Metrics Available

Definition: The total number of times all object detail pages were viewed within the PWA, including repeated views by the same user. This total is based on the filters applied by the operator when viewing the dashboard.

Context: This metric is represented as a single number that provides insight into how frequently visitors engage with all object detail pages in the app, regardless of whether the same object is viewed multiple times by the same visitor. It encompasses all interactions across different objects, offering a comprehensive view of overall engagement within the selected filters.

Example: If Visitor 1 views Object A three times, Object B twice, Object C once, and Object D four times, while Visitor 2 views Object A twice, Object B once, Object C three times, and does not view Object D, the Total Object Views would be calculated by summing all of these views: (3 + 2 + 1 + 4 + 2 + 1 + 3), resulting in 16 views.

How it is Useful: This metric provides a cumulative total of all object page views, serving as a broad gauge of visitor engagement with the collection. Significant increases or decreases over time can signal trends in visitor interest with the Objects you have on display. A rise in views may suggest heightened interest in specific objects, while a decline might indicate the need for content updates, or marketing efforts.

Definition: The estimated total number of unique visitors who viewed any Object detail page in the PWA, based on the filters applied in the dashboard. Repeated views by the same user across different Objects are counted as one unique user.

Context: This metric reflects the number of distinct visitors engaging with Object detail pages across the PWA within the specific date range and filters set by the operator. Unlike Total Object Views, which counts every interaction, this metric focuses solely on unique users, providing insight into the breadth of engagement across all Objects.

Example: If User A views 4 different Object pages and User B views 3 Object pages multiple times during the filtered time period, the Total Estimated Unique Users will count them as 2 unique users, despite multiple views across different Objects.

How it is Useful: This metric helps gauge the total reach of Object engagement by focusing on unique visitors within the filters applied in the dashboard. It provides a clearer picture of the number of distinct visitors interacting with Objects during the filtered period, offering valuable insights into exhibit popularity. This data can guide decisions on exhibit promotion, content development, or broader marketing strategies that are better aligned with visitor engagement patterns in the filtered data.

Definition: A line chart showing the total number of object views per day, with daily nodes plotted to help identify trends in visitor engagement. This includes all views, meaning if the same person views the same object more than once, each view is counted.

Context: This metric visually tracks how often object detail pages are viewed on a day-to-day basis, giving insights into daily fluctuations in visitor interaction with objects. Repeated views from the same visitor are included, allowing for a full picture of engagement.

Example: Each day's total views are calculated based on visitor activity. For example, on Monday, 80 visitors accessed object pages, with 30 of them revisiting the same objects later in the day, resulting in a total of 110 views. With each day's total represented as a data point on the line chart, the chart might display 110 views for Monday, 90 for Tuesday, 135 for Wednesday, 65 for Thursday, and 200 for Friday. This allows operators to analyze engagement patterns, noting peaks like Friday and dips such as Thursday throughout the week.

How it is Useful: This metric provides an overall view of visitor engagement with your objects on a daily basis. While it doesn't show individual object performance, it helps identify patterns in overall visitor activity, such as spikes in engagement on certain days. This can help operators correlate increased interest with events, promotions, or specific days of the week, offering insights into broader engagement trends across the entire collection.

Definition: A pie chart that provides a detailed breakdown of how visitors accessed Object detail pages within the app, represented as percentages. The center of the pie chart displays the total number of object page views, and when you hover over any slice, you can see the number of views attributed to each specific source. The sources are categorized as follows:

Context: This metric highlights how visitors are navigating to objects, showing which entry points are most popular for accessing content related to all your objects in your venue.

Example: Suppose your objects have 100 total views. Of those, 60% come from visitors scanning QR codes, 25% from searching for the object within the app, and 15% from accessing it through nearby object lists. The breakdown shows the largest slice in the pie chart as Scans, the second largest as Search, and the third largest slice represents Nearby. This visualization helps demonstrate how visitors are interacting with your objects, with Scans being the most common method, followed by Searches and Nearby recommendations.

How it's useful: This metric helps institution operators understand how visitors prefer to access objects. For instance, if most views come from Scans, operators might want to ensure that QR codes are placed prominently throughout the venue. If searches are frequently utilized, it may indicate a need for keyword-rich tagging. By identifying these trends, operators can gain insights into visitor behavior and engagement patterns.

Definition: A ranked table displaying popularity of each of your objects and how it was accessed. The table includes sortable columns for total views, unique users, and all of the view sources such as direct views, searches, scans (QR or NFC), tags, spaces, and nearby objects. Each metric and view source is detailed in the sections above.

