Built for teams needing a shared customer database

The customer database that stops being a spreadsheet once your team passes 10 people.

Teams running on a shared Google Sheet or a CSV passed around by email hit the customer database question when the sheet breaks: duplicate records, inconsistent fields, no activity history, no consent context. Strkr ships a real customer database as the CRM substrate.

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What this audience is actually dealing with

The pains that bring buyers here.

A customer database starts as a spreadsheet and ends as a liability. The pain points below are what Strkr onboarding hears when the team is upgrading from Sheets, from a legacy database, or from a lightweight contact-management tool.

The spreadsheet collapse

A Google Sheet that nobody trusts past row 400.

A customer spreadsheet starts clean on row 1. By row 400 it carries duplicates, inconsistent field values, stale contact info, and no activity history. The team stops trusting it, starts emailing CSV copies around, and the single source of truth fragments. Strkr ships a real customer database with deduplication, field validation, and activity history on day one.

The duplicate record flood

The same customer in the database three times.

Without identity resolution the customer database accumulates duplicate records. The same contact appears as "John Smith," "J. Smith," and "jsmith@acme.co" with inconsistent attributes. Strkr identity resolution matches on email, phone, and external ID with fuzzy duplicate detection from Strkr AI. Duplicates merge or flag for admin review automatically.

The missing activity history

A customer record with no conversation history.

A spreadsheet or lightweight contact tool holds the customer attributes but no activity history. The team cannot see what the customer said last week, which email they opened, which deal they considered. Strkr activity timeline captures emails, calls, meetings, SMS, pipeline moves, support interactions, and marketing engagement on every record.

The field validation gap

Phone numbers in six different formats.

A spreadsheet accepts any value in any field. Phone numbers appear as "555-1212," "(555) 1212," "+1 555 1212," and "555.1212". Email addresses include trailing spaces, typos, and malformed strings. Strkr field validation enforces format rules at write time, which keeps the data queryable and deliverable.

The consent context gap

Marketing to customers who opted out two years ago.

A customer database without consent context cannot answer the question "can I send a marketing email to this customer." The team sends anyway and discovers the TCPA settlement arrived. Strkr consent context tracks per-channel consent (email, SMS, data processing) with the source of each consent captured for the audit log.

The API poverty

A database that cannot talk to anything.

A spreadsheet has no API. A lightweight contact tool has a decorative API. The team cannot sync customer data to the ERP, the data warehouse, or downstream SaaS tools without manual CSV exports. Strkr ships REST and GraphQL APIs with webhook outbound events on every record change.

What a real customer database ships with

The eight surfaces that make Strkr a database, not a spreadsheet.

The cards below are the database surfaces Strkr ships. Each one closes a spreadsheet failure mode.

Record types

Account, contact, deal, custom objects.

Strkr ships a tunable schema with account, contact, deal, and custom objects as first-class records. The admin can model their domain (equipment, policy, matter, patient, project, product line) as custom objects with the same treatment as standard objects. Custom objects available on the Starter tier at $29 per seat.

Field library

Twelve field types with validation.

Strkr ships 12 field types (text, long text, number, currency, date, date-time, picklist, multi-select picklist, user picker, record picker, boolean, URL) with validation rules at write time. Required-field marking, format validation (email, phone, URL), and picklist value enforcement keep the data queryable.

Identity resolution

Match on email, phone, external ID.

Strkr identity resolution matches records on email, phone, and external ID. When a webhook fires from the marketing tool with an email that matches a Strkr contact the activity lands on the right contact record. Strkr AI fuzzy duplicate detection catches near-matches ("j.anderson@acme.co" versus "josh.anderson@acme.co") with configurable auto-merge threshold.

Activity timeline

Emails, calls, meetings, SMS, pipeline moves.

The activity timeline captures emails (from Gmail or M365 bi-directional sync), calls (from the dialer coming on voice SDK target 2027-Q1), meetings (from Zoom or Teams), SMS and MMS (from Strkr Messaging), pipeline moves (automatic), and custom activity (manually logged). The timeline runs on every record.

Consent context

Per-channel consent with audit log.

