[Steve Side Hustler]

Hi everyone — I run a small sales team and we’d like to use AI to rank leads so we can focus follow-up. We don’t have a data scientist and our team is non-technical. Is it realistic to build a useful lead scoring model without hiring an expert?

Quick context: we have basic CRM data (interactions, company size, source, recent activity) and a small budget. I’m looking for practical, low-risk options.

Questions for the group:

• What no-code/low-code tools or AutoML services actually work for lead scoring?
• What minimal data and preparation are needed to get reasonable results?
• How should a non-technical team validate the model and avoid common pitfalls?
• Any real-world tips, tutorials, or honest experiences to share?

I appreciate any suggestions, step-by-step tips, or warnings from people who tried this. Thanks!

[aaron]

Short answer: Yes. You can build a practical AI-driven lead scoring model without hiring a data scientist — if you keep it simple, metric-driven, and iterative.

The problem: Most small businesses either 1) ignore lead quality and waste sales time or 2) try complicated ML and stall. Both cost revenue.

Why it matters: A usable lead score increases salesperson efficiency, raises conversion rates, and shortens sales cycles. Even a basic model that reliably surfaces the top 20% of leads can lift conversions by double digits.

What I’ve learned: Start with rules + basic stats, then automate. You’ll get 80% of the value from simple features (company size, source, engagement) and straightforward weighting. Don’t optimize for perfection — optimize for faster, measurable wins.

1. What you’ll need
   • CSV export of recent leads with these columns: lead source, industry, company size, job title, page views, emails opened, demo requested, outcome (won/lost), deal value, date.
   • Spreadsheet tool (Excel or Google Sheets) and an LLM (ChatGPT or similar) for guidance.
   • 1 salesperson and 1 marketer to validate results.
2. How to build it (step-by-step)
   1. Clean data: remove duplicates, standardize job titles, fill missing key fields.
   2. Choose 6–8 features: source, job title seniority, company size, pages visited, email opens, demo requested, time since last activity.
   3. Create simple weights: assign 0–10 points per feature. Example: Demo requested = 10, VP+ title = 8, Company 50+ = 6, Source=paid=5, Email opened=2, >5 pages=4.
   4. Score every lead by summing points; bucket into High (18+), Medium (10–17), Low (<10).
   5. Validate: compare buckets to historical outcomes — calculate conversion rate per bucket and adjust weights.
   6. Automate: set spreadsheet formulas and conditional formatting; export to CRM or use Zapier to push top leads to sales inbox.

Metrics to track

• Conversion rate by bucket (High/Med/Low)
• % of closed-won from High bucket
• Average time-to-close per bucket
• Lead triage time saved (minutes per lead)

Common mistakes & fixes

• Using too many features — fix: reduce to top 8 drivers.
• Small sample size — fix: use at least 200 historical leads or combine 6–12 months.
• Ignoring feedback — fix: weekly review with sales and recalibrate weights.

One robust AI prompt (copy-paste):

“Act as a senior data analyst for a B2B SaaS company. I have a CSV with columns: lead_id, source, job_title, company_size, industry, pages_viewed, emails_opened, demo_requested (yes/no), date, outcome (won/lost), deal_value. Propose 6–8 features to use for lead scoring, suggest point-based weights for each feature, provide the exact Excel formulas to compute the score and bucket thresholds, and describe how to validate the model and what metrics to track. Keep recommendations simple and explain expected lift in conversion for High bucket.”

7-day action plan

1. Day 1: Export CSV and list key columns; gather salesperson feedback on what signals matter.
2. Day 2: Clean data in spreadsheet and standardize fields.
3. Day 3: Create feature list and initial weights; build scoring formulas.
4. Day 4: Run historical validation; calculate conversion by bucket.
5. Day 5: Adjust weights; present results to sales; agree handoff rules.
6. Day 6: Automate push to CRM or email alerts for High leads.
7. Day 7: Start A/B test: scored routing vs. current routing; measure 30-day conversions.

Your move.

[Becky Budgeter]

Small correction: 200 historical leads is a good target for solid statistics, but you can start with fewer by combining months, labeling a recent sample manually (salesperson feedback), or running a rolling-window test. The key is to validate early and iterate — not to wait for a perfect dataset.

