Navigating the Challenges of Outbound ABM: A Practical Guide to Data-Driven Lead Generation
Account-based marketing (ABM) presents unique challenges, especially in acquiring high-quality data for effective cold outreach campaigns. In a dynamic SaaS industry landscape, having a refined strategy for lead generation is not just an advantage; it's a necessity. Skief Labs, leveraging its expertise in outbound marketing, shares insights from a real-life case study, offering a practical roadmap for businesses facing similar challenges.
In this article, we delve into the intricate process of building a high-quality database for ABM campaigns without leveraging the services of external database providers. The following critical stage will be detailed:
- Data Acquisition Phase: This stage focuses on identifying and validating target accounts, enriching company data through LinkedIn research, and configuring tailored Sales Navigator queries to ensure a high-quality, relevant list of prospects for effective outreach.
- Data Quality and Scoring Phase: This stage emphasizes the refining and scoring of prospect data, including standardizing company names and job titles, cleaning names, verifying current company affiliations, and classifying prospects based on job titles. These steps are critical to ensure the precision and relevance of the target audience for outreach.
- Enrichment and Verification Phase: This stage focuses on the meticulous enrichment and verification of email addresses. By employing diverse tools for email enrichment and validation, the process aims to maximize the accuracy and completeness of the prospect data, essential for the effectiveness of outbound marketing efforts, and to find the best working ones for this client.
This short practical guide aims to provide a comprehensive understanding of overcoming the common hurdles in ABM, ensuring your cold-emailing efforts are efficient and effective.
Overview of the workflow
1) Data Acquisition Phase
This phase forms the foundation of the lead generation process, focusing on identifying and compiling a relevant list of target accounts and their detailed profiles. Each step is crafted to ensure accuracy and relevance, setting the stage for effective outreach.
We also have published a more comprehensive guide detailing the data acquisition workflows. You can find it here.
A) Validation of Target Accounts
- Objective: To ensure efforts are concentrated on the most relevant companies.
- Method: A combination of LinkedIn research and manual verification is employed to ensure the accuracy of the target list.
- Outcome: A refined list of 367 potential companies in the taregt industry.
B) Extraction of Company Details from LinkedIn
- Objective: To enrich the dataset with detailed company information, aiding informed decision-making in subsequent stages.
- Method: Data extraction and scraping tools like Phantombuster or CaptainData are utilized to gather comprehensive data.
- Outcome: An enriched dataset of 367 companies, complete with unique company IDs and industry details.
C) Configuration of Custom Sales Navigator Search Queries
- Objective: To tailor search queries for each target account, enhancing the relevance of extracted contact profiles.
- Method: Customizing Sales Navigator search queries for each account to target specific profiles within key accounts.
- Outcome: Creation of 367 unique Sales Navigator search URLs, each targeting the marketing department of the respective companies.
D) Extraction of Sales Navigator Research
- Objective: To compile a consolidated list of prospects along with their LinkedIn profiles for additional scoring and enrichment.
- Method: Employing data extraction tools like Phantombuster or CaptainData, with looping extraction for each URL.
- Outcome: Successful extraction of 6,115 unique, deduplicated profiles from 331 companies, acknowledging that not all companies yielded relevant profiles, a common occurrence in targeted searches.
Each step in this phase is meticulously executed to build a robust database of potential leads, ensuring that the subsequent phases of the lead generation process are based on high-quality, relevant data. However, the LinkedIn Sales Navigator search engine can sometimes yield false positives, which must be carefully filtered out to minimize the risk of spam complaints (hello Google) and other associated challenges.
2) Data Quality and Scoring Phase
This phase is dedicated to refining the data for precision in targeting, ensuring that the outreach connects with the most relevant prospects. It involves meticulous steps for data standardization and scoring.
A) Clustering Based on Company Name and Job Title
- Objective: To achieve consistency and high data quality, vital for accurate company clustering and analysis.
- Method: Utilization of OpenRefine and algorithms like fingerprint, n-gram, and Levenshtein for precise clustering.
- Outcome: Company names and job titles are standardized, enhancing data uniformity.
B) Cleaning of First and Last Names
- Objective: To improve the enrichment process by ensuring clean and complete names, particularly addressing LinkedIn’s occasional incomplete (hidden) last names.
- Method: A mix of manual and logical cleaning methods, with potential support from custom-trained AI tools.
- Outcome: Thoroughly cleaned names, with identification of any incomplete last names, thus preventing unnecessary enrichment efforts.
C) Verification of Current Company ID
- Objective: To confirm the current employment status of prospects at target companies, increasing the relevance of outreach.
- Method: Matching the contact table's company ID with the account table's company ID for verification.
- Outcome: A focused list of 6034 prospects confirmed to be working at the target companies.
D) Scoring Based on Job Title
- Objective: To fine-tune targeting by segmenting the audience based on job titles, using keyword matching and hierarchical classification.
