Mautic Community Forums

Reporting View - Database Schema for Business Intelligence (BI) Reporting

Hey Guys

Been trying to map out the database to report on Email Performance

Does anyone have previous built database views they would be willing to share?

Elements I am still struggling with

Per email / contact

  • Unique Clicks / total clicks
  • Latest / First Device used on email

SELECT DISTINCT
mautic_email_stats.id AS id,
mautic_emails.subject AS Email_Subject,
mautic_leads.firstname AS firstname,
mautic_leads.lastname AS lastname,
mautic_leads.company AS company,
mautic_leads.email AS email,
mautic_email_stats.is_read AS is_read,
mautic_email_stats.is_failed AS is_failed,
mautic_email_stats.open_count AS open_count,
mautic_email_stats.last_opened AS last_opened,
mautic_email_stats.date_read AS date_read,
mautic_email_stats.viewed_in_browser AS viewed_in_browser,
mautic_email_stats.date_sent AS date_sent,
mautic_email_stats.email_address AS email_address,
mautic_lead_donotcontact.date_added AS Unsubscribe_Date,
mautic_leads.phone AS phone,
mautic_lead_lists.name AS Email_Segment_Name,
mautic_categories.title AS Email_Category_Name
FROM
(((((((
mautic_email_stats
LEFT JOIN mautic_leads ON ((
mautic_email_stats.lead_id = mautic_leads.id
)))
LEFT JOIN mautic_lead_devices ON ((
mautic_lead_devices.lead_id = mautic_leads.id
)))
LEFT JOIN mautic_email_stats_devices ON (((
mautic_email_stats_devices.device_id = mautic_lead_devices.id
)
AND ( mautic_email_stats_devices.stat_id = mautic_email_stats.id ))))
LEFT JOIN mautic_lead_lists ON ((
mautic_email_stats.list_id = mautic_lead_lists.id
)))
LEFT JOIN mautic_emails ON ((
mautic_email_stats.email_id = mautic_emails.id
)))
LEFT JOIN mautic_lead_donotcontact ON (((
mautic_lead_donotcontact.channel_id = mautic_emails.id
)
AND ( mautic_lead_donotcontact.lead_id = mautic_email_stats.lead_id ))))
JOIN mautic_categories ON ((
mautic_emails.category_id = mautic_categories.id
)))
WHERE
( mautic_categories.title = ‘XXXXX’ )
ORDER BY
mautic_email_stats.date_sent DESC

Thought I would monitor the queries coming in from the default reports
Selecting all the fields and changed the limit from 100 to 10,000

