Visits

Visits data tracks traffic metrics, such as website visits, store footfall, or other forms of consumer engagement that indicate interest or exposure to your business. This includes sessions from various channels like direct access, paid search, or email campaigns, helping to understand how traffic correlates with conversions and sales.

Monitoring visits is key to analyzing consumer behavior and funnel efficiency. Alviss AI uses this data to model how traffic scales with outcomes like revenue, quantifying the impact of marketing channels on demand. For example, it can reveal how organic search visits drive in-store purchases, supporting optimizations for acquisition strategies. Sourced from analytics tools (e.g., Google Analytics for web, sensors/counters for physical stores), this data enhances attributions, simulations, predictions, and response curves by linking upstream engagement to downstream results.

Data Requirements

The Visits 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 visit 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).
• Channel (string, required): The traffic source or channel (e.g., "BrandedPaidSearch", "Direct", "Display", "Email", "GenericPaidSearch", "OrganicSearch").
• Visits (float, required): The number of visits (e.g., 11339). Can be fractional for weighted or averaged data; handle missing values with blanks or zeros—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 channel per date, etc.).

• Wide Format (Useful for spreadsheets): Columns represent different channels, with rows as dates. The first few rows define fixed attributes (e.g., Country, Region).

Best Practices

• Consistency: Ensure dates align with your project's periodicity (e.g., daily visits if the project is set to daily 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.
• Channel Tracking: Use clear, consistent channel names to enable segmentation. Align with media or sales channels for cross-analysis.

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