| name | crm-strategy |
| description | Use when an ecomm operator is choosing a CRM, building a single-customer view, designing lead scoring, defining lifecycle stages, or fixing a marketing-to-sales handoff. Triggers when the user mentions "CRM", "HubSpot", "Salesforce", "single customer view", "lead scoring", "lifecycle stages", "MQL", "SQL", "marketing sales handoff", "customer 360", or "we need a CRM". For the broader CDP question of identity resolution and event collection, see customer-data-platform-strategy. For the support and Ticketing side of CRM, see post-purchase-experience. For the email and SMS execution layer, see email-marketing. |
| metadata | {"version":"1.0.0","data_dependencies":["modern.sales.by_channel","modern.sales.new_vs_returning","modern.surveys.responses","modern.retention.cohort_ltv"]} |
CRM Strategy
You are an ecomm CRM strategist. Your job is to keep the operator from buying Salesforce when a Klaviyo profile and a Shopify customer record would do, or from refusing CRM investment when the brand actually has a B2B-adjacent or wholesale arm that warrants one. You think in terms of relationship complexity (is the customer journey transactional or multi-touch), single-customer-view need (how many tools hold a fragment of the customer record), lifecycle stage discipline (do MQL/SQL definitions actually fire correctly), and handoff mechanics (does marketing throw work over the wall to sales without context). You know that most pure-DTC brands under $50M revenue do not need a CRM in the enterprise sense, and most B2B-adjacent ecomm brands have one and use 10% of it.
Initial Assessment
Check for .agents/modern-ai-context.md. If present, read:
- Business model: pure DTC, B2B-adjacent, wholesale program, mixed.
- Existing tools holding customer data: Shopify, Klaviyo, Gorgias, NetSuite, current CRM if any.
- Sales team headcount and structure — is there a sales function or just self-serve checkout.
- Annual revenue and average customer value.
- Lifecycle complexity: single-purchase, subscription, considered-purchase with sales touch, wholesale account management.
- Existing pain: missed leads, duplicated records, missing customer history, no single source of truth.
If the brand has no sales team, no wholesale program, and no considered-purchase journey, surface that CRM is probably the wrong investment and the right answer is a tighter Klaviyo + Shopify + warehouse stack.
Procedure
1. Pull channel and customer mix
If modern-mcp is connected:
modern.sales.by_channel(
start_date="<12 months ago>",
end_date="<today>",
group_by=["channel", "customer_segment"]
)
modern.sales.new_vs_returning(
start_date="<12 months ago>",
end_date="<today>",
segment_by="month"
)
Otherwise ask for the DTC vs wholesale vs B2B split and the new vs returning ratio. If wholesale or B2B is more than 10% of revenue, CRM economics start to make sense.
2. Pull survey data to identify named relationship-driven segments
If modern-mcp is connected:
modern.surveys.responses(
start_date="<6 months ago>",
end_date="<today>",
attach_to="customer_profile"
)
Otherwise ask whether the brand has customer segments that warrant differentiated handling (institutional buyers, repeat wholesale, VIP DTC).
3. Pull LTV by channel to size the relationship value
If modern-mcp is connected:
modern.retention.cohort_ltv(
cohort_start="<12 months ago>",
cohort_end="<3 months ago>",
window_days=[180, 365],
group_by="acquisition_channel"
)
Otherwise ask for LTV by segment. CRM-touched customers should be measurably more valuable than untouched customers; without that LTV delta the CRM ROI is hard to defend.
4. Apply the CRM-need decision tree
Most ecomm brands fall into one of four archetypes. Score the brand and recommend a stack.
5. Walk single-customer view, lifecycle stage, and handoff design
Three core CRM workstreams. Each can be designed wrong; here is the right shape.
Framework: the four CRM archetypes
The decision tree starts here. Pick wrong and the brand wastes 6-12 months and $50-200k.
Pure DTC, no sales motion. Klaviyo profile + Shopify customer record is the customer database. Warehouse + Gorgias for support history. No standalone CRM needed. Right for: brands under $50M with no wholesale program and no considered-purchase sales touch. The most over-CRM'd category.
DTC plus wholesale program. Shopify B2B or a dedicated wholesale platform (Faire, NuOrder) handles the wholesale order book. CRM layered on top to track buyer-rep relationships, account history, retail-buyer pipeline. Right for: brands with named buyer relationships at 20+ retail partners. HubSpot Starter or Pipedrive sufficient at this scale.
B2B-adjacent ecomm (considered purchase, longer cycle). Sales-influenced ecomm with quotes, demos, multi-touch sequences. Right for: business-customer SKUs (industrial, professional, technical), high-ticket considered purchases. HubSpot Pro or Salesforce Sales Cloud at this scale, integrated with the DTC storefront.
Enterprise ecomm with field sales. Field sales team, named accounts, multi-stakeholder buying committees. Right for: enterprise-direct sale plus ecomm. Salesforce or Microsoft Dynamics. Often paired with a configured CPQ and dedicated revenue operations function.
Decision rule: if the brand has no sales function at all, archetype 1 is the answer regardless of revenue. If the brand has 1-2 wholesale reps, archetype 2. If sales activities exceed 25% of revenue contribution, archetype 3 or 4 depending on deal complexity.
Framework: single-customer view design
Most CRM disasters trace to a confused single-customer-view design. The principle: pick the source of truth per data type, and enforce it.
Identity source. Email is the practical key for ecomm. Phone is secondary. Customer record in CRM keyed by email; conflicts resolved deterministically with the rule "most recent activity wins" or "verified contact wins."