• Do
  • Keep features to 6–8 clear signals (source, job seniority, company size, engagement, demo interest, recent activity).
  • Start with simple point weights and buckets you can explain to sales.
  • Validate against past outcomes and get weekly sales feedback to adjust.
• Do not
  • Overfit with dozens of features with small samples.
  • Trust the first weights forever — treat them as hypotheses.
  • Ignore handoff rules (who acts on High leads and when).

Worked example — what you’ll need, how to do it, what to expect

1. What you’ll need
   • A CSV export (even 50–200 rows will work to start) with: source, job_title, company_size, pages_viewed, emails_opened, demo_requested (yes/no), date, outcome (won/lost).
   • Google Sheets or Excel, one salesperson to review results, and a simple way to push High leads to inbox or CRM (manual copy or Zapier).
2. How to do it (step-by-step)
   1. Clean: remove duplicates, standardize job titles into buckets (e.g., Admin, Manager, Director, VP+), and bin company_size (1–10, 11–50, 51–200, 200+).
   2. Pick features (example): demo_requested, job_seniority, company_size, pages_viewed, emails_opened, source.
   3. Assign simple points (example): demo=10, VP+=8, company 51–200=6, pages>5=4, email_open>1=2, paid_source=3. Keep totals easy to explain (0–30).
   4. Score each lead by summing points; create buckets like High (18+), Medium (10–17), Low (<10). These thresholds are starting points — expect to adjust after validation.
   5. Validate: calculate conversion rate and average deal value per bucket using your historical data. If High contains very few conversions, lower thresholds or reweight features. If too many, raise them.
   6. Automate: add formulas and conditional formatting in the sheet; push High leads manually at first, then automate with your CRM or a simple integration once you trust the scores.
3. What to expect
   • Within 1–2 weeks: a readable score and clear High/Medium/Low lists for sales triage.
   • Within 4–8 weeks: measurable lift if sales focuses on High (track conversion rate and time-to-close by bucket).
   • Ongoing: weekly weight tweaks and monthly review of metrics.

Simple tip: label 50 recent leads together with a salesperson — that small manual step drastically improves weight choices.

Question: how many leads do you get per month? That helps me suggest the right validation window and sample plan.

[Jeff Bullas]

Nice point — good call on starting with fewer than 200 leads if needed. Labeling a recent sample with sales and using a rolling window are practical shortcuts that keep momentum.

Here’s a compact, do-first plan to get a usable lead score live in days — no data scientist required.

What you’ll need

• CSV export (50–200 rows to start; more if available) with: lead_id, source, job_title, company_size, pages_viewed, emails_opened, demo_requested (yes/no), date, outcome (won/lost).
• Google Sheets or Excel and one salesperson for quick labeling/validation.
• Simple automation option (manual copy, Zapier, or CRM import).

Step-by-step (fast build)

1. Clean: remove duplicates, standardize job_title into buckets (Admin, Manager, Director, VP+), and bin company_size (1–10, 11–50, 51–200, 200+).
2. Pick 6 features: demo_requested, job_seniority, company_size, pages_viewed, emails_opened, source.
3. Assign simple points (example): Demo=10, VP+=8, Company 51–200=6, Pages>5=4, Emails>1=2, Paid source=3.
4. Compute score in a new column and bucket: High (18+), Medium (10–17), Low (<10).
5. Validate: compare bucket conversion rates to historical outcomes and adjust weights. Focus on lift for High bucket first.
6. Automate: conditional formatting for High, manual push to sales first, then integrate with CRM when trusted.

Exact Excel formula example (copy-paste, adapt columns)

Assume columns: C=source, D=company_size, E=job_title_seniority, F=pages_viewed, G=emails_opened, H=demo_requested. Put this in I2 for score:

=IF(H2=”yes”,10,0) + IF(E2=”VP+”,8,IF(E2=”Director”,6,IF(E2=”Manager”,3,0))) + IF(D2>=51,6,IF(D2>=11,3,1)) + IF(F2>5,4,IF(F2>2,2,0)) + IF(G2>1,2,0) + IF(C2=”paid”,3,0)

Bucket formula (J2):

=IF(I2>=18,”High”,IF(I2>=10,”Medium”,”Low”))

Worked example — what to expect

• Day 1–2: Clean data and label 50–100 recent leads with a salesperson.
• Day 3: Build scores and buckets; review conversion rates by bucket.
• Week 2–4: Tweak weights weekly and route High leads to sales. Expect to see early lift within 4–8 weeks.

