build history instead of isolated snapshots.
What the pricing dataset contains
Every delivery arrives with a fixed schema, so your pricing model always reads the same columns. These are the fields we usually deliver; the final set is agreed at kickoff based on what your decisions actually require.
Fields delivered
- SKU
- Retailer internal identifier, the key for tracking one listing over time.
- Product name
- Title as published, normalized to enable matching across retailers.
- Current price
- Selling price at the moment of capture, discounts already applied.
- Previous price
- List price before the discount, when the site publishes it. Distinguishes a promotion from a repricing.
- Currency
- Essential in multi-country monitoring so figures are never mixed.
- Availability
- In stock, out of stock, or limited. A price on an unavailable product does not compete.
- Competitor
- Which retailer the record belongs to, for segmenting the analysis by player.
- Category
- Site classification, useful for aggregating and comparing by segment.
- Source URL
- Direct link to the listing so any figure can be audited against the origin.
- Timestamp
- Exact capture moment. Without it there is no price history, only a snapshot.
- EAN / GTIN
- Universal code. When it exists, it is the most reliable key for matching the same product across retailers.
- Promotion
- Type of active offer: coupon, bundle, installments, bank discount.
Sources we typically monitor
These are examples of the kind of source we cover; the actual list is built from your competitive set. If a retailer matters to your business and publishes its prices, we can monitor it.
- MercadoLibre
- Amazon
- Falabella
- Frávega
- Coppel
How to know if you need to automate your Pricing Strategies
If your sales team spends countless hours comparing market value against competitors manually via spreadsheets, and still always lags behind discounts, you need this service. The symptom is easy to recognize: the price report is ready on Thursday with Monday data, and by the time someone reads it the market has already moved twice. Manual monitoring does not fail because the team is slow — it fails because the frequency a person can sustain is not the frequency at which prices change.
How we help you
Our Price Intelligence service extracts, cleanses, and standardizes rate fluctuations across thousands of platforms to provide you with actionable alerts about your catalog. The hard part is rarely reading a price: it is making sure the price you read belongs to the product you think it does, at a unit you can actually compare, captured at a moment you can date. That is where a comparison stops being a spreadsheet and starts being a decision input.
You define which products and stores to monitor. We set up the tracking, including the sources that block automated access.
We deliver updated prices at the frequency your case needs, with a stable schema your pricing model can read without adaptation.
We match your products with equivalent competitor products so the comparison is useful, and we normalize sizes and bundles to a comparable unit.
When a store changes, we handle it before your report arrives with gaps — the maintenance is part of the service, not an extra.
The power of expert talent ready to provide you with the best pricing strategies
Tell us which products and which competitors matter to your business, and we will show you what the dataset would look like before you commit to anything. Most projects start with a single category to validate that the data answers the question, and grow from there.
Frequently asked questions
Clarify your doubts about how our data process works.
We define the key together: universal code (if it exists), normalized title, brand, model, key attributes. With that key we group equivalent products so your comparison is useful.
At the frequency your case needs: daily, several times a day, weekly, or monthly. For active pricing we usually recommend at least daily; for strategic analysis, weekly is sufficient.
Yes. Beyond price we can capture stock, promotions, discounts, coupons, bundles, and other attributes that affect the pricing decision. We agree on this in the scope definition at the start.
Our team monitors every delivery. When there are changes that affect the capture, we adjust the extraction before the next run. If the change prevents covering that source, we notify you with options.
CSV, Excel, JSON, API, or direct import to your database or pricing tool. We define the schema and destination at the start.
We normalize to a comparable unit — price per liter, per kilo, per unit — so a 2-liter bottle and a six-pack of 500 ml can be compared honestly. Without that normalization, a price comparison across retailers produces conclusions that look precise and are wrong.
Yes, when the site shows it. Having list price and current price side by side is what lets you tell a real promotion from a permanent repricing — two situations that call for opposite responses.
Included. When a retailer redesigns its site or changes how it renders prices, adjusting the extraction is our job, not an extra. That is the actual difference between buying a scraper and contracting a data service.
History is built from the moment monitoring starts: each capture is stored with its timestamp, so after a few weeks you have a series instead of a snapshot. Retroactive history is a different matter — a price that was never published anywhere cannot be recovered, so if seasonality analysis is part of the plan, the earlier monitoring starts the better.
Need to add engineers to your team instead of receiving ready-made data?
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