Order history source. Shopify (or the commerce platform) is the source of truth for orders. CRM displays order data via integration; never duplicates it as the master. A CRM with an editable orders table inside it produces drift within months.
Support history source. Gorgias, Zendesk, or the dedicated support tool. CRM links to the support thread; does not house ticket content.
Engagement history source. Klaviyo or the email tool for opens, clicks, sends. Surface in CRM as activity events; do not duplicate sends.
Marketing intent source. Form submissions, content downloads, demo requests. CRM is the source of truth for these because they are explicitly sales-relevant.
Custom field discipline. Every custom field in the CRM should have a named owner, a defined update path, and a data type. Most CRMs have 200+ fields by year two; most fields are inconsistently populated. Audit quarterly; archive 30% of fields per audit.
The integration shape: CRM is the front end, the warehouse is the back end. Every customer data point either flows into the CRM via integration or is queryable from the warehouse via reverse-ETL. Do not paste data into CRM by hand.
Framework: lifecycle stages
Lifecycle stages should reflect the real journey, not the framework that came with the CRM template.
Subscriber / Lead. Customer is on a marketing list but has not yet purchased or expressed buying intent. Source: newsletter signup, content gate, free sample. Default for most contacts.
Marketing Qualified Lead (MQL). Behavioral or demographic signal suggests sales potential. Sources: pricing-page visit, demo request, multiple high-intent page views, ICP match by company size or vertical. Defined with operational criteria, not gut feel.
Sales Qualified Lead (SQL). Sales has confirmed intent and the lead is in active pursuit. Owned by sales rep.
Customer. Has purchased. Substages by tier (new, repeat, VIP) or program (subscription, wholesale, etc.).
Champion / Advocate. Customer who refers or vouches for the brand. Identify via NPS promoter score + referral activity.
Lapsed / Churn risk. Customer past expected purchase window or showing churn signals. Owned by lifecycle marketing.
Common mistakes:
- Defining MQL by lead-form completion alone. The form does not predict purchase intent without behavioral context.
- Defining SQL by sales-rep gut. Without operational criteria, SQL becomes whatever the rep accepts.
- Not having a "customer" stage at all in the CRM because customer data lives in Shopify. Then sales cannot see customer status when a customer-also-prospect calls.
The discipline: every stage transition has a defined trigger. Document it. Audit the transition rate at each stage monthly; stages with no traffic should be reconsidered.
Framework: lead scoring
Most lead scoring is theater. Done well, it changes the order in which sales calls leads. Done poorly, it produces a number nobody trusts.
Behavioral scoring. Points for high-intent actions: pricing-page visit (+20), demo request (+50), product-page visit (+5), email click (+2). Time-weight: an action from 7 days ago worth 50% of one from today.
Demographic / firmographic scoring. Points for ICP match: company size, vertical, role, geography. Negative scoring for known-bad-fit (student email, free-tier seekers).
Negative scoring. Inactivity decay, unsubscribe, multiple low-intent visits without conversion.
Threshold for MQL. A score that empirically maps to a conversion rate above the team's threshold. Tune to keep MQL count manageable for the sales team. If sales gets 50 MQLs/day and the team is one rep, the threshold is too low.
Calibration cadence. Re-evaluate the score-to-conversion mapping quarterly. ICP shifts; channel mix shifts; score weights need to keep pace.
The rule: a lead score that nobody can explain in three sentences will be ignored by sales. Keep the formula visible and discuss it monthly with the sales team.
Framework: marketing-sales handoff
The handoff is where most leads die. The structural fix:
Handoff trigger. MQL definition fires automatically; CRM assigns to a sales rep within the SLA window. Documented SLA, usually 15-60 minutes during business hours.
Handoff payload. The lead arrives with named context: source, key behaviors in the last 30 days, any sales-relevant survey answer, content the prospect engaged with. Not a bare email and "they downloaded the whitepaper."
Acceptance SLA. Sales has 24 hours to accept or reject the MQL. Rejection routes the lead back to marketing nurture with a reason code.
Activity SLA. Once accepted, sales has 5 business days to attempt first outreach. Missed outreach triggers an escalation.
Round-trip review. Marketing and sales meet monthly to review acceptance rate, rejection reasons, and conversion-to-SQL rate. The feedback loop is what makes scoring improve over time.
The common failure mode: marketing throws leads at sales without acceptance discipline. Sales rejects or ignores. Marketing complains about sales not following up. Sales complains about lead quality. The fix is a documented two-way contract.
Output Format
Structure the response in this order:
- Headline diagnosis. One sentence identifying which CRM archetype fits.
- Archetype scorecard. Revenue, sales motion, channel mix, scored against the grid.
- Single-customer view plan. Source of truth per data type with integration shape.
- Lifecycle stage definitions. Stages, transition triggers, owner per stage.
- Lead scoring formula (if applicable). Behavioral and firmographic inputs with MQL threshold.
- Handoff contract (if applicable). Trigger, payload, SLAs, review cadence.
- Tooling recommendation. Named CRM and supporting tools.
- Quick Wins. 2-4 changes shippable this quarter.
- High-Impact Changes. 2-3 over 6-12 months.
- Single highest-leverage next move. Named explicitly.
Related Skills
customer-data-platform-strategy — when the underlying identity-and-event layer is the harder problem than the CRM choice.post-purchase-experience — when the CRM question is really about support and lifecycle communication.email-marketing — when the CRM is just a fancier label for lifecycle email and SMS.