Consent context tracks per-channel consent (marketing email, SMS, data processing, phone calls) with the source of each consent (web form opt-in, verbal consent captured on a call, email double-opt-in, in-person consent). Every outbound send checks the appropriate consent context before firing.

Audit log

Every read and write with 90-day retention.

The audit log captures every record read, every record write, every email send, every SMS send, every workflow execution, and every admin console action. 90-day default retention is tunable per tenant up to the compliance ceiling.

Permission scoping

Role-based access on 54 resource types.

The admin console ships with permission scoping on 54 resource types across the standard role matrix (SDR, AE, SE, CSM, RevOps, admin, CRO). The admin can tune permissions per role or configure explicit record-level sharing for cross-team collaboration.

Open API

REST and GraphQL with webhook spec.

Strkr ships REST and GraphQL APIs with webhook outbound events on every record change (HMAC-signed). The REST API covers simple resource operations, the GraphQL API covers complex graph traversals. Rate limits are built for real integration volume. OAuth 2.0 authentication with per-tenant client credentials or user-scoped tokens.

Customer database patterns

What teams actually use the database for.

The cards below are the patterns Strkr onboarding sees across customer database deployments. Each pattern maps to a team shape and use case.

The sales motion database

Accounts, contacts, deals, pipeline.

Teams running a sales motion use the customer database as the sales substrate: accounts carry company info, contacts carry people info, deals carry opportunity info, pipeline tracks progression. The sales rep works from the database daily, the manager runs forecast against it weekly, the CRO reviews the executive dashboard off it.

The services firm database

Clients, engagements, matters, projects.

Services firms (agencies, consultancies, law firms, accounting firms) use the customer database with client, engagement, matter, or project as custom objects. The engagement history, scope documents, and billing milestones live on the client record. The next partner conversation picks up from the structured context.

The healthcare database

Patients, visits, referrals, consent.

Healthcare teams use the customer database with patient, visit, referral, and consent as first-class records under the Strkr HIPAA BAA. The patient history, PHI consent context, appointment scheduling, and referral tracking live on the patient record. Audit log coverage closes the HIPAA documentation requirement.

The field service database

Jobs, site visits, crews, work orders.

Field service teams (home services, equipment installation, commercial maintenance) use the customer database with job, site visit, crew, vehicle, and work order as custom objects. The dispatcher routes jobs, the field reps log activity from tablet, the office team sees the full history on the client record.

The MSP database

Accounts, assets, incidents, maintenance.

Managed service providers use the customer database with account, asset, incident, and scheduled maintenance as records. The asset inventory, incident response history, and maintenance cadence live on the account. The MSP coordinates service delivery and renewal from the shared database.

The nonprofit database

Donors, campaigns, pledges, programs.

Nonprofit teams use the customer database with donor, campaign, pledge, and program as first-class records. The donor history, pledge tracking, campaign engagement, and program participation live on the donor record. The development team runs stewardship from the shared database.

The membership database

Members, dues, benefits, engagement.

Membership organizations use the customer database with member, dues payment, benefit entitlement, and engagement signal as records. The membership history, dues tracking, and benefit utilization live on the member record. The membership team manages retention and engagement from the shared database.

How Strkr AI serves the customer database

AI jobs that fit the database motion.

The AI Enhancement add-on at $49 per month with 5,000 Strkr AI calls included covers the AI jobs below that make the customer database feel useful.

Fuzzy duplicate detection

Catch near-matches on merge.

Strkr AI runs fuzzy duplicate detection on records. A submission from "j.anderson@acme.co" matches an existing record for "josh.anderson@acme.co" or "josh@acme.co" with confidence scoring. The admin can configure the auto-merge threshold per object type. The detection runs on form submissions, API inserts, and manual record creation.

Fit scoring

Lead and account quality from database context.

Strkr AI runs fit scoring (0 to 100) on records with reasoning text. The scoring considers the record attributes (industry, size, geography, source) and the activity history (engagement, product usage, support tickets). The fit score surfaces on the record detail and in list views for prioritization.

Record enrichment

Fill in missing attributes from available signal.

Strkr AI runs record enrichment on records with missing attributes. The enrichment pulls from available signal (email domain, company name, prior deal context) to suggest missing values (company size, industry, geography). The admin reviews and accepts or ignores suggestions.

