Array enhances account targeting and sales efficiency with Kernel

In 2024, Array's RevOps team faced a common challenge in scaling their sales function: effectively segmenting and prioritizing accounts without extensive manual research. Traditional data providers delivered overly broad or inaccurate classifications, limiting efficiency.

Array needed to precisely distinguish target verticals, such as Personal Loans, Financial Management, and Banks, without overwhelming manual processes

The main challenges were:

• Vertical classification: Difficult accurately categorize accounts at scale, especially when exploring new verticals
• Fit tiering: Ability to rank accounts by ICP fit
• Size prediction: Challenging to prioritize GTM effort by expected deal size
• Coverage gaps: Untapped high-quality accounts from their CRM

As the sales team expanded, honing in on this precision became increasingly critical to avoid operational inefficiencies.

Finding the right partner

After exploring multiple providers, Array selected Kernel for its ability to:

• Provide granular and customized industry segmentation.
• Deliver automated and reliable estimations for account prioritization.
• Continuously improve based on sales team feedback.

Solution

‍

With Kernel, Array deployed AI agents that work directly in Salesforce, with no need for additional tools or platforms.

First, Kernel's cleaning agent validates all accounts in Array's CRM. It fixes broken website links, removes duplicates, and maps each account to the correct LinkedIn profile. 

Using this clean foundation, Kernel’s research agent maps accounts to Array's precise industry segments. Instead of broad categories like "Finance", accounts are classified into specific verticals such as "Personal Loans" or "Banks". Each classification comes with transparent reasoning and confidence levels.

Kernel’s prioritization agent then tiers accounts based on their potential fit. The model analyzes company growth metrics, funding history, business models, and team composition. Each tier assignment includes the key factors that influenced the decision.

Most critically, Kernel’s estimation agent provides an estimate of the potential ARR for each account. The model combines customer counts, website traffic, app usage data, and industry benchmarks to estimate deal size potential specifically for Array. Like all Kernel data points, these predictions include detailed reasoning.

The entire system improves through rep feedback. When reps spot incorrect data, they submit quick notes in Salesforce. Kernel processes this feedback, updating the model and sharing insights with RevOps leadership during monthly reviews.

Impact

With Kernel, Array identified 6,000 new high-value target accounts missing from Array's CRM, expanding their pipeline by millions in ARR. 

By automatically disqualifying poor-fit accounts and precisely tiering the good ones, reps now focus their time on the accounts most likely to convert, increasing scheduling efficiency from 6% to 21%.

The impact extends beyond just new accounts. Array's RevOps team now spends less time fighting data quality issues and more time on strategic initiatives. The automation has also allowed Array to consolidate their tech stack, reducing annual SaaS spend.

‍