~600s run time for 30k records

SELECT
l.email AS email1,
e.NAME AS e_name1,
l.firstname AS firstname1,
l.lastname AS lastname1,
DATE( es.date_sent ) AS date_sent1,
DATE( es.date_read ) AS date_read1,
IF
( cut.hits IS NULL, 0, 1 ) AS is_hit1,
IFNULL( cut.hits, 0 ) AS hits1,
IFNULL( cut.unique_hits, 0 ) AS unique_hits1,
IF
( dnc.id IS NOT NULL AND dnc.reason = 1, 1, 0 ) AS unsubscribed1,
c.title AS category_title1,
e.SUBJECT AS subject2,
e.sent_count AS sent_count1,
es.is_read AS is_read1,
es.viewed_in_browser AS viewed_in_browser1,
vp.id AS id1,
vp.SUBJECT AS subject1,
DATE( e.variant_start_date ) AS variant_start_date1,
IF
( dnc.id IS NOT NULL AND dnc.reason = 2, 1, 0 ) AS bounced1,
cmp.NAME AS name1,
clel.campaign_id AS campaign_id1,
c.id AS category_id1,
comp.companyaddress1 AS companyaddress11,
comp.companyaddress2 AS companyaddress21,
comp.companyannual_revenue AS companyannual_revenue1,
comp.companycity AS companycity1,
comp.companyemail AS companyemail1,
comp.companyname AS companyname1,
comp.companycountry AS companycountry1,
comp.companydescription AS companydescription1,
comp.companyfax AS companyfax1,
comp.id AS comp_id1,
comp.companyindustry AS companyindustry1,
comp.companynumber_of_employees AS companynumber_of_employees1,
comp.companyphone AS companyphone1,
comp.companystate AS companystate1,
comp.companywebsite AS companywebsite1,
comp.companyzipcode AS companyzipcode1,
l.address1 AS address11,
l.address2 AS address21,
l.attribution AS attribution1,
l.attribution_date AS attribution_date1,
l.city AS city1,
l.company AS company1,
l.country AS country1,
l.facebook AS facebook1,
l.fax AS fax1,
l.foursquare AS foursquare1,
l.googleplus AS googleplus1,
l.id AS contactId1,
l.instagram AS instagram1,
l.linkedin AS linkedin1,
l.mobile AS mobile1,
l.custom_field_a_id AS custom_field_a_id1,
l.custom_field_b AS custom_field_b1,
l.custom_field_c AS custom_field_c1,
l.phone AS phone1,
l.points AS points1,
l.position AS position1,
l.preferred_locale AS preferred_locale1,
l.skype AS skype1,
l.state AS state1,
l.title AS title1,
l.twitter AS twitter1,
l.website AS website1,
l.zipcode AS zipcode1,
e.created_by_user AS e_created_by_user1,
e.date_added AS e_date_added1,
e.date_modified AS e_date_modified1,
e.description AS e_description1,
es.email_address AS email_address1,
e.id AS e_id1,
i.ip_address AS ip_address1,
es.is_failed AS is_failed1,
e.is_published AS e_is_published1,
companies_lead.is_primary AS is_primary1,
e.lang AS lang1,
e.modified_by_user AS e_modified_by_user1,
es.source AS source1,
es.source_id AS source_id1,
e.publish_down AS e_publish_down1,
e.publish_up AS e_publish_up1,
IF
( es.date_read IS NOT NULL, TIMEDIFF( es.date_read, es.date_sent ), ‘-’ ) AS read_delay1,
es.retry_count AS retry_count1,
e.revision AS revision1
FROM
mauvr_email_stats es
LEFT JOIN mautic_emails e ON e.id = es.email_id
LEFT JOIN mautic_leads l ON l.id = es.lead_id
LEFT JOIN mautic_ip_addresses i ON i.id = es.ip_id
LEFT JOIN mautic_emails vp ON vp.id = e.variant_parent_id
LEFT JOIN mautic_categories c ON c.id = e.category_id
LEFT JOIN (
SELECT
COUNT( ph.id ) AS hits,
COUNT(
DISTINCT ( ph.redirect_id )) AS unique_hits,
cut2.channel_id,
ph.lead_id
FROM
mautic_channel_url_trackables cut2
INNER JOIN mautic_page_hits ph ON cut2.redirect_id = ph.redirect_id
AND cut2.channel_id = ph.source_id
WHERE
cut2.channel = ‘email’
AND ph.source = ‘email’
GROUP BY
cut2.channel_id,
ph.lead_id
) cut ON e.id = cut.channel_id
AND es.lead_id = cut.lead_id
LEFT JOIN mautic_lead_donotcontact dnc ON e.id = dnc.channel_id
AND dnc.channel = ‘email’
AND es.lead_id = dnc.lead_id
LEFT JOIN mautic_campaign_lead_event_log clel ON clel.channel = “email”
AND e.id = clel.channel_id
AND clel.lead_id = l.id
LEFT JOIN mautic_campaigns cmp ON cmp.id = clel.campaign_id
LEFT JOIN mautic_companies_leads companies_lead ON l.id = companies_lead.lead_id
LEFT JOIN mautic_companies comp ON companies_lead.company_id = comp.id
WHERE
es.date_sent IS NULL
OR ( es.date_sent BETWEEN ‘2019-11-20 13:00:00’ AND ‘2019-12-21 03:27:11’ )
LIMIT 100000 OFFSET 0

After much pain and suffering I have come up with this solution

Daily Scheduled Event in phpmyadmin
Daily roll up of the the the email activity report into one table

I had to play some fun games with setting the table, then emptying it before inserting.
Imported data in Mautic wasnt clean and kept throwing errors, this way the data is structured and pushes through ignoring the errors.

If anyone knows a way to Create a table and ignoring errors, please let me know!

