The four-step, 60–180 minute test (quick overview)
- Step 1 — Map downstream uses (10–30 minutes): list every report, automation, SLA or handoff that reads the field (use a quick HubSpot list, Salesforce report, Marketo/Pardot segment or your spreadsheet). Note the owners and what breaks when the value is wrong.
- Step 2 — Sample and measure (30–60 minutes): extract a random sample from the source (CRM list or export to Sheets) and record error, blank and ambiguous rates using a simple checklist. Expect 1–2 minutes per record for manual review.
- Step 3 — Estimate cost per error (15–30 minutes): for each downstream use, estimate the time or harm when the field is wrong (manual fixes, wrong emails, lost lead follow-up, failed deliveries). Convert to hours or £ per error.
- Step 4 — Pilot or A/B (30–60 minutes setup; 2–4 weeks run): run a short pilot where one path uses cleaned/blocked values and the other uses the status quo; measure the real difference in manual fixes, SLA breaches, conversions or delivery failures.
Sampling plan, simple calculations and break‑even
Sampling plan (template): pick a random sample using your CRM's list/report export into Sheets. If the population is small, sample 50–100 records. For 1k–10k records sample ~200. For very large sets 300 is often enough to get a useful error-rate estimate. In Sheets add columns: record id, current value, classification (valid/blank/invalid), reviewer time (mins) and quick notes.
Break‑even in three lines (hours):
- Hours saved per period = Records per period × (error_rate_before − error_rate_after) × time_to_fix_per_error
- Cleaning cost (hours) = time_to_clean_field (est.)
- Months to break even = Cleaning cost / Hours saved per month
Example: you have 500 records/month, error rate now 20% and cleaning will drop it to 5% (15% improvement). If each error costs 0.25 hours to fix, monthly hours saved = 500 × 0.15 × 0.25 = 18.75 hours. If cleaning takes 30 hours, break‑even ≈ 1.6 months. If you prefer £, multiply hours by average hourly cost.
Keep this low‑tech: do the sums in Sheets; pull the sample with HubSpot lists, Salesforce reports or a Marketo/Pardot segment; review manually and score the numbers.
Two short examples and clear stop/continue signals
Example A — marketing→sales `source` field: map shows three automations that assign lead owner and fire sales emails. Sample 200 leads: 26% blank/invalid. Estimate: each wrong source causes 0.5 hours of follow‑up and misrouting per lead on average. If you handle 300 leads/month and cleaning reduces invalids to 6%, the maths usually favours cleaning (30+ hours saved per month). Pilot by splitting new leads for 4 weeks: route one half through a small validation step (dropdown or manual check) and compare handoffs and owner reassigns.
Example B — postcode used for delivery automations: sample 150 records and find 12% malformed postcodes. Each failed automated delivery attempt costs a failed delivery task (0.75 hours) plus potential customer contact. For small teams, if deliveries per month are low you might accept the field as‑is or add a simple validation rule to block bad values at entry (cheaper than bulk cleaning). Run a short pilot that flags bad postcodes and tracks how many manual interventions are prevented.
Stop/continue signals (practical): continue to clean if break‑even is within 1–3 months or if errors cause customer or compliance risk; block inputs (add a validation/fallback) if cleaning is expensive but you can stop automations running on that field; accept as‑is if expected months to recover >12 and errors only cause minor, rare admin. Use an A/B pilot to prove impact before committing to full clean.
If you want a ready Sheets template or a quick run‑through of the test using your CRM (HubSpot, Salesforce, Marketo or Pardot), Optira can help set it up and run the pilot.