Sherlock review

Sherlock is product engagement analytics for SaaS teams. It turns in-app activity into engagement scores for individual users and accounts, with scores normalized from 1 to 100. The idea is to help teams spot product health, activation and engagement signals, then use them across go-to-market workflows.

Teams can choose which events count toward a score and assign each event a weight from 0 to 10. Sherlock also offers ranked views of engaged, active, rising or falling users, plus account-level scores. Trend tracking covers engagement over time and activation against onboarding milestones. Cohorts let teams compare groups such as trial and paid users, while event impact analysis highlights in-app activities and feature interactions most correlated with engagement.

That is a focused feature set, not a general-purpose product analytics description. The published information does not specify integrations, implementation requirements or how Sherlock defines its underlying event data.

Sherlock’s clearest strength is the connection between product activity and engagement scoring. Teams can select tracked events and set their relative weights, rather than relying on a single fixed score. The 1–100 scale is designed to make user and account engagement easier to compare, while leaderboards help surface people or accounts whose engagement is rising or falling.

The feature set also ties scoring to practical SaaS questions: whether users are activating against onboarding milestones, how engagement changes over time, and whether trial users behave differently from paid users. Event impact analysis adds a way to identify activities and feature interactions correlated with engagement. That can help teams decide what signals to monitor in customer-facing workflows.

• User and account scoring, with ranked views for several engagement states.
• Custom event selection and weights from 0 to 10.
• Trend tracking, onboarding milestones and cohort comparisons.
• Event impact analysis focused on correlation with engagement.

These are useful capabilities on paper for teams that want a defined engagement layer. The published details do not establish how the scores are calculated beyond event weighting, so buyers should ask how the model handles missing, noisy or changing event data.

The biggest issue is that the published pricing details do not clearly match the product described. They list Team at $49 per month for up to 5 users, unlimited hosts and $49 in monthly usage; Business at $349 per month for up to 100 users, multiple organizations, unlimited hosts and $349 in monthly usage; and Enterprise at $4,000 per month, with unlimited users and customer-quoted usage and terms. The listed audiences refer to monitoring hosts, not product engagement analytics.

The same pricing data says extra usage costs $0.30 per GB ingested for uncompressed logs and traces, $0.10 per GB stored monthly for logs and traces, and $0.12 per million metric samples. Those units need clarification before comparing Sherlock with an engagement analytics tool.

No free plan is listed. Annual prices are not published, and the free-trial field is null, so trial availability is unclear. We would confirm the plans, billing unit and what counts as included usage directly with Sherlock before switching.

Sherlock’s listed monthly prices start at $49 for Team, rise to $349 for Business, and reach $4,000 for Enterprise. That’s a $300 jump from Team to Business, then $3,651 more for Enterprise. Team includes up to 5 users, unlimited hosts, and $49 in monthly usage; Business includes up to 100 users, multiple organizations, unlimited hosts, and $349 in monthly usage. Enterprise has unlimited users, with usage and terms quoted per customer.

Annual billing prices are not published. No free plan is listed, and trial availability is not published. The pricing information also lists overage charges of $0.30 per GB ingested for uncompressed logs and traces, $0.10 per GB stored monthly, and $0.12 per million metric samples. Those usage units don’t obviously match Sherlock’s engagement-scoring description, so confirm that these charges apply to the product and ask what counts toward included usage before signing.

Plan to map your existing product events to Sherlock’s tracked events and weights, then check that user and account identifiers line up. That is a sensible migration checklist based on Sherlock’s custom scoring and account-level analytics, not a documented estimate of effort. The published information does not specify implementation steps, integrations, migration services, data export formats, or whether historical activity can be imported.

Before switching, ask for a sample export and confirm whether you can retrieve raw event data, scores, cohorts, and score history if you leave. Also document your event definitions and weights outside the product. The published sources include no Reddit posts or comments about Sherlock, so we can’t substantiate common reasons customers leave or report user experiences. If data portability or migration support is a deciding factor, get those terms in writing rather than treating them as included.