TEXT ANALYTICS FOR PRODUCT MANAGERS
2-Week Sprints ClickMasters delivers in 2-week sprints with demo and retrospective the PM sees working software every fortnight, not monthly status reports
What you get
- 2-Week Sprints Working Software Every Fortnight
- Acceptance Criteria Agreed Before Sprint Starts
- Feature Flags for PM-Controlled Rollout
- Analytics Events in Every Story
- Risk Escalation Within 4 Hours, Not at Sprint Review
- Definition of Done With PM Sign-Off
Why PRODUCT MANAGERS AND PRODUCT LEADERS choose ClickMasters
Product managers adding AI capabilities to their product roadmap need to answer three questions before committing to an AI feature: what specific user job will the AI do (not 'improve the product with AI' but 'reduce the time users spend on task X from 20 minutes to 2 minutes'), how will we evaluate whether it is working (the quality benchmark and the metrics), and what is the failure mode (what does the user experience when the AI is wrong, and is that failure mode acceptable). ClickMasters helps PMs answer these questions during scoping. A development partner who treats acceptance criteria as suggestions, escalates timeline risks at the sprint review rather than when they are identified, and instruments analytics after being asked rather than by default is not a partner it is a source of stakeholder communication problems for the PM. ClickMasters is structured to be the opposite of this: process-integrated, measurement-first, and transparently communicative.
Built for PRODUCT MANAGERS AND PRODUCT LEADERS
Overview
ClickMasters delivers text analytics in the way PMs need it delivered: sprint-based with working software every 2 weeks, acceptance criteria agreed before the sprint starts, analytics events instrumented as part of every story, and feature flags so the PM controls rollout. No black boxes. No surprise timeline misses. No missing instrumentation requests.
User Stories
ClickMasters engineers participate in story refinement ambiguous stories are challenged before they enter the sprint, not mid-sprint when the cost of ambiguity is highest
Feature Flags
Every ClickMasters product engagement includes feature flag infrastructure PMs control feature rollout to user segments without waiting for a deployment
Analytics-First
Every feature includes agreed analytics events PMs measure feature impact from day one, not after requesting instrumentation as a follow-up task
AI Feature Discovery for PMs
The PM's AI feature discovery process: job mapping (identify the specific user job the AI will automate or augment 'the user spends 20 minutes writing a meeting summary after each call' is a specific job that AI can address; 'improve user experience' is not), quality benchmark (define 20-50 test cases representing the range of inputs the AI will encounter varied complexity, edge cases, and failure scenarios the benchmark that measures whether the AI is ready to ship), and cost modelling (estimate the LLM API cost per user per month at expected usage rates a feature that costs $3 per user per month for a $10/month product has negative unit economics and must be redesigned before build begins).
AI Feature Prioritisation in the PM Roadmap
AI feature prioritisation framework for PMs: reach (how many users have the problem the AI will solve? a feature that helps 80% of users is higher priority than one that helps 10%), impact (how much will the AI improve the experience? reduce task time from 20 minutes to 2 minutes is higher impact than reduce from 5 minutes to 3 minutes), confidence (how confident are we that the AI can perform this task reliably? a classification task with clear categories is higher confidence than an open-ended generation task), and effort (how complex is the implementation? a single API call with structured output is lower effort than a multi-step reasoning chain with retrieval). RICE score (Reach x Impact x Confidence / Effort) provides a consistent prioritisation framework across AI and non-AI features on the same roadmap.
AI Feature Communication for PM Stakeholders
Communicating AI features to stakeholders: set expectations precisely (AI features have variable quality communicate the expected quality level to stakeholders before release, not after: 'this feature will correctly classify 90% of inputs, and for the remaining 10%, it will present the user with a manual review option'), demonstrate the failure mode (show stakeholders what the AI does when it is wrong stakeholders who have not seen the failure mode will be surprised by it in production, stakeholders who have seen it during the demo have calibrated expectations), and measure and report quality over time (include AI quality metrics in the PM's regular stakeholder updates thumbs up/down rate, task completion rate, and error rate so that quality trends are visible before they become customer complaints).
Text Analytics for Product Managers Sprint-Based, Measurable, PM-Led
Acceptance criteria driven. Analytics-first. Feature flags standard.
Transparent pricing
TEXT ANALYTICS pricing
Fixed-price engagements tailored to your scope. All amounts in USD.
AI Feature Discovery
Job mapping, benchmark definition, cost model, quality criteria, prioritisation
3-5 days
$2,500-$5,000
AI Feature Sprint
LLM integration, quality evaluation, feedback loop, cost monitoring, analytics
2 wks
$10,000-$22,000
AI Quality Dashboard
Thumbs up/down, LLM-as-judge, quality trends, cost per user, PM dashboard
1-2 wks
$4,000-$8,000
AI A/B Testing
Variant framework, metric definition, statistical analysis output
1-2 wks
$4,000-$8,000
AI PM Retainer
Feature development, quality monitoring, model updates, cost optimisation
Ongoing
$5,000-$10,000/mo
Frequently Asked Questions
Book a PM Discovery Session in 48 Hours
Story mapping + metrics definition + sprint process design.