Common mistakes & fixes

• Too many features — keep to top 6–8 signals and prune noise.
• Small sample panic — label recent leads manually to bootstrap weights.
• No handoff rules — agree who calls High leads and within what time window.

Copy-paste AI prompt

“Act as a senior data analyst for a small B2B company. I have a CSV with columns: lead_id, source, job_title, company_size, pages_viewed, emails_opened, demo_requested (yes/no), date, outcome (won/lost). Propose 6–8 features for lead scoring, suggest point-based weights, provide exact Excel formulas to compute score and buckets, and a short validation plan with metrics to track. Keep it simple and explain expected conversion lift for the High bucket.”

Quick reminder: start small, get sales to trust one clear list (High), then iterate. Momentum beats perfection.

[Becky Budgeter]

Nice, you’ve got a solid, practical plan — one small refinement: don’t lock in bucket thresholds (18/10) before you see your score distribution. Use those as starting rules, then set final High/Medium/Low cutoffs based on where the top converting historical leads fall (often the top 15–25% by score). Also make your spreadsheet robust to messy text (trim and normalize job titles/source) so scores aren’t skewed by capitalization or extra spaces.

What you’ll need

• CSV export (50–200 rows to start; label extra if you have fewer) with: lead_id, source, job_title, company_size, pages_viewed, emails_opened, demo_requested (yes/no), date, outcome (won/lost).
• Google Sheets or Excel and one salesperson to label/validate.
• Simple automation option: manual copy, Zapier, or CRM import for High leads.

Step-by-step (do this)

1. Clean: remove duplicates, standardize job_title into buckets (Admin, Manager, Director, VP+), normalize source text (TRIM/LOWER), and bin company_size (1–10, 11–50, 51–200, 201+).
2. Pick 6 features: demo_requested, job_seniority, company_size, pages_viewed, emails_opened, source. Keep it explainable to sales.
3. Assign simple points (starting example): Demo=10, VP+=8, Company 201+=8, Company 51–200=6, Pages>5=4, Emails>1=2, Paid source=3. Pick round numbers so totals are clear.
4. Compute score: add the points into a new column. Check the score distribution (percentiles) and choose buckets so High is roughly the top 15–25% by score, Medium the next 30–40%, Low the rest.
5. Validate: compare buckets to historical outcomes — conversion rate and average deal size per bucket. Adjust weights where a feature consistently under/over-predicts.
6. Automate: add formulas and conditional formatting, push High leads manually at first, then automate with Zapier/CRM once trusted.

Quick, safer Excel formula example (adapt columns)

Assume: C=source, D=company_size, E=job_title_seniority, F=pages_viewed, G=emails_opened, H=demo_requested. In I2:

=IF(TRIM(LOWER(H2))=”yes”,10,0) + IF(TRIM(UPPER(E2))=”VP+”,8,IF(TRIM(UPPER(E2))=”DIRECTOR”,6,IF(TRIM(UPPER(E2))=”MANAGER”,3,0))) + IF(VALUE(D2)>=201,8,IF(VALUE(D2)>=51,6,IF(VALUE(D2)>=11,3,1))) + IF(F2>5,4,IF(F2>2,2,0)) + IF(G2>1,2,0) + IF(TRIM(LOWER(C2))=”paid”,3,0)

What to expect

• Days 1–3: cleaned data, initial scores, and a labeled sample.
• Week 1–2: validate buckets against outcomes and tweak weights.
• Weeks 4–8: measurable lift if sales prioritizes High leads; keep weekly tweaks for the first month, monthly after.

Simple tip: label 50–100 recent leads with a salesperson now — that small step beats guessing weights. One question: how many leads do you get per month? That helps me suggest the right validation window.

[aaron]

Strong point on dynamic thresholds and text normalization — that one change prevents “threshold drift” and bad scores from messy inputs. Here’s how to turn that into immediate lift, plus three upgrades that move you from workable to revenue-grade.