BEGIN

DROP TABLE IF EXISTS email_stats_daily_rollup;
CREATE TABLE email_stats_daily_rollup AS
SELECT
l.email AS email1,
e.name AS e_name1,
l.firstname AS firstname1,
l.lastname AS lastname1,
DATE( es.date_sent ) AS date_sent1,
DATE( es.date_read ) AS date_read1,
IF
( cut.hits IS NULL, 0, 1 ) AS is_hit1,
IFNULL( cut.hits, 0 ) AS hits1,
IFNULL( cut.unique_hits, 0 ) AS unique_hits1,
IF
( dnc.id IS NOT NULL AND dnc.reason = 1, 1, 0 ) AS unsubscribed1,
c.title AS category_title1,
e.subject AS subject2,
e.sent_count AS sent_count1,
es.is_read AS is_read1,
es.viewed_in_browser AS viewed_in_browser1,
– vp.id AS id1,
– vp.subject AS subject1,
DATE( e.variant_start_date ) AS variant_start_date1,
IF
( dnc.id IS NOT NULL AND dnc.reason = 2, 1, 0 ) AS bounced1,
cmp.name AS name1,
clel.campaign_id AS campaign_id1,
c.id AS category_id1,
comp.companyaddress1 AS companyaddress11,
comp.companyaddress2 AS companyaddress21,
comp.companyannual_revenue AS companyannual_revenue1,
comp.companycity AS companycity1,
comp.companyemail AS companyemail1,
comp.companyname AS companyname1,
comp.companycountry AS companycountry1,
comp.companydescription AS companydescription1,
comp.companyfax AS companyfax1,
comp.id AS comp_id1,
comp.companyindustry AS companyindustry1,
comp.companynumber_of_employees AS companynumber_of_employees1,
comp.companyphone AS companyphone1,
comp.companystate AS companystate1,
comp.companywebsite AS companywebsite1,
comp.companyzipcode AS companyzipcode1,
l.address1 AS address11,
l.address2 AS address21,
l.attribution AS attribution1,
l.attribution_date AS attribution_date1,
l.city AS city1,
l.company AS company1,
l.country AS country1,
l.facebook AS facebook1,
l.fax AS fax1,
l.foursquare AS foursquare1,
l.googleplus AS googleplus1,
l.id AS contactId1,
l.instagram AS instagram1,
l.linkedin AS linkedin1,
l.mobile AS mobile1,
– l.pagespeed_submission_id AS pagespeed_submission_id1,
– l.pagespeed_url AS pagespeed_url1,
– l.pagespeed_url_report AS pagespeed_url_report1,
l.phone AS phone1,
l.points AS points1,
l.position AS position1,
l.preferred_locale AS preferred_locale1,
l.skype AS skype1,
l.state AS state1,
l.title AS title1,
l.twitter AS twitter1,
l.website AS website1,
l.zipcode AS zipcode1,
e.created_by_user AS e_created_by_user1,
e.date_added AS e_date_added1,
e.date_modified AS e_date_modified1,
e.description AS e_description1,
es.email_address AS email_address1,
e.id AS e_id1,
– i.ip_address AS ip_address1,
es.is_failed AS is_failed1,
e.is_published AS e_is_published1,
companies_lead.is_primary AS is_primary1,
e.lang AS lang1,
e.modified_by_user AS e_modified_by_user1,
es.source AS source1,
es.source_id AS source_id1,
e.publish_down AS e_publish_down1,
e.publish_up AS e_publish_up1,
IF
( es.date_read IS NOT NULL, TIMEDIFF( es.date_read, es.date_sent ), ‘-’ ) AS read_delay1,
es.retry_count AS retry_count1,
e.revision AS revision1
FROM
mautic_email_stats es
LEFT JOIN mautic_emails e ON e.id = es.email_id
LEFT JOIN mautic_leads l ON l.id = es.lead_id
– LEFT JOIN mautic_ip_addresses i ON i.id = es.ip_id
– LEFT JOIN mautic_emails vp ON vp.id = e.variant_parent_id
LEFT JOIN mautic_categories c ON c.id = e.category_id
LEFT JOIN (
SELECT
COUNT( ph.id ) AS hits,
COUNT(
DISTINCT ( ph.redirect_id )) AS unique_hits,
cut2.channel_id,
ph.lead_id
FROM
mautic_channel_url_trackables cut2
INNER JOIN mautic_page_hits ph ON cut2.redirect_id = ph.redirect_id
AND cut2.channel_id = ph.source_id
WHERE
cut2.channel = ‘email’
AND ph.source = ‘email’
GROUP BY
cut2.channel_id,
ph.lead_id
) cut ON e.id = cut.channel_id
AND es.lead_id = cut.lead_id
LEFT JOIN mautic_lead_donotcontact dnc ON e.id = dnc.channel_id
AND dnc.channel = ‘email’
AND es.lead_id = dnc.lead_id
LEFT JOIN mautic_campaign_lead_event_log clel ON clel.channel = “email”
AND e.id = clel.channel_id
AND clel.lead_id = l.id
LEFT JOIN mautic_campaigns cmp ON cmp.id = clel.campaign_id
LEFT JOIN mautic_companies_leads companies_lead ON l.id = companies_lead.lead_id
LEFT JOIN mautic_companies comp ON companies_lead.company_id = comp.id
WHERE
es.date_sent IS NOT NULL
limit 10

;



