Define a data-driven ICP before building the commercial process
Why: An efficient commercial process requires knowing who you are selling to. Without a defined ICP, teams spend resources on unqualified leads, lowering win rate and extending the sales cycle.
Do: Analyze customers with NPS > 8, churn below average, and LTV above average. Identify common characteristics (industry, size, buyer role, tech stack). Document the ICP as a written profile and update it quarterly with new data.
Don't: Don't use anecdotal intuitions or team opinions as the only source. Don't treat the ICP as static: markets evolve.
Standardize pipeline stages with verifiable milestones
Why: Ambiguous pipeline stages (e.g., "in discussion") make forecasts unreliable and managers cannot diagnose where opportunities stall.
Do: Each stage must have a verifiable exit criterion (e.g., "Proposal" = document sent and received; "Negotiation" = contract term sent). Salespeople update the stage only when the criterion is met, not out of optimism.
Don't: Don't allow salespeople to skip multiple stages without documentation. Don't have more than 6-7 stages: excessive detail increases operational overhead without improving the forecast.
Implement an explicit SLA for the MQL-to-SQL handoff
Why: Lead response speed has a direct impact on conversion rates: industry studies indicate that contacting a lead quickly after a form submission significantly increases qualification probabilities compared to a delayed contact. (Source: cited in industry lead response time studies; specific values vary by sector.)
Do: Define in the marketing-sales contract within how many business hours each SQL must be contacted. Automate notification to the assigned salesperson. Track and report SLA compliance weekly.
Don't: Don't leave SQLs in queue without an owner. Don't rely only on email for notifications: use CRM push notifications or Slack to ensure immediate visibility.
Log all activities in the CRM, including unanswered calls
Why: The quality of activity data is the foundation for coaching, forecasting, and sales cycle analysis. A CRM with missing activities produces distorted analyses.
Do: Configure email and calendar integration for automatic activity logging. Ask salespeople to log unanswered calls and emails without replies as well. Use mobile CRM for field salespeople.
Don't: Don't make activity logging optional in the first weeks: data entry behavior solidifies in the first 30 days of adoption.
Use lead scoring as prioritization, not a substitute for commercial judgment
Why: Lead scoring is a prioritization tool that separates warm leads from cold ones, but does not guarantee qualification: a lead with high behavioral score might be a competitor studying the product.
Do: Use the score to rank the SDR work queue. Add negative criteria (excluded industries, competitors, students) to lower the score of irrelevant leads. Review scoring weights quarterly, comparing them with conversion data.
Don't: Don't automatically pass all leads above a threshold to SQL without human review. Don't use overly generous scoring that leads to an inflated pipeline.
Integrate CRM with email automation tools for nurturing sequences
Why: The majority of leads are not ready to purchase at the moment they enter the database. An automated nurturing program keeps the lead engaged until maturity, reducing opportunity loss from inactivity.
Do: Segment leads by lifecycle stage and create sequences of relevant content (3-5 emails over 2-3 weeks). A/B test email subjects. Log all interactions in the CRM as touchpoints.
Don't: Don't send the same sequence to all leads regardless of industry or stage. Don't continue nurturing leads marked as unfit.
Conduct regular pipeline reviews with structured criteria
Why: The pipeline review is the primary tool for coaching salespeople and updating the forecast. Without a structured cadence, opportunities stagnate without corrective actions.
Do: Hold the pipeline review weekly for each salesperson (15-30 minutes). Use standard criteria: date of last contact, next planned action, identified risks, realistic close date. Document actions in the CRM.
Don't: Don't make the pipeline review a passive report. The value lies in the manager's questions: "What is blocking this deal?", "Who is the decision-maker?", "What is the concrete next step?".
Monitor customer health score and intervene before contract renewal
Why: Recovering a customer who has already decided to cancel is much more expensive than preventing churn with a proactive intervention. The aggregated health score gives warning signals weeks before renewal.
Do: Define the signals to monitor: login frequency, usage of key features, response to NPS surveys, number of open tickets. Configure automatic alerts for declining scores. The CSM must intervene with an EBR (Executive Business Review) or check-in when the score drops.
Don't: Don't wait for the customer to communicate their intention to cancel. Don't rely only on subjective signals ("they seem satisfied").
Systematically document and analyze lost opportunities
Why: Won/lost analyses are among the most valuable sources of competitive and process information. Every lost opportunity contains data on pricing, product, competitors, and the sales process.
Do: Create a required "loss reason" field with standardized categories (price, features, competitor, timing, no decision). Analyze this data monthly. Share insights with product management and marketing.
Don't: Don't accept "the customer didn't buy" as a loss reason. Don't limit analysis to large deals: trends emerge from analyzing the full dataset.
Maintain data hygiene with structured data governance processes
Why: Outdated or duplicate CRM data produces campaigns sent to the wrong contacts, inaccurate forecasts, and distorted analyses. B2B contacts degrade at a rate of 25-30% annually.
Do: Configure automatic deduplication rules. Enrich records with external sources at least quarterly. Make critical fields (verified email, role, account) required for the lead-to-MQL transition. Assign data quality responsibility to a data steward.
Don't: Don't leave data hygiene to annual one-off initiatives. Don't enrich data from unverified sources that may introduce fictitious records.
Separate SDR and AE roles to optimize specialization and throughput
Why: Role specialization increases efficiency: SDRs are optimized for volume and qualification, AEs for negotiation and closing. A salesperson doing both is less efficient at both.
Do: Define clear SDR-to-AE handoff criteria: what qualifies an SQL and when the handoff occurs. Share both quota and feedback between SDR and AE to align incentives.
Don't: Don't introduce the SDR/AE separation before lead volume justifies it. In small teams the distinction adds overhead without benefit.
Align marketing and sales KPIs around shared metrics
Why: Misalignment between marketing (lead volume) and sales (lead quality) is one of the most common causes of commercial inefficiency. Marketing generates MQLs that sales doesn't work; sales complains about lead quality without data.
Do: Define the MQL and SQL definition together. Build a mutual SLA: marketing commits to MQL volume and quality, sales commits to response time and pipeline coverage. Report shared funnel metrics in a dashboard visible to both teams.
Don't: Don't use different metrics for the two teams that prevent dialogue. Don't hold alignment reviews only during conflicts: structured cadence prevents problems.