Context: This metric provides a detailed view of object popularity and visitor access patterns. Each row in the table represents a specific object, with columns displaying total views, unique views, and how visitors accessed each object’s detail page. By clicking on the column headers, you can sort the data to easily analyze interaction trends and access sources across your collection. Note that the table displays only six objects at a time; use the arrow buttons in the bottom right corner to view additional objects.

Example: The table is ranked by total views by default, with the most-viewed object at the top. For instance, Object A might be ranked first with 500 total views and 350 unique views, including 200 views from scans, 100 from direct views, and 150 from searches. Object B, ranked second, could have 400 total views and 380 unique views, with the majority of views coming from searches and nearby objects. By default, Object A would appear above Object B because it has more total views. However, if you sort the table by unique views by clicking on that header, Object B would move to the top of the list, as it has a higher number of unique views compared to Object A. This allows you to easily identify not just the most-viewed objects, but also those with the highest number of unique visitors.

How it’s Useful: This metric helps identify which objects are the most popular and how visitors are discovering them. By analyzing the breakdown of views by source, operators can understand which methods of discovery are most effective. For instance, "Views from Object" shows how often an object was accessed from the Nearby section of another object, highlighting how objects within the same gallery engage visitors. Similarly, "Views from Tags" illustrates how interconnected objects influence visitor behavior. This data can help operators enhance signage, marketing materials, or the organization of objects within the PWA.

Definition: A ranked table showing media associated with objects, including sortable columns for the number of plays, media type (e.g., audio, video), average completion rate percentage, average played seconds, and full duration seconds(the total length of the file.)

Context: This table provides insights into media engagement linked to objects. By default, it ranks media by the number of plays, but you can sort by other columns to analyze media effectiveness, including average completion rates and play durations. Each row represents a media item, displaying its play metrics and engagement details.

Example: Media A might top the list with 200 plays, audio type, an average completion rate of 40%, an average played duration of 1 minutes, and a full duration of 2.5 minutes. Media B, with 150 plays, could be a video with a 60% completion rate, an average played duration of 1.5 minutes, and a full duration of 3 minutes and it would be displayed below Media A by default. Sorting by other columns will show which media has the longest engagement and other patterns which could result in these two pieces of media swapping places such as sorting by average completion percentage.

How It’s Useful: This metric highlights which media is most popular and engaging. By examining these details, operators can gauge visitor preferences and the effectiveness of media formats. For instance, high completion rates for audio might indicate strong engagement, while lower rates for videos could signal a need for content improvement. This data could inform decisions on media content creation and enhancement strategies.

Definition: A pie chart displaying the objects most frequently saved to visitors’ “My Visit” lists, with the total number of saves shown in the center. Users can hover over individual slices to see the exact number of times each object was saved. If objects are added to My Visit and then removed, they will still be counted in this metric.

Context: This metric highlights objects that visitors are interested in prioritizing during or after their visit. The size of each pie slice reflects the proportion of saves for that object, providing insight into visitor preferences. Hovering over a slice reveals the exact number of saves.

Example: In the pie chart, Object A may represent 20% of all saves, with 150 total saves when hovered over, while Object B represents 15%, with 120 saves. The relative size of each slice indicates the popularity of each object.

How It’s Useful: This metric provides a deeper understanding of visitor engagement beyond just object views. Objects saved to “My Visit” are likely ones that visitors find most compelling, either to prioritize during their visit or to revisit later. By identifying which objects are saved most often, operators can understand which items resonate most with visitors. This insight can inform decisions about exhibit highlights, potential promotional opportunities, or marketing strategies, as it shows a higher level of personal significance attached to these objects.

Definition: A pie chart that displays how many times visitors viewed the location of an object on the map, broken down by object name. The center of the pie chart shows the total number of location views for all objects combined.

Context: This metric helps identify which objects visitors are most interested in locating within the venue. Each slice of the pie chart represents a percentage of the total location map views, with larger slices indicating objects that were more frequently sought out by visitors. Hovering over the chart reveals the exact number of views for each object.

Example: If Object A accounts for 40% of the location views, it will have the largest slice of the pie, showing that it was the most sought-after on the map. Object B, with 25%, will have a smaller slice, and so on. The remaining objects, with fewer map views, will be represented by even smaller portions, giving a clear visual of object interest in relation to physical location.

How It’s Useful: This metric allows operators to see which objects are most often sought out by visitors on the map, providing insights into high-traffic areas or objects that are difficult to locate. Operators can use this information to better understand visitor patterns and adjust object placement, signage, or promotional materials. Additionally, if certain objects consistently receive more map views, it may indicate that visitors are prioritizing finding them during their visit, giving operators data that could support future content strategies or exhibit layouts.