Get started with Dreamdata
How it works
How set up Dreamdata Web tracking (analytics.js) manually
How to invite your colleagues to Dreamdata
How to set up up Dreamdata web tracking (analytics.js) using Google Tag Manager
How to track forms adding the auto-identify script via Google Tag Manager.
Onboarding for paying customers [VIDEO]
Onboarding process for free customers [VIDEO]
Setting Up Dreamdata
The Onboarding Process
What is Dreamdata? [VIDEO]
Content Performance - Dashboard Options
Which channel performs best for different content?
Which content generates pipeline?
Measuring influenced pipeline for B2B content - the true conversion metric
Setup Content Reporting
What KPI to measure the effect of B2B content?
Attribution Models- dashboard explanation
Data Driven Attribution
Types of Attribution Models
Google Display Ads
Google Search Ads
Return on Ads Spend
Performance vs. Revenue attribution: A guide on when to use what
Setting up AdRoll
Setting up Bing Ads
Setting up Capterra
Setting up Close
Setting up Data Export to BigQuery of CRM Properties
Setting up Facebook Ads
Setting up G2 Crowd
Setting up Google Ads
Setting up Google Search
Setting up Google Sheets
Setting up HubSpot
Setting up Intercom
Setting up LinkedIn Ads
Setting up Marketo
Setting up Microsoft Dynamics
Setting up Pardot
Setting up Pipedrive
Setting up Salesforce
Setting up Twitter Ads
Setting up Zapier integration & Zaps for Lead Gen forms/Lead Ads
Setting up Zendesk Sell
Setting up Zoho CRM
Connect to AWS Redshift using AWS Glue
Connect your Dreamdata data to Amazon Redshift
Connect your Dreamdata data to Snowflake
Getting Started with Google Data Studio Templates
Google Cloud Storage
Google Connected Sheets
Guides for Google Data Studio Reporting
How does Dreamdata track all relevant on-site customer data?
Load analytics.js from your own domain
Pardot iframe form tracking
Server Side Analytics APIs
Setting up Dreamdata Web tracking (analytics.js)
Setting up tracking with Segment
Tracking Hubspot Forms with auto-identify script
Tracking iframes with auto-identify script
Tracking using Sleeknote or Drift
Setting up your customised Stage Models
Setting up your default Stage Models
Can I exclude content or websites from being tracked?
Roles and Permissions
Some of my deals are flagged with "no-tracking". What does it mean?
What does Visitors, Contacts and Companies mean?
What is a Stage Model?
What is a company in Dreamdata?
What is a session?
Why are my dashboards empty?
Quick learning videos!
Are you using G2?
Do you know how your company is generating money?
Do you know which of your Marketing activities had the biggest impact on pipeline and revenue?
Dreamdata Content Analytics: Discover the real value of your content
Find the content that generates most pipeline
Helping BDRs break through to the hottest accounts
How Content Analytics tracks the influence of content of pipeline and revenue
How to cut the cost of your Google Search Ads
How to easily build a retargeting audience with Dreamdata
How to see the value of B2B Google Ads in pipeline and revenue generated
How to set up content categories on Dreamdata
Performance vs. Revenue Analytics reports- when to apply them best!
See the value of SEO in pipeline and revenue generated
What attribution really is and why you should care!
Which of your emails produce pipeline and revenue?
Updated by Mikkel Settnes
How to use Dreamdata to measure which content is affecting your pipeline
In Dreamdata, the pipeline stages are defined using the Stage option.
First step is to choose which stage to analyse. Below we use Marketing Qualified Lead (MQL) as an example.
To analyze which content views in a selected time period are influencing MQLs, we set the dashboard options as shown.
This will setup the report to investigate:
- Which content influences the most MQLs?
- How many MQLs was affected by content views happening in the selected time period?
For each content view we track if that view becomes part of a journey leading to a MQL, SQL, NewBiz etc.
When new MQLs are created, the numbers for the content that is part of those journeys are therefore updated. It is worth noting that we usually see a lag between views and, for example, MQL creation.
The Group By option indicates the granularity of the content we want. The default is URL, but if we are tracking a substantial amount of individual url’s, it might provide a clearer view to aggregate them by content category. Note that content categories need to be setup within the Dreamdata application (see here)
The first graph of the report shows the top 10 urls based on how many Leads (=MQLs) they influenced.
In other words, how many MQL journeys contain a view of the specific content, meaning that one of the stakeholders in that journey has consumed the specific content.
This generates a quick overview of the content (= url’s or categories) that influenced the most of the selected pipeline stage.
The summary table shows the detailed performance of each url or category. Note that no secondary group by is selected in this analysis, so it is showing all (= no split groups).
The Performance indicator (High, Low, Average, Insufficient Data) is based on the Influenced Leads per Session in the following way, and is meant to ease the identification of content pieces to scale
- High: top 25% of the Influenced Leads per Session with sufficient data
- Low: bottom 25% of the Influenced Leads per Session with sufficient data
- Insufficient Data: content with fewer than 250 sessions. Here there is not enough data and we should therefore treat the value of Influenced Leads per Session with more uncertainty.
In this table, we can identify the content that is performing well i.e. have a high number of Influenced Leads and compare content with different traffic using the Influenced Leads per Session metric.
A high Influenced Leads per Session % indicates that many of the sessions are connected to actual Leads. Meaning that readers of this content piece have a high likelihood to become Leads at a later point in time. We therefore aim to scale the content that show a high number of Influenced Leads and where the sessions effectively influence Leads (= Influenced Leads per Session)
See here for a detailed description of the importance of measuring the influence over a long period of time.
A high Influenced Leads per Session % that is partially caused by a low number of sessions, indicates that the content is currently primarily read by Leads. Therefore, it is a good candidate to drive more traffic to, in order to see if the Influenced Leads per Session % keeps being high when increasing traffic.
The over-time views can be used to follow the progress of specific urls or categories over time. The time aggregation is controlled by the option Aggregation at the top of the page.
We follow the Influenced Leads per Session % to analyze if the content performance increases or decreases over time i.e. are the % of sessions that influenced leads increasing or decreasing with time.
In the last time series we follow how the number of Influenced Leads evolves over time (to change the metric of this graph use the Metric On Graph option at the top of the page)