Record summarization

The next-conversation primer.

For records with long activity histories (50-plus timeline events) Strkr AI drafts a record summary on the detail. The summary covers the recent arc, open commitments, current status, and recommended next step. The team member reads the summary before the next conversation.

Health scoring

Account health across adoption and engagement.

Strkr AI runs health scoring on accounts based on adoption signal, support signal, executive engagement, renewal proximity, stakeholder stability, and payment history. The composite health score (0 to 100) surfaces on the account record with red, yellow, green categorization.

Risk signals

Churn, slip, and discount drift.

Strkr AI runs batched risk analysis (up to 30 records per batch) surfacing records trending toward risk. Common risk patterns include renewal risk on accounts, forecast slippage on deals, and discount drift on quotes. The risk signals surface on dashboards with reasons.

Head-to-head

Strkr customer database vs spreadsheet or lightweight tool.

The comparison below is Strkr customer database versus the common pattern of running on a shared spreadsheet or a lightweight contact-management tool.

Feature Strkr Shared spreadsheet or lightweight tool
Field validation at write Enforced, 12 field types. None.
Identity resolution Email, phone, external ID with AI fuzzy. Manual deduplication.
Activity timeline Emails, calls, meetings, SMS, moves. None.
Consent context Per-channel with audit log. Varies, often absent.
Audit log 90-day default on every event. None.
Role-based permissions 54 resource types. View or edit per sheet.
API access REST, GraphQL, webhook spec. Decorative or none.
Workflow automation ~140 blocks in workflow engine. Scripts or manual.
Multi-user concurrency Row-level locking, no conflicts. Last-write-wins conflicts.
How teams use Strkr

Four customer database deployment patterns.

The playbooks below are patterns Strkr onboarding sees across teams upgrading from spreadsheet or lightweight tooling to a real customer database.

Pattern A

The 18-rep team off Google Sheets.

An 18-rep SaaS SDR team ran their lead database in a shared Google Sheet. The sheet had grown to 2,400 rows with duplicates, inconsistent fields, and no activity history. Strkr onboarding ran an 11-business-day migration (shorter than default because there was no legacy CRM to disentangle). The Sheet contents imported, deduplicated via Strkr AI fuzzy matching, and landed with field validation enforced. The team closed the Sheet. First-week activity logging ran at 94 percent compliance.

Pattern B

The 42-rep agency off Airtable.

A 42-rep digital agency ran their client database in Airtable with eight linked tables (clients, projects, invoices, deliverables, team members, contracts, templates, retainers). Airtable worked at 24 clients but strained at 240. Strkr onboarding migrated the Airtable schema to Strkr with the agency-specific objects (client, project, retainer, deliverable) as custom objects. The Airtable workspace closed. The team gained workflow automation, role-based permissions, and the AI runbook that Airtable did not ship.

Pattern C

The 60-rep MSP off a legacy CRM database.

A 60-rep MSP ran a legacy on-premise CRM database (ConnectWise) that had grown over 11 years. The database had drifted and the admin console was slow. Strkr onboarding ran a 24-business-day migration preserving the asset, incident, and maintenance records. The AI Enhancement add-on landed with fit scoring on inbound incidents to help triage. Admin console load time dropped from 11 seconds to under 400 milliseconds. The on-premise infrastructure retired.

Pattern D

The 24-rep nonprofit off Raiser Edge.

A 24-rep nonprofit ran Raiser Edge for donor management at about $360 per user per month. The team wanted a more modern database with better reporting and workflow automation. Strkr onboarding configured the donor, campaign, pledge, and program objects as custom records. The 14-business-day migration preserved the full donor history (23 years of records). Monthly spend dropped roughly 72 percent. The development team gained workflow automation for stewardship cadence.

Pattern E

The 180-rep enterprise consolidating three databases.

A 180-rep enterprise SaaS team ran three customer databases (Salesforce for sales motion, Zendesk for support, Mixpanel for product usage) with manual reconciliation on QBR prep. Strkr onboarding ran a 32-business-day migration consolidating sales motion onto Strkr with Zendesk and Mixpanel staying as source systems feeding webhooks to Strkr. The three databases became one unified customer record with support and product signal landing on the account.

