View insights and observations

11 min

Note

The terms audiences and segments are interchangeable. Optimizely has updated all segments to audiences on the Audiences page and is updating the UI on other pages. See Understand audiences to get information on audiences.

Optimizely Data Platform (ODP) automatically assesses insights and observations for an account using available underlying data (like customers, products, orders, and page views). ODP evaluates the data nightly and makes it available for standard audiences, reports, and custom Liquid building. For real-time evaluation based on events, use filters and behaviors instead.

Availability

In standard audiences

Insights and observations available for a customer's attributes like their country, topic of interest, and likelihood of ordering can help you build a standard audience in Customers > Audiences.

In custom report columns (rocket columns)

Go to Reports > Custom Reports.

Select your desired report or click New Report.

Expand the Column(s) menu to select the columns you want in a report. Click Apply to add the column data to the report.

In custom Liquid

You can reference the insights and observations in Liquid syntax when building out campaign touchpoints. Use the following prefixes with the Liquid suffix according to the category:

Customer observations – customer.observations.suffix (for example, customer.observations.order_count)

Customer insights – customer.insights.suffix (for example, customer.insights.order_likelihood)

Product observations –

Customer – customer.observations.product.observations.suffix where product can be first_ or last_ (for example, customer.observations.first_product.observations.repeated_sale_ratio)

Product grid – product.observations.suffix (for example, product.observations.repeated_sale_ratio)

Product insights –

Customer – customer.insights.product.insights.suffix where product can be first_ or last_ (for example, customer.insights.first_product.insights.up_sell_category)

Product grid – product.insights.suffix (for example, product.insights.up_sell_category)

Order Observations – order.observations.suffix (for example, order.observations.order_number)

Proper use of percentiles

Observations and insights based on percentiles use a range of 0 to 100, where 100 is the highest percentile. For example, a value of "At Least 90" when building an audience with Order Count Percentile includes customers with more orders than 90% of the customers in the account, meaning the top 10% of customers by Order Count.

Customer information

Customer observations

Name	Liquid suffix	Description
Order count	order_count	Number of orders purchased.
Order count percentile	order_count_percentile	Percentile by order purchased.
First order date and time	first_order_ts	Date and time of first observed order.
Last order date and time	last_order_ts	Date and time of last observed order.
First product ID	first_product_id	Product ID of the highest-priced product in the first order.
Last product ID	last_product_id	Product ID of the highest-priced product in the last order.
Total revenue	total_revenue	Sum of the value of all observed orders (net).
Total revenue percentile	total_revenue_percentile	Percentile by observed orders.
Average order revenue	average_order_revenue	Average revenue across observed orders (net).
Average order revenue percentile	average_order_revenue_percentile	Percentile by average revenue.
First visit date and time	first_visit_ts	Date and time of first observed page view.
Last visit date and time	last_visit_ts	Date and time of last observed page view.
Session count	session_count	Number of observed sessions.
Session count percentile	session_count_percentile	Percentile by observed sessions.
Email domain	email_domain	The domain name of the email address.
Mailbox provider	mailbox_provider	The organization that provides the email box.
Discount order ratio	discount_order_ratio	The ratio of orders that involved a discount (line item level, order level, or discount code).
Discount order percentile	discount_order_percentile	Percentile by discount order ratio.
Discount usage	discount_usage	Restatement of discount order ratio into "Always," "Sometimes," or "Never," based on the ratio of orders with a discount.
Acquisition Source	acquisition_source	Same as first_source, when populated, otherwise filled in as "unknown".
First source	first_source	UTM source of the first event.
First medium	first_medium	UTM medium of the first event.
First campaign	first_campaign	UTM campaign of the first event.
First event date and time	first_event_ts	Date and time of the first event.
One-time send count (last 365 days)	one_time_send_count	Number of one-time sends in the last 365 days.
One-time click rate (last 365 days)	one_time_click_rate	Click rate (based on sends) of one-time sends in the last 365 days (in decimal, that is, 0.05 is 5%).
Last seen subscribed	subscribed	The marketing consent status for the most recently seen email address (True or False).
Last seen email status	email_status	The deliverability status for the most recently seen email address.
Days until birthday	days_until_birthday	The computed number of days until the birthday, if at least the birthday month is known.
Computed age	computed_age_years	The computed age of the customer, if at least birthday year is known.

