Competitor Brand

Competitor brand data captures metrics about your competitors' brands in the market, such as awareness, liking, penetration, consideration, and recommendation. These metrics help quantify how competitors' brand strength influences market dynamics, customer preferences, and your own business performance.

Similar to your own brand data, these metrics are often sourced from third-party providers like YouGov. Alviss AI uses this data to model competitive effects on your sales, KPIs, and market share. By including competitor tracking, you can perform comparative analyses, identify threats or opportunities, and refine strategies accordingly.

Data Requirements

The Competitor Brand Data file must include the following columns (headers). All columns are required unless marked as optional:

• Country (string, required): A three-letter country code per ISO 3166 standard (e.g., "SWE" for Sweden), indicating where the metrics were collected.
• Region (string, required): The region within the country (e.g., "all" for nationwide or a specific ISO 3166-2 code like "SE-AB" for Stockholm County in Sweden).
• Grouping (string, optional): For additional segmentation, such as product categories, demographics, or sales territories (e.g., "all" if not applicable).
• Date (date, required): The collection date in ISO 8601 format (YYYY-MM-DD).
• Competitor (string, required): The name or identifier of the competitor company (e.g., "Competitor1").
• Brand (string, required): The specific brand name or identifier associated with the competitor (e.g., "AwesomeInsurance"). Use this if competitors have multiple brands; otherwise, it can match the Competitor field.
• Metric (string, required): The metric type (e.g., "Unaided Awareness", "Aided Awareness", "Consideration", "Likeability", "Penetration", "Recommendation").
• Value (float, required): The metric value (e.g., a percentage like 87.65). Use consistent units (e.g., percentages as decimals or whole numbers; specify in your metadata if needed). Handle missing values with NaN or leave blank—Alviss AI will flag inconsistencies during upload.

Supported Formats

Data can be uploaded in long (tidy) or wide (pivoted) format. Choose based on your data pipeline:

• Long Format (Recommended for flexibility): Each row represents a single observation (one metric per competitor, brand, date, etc.).

• Wide Format (Useful for spreadsheets): Columns represent combinations of Brand and Metric, with rows as dates. The first few rows define fixed attributes (e.g., Country, Region, Competitor).

Best Practices

• Consistency: Ensure dates align with your project's periodicity (e.g., weekly data if the project is set to weekly granularity). Mismatched granularity will cause upload errors.
• Data Quality: Check for outliers, missing values, or inconsistencies before upload. Use the Activities dashboard to visualize and validate post-upload.
• Granularity Alignment: All data in a project must match the chosen periodicity (set during project creation). For details, see Projects.
• Competitor Tracking: Include multiple competitors for robust analysis. Use clear, consistent naming for Competitor and Brand fields to enable easy filtering and comparison.

For uploading instructions, see Upload Data. If you encounter issues, contact support or refer to the API for programmatic uploads.