diff --git a/addons/crm/report/crm_lead_report.py b/addons/crm/report/crm_lead_report.py
index da5167da1e6..25598abc3ea 100644
--- a/addons/crm/report/crm_lead_report.py
+++ b/addons/crm/report/crm_lead_report.py
@@ -81,7 +81,6 @@ class crm_lead_report(osv.osv):
'type_id':fields.many2one('crm.case.resource.type', 'Campaign', readonly=True),
'state': fields.selection(AVAILABLE_STATES, 'Status', size=16, readonly=True),
'company_id': fields.many2one('res.company', 'Company', readonly=True),
- 'email': fields.integer('# Emails', size=128, readonly=True),
'probability': fields.float('Probability',digits=(16,2),readonly=True, group_operator="avg"),
'planned_revenue': fields.float('Planned Revenue',digits=(16,2),readonly=True),
'probable_revenue': fields.float('Probable Revenue', digits=(16,2),readonly=True),
@@ -137,7 +136,6 @@ class crm_lead_report(osv.osv):
c.planned_revenue,
c.planned_revenue*(c.probability/100) as probable_revenue,
1 as nbr,
- (SELECT count(id) FROM mail_message WHERE model='crm.lead' AND res_id=c.id AND email_from is not null) AS email,
date_trunc('day',c.create_date) as create_date,
extract('epoch' from (c.date_closed-c.create_date))/(3600*24) as delay_close,
abs(extract('epoch' from (c.date_deadline - c.date_closed))/(3600*24)) as delay_expected,
diff --git a/addons/crm/report/crm_lead_report_view.xml b/addons/crm/report/crm_lead_report_view.xml
index a17633d0ef6..0e5df8837f1 100644
--- a/addons/crm/report/crm_lead_report_view.xml
+++ b/addons/crm/report/crm_lead_report_view.xml
@@ -25,7 +25,6 @@
-
@@ -148,7 +147,6 @@
-
@@ -167,7 +165,7 @@
{'search_default_year': 1,'search_default_lead': 1, "search_default_user":1, "search_default_this_month":1, 'group_by_no_leaf':1, 'group_by':[]}
tree,graph
[]
- Leads Analysis allows you to check different CRM related information. Check for treatment delays, number of responses given and emails sent. You can sort out your leads analysis by different groups to get accurate grained analysis.
+ Leads Analysis allows you to check different CRM related information like the treatment delays or number of leads per state. You can sort out your leads analysis by different groups to get accurate grained analysis.