Every contact goes through an extensive process combining data collection, automation, human validation and AI processes to provide you with end-to-end automated workflows.
Below is a representation of the process highlighting each step a contact will go through.
Each section will provide more detail on the step in the overall process. As a final thought before jumping in, each step within this journey can be used independently.
Already have company names? Perfect, we will perform the identification and following steps. Just need a list to be verified? No problem, we can do that too.
No matter the state of your current contract and company data is process is modular and can accommodate it. Reach out to your account manager should you have any questions.
Launch Workflow
The first step in the journey to a conversation is to launch a new workflow. You can look at best practices on how to do so here.
Once finalized, the targeting information will be added to our system and processed. By default, our system will seek 300 contacts to add to the newly launched workflow.
Identify Companies
Our system will first look for companies that fit the profile provided, this includes information like employee size, industry, estimated revenue figures and company location. This can range anywhere from hundreds to thousands of potential company matches.
We then look at the custom criteria that were submitted and filter out the companies that do not fit the profile.
For example, if your workflow was targeting computer software companies that are also using Hubspot as a CRM then only companies where we have a match on the technology tag will be able to progress.
Identify Contacts
Once we have shortlisted these companies we look for decision-makers that matter to you. We look at the ideal profile submitted as well as job titles and seniority provided and start looking for those ideal matches from the companies identified.
Once we have gathered these profiles we run the contacts through another custom criteria validation. For example, if your criteria require the contact to have worked at a bank previously this would be added here.
Human Validation & Data Accuracy
Where we cannot run custom criteria validation checks programmatically, we have a crowd of researchers who can validate the data provided and further ensure accuracy. This step is crucial to maintaining our data accuracy and is why we surpass other databases in quality and accuracy every time.
Checkpoint
So what do we have at his point?
We have:
- Identified thousands of potential companies
- Identified all potential contacts at those companies
- Filtered out all companies and contacts that do not meet our criteria through human validation and custom requirements.
At this checkpoint our TAM (total addressable market) research is complete and we can commence the appending of data points and contact information.
Based on our estimates, this process manually would take up to 5 minutes per contact depending on the targeting information provided. Obviously, you are using Ubico so you gain all that time back ;).
5 X # of contacts sourced = minutes saved identifying your TAM
Identify Contact Information
Once the contact and company have been approved we now start building the data and ensuring the accuracy. Quality data is important to you and therefore it's paramount to us. Once we have found an email address we run it through 3 validation steps to know if we can send it to the email address or not.
I won't get into the technicals here however this validation process yields an average delivery rate of 98%. For reference, the industry average for outbound emails is a bounce rate of 10-15%.
Less bounces means:
- More delivered messages for you
- A cleaner database (you are pushing your data to your CRM right?)
- Healthier domain reputation.
Enrichment Data
With a validated email address, we now know that this contact and company fit our criteria for engagement but there is one more step which is to enrich the contact and company with up to 54 data points. This includes information such as the company location, industry information, social profiles, technologies used, contact role in the company, and so much more.
This information is available to be pushed to your CRM directly and removes the need for manual data entry and fetching of individual data points.
Data Cleaning
An often overlooked step in the data build process is standardizing and cleaning all your data.
Data hygiene is important to us.
Company names, names, titles, links and other data points get cleaned in order to be ready for email sequences. It is not uncommon that we hear teams doing this manually or skipping it altogether.
Easy example: Company name 'Acme Inc' becomes 'Acme'.
Your CRM admin and/or sales ops person will thank us for this.
Duplicate Detection
All Ubico users and teams have built-in duplicate detection to ensure that no two folks are reaching out to the same contact. This duplication detection is done for every new contact either sourced or uploaded to maintain data integrity across the organization. This is an arduous task for most teams since managing this data at the top of your sales funnel is often messy, scattered and spread across numerous tools.
By baking it to the workflow directly you no longer waste time managing contacts and determining if they already exist within your organization.
Contact Engagement
We now start engaging with the contact based on the messaging you have set up. Remember those data points we cleaned earlier? Those are now being merged and sent. In the event that there is a problem with a data point we also have safety measures in place to ensure that no contact with missing data (say {FirstName}) is going to receive an email (this assumes you are using our variable selector, please don't copy paste variable tags from other outreach platforms).
Contacts will receive the next step in your workflow unless...
A Reply is Received
We automatically detect responses from contacts and remove them from the workflow. You also have the option to manually mark a contact as responded in the dashboard directly. This is typical if say the contact responds to your call or LinkedIn message.
Reply Categorization
A reply is received and now processed and categorized. We use AI to classify and categorize emails based on the type.
Here are the types of responses we classify:
- 'Interested' type conversation
- 'Objection' type conversation
- 'Not now' type conversation
- 'Forwarded' type conversation
- 'Out of office' type response
- 'Bounce' type response
Once classified we move the conversation to the appropriate folder in your email client. This makes sorting and prioritizing tasks even easier.
Checkpoint
So what do we have at his point?
We have:
- Identified your TAM
- Validated contact information and sourced enriched data points
- Engaged with your contacts
At this checkpoint our TAM, data accuracy and contact engagement are complete.
Based on our estimates, this entire workflow saves on average 10 minutes per contact depending on the targeting information provided.
10 X # of contacts sourced = minutes saved in your outreach workflow
Workflow Optimization
The final step in the process is to add the contact information, company information and engagement data (basically all the data generated throughout the workflow) to our AI model which will be used to optimize the workflow moving forward. This gives you and your team a stronger understanding of who to look for and what their attributes are.
These thousands of data points are fed into our AI which is seeking to answer one question:
'Which targeting parameters are most likely to generate conversations?'
Parameters are then modified to your existing workflow to optimize the workflow moving forward and source contacts that are most likely to result in a conversation given the historical dataset.
So you want to do this yourself?
Goodluck. (Kidding)
Sort of... if you were to reproduce this workflow in its entirety you would need:
- Lead researcher(s) - for finding your TAM
- Validation tools - for data accuracy and finding contact information
- Enrichment tools - for data enrichment
- Data cleaning and standardization tools - data hygiene
- Email automation software - for contact engagement,
- Machine learning and data scientist - for analysis and workflow optimization
I could add up the average cost for what it would look like to reproduce this workflow internally however Ubico is simply always cheaper.
