Customer Activity

The unified data model for website and app analytics. Sessions, page views, events, and customer engagement consolidated across every digital touchpoint, with bounce, returning-visitor, and funnel-conversion rates pre-calculated.

What this data model represents

Grain: one row per user × session × activity. Metrics, grouped by category — every number you can compute on this data model. Expand below for examples in each.

Show sample metrics in each category

Dimensions, grouped by category — every way you can split, filter, or group those metrics. Expand below for examples in each.

Show sample dimensions in each category

Source: Google Analytics 4, customer activity streams, device + geo enrichment, and product-interaction events, refreshed daily. What’s special: session-based and identity-resolved metrics are pre-aggregated. Bounce Rate accounts for single-activity sessions, Returning Visitor % tracks multi-session users, and funnel CVRs map the full View → Cart → Checkout → Purchase path. Metrics like Pages per Session and Traffic-to-Purchase CVR stay accurate at any time grain and across any dimension — you don’t have to re-derive them per query.

Slice by

Every dimension you can group or filter by.

Dimension	Description
User identity
`user_id`	Unique identifier for authenticated users
`user_pseudo_id`	Pseudo identifier for anonymous visitors
`is_customer_flag`	Boolean — user is an identified customer
`customer_id`	Unique customer identifier for transacting users
Session & activity
`session_id`	Unique session identifier
`activity_timestamp`	When the activity occurred
`activity_name`	Name of the activity / event
`session_count_per_user`	Number of sessions for this user
`activity_count_per_session`	Number of activities within this session
Traffic source
`source`	Traffic source (google, facebook, direct, …)
`medium`	Marketing medium (organic, cpc, referral, …)
`channel_grouping`	Grouped channel classification
`campaign_id`	Marketing campaign identifier
`campaign_name`	Marketing campaign name
`content`	Ad content or creative identifier
`term`	Search term / keyword from paid campaigns
Page & content
`page_location`	Full URL of the page
`page_view_path`	Path portion of the page URL
`page_title`	Title of the page
`collection`	Product collection identifier
`search_term`	Internal site-search query
Product
`product_variant_id`	Product variant identifier
`product_name`	Product name
`product_category`	High-level product category
`product_subcategory`	Product subcategory
`product_color`	Color variant
Device
`device_category`	Desktop, mobile, tablet
`device_operating_system`	OS of the device
`device_browser`	Browser used
`device_mobile_brand_name`	Mobile device brand
`device_mobile_model_name`	Mobile device model
Geographic
`city`	City of the user
`region`	State or region
`country`	Country
`sub_continent`	Sub-continental region
`continent`	Continent
Time
`full_date`	Date, time component stripped
`full_timestamp`	Full timestamp
`day_name`	Monday, Tuesday, …
`type_of_day`	Weekday / Weekend
User journey
`first_activity_timestamp`	First-ever activity for this user
`last_activity_timestamp_for_activity`	Most recent activity for this user
`days_since_last_activity`	Days since previous activity
`time_to_activity`	Days from first to current activity
Technical
`order_id`	Order identifier for purchase events
`stream_id`	Data-stream identifier
`platform`	Web, app, …

Use it to answer

What’s our true conversion rate from session to purchase, and where in the funnel are we losing people?
Which traffic sources and channels drive the highest-quality visitors — by engagement, not just volume?
How do mobile vs. desktop users behave differently — pages per session, add-to-cart rate, checkout CVR?
What’s the bounce rate by landing page or campaign, and which pages need work?
How many of our visitors are returning, and what’s the DAU/WAU/MAU stickiness ratio?
Which geographic regions show the strongest engagement and conversion?
Which products get viewed often but rarely added to cart — a PDP or pricing problem?
What share of sessions come from paid vs. organic vs. referral, and how does each convert?

Available metrics

Everything you can compute on this data model.

Metric	Formula
Sessions & activity
Web Traffic (Sessions)	Count of unique website sessions
Customer Sessions	Count of sessions by identified customers
Total Sessions	Count of all sessions regardless of activity type
Users	Count of unique identified users
Pseudo Users	Count of unique anonymous visitors
Customers (Transacting)	Count of unique customers with transactions
Total Activities	Count of activities across all sessions
Page views & navigation
Page View Sessions	Sessions containing at least one page view
Page View Activities	Count of individual page-view events
Pages per Session	Avg page views per session
View Item Sessions	Sessions with a product detail page view
Add to Cart Sessions	Sessions where items were added to cart
Checkout Sessions	Sessions that reached checkout
Purchase Sessions	Sessions that completed a purchase
Referral Sessions	Sessions from referral sources
Paid Sessions	Sessions from paid advertising
Conversion & engagement
Visits per Customer	Avg sessions per identified customer
% PLP Sessions	Sessions viewing product-listing pages ÷ Total Sessions × 100
% PDP Sessions	Sessions viewing product-detail pages ÷ Total Sessions × 100
% Sessions Adding to Cart	Add to Cart Sessions ÷ Total Sessions × 100
Checkout CVR	Purchase Sessions ÷ Checkout Sessions × 100
Traffic-to-Purchase CVR	Purchase Sessions ÷ Total Sessions × 100
Traffic source
Referral Traffic	Count of sessions from referral sources
Paid Traffic	Count of sessions from paid channels
Organic Sessions	Count of sessions from organic search
User behaviour
Avg Sessions per User	Total Sessions ÷ Pseudo Users
Returning Visitors	Users with more than one session
% Returning Visitors	Returning Visitors ÷ Pseudo Users × 100
Return Sessions	Sessions from returning visitors
Return Visit Rate	Return Sessions ÷ Total Sessions × 100
Avg Activities per Session	Total Activities ÷ Total Sessions
Avg Activities per User	Total Activities ÷ Pseudo Users
Avg Days Since Last Activity	Avg(days between consecutive activities per user)
Avg Time to Activity	Avg(days from first to last activity per user)
Active users
DAU	Unique users active in the last 1 day
WAU	Unique users active in the last 7 days
MAU	Unique users active in the last 30 days
DAU / WAU	DAU ÷ WAU
DAU / MAU	DAU ÷ MAU
WAU / MAU	WAU ÷ MAU
Bounce & exit
Bounced Sessions	Sessions with only one activity
Bounce Rate	Bounced Sessions ÷ Total Sessions × 100
Revenue per session
Gross Sales per Session	Gross Revenue ÷ Total Sessions
Net Sales per Session	Net Revenue ÷ Total Sessions

Not available in this data model

If you need order-level revenue, fulfilment, or ad-platform metrics, query a different data model.

Looking for…	Use
Net Revenue, AOV, gross/net order counts, returns, cancellations, discount uplift, Customer Lifetime Value	Sales
Shipment status, delivery SLA, RTO rate, return-pipeline timing	Fulfilment
Ad spend, ROAS, CPA, impressions, clicks by campaign / adset / ad	Meta Ads · Google Ads

​What this data model represents

​Slice by

​Use it to answer

​Available metrics

​Not available in this data model

What this data model represents

Slice by

Use it to answer

Available metrics

Not available in this data model