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This is unreleased documentation for Rasa & Rasa Pro Documentation Main/Unreleased version.
For the latest released documentation, see the latest version (3.x).

Version: Main/Unreleased

Example queries

This section helps you get started with analyzing your assistant's conversations. The examples use SQL queries together with an example visualization in Metabase.

For more metrics and categories of conversations, see Types of metrics.

Number of sessions per month

A common high-level usage metric of your assistant is the number of sessions per month. Here is how it would look as an SQL query:

SELECT
date_trunc('month', "public"."rasa_session"."timestamp") AS "first_seen",
count(*) AS "count"
FROM "public"."rasa_session"
GROUP BY 1
ORDER BY 1 ASC
Number of sessions per month visualized in Metabase.
Number of sessions per month visualized in Metabase.

Number of sessions per channel

If you're connecting your assistant to multiple channels, it could be useful to look at the number of sessions per channel, let's say per week. The query you would need for this metric is:

SELECT
"public"."rasa_sender"."channel" AS "channel",
"public"."rasa_sender"."first_seen" AS "timestamp",
count(distinct "public"."rasa_sender"."sender_key") AS "count"
FROM "public"."rasa_sender"
GROUP BY 1, 2
ORDER BY 1 ASC, 2 ASC
Number of sessions per channel visualized in Metabase.
The number of sessions per channel as visualized in Metabase.

Top N intents

To improve your assistant, you could look into the variety of intents your users express. The query below selects the top 5 intents which could help you have a good perspective on that topic:

SELECT
"public"."rasa_user_message"."intent" AS "intent",
count(*) AS "count"
FROM "public"."rasa_user_message"
GROUP BY 1
ORDER BY 2 DESC, 1 ASC
LIMIT 5
Top 5 intents visualized in Metabase.
Top 5 intents visualized in Metabase.

Moreover, you can look for the intent distribution over time:

SELECT
"public"."rasa_user_message"."intent" AS "intent",
date_trunc('month', "public"."rasa_user_message"."timestamp") AS "timestamp",
count(*) AS "count" FROM "public"."rasa_user_message"
GROUP BY 1, 2
ORDER BY 1 ASC, 2 ASC
Intent distribution over time visualized in Metabase.
Intent distribution over time visualized in Metabase.

Escalation rate

The escalation rate or human hand-off rate is a measure of the number of conversations the assistant passes to a human agent. This metric can help you gain a better understanding of what happens during a conversation. Let's say you have an intent named handoff_to_support. You'll get the escalation rate over time with this sample query:

WITH "sessions" AS (
SELECT
"public"."rasa_user_message"."session_id" AS "session_id",
date_trunc('month', "public"."rasa_user_message"."timestamp") AS "timestamp",
(
CASE "public"."rasa_user_message"."intent"
WHEN 'handoff_to_support'
THEN 1 ELSE 0
END
) AS "has_handoff_to_support"
FROM "public"."rasa_user_message"
),
"sessions_with_handoff" AS (
SELECT
"session_id",
"timestamp",
SUM("has_handoff_to_support") AS "has_handoff_to_support"
FROM "sessions"
GROUP BY 1, 2
)
SELECT
"timestamp",
SUM("has_handoff_to_support") / count(*) AS "escalation_rate"
FROM "sessions_with_handoff"
GROUP BY 1 ASC
ORDER BY 1 ASC
Escalation rate visualized in Metabase.
Escalation rate visualized in Metabase.

Abandonment rate

Abandonment rate can be defined in many different custom ways, however here we'll define it as a session ending without a user message after a specific message was uttered by the bot, e.g. utter_ask_name. You could adapt the metric to detect sessions ending without a user message after a specific set of intents. The SQL query would look like this:

WITH "sessions" AS (
SELECT
DISTINCT ON ("public"."rasa_event"."session_id") "public"."rasa_event"."session_id",
"public"."rasa_event"."timestamp" AS "timestamp",
(
CASE
WHEN "public"."rasa_bot_message"."template_name" = 'utter_ask_name'
THEN 1 ELSE 0
END
) AS "is_abandonned"
FROM "public"."rasa_event"
INNER JOIN "public"."rasa_bot_message"
ON "public"."rasa_event"."id" = "public"."rasa_bot_message"."event_id"
WHERE "public"."rasa_event"."event_type" = 'bot'
ORDER BY 1, 2 DESC
)
SELECT
date_trunc('month', "timestamp") AS "timestamp",
SUM("is_abandonned")::float / count(*) AS "abandonment_rate"
FROM "sessions"
GROUP BY 1
ORDER BY 1 ASC
Abandonment rate visualized in Metabase.
Abandonment rate visualized in Metabase.