Customer behavior evidence and reporting workspace

For

Marketing and product teams acting on customer behavior data

Solves

Behavior data sits in separate analytics, feedback and social tools, so teams cannot connect what customers do, say and cost without manual exports.

Delivers

Evidence-backed behavior report with source links and approval states

Built in

about 5 weeks of creation time, MVP in 5 days

Investment

$12,500 for the MVP, $42,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce the time from scattered behavior data to a reviewed, decision-ready report.

1. Ask questions in plain English and return answers about user behavior.
2. Organize households into groups and behavioral segments.
3. Identify top customers from segment and spend data.
4. Visualize sessions and heatmaps of product interaction.
5. Run card sorting, preference and usability studies on the site.
6. Collect NPS, CSAT scores and bug reports from users.
7. Connect analytics, feedback and social tools into one customer view.
8. Compare market share and locate growth opportunities from spend data.
9. Report campaign insights across multiple channels.
10. Suggest post ideas from trending topics.
11. Plan and schedule posts across platforms.
12. Show engagement and audience behavior in a dashboard.
13. Coordinate content strategy across team members.
14. Apply customizable templates to posts and reports.
15. Support live user interviews for deeper insight.
16. Link tutorials, webinars and case studies to the relevant analysis step.

Everything these tools do, in one app

• AI-powered insights Ask questions in plain English and get instant answers about user behavior.Found in Crowd
• Customer segmentation Organizes households into groups and behavioral segments to identify top customers.Found in Spatial.ai
• Session recordings and heatmaps Visualize how users interact with your product in real time.Found in Crowd
• Research studies Conduct card sorting, preference tests, usability studies, and more directly on your website.Found in Crowd
• Feedback collection Collect NPS, CSAT scores, and bug reports directly from users.Found in Crowd
• Data integration Connect with other tools to centralize customer data.Found in Crowd, Spatial.ai
• Consumer spend analysis Compare market share and locate growth opportunities using spend data.Found in Spatial.ai
• Multi-channel campaign insights Provides insights for tailored campaigns across multiple channels.Found in Spatial.ai
• AI content generation Suggests post ideas based on trending topics.Found in buzzabout
• Post scheduling Plan and schedule posts across multiple platforms.Found in buzzabout
• Analytics dashboard Provides insights into engagement and audience behavior.Found in buzzabout
• Team collaboration Tools for teams to coordinate content strategies effectively.Found in buzzabout
• Customizable templates Create visually appealing posts quickly.Found in buzzabout
• Live user interviews Supports live interviews for deeper insight gathering.Found in Crowd
• Educational resources Access tutorials, webinars, and case studies to leverage data effectively.Found in Spatial.ai

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted analytics events, feedback responses, social posts and spend records

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted analytics events

4. 3

   Feedback responses

5. 4

   Social posts and spend records

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Evidence-backed behavior report with source links and approval states

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One agreed data schema and permissioned source set; final interpretation and campaign decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Data sources and permissions, Analysis workspace, Report and review. Use a source list with connection states, a central canvas for segments, sessions and feedback, and a right-hand panel for evidence, comments and approval. Let users compare periods and segments side by side. Display draft, changes requested and approved states. Provide a shareable report link with comments anchored to the relevant chart or quote. Make the task-specific outcome evidence-backed behavior report visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source connection states, segment definitions, comment threads, approval states, usage allowances, report versions, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Customer-owned analytics, feedback and social accounts and permitted spend data. Cloud data storage, analytics and feedback platform import/export and reporting destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   5 days

   One buyer segment, one recurring use case; first modules: ask questions in plain English and return answers about user behavior; organize households into groups and behavioral segments. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will marketing and product teams acting on customer behavior data use it to solve "behavior data sits in separate analytics, feedback and social tools, so teams cannot connect what customers do, say and cost without manual exports"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Reviewed reports per analyst hour and decisions traced to evidence.
4. Measure, then decide. Track reviewed reports per analyst hour and decisions traced to evidence; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One agreed data schema and permissioned source set; final interpretation and campaign decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: ask questions in plain English and return answers about user behavior; organize households into groups and behavioral segments. Support the third module with operator review: identify top customers from segment and spend data. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around the evidence-backed behavior report. Retain the explicit scope boundary: One agreed data schema and permissioned source set; final interpretation and campaign decisions remain human.

What the build depends on. Data upload and preview, asynchronous analysis jobs, editable version history, reviewer access and tested export formats. High-fidelity analysis requires specialist data QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One agreed data schema and permissioned source set; final interpretation and campaign decisions remain human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined data package. Offer a monthly analysis allowance after repeat demand. Quote complex multi-source integrations or specialist research separately. These are test prices, not market benchmarks. Package the initial sale as one bounded evidence-backed behavior report. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce the time from scattered behavior data to a reviewed, decision-ready report. Demonstrate a concrete evidence-backed behavior report using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Marketing and product teams acting on customer behavior data professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample evidence-backed behavior report from a small authorized input set, with a transparent calculation of reviewed reports per analyst hour and decisions traced to evidence and no promised savings.

The first 30 days

1. Week 1: interview five marketing and product teams acting on customer behavior data and inspect a recent example of behavior data sitting in separate analytics, feedback and social tools.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure reviewed reports per analyst hour and decisions traced to evidence, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Reviewed reports per analyst hour and decisions traced to evidence. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Reviewed reports per analyst hour and decisions traced to evidence; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs an evidence-backed behavior report. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved segment definitions, source mappings and review examples, together with reliable delivery for a narrow marketing and product niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for marketing and product teams acting on customer behavior data. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Crowd, Spatial.ai and buzzabout, plus spreadsheets and manual exports. Compare this product with the buyer's present method on reviewed reports per analyst hour and decisions traced to evidence. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Data processing, storage, reviewer hours, client revision rounds and licensed source data. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of the evidence-backed behavior report. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve data permissions, source attribution, consent records and usage rights. Named owners approve substantive interpretations and campaign scope. One agreed data schema and permissioned source set; final interpretation and campaign decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.