Upgrade from spreadsheet to a real customer database.

Book a 30 minute call. We will walk through the record types, the field library, the identity resolution, the activity timeline, and the API surface with your actual team shape.

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Common questions

What buyers in this bucket ask most.

Is Strkr a customer database or a CRM?

Both. Strkr ships the customer database as the CRM substrate. The record types (account, contact, deal, custom objects), field library, identity resolution, activity timeline, consent context, audit log, and API are the database capability. The pipeline view, forecast module, workflow engine, reports, dashboards, Strkr AI, and Strkr Messaging are the CRM capability built on top. For teams needing just a shared customer database Strkr covers the use case. For teams needing full CRM capability on top of the database Strkr covers that too.

How does Strkr handle duplicate records?

Strkr identity resolution matches records on email, phone, and external ID at write time. When a new record would duplicate an existing one the system merges automatically (if the match is exact) or flags for admin review (if the match is partial). Strkr AI fuzzy duplicate detection catches near-matches ("j.anderson@acme.co" versus "josh.anderson@acme.co") with confidence scoring. The admin can configure the auto-merge threshold per object type. For teams with existing duplicate records in their legacy data, the Strkr migration import runs a bulk deduplication pass with the admin reviewing matches above the auto-merge threshold.

What does field validation enforce?

Strkr field validation enforces format rules at write time. Email fields validate email format and reject malformed strings. Phone fields validate phone format and normalize to a consistent representation. Required fields reject writes with null or empty values. Picklist fields enforce the allowed value list. URL fields validate URL format. Number and currency fields enforce numeric type. Date fields validate date format. The validation runs on all write paths (manual entry, form submission, API insert, webhook write) so the database stays queryable and deliverable.

How does consent context work?

Consent context lives on the contact record with per-channel consent (marketing email, SMS, data processing, phone calls) and the source of each consent (web form opt-in on 2026-03-14, verbal consent captured on a call 2026-05-20, email double-opt-in confirmed 2026-07-02, in-person consent). Every outbound send checks the appropriate consent context before firing. SMS respects STOP, UNSUBSCRIBE, QUIT, CANCEL, END, OPTOUT, STOPALL, ARRET, and REVOCAR enforcement. Email respects marketing consent and GDPR double-opt-in. The audit log captures every consent change and every outbound send with consent context.

What does the API let us do with the customer database?

The Strkr API covers three primary surfaces. First, the REST API for simple resource operations (CRUD on records, bulk operations, search) with rate limits built for real integration volume. Second, the GraphQL API for complex graph traversals (an account with all contacts, all deals, all activity, all custom-object references in one query). Third, the webhook spec with HMAC signing on every record change for event-driven integrations (data warehouse pipelines, downstream SaaS tools, custom analytics). Authentication is OAuth 2.0 with per-tenant client credentials or user-scoped tokens.

Can we export our full customer database at any time?

Yes. The data is yours and so is the structure. Strkr exports the full record graph (standard objects, custom objects, relationships, custom fields, activity history, attachments) in documented JSON on any paid tier at any time. The export is self-service, not partner-gated or tier-gated. For teams migrating out the exported data lands in a structure that any downstream CRM, customer database, or data warehouse can read with a parsing adapter. There is no vendor-lock-in on export. Scheduled CSV export to SFTP or S3 is also available for teams running daily or weekly data pipelines.

What is the pricing for a customer database deployment?

Strkr runs flat per seat: $29 Starter, $59 Pro, $119 Scale, with a 14-day trial on paid plans. Custom objects, field library, identity resolution, activity timeline, consent context, audit log, workflow engine, and API are available on every paid tier. For a 24-rep team on Starter the monthly spend is $696. For a 24-rep team on Pro (with advanced reporting and dashboard builder) the monthly spend is $1,416. Add $49 for AI Enhancement if the team wants the AI jobs (fuzzy duplicate detection, fit scoring, record summarization, health scoring). SMS and MMS metered through Strkr Messaging at 2 cents per segment and 5 cents per MMS.

Also built for

Related audiences.

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