-- Phase 2 of query

TRUNCATE TABLE email_stats_daily_rollup;
INSERT IGNORE email_stats_daily_rollup

SELECT
l.email AS email1,
e.name AS e_name1,
l.firstname AS firstname1,
l.lastname AS lastname1,
DATE( es.date_sent ) AS date_sent1,
DATE( es.date_read ) AS date_read1,
IF
( cut.hits IS NULL, 0, 1 ) AS is_hit1,
IFNULL( cut.hits, 0 ) AS hits1,
IFNULL( cut.unique_hits, 0 ) AS unique_hits1,
IF
( dnc.id IS NOT NULL AND dnc.reason = 1, 1, 0 ) AS unsubscribed1,
c.title AS category_title1,
e.subject AS subject2,
e.sent_count AS sent_count1,
es.is_read AS is_read1,
es.viewed_in_browser AS viewed_in_browser1,
– vp.id AS id1,
– vp.subject AS subject1,
DATE( e.variant_start_date ) AS variant_start_date1,
IF
( dnc.id IS NOT NULL AND dnc.reason = 2, 1, 0 ) AS bounced1,
cmp.name AS name1,
clel.campaign_id AS campaign_id1,
c.id AS category_id1,
comp.companyaddress1 AS companyaddress11,
comp.companyaddress2 AS companyaddress21,
comp.companyannual_revenue AS companyannual_revenue1,
comp.companycity AS companycity1,
comp.companyemail AS companyemail1,
comp.companyname AS companyname1,
comp.companycountry AS companycountry1,
comp.companydescription AS companydescription1,
comp.companyfax AS companyfax1,
comp.id AS comp_id1,
comp.companyindustry AS companyindustry1,
comp.companynumber_of_employees AS companynumber_of_employees1,
comp.companyphone AS companyphone1,
comp.companystate AS companystate1,
comp.companywebsite AS companywebsite1,
comp.companyzipcode AS companyzipcode1,
l.address1 AS address11,
l.address2 AS address21,
l.attribution AS attribution1,
l.attribution_date AS attribution_date1,
l.city AS city1,
l.company AS company1,
l.country AS country1,
l.facebook AS facebook1,
l.fax AS fax1,
l.foursquare AS foursquare1,
l.googleplus AS googleplus1,
l.id AS contactId1,
l.instagram AS instagram1,
l.linkedin AS linkedin1,
l.mobile AS mobile1,
– l.pagespeed_submission_id AS pagespeed_submission_id1,
– l.pagespeed_url AS pagespeed_url1,
– l.pagespeed_url_report AS pagespeed_url_report1,
l.phone AS phone1,
l.points AS points1,
l.position AS position1,
l.preferred_locale AS preferred_locale1,
l.skype AS skype1,
l.state AS state1,
l.title AS title1,
l.twitter AS twitter1,
l.website AS website1,
l.zipcode AS zipcode1,
e.created_by_user AS e_created_by_user1,
e.date_added AS e_date_added1,
e.date_modified AS e_date_modified1,
e.description AS e_description1,
es.email_address AS email_address1,
e.id AS e_id1,
– i.ip_address AS ip_address1,
es.is_failed AS is_failed1,
e.is_published AS e_is_published1,
companies_lead.is_primary AS is_primary1,
e.lang AS lang1,
e.modified_by_user AS e_modified_by_user1,
es.source AS source1,
es.source_id AS source_id1,
e.publish_down AS e_publish_down1,
e.publish_up AS e_publish_up1,
IF
( es.date_read IS NOT NULL, TIMEDIFF( es.date_read, es.date_sent ), ‘-’ ) AS read_delay1,
es.retry_count AS retry_count1,
e.revision AS revision1
FROM
mautic_email_stats es
LEFT JOIN mautic_emails e ON e.id = es.email_id
LEFT JOIN mautic_leads l ON l.id = es.lead_id
– LEFT JOIN mautic_ip_addresses i ON i.id = es.ip_id
– LEFT JOIN mautic_emails vp ON vp.id = e.variant_parent_id
LEFT JOIN mautic_categories c ON c.id = e.category_id
LEFT JOIN (
SELECT
COUNT( ph.id ) AS hits,
COUNT(
DISTINCT ( ph.redirect_id )) AS unique_hits,
cut2.channel_id,
ph.lead_id
FROM
mautic_channel_url_trackables cut2
INNER JOIN mautic_page_hits ph ON cut2.redirect_id = ph.redirect_id
AND cut2.channel_id = ph.source_id
WHERE
cut2.channel = ‘email’
AND ph.source = ‘email’
GROUP BY
cut2.channel_id,
ph.lead_id
) cut ON e.id = cut.channel_id
AND es.lead_id = cut.lead_id
LEFT JOIN mautic_lead_donotcontact dnc ON e.id = dnc.channel_id
AND dnc.channel = ‘email’
AND es.lead_id = dnc.lead_id
LEFT JOIN mautic_campaign_lead_event_log clel ON clel.channel = “email”
AND e.id = clel.channel_id
AND clel.lead_id = l.id
LEFT JOIN mautic_campaigns cmp ON cmp.id = clel.campaign_id
LEFT JOIN mautic_companies_leads companies_lead ON l.id = companies_lead.lead_id
LEFT JOIN mautic_companies comp ON companies_lead.company_id = comp.id
WHERE
es.date_sent IS NOT NULL
– limit 1000

;

END