- Method: Part A: Matching job titles with a predefined list of keywords and combinations (manually or with AI tools). Part B: Classifying hierarchical levels (e.g., 'manager', 'director') using keyword classification (manually or with AI tools).
- Outcome: Identification of 4791 prospects as a perfect fit, constituting 78% of the total dataset.
The identification of 22% of false positives of the total dataset, highlights the criticality of the Data Quality and Scoring Phase in lead generation:
- Precision in Targeting: A high percentage of relevant prospects ensures focused and impactful marketing efforts.
- Efficient Resource Allocation: By filtering out less relevant contacts, this phase directs resources efficiently toward engaging the most promising leads.
- Foundation for Success: This accurate segmentation lays the groundwork for more effective outreach, increasing the likelihood of positive engagement and conversions.
This phase is crucial for creating a targeted and efficient lead-generation campaign, crucially mitigating deliverability issues. With major ESPs like Google and Yahoo now requiring a spam complaint rate below 0.3% for large bulk senders (over 5k emails daily), the accuracy in this phase helps ensure compliance and avoid potential complications.
Note: the percentage of false positives will depend on the quality of your Sales Navigator query.
Contacts distribution by company size
3) Enrichment Phase
The Enrichment Phase plays a vital role in refining the lead generation process by ensuring the accuracy and completeness of prospect email data, crucial for successful outbound marketing campaigns.
A) Email Enrichment
- Objective: To locate the professional email addresses of prospects.
- Method: Utilization of four diverse enrichment tools (Dropcontact, Anymailfinder, Icypeas, Apollo) to assess and maximize enrichment performance.
- Outcome: Successful enrichment of over 14,000 emails, enhancing the database for outreach.
B) Email Verification and Hard Bounce Management
- Objective: To verify the validity of email data and reduce the incidence of hard bounces, thereby maintaining the domain's reputation.
- Method: A combination of tools like Mailfloss and internal validation processes.
- Outcome: 4520 prospects with verified emails, ensuring a high level of data reliability for the campaign (74% of the global list or 94% of those sent for enrichment).
Achieving a 94% enrichment rate from the sent-to-enriched process significantly surpasses industry averages, typically ranging from 50% to 80%. This exceptional rate indicates a highly effective approach, reflecting the quality of the initial dataset and the efficiency of using multiple enrichment tools. Such a high success rate in email enrichment not only enhances the reach and potential engagement of outbound marketing campaigns but also provides a competitive advantage, leading to higher conversion rates and better overall campaign effectiveness.
Bonus: Analysis of email enrichment tools
Performance of each provider
Overview of the performance of the combination of providers
This table provides a clear layout of the performance in terms of unique verified emails and their corresponding percentages of the total unique profiles. It shows that the best trio combination covers 84.00% of the unique profiles, indicating a strong performance in email enrichment coverage. The overlap percentage indicates the degree of similarity between Dropcontact and Apollo, which is over half of the unique profiles at 55.46%.
This detailed table shows the effectiveness of each combination in providing unique verified emails, allowing for a comprehensive comparison of the performance across different sets of providers. The highest coverage is offered by the trio of Dropcontact, Anymailfinder, and Apollo, with 84.00% of the unique verified emails.
Heatmap – unique verified emails & overlaps
The heatmap now displays the percentage of overlaps between providers in terms of unique verified emails. The color intensity represents the percentage of the total unique profiles that are shared between each pair of providers. This percentage-based view allows for an immediate understanding of the degree to which providers' results overlap, providing a clearer picture for strategic decisions on provider selection and combination.
Chart of unique and overlapping emails
The stacked bar chart visualizes the unique and overlapping emails for each provider. The green portion at the bottom of each bar represents the number of unique emails that each provider has verified without overlap, while the red portion above represents the number of emails where there was an overlap with one or more of the other providers. This chart helps in understanding not just the total output of each provider, but also how much unique value each is adding, which can be critical when deciding which providers to use in tandem or which might be redundant.
Analysis of the performance based on the company size
- SMBs: For companies with less than 1000 employees, Apollo is the single provider with the highest percentage of unique verified emails in the segment of small companies, indicating a strong individual performance.
- Large companies: For companies with 1000 or more employees, Apollo again shows the highest individual performance among the providers for large companies
ConclusionIn the intricate dance of Account-Based Marketing, the precision of one's steps—from selecting the ideal companies to the final flourish of reaching out—can elevate a campaign from mere noise to a resonant symphony of engagement. Our deep-dive analysis crystallizes the essence of this precision: a meticulously curated company list lays the groundwork; rigorous data cleaning and smart scoring refine the prospects; and the astute application of enrichment tools brings the clarity of verified contacts.
At Skief, we understand that the alchemy of ABM is part art, part science. By harnessing our analytical acumen and technological savvy, we empower companies to navigate the ABM landscape with confidence and finesse. Let us orchestrate your outreach so that each contact resonates with potential and every campaign crescendos to success. Book your free discovery call here.