Customer insights

Name	Liquid suffix	Description
Engagement rank	engagement_rank	Percentile engagement for each customer based on the amount of time spent with your brand, weighted to more recent days. A measure of "headspace" occupied, is correlated to future engagement.
Order likelihood	order_likelihood	Likelihood of ordering in the next six weeks.
Days until next order	ttno_days	Estimated number of days until the next order, based on those likely to order in the next six weeks.
Winback zone	winback_zone	Customer's designation as "Engaged," "Winback," or "Churned".
Winback type	winback_type	Customers with the value "New" entered "Winback" during the latest model run (evaluated nightly).
Probability still a customer	probability_alive	Customer's liveness probability from the churn prevention model.

Product information

Product observations

Name	Liquid suffix	Description
Bestseller rank (seven days)	bestseller_rank_7_day	The sales ranking by units sold over the last seven days calculated at the parent product level, if applicable. "1" is the highest rank.
Bestseller rank (30 days)	bestseller_rank_30_day	The sales ranking by units sold over the last 30 days calculated at the parent product level, if applicable. "1" is the highest rank.
Detail views per day	views_per_day	The average product detail page views per day, weighted to more recent days. Calculated at the parent product level, if applicable.
Detail views per day percentile	views_per_day_percentile	Percentile by average product detail page views per day.
Discounted sale ratio	discounted_sale_ratio	The ratio of product purchases sold on discount to all product purchases, weighted to more recent days.
Discounted sale percentile	discounted_sale_percentile	Percentile by discount sale ratio.
First sale ratio	first_sale_ratio	The ratio of product purchases that were first-time buys to all product purchases, weighted to more recent days.
First sale percentile	first_sale_percentile	Percentile by first buy ratio.
PDP views per day	pdp_per_day	Average product detail page views per day, weighted to more recent days.
PDP views per day percentile	pdp_per_day_percentile	Percentile by average product detail page views per day.
Revenue per day	revenue_per_day	The average revenue per day weighted to more recent days.
Revenue per day percentile	revenue_per_day_percentil	Percentile by average revenue per day.
Revenue rank (seven days)	revenue_rank_7_day	The sales ranking by revenue sold over the last seven days calculated at the parent product level, if applicable. "1" is the highest rank.
Revenue rank (30 days)	revenue_rank_30_day	The sales ranking by revenue sold over the last 30 days calculated at the parent product level, if applicable. "1" is the highest rank.
Sales per day	sales_per_day	The average sales per day weighted to more recent days.
Sales per day percentile	sales_per_day_percentile	Percentile by average sales per day.
Repeated sale ratio	repeated_sale_ratio	The ratio of product purchases that were a repeat purchase of the product, weighted to more recent days.
Repeated sale percentile	repeated_sale_percentile	Percentile by repeated sale ratio.
Views before purchase	views_before_purchase	The average product detail page views before the first purchase of this product, weighted to more recent days. Calculated at the parent product level, if applicable.
Views before purchase Percentile	views_before_purchase_percentile	Percentile by average product detail page views before a first purchase.
Viewed Rank (seven days)	views_rank_7_day	The product detail pageview ranking over the last seven days calculated at the parent product level, if applicable. "1" is the highest rank.
Viewed rank (30 days)	views_rank_30_day	The product detail page view ranking over the last 30 days calculated at the parent product level, if applicable. "1" is the highest rank.

Product insights

Name	Liquid suffix	Description
Up-Sell category	up_sell_category	Category of products that are frequently purchased with this one.
Cross-sell category	cross_sell_category	Category of products that are frequently purchased after this one.
Replenishment days	replenishment_period_days	The average number of days before a reorder of the product.

Order information

Order observations

Name	Liquid suffix	Description
Order number	order_number	The order number that the behavior is associated with.