Start with a marketing question
Choose a decision the dataset should inform: which product categories competitors emphasize, how advertised prices change, what customers discuss in reviews or which topics appear in search results. A clear question determines the sources, record types and collection frequency.
Set the market, language, time window and comparison group before collecting. Public web observations can complement your own campaign and sales data, but they do not reveal a competitor’s private revenue, conversion rate or advertising return.
Monitor competitors and product positioning
Collect supported product or listing fields such as titles, descriptions, categories, attributes and source URLs. Compare how competitors describe their offers, organize their catalogs and introduce or remove products.
Use stable source IDs and a product-matching strategy across stores. Similar titles do not prove that two listings represent the same model, variant or package. Define how your analysis will handle bundles, sizes and missing specifications.
Compare pricing, promotions and assortment
Build repeatable observations of advertised prices and availability from supported ecommerce sources. Retain currency, collection time and any available variant, seller, promotion or delivery context alongside each observation.
For a fair comparison, decide whether you are comparing base prices or total delivered prices, and use equivalent products and search conditions. A current listing price is an observation, not proof of a completed sale or a historical price trend. Recurring collections create the timeline you can analyze.
Research customer review themes
Where supported, collect review text, ratings, dates and product references to explore recurring praise, complaints, purchase considerations and unmet needs. Retain original URLs so an analyst can read the context behind a theme.
Sentiment classification, topic grouping and summarization are downstream analysis steps. Review samples and ambiguous cases, and separate product feedback from delivery or seller complaints. Reviews reflect the people who posted them and should not be presented as a representative survey of all customers.
Study search results and content opportunities
Use supported search and content tools to collect result titles, URLs, snippets or article text. Compare the topics and questions visible for a defined set of queries, and identify material for an editorial research workflow.
Record query, language, region and collection time. Search positions vary with the conditions and time of collection. Do not infer search volume, website traffic or campaign attribution from a result page alone; those questions need additional data.
Track public market and brand signals
Supported public posts, company pages, news and business listings can contribute observations about launches, locations, messaging and market activity. Define which entities and keywords belong in your monitoring scope.
Keep source-provided engagement metrics distinct from your own calculated measures. A post count or a change in listed availability is a signal to investigate, not a direct measure of market share, demand or brand preference. Compare consistent sources and time windows.
Design a repeatable marketing dataset
Specify the schema, required fields, stable identifiers and timestamp policy. Decide whether the analysis needs complete snapshots or changes since a prior collection, and confirm the selected actor supports the required update behavior.
JSON can preserve nested records; CSV can support spreadsheet analysis when the fields are flat. Custom transformations and dashboard integration should be scoped explicitly. Validate a sample against the actual source and your analysis tool before scheduling larger runs.
Send your marketing data brief
Include the marketing question, competitors or representative source URLs, target market, required fields, expected volume and refresh frequency. Share an example table or report input so we can discuss the collection scope.
Existing actors may cover the sources you need; custom work begins with a feasibility assessment. Scraper outputs are source data. Strategy recommendations, finished dashboards, sentiment models and campaign attribution are not included by default.
Common questions
Can I use this data for competitor analysis?
Yes, when supported source fields answer your question. Define comparable products or entities and keep collection conditions consistent. Public observations do not expose private competitor performance.
Can I monitor competitor prices over time?
Choose a scraper with the required price fields and suitable recurring-run behavior. Retain collection times and product identifiers to build your own history; do not assume that a first run provides past prices.
Do review scrapers automatically provide sentiment analysis?
Check the actor’s documented output. Collecting reviews and classifying their sentiment are separate steps. Custom analysis or preparation requirements should be described in your brief.
Can I export marketing research data for a spreadsheet or dashboard?
Check the actor’s supported exports and match its fields to your destination. Include custom CSV mapping, data transformations or integration requirements when discussing a project.
Is this a marketing analytics dashboard?
The service provides data collection and scoped custom data work. You can use the output in your analysis tools; a finished dashboard or a strategy report is not included by default.
