Salesforce Software / SaaS Global

Grammarly

Marketing Personalization · Sales Automation

Manually gathered marketing leads aren’t ready for sales. Grammarly’s marketing operations team used to spend hours manually building email lists based on user open rates or perceived interest in upgraded plans.

Better email relevance and frequency have led to an unsubscribe rate of just 0.04%, whereas the industry standard is about 2%.
Einstein 1 Marketing identifies quality leads, increasing conversions by 30%.
It previously took 60 to 90 days to convert and close those deals.
Kelli Meador Senior Marketing Operations Manager, Grammarly Automated email sends increase relevance, leading to just a 0.04% unsubscribe rate.

Reported impact

Business outcome

Reported outcomes include Einstein 1 Marketing identifies quality leads, increasing conversions by 30%., Marketing ops now sends about 200 high-quality leads per month, resulting in a 30% increase in MQL conversion., and This has led to an 80% increase in customers upgrading their accounts..

  • Productivity Improvement
  • Revenue Growth

Solution

Einstein 1 Marketing identifies quality leads, increasing conversions by 30%. Now, Account Engagement identifies multiple Grammarly users who work at the same company, predicting the potential need for a business account. Einstein Account Insights then scores the leads based on the number of users they have and how they engage with Grammarly resources. Sales can then view the scores without leaving the account record . Automation also routes leads to the best-suited salesperson. With Salesforce, Grammarly prioritizes quality leads over quantity. Marketing ops now sends about 200 high-quality leads per month, resulting in a 30% increase in MQL conversion. This has led to an 80% increase in customers upgrading their accounts. It previously took 60 to 90 days to convert and close those deals.

Data flow

Operational data is captured in Salesforce workflows, analyzed in Tableau, and then fed back to teams making decisions about how to qualify leads, prioritize accounts, and coordinate seller outreach.

Solution architecture

4 components · 2 layers
  1. Serving
    • Slack Delivers alerts and collaboration context that help teams qualify leads, prioritize accounts, and coordinate seller outreach.
    • Sales Cloud Stores account, opportunity, and workflow data teams use to qualify leads, prioritize accounts, and coordinate seller outreach.
    • Tableau Provides dashboards and analytics teams use while they qualify leads, prioritize accounts, and coordinate seller outreach.
  2. Orchestration
    • Account Engagement Captures engagement signals and automates lead qualification and nurture.

Architecture clues

  • Slack is used as a workflow surface for internal support or collaboration.

Evidence from the source

Einstein 1 Marketing identifies quality leads, increasing conversions by 30%.
Grammarly’s marketing operations team used to spend hours manually building email lists based on user open rates or perceived interest in upgraded plans.
However, the list often included spam bots or accounts that weren’t ready to convert, creating the need to generate higher-quality leads and optimize the sales team’s time.
Manually gathered marketing leads aren’t ready for sales.
Marketing ops now sends about 200 high-quality leads per month, resulting in a 30% increase in MQL conversion.
Marketing would then send about 400 organic marketing qualified leads (MQLs) to sales each month.
Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    8 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Salesforce

    Grammarly customer story

    First-party
    https://www.salesforce.com/customer-stories/grammarly-lead-scoring-ai/
    Source type
    Vendor-published customer story
    Published
    Mar 15, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Mar 15, 2024 · Salesforce · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.