Dashboard Access

There is no limit on the number of custom dashboards, views, or saved filters you can create. Each team member can build and save their own personalized views, and any view can be shared with the entire team or specific members with view-only or edit permissions.

Dashboard data refreshes automatically as new scraping jobs complete. On real-time plans, this means data can be as fresh as 5 minutes old. On standard plans, data refreshes according to your scraping schedule (hourly, daily, etc.). A manual refresh button is available for on-demand updates.

Yes. Pro and Enterprise plans include embeddable chart URLs that can be inserted into Notion, Confluence, internal dashboards, or any tool that accepts iframe embeds. A public share link option is also available for sharing specific views with external stakeholders without requiring a DataWeBot login.

Pro plans support up to 10 concurrent users and Enterprise plans support unlimited users. User seats are managed through the team settings panel where you can invite members, assign roles (Admin, Analyst, Viewer), and control which data sources each role can access.

Yes. Any dashboard view can be scheduled as an automated PDF or CSV report delivered via email on a daily, weekly, or monthly basis. You can also set up alert-triggered reports that send a snapshot of relevant dashboards whenever a significant data change is detected.

No. Every customer's data is fully isolated in separate database namespaces. There is no shared data environment. Your competitors cannot see your data, and you cannot see theirs. All data is encrypted at rest and in transit, and access is strictly controlled by your account credentials and team permissions.

Self-service analytics enables non-technical team members like category managers, merchandisers, and pricing strategists to explore and analyze data without relying on data engineers or SQL queries. It is valuable because business decisions in ecommerce move fast — waiting days for a custom report from the analytics team means opportunities are missed. Self-service dashboards put data exploration directly in the hands of the people making daily pricing and assortment decisions.

A KPI dashboard displays a fixed set of pre-defined metrics and is designed for at-a-glance monitoring — think executive scorecards with revenue, margin, and competitive position indicators. An exploratory dashboard is designed for ad-hoc analysis, allowing users to drag and drop fields, apply filters, and drill into data to answer unplanned questions. Most ecommerce teams benefit from both: KPI dashboards for daily health checks and exploratory dashboards for investigating specific competitive situations.

Role-based access control (RBAC) restricts dashboard access based on each user's role within the organization. For example, a pricing analyst might see full competitor pricing data, while a marketing manager sees only trend summaries. RBAC matters because ecommerce data often includes commercially sensitive information like margin data and pricing strategies that should not be visible to every team member, especially in large organizations with many users.

Line charts are essential for tracking price and sales trends over time. Heatmaps effectively display competitor pricing across large product matrices. Scatter plots reveal the relationship between price position and sales velocity. Treemaps show category-level market share distribution at a glance. The most valuable visualization depends on the question being asked — trend questions need time-series charts, comparison questions need bar charts, and distribution questions need histograms or box plots.

A data annotation is a note or comment attached to a specific data point, time period, or chart element within a dashboard. Analysts use annotations to mark significant events like competitor promotions, supply chain disruptions, or marketing campaigns that explain unusual data patterns. Annotations create institutional knowledge that persists across team members, ensuring context is not lost when the person who noticed an anomaly is unavailable.

Alert thresholds define the conditions that trigger a notification — for example, alerting when a competitor drops price by more than 5% or when your price position falls below the market average. Effective thresholds balance sensitivity with noise: too tight and you receive constant alerts for minor fluctuations, too loose and you miss important market moves. Best practice is to set different thresholds by product category based on typical price volatility in each segment.