5-minute win: make High/Medium/Low dynamic today. If your total score is in column I, set two cells for cutoffs and re-bucket automatically.

High cutoff (top 20%) in M1: =PERCENTILE.INC(I:I,0.8)Medium cutoff (next 40%) in M2: =PERCENTILE.INC(I:I,0.4)Bucket in J2: =IF(I2>=M1,”High”,IF(I2>=M2,”Medium”,”Low”))

The problem: Static cutoffs and unclean inputs inflate scores for noisy leads and stale records. Sales wastes time on the wrong 10–20% of the list.

Why it matters: You’re not trying to be perfect — you’re trying to concentrate wins. Getting the top 20% truly “hot” can deliver double-digit conversion lift and faster closes without adding headcount.

What experience shows: The biggest jumps come from three simple upgrades — dynamic thresholds, a freshness penalty, and a proper validation loop with shadow routing. Keep it explainable. Iterate weekly for the first month.

1. Upgrade 1: Dynamic thresholds (done above)
   • What you’ll need: Your score column and 5 minutes.
   • How to do it: Use the percentile formulas above; re-bucket daily or weekly.
   • What to expect: Cleaner High list that flexes with lead volume and seasonality.
2. Upgrade 2: Freshness penalty to stop stale “Highs”
   • What you’ll need: A Days_Since_Last_Activity column (K).
   • How to do it: Subtract up to 6 points as leads age. In L2 (Penalty): =-MIN(6,ROUNDUP(K2/7,0))New total in I2: =BaseScore + L2
   • What to expect: High bucket rotates toward recently engaged prospects; contact rates improve.
3. Upgrade 3: Value-aware weighting
   • What you’ll need: Deal value or a proxy (company size tier).
   • How to do it: Add 0–4 bonus points for expected value. Example: 1–10=0, 11–50=1, 51–200=3, 201+=4.
   • What to expect: Sales spends marginal time on higher-ROI prospects; revenue per 100 leads rises.
4. Upgrade 4: Shadow routing validation
   • What you’ll need: Two weeks, sales agreement on a test.
   • How to do it: Continue normal routing, but flag High leads. Have sales fast-track half of Highs (A) and treat the other half as usual (B). Keep all else identical.
   • What to expect: A clean read on lift from prioritization, separate from marketing changes.

Metrics that prove it’s working

• Conversion rate by bucket (High/Med/Low) — High should be 2–5x Low after tuning.
• Precision@Top20% — of the top 20% by score, what % become opportunities or closed-won.
• Time-to-first-touch for High leads — target <15 minutes; faster contact lifts win rate.
• Revenue per 100 leads — compare before/after implementing the score.
• % of closed-won originating from High — aim for >65% within 6–8 weeks.

Common mistakes and fast fixes

• Mistake: Locking in weights. Fix: Adjust weekly using bucket conversion deltas; nudge any over-weighted feature down 1–2 points if it underperforms for two weeks.
• Mistake: Email open noise (bots). Fix: Cap email points at 2; only count opens if there was a click or a pageview in the same week.
• Mistake: Ignoring aging. Fix: Apply the freshness penalty and re-bucket weekly.
• Mistake: One-size-fits-all by channel. Fix: Keep a single score, but allow source-specific adjustments (e.g., paid search +2, events +3) validated monthly.
• Mistake: No SLA on High leads. Fix: Assign owners and a 15-minute response target; monitor compliance.

Copy-paste AI prompt

“Act as a revenue operations analyst. I have a lead scoring sheet with columns: lead_id, source, job_title, company_size, pages_viewed, emails_opened, demo_requested (yes/no), date, days_since_last_activity, outcome (won/lost), deal_value (optional). 1) Propose point weights for each feature (keep totals ~30). 2) Add a freshness penalty that subtracts 1 point per 7 days since last activity (max -6). 3) Provide exact Excel formulas to calculate total score, dynamic percentile-based buckets (top 20% High, next 40% Medium), and conditional formatting rules. 4) Outline a two-week shadow routing test and the KPIs to judge success (conversion by bucket, Precision@Top20%, time-to-first-touch). Keep explanations simple and note expected lift ranges.”

7-day action plan (crystal clear)

1. Day 1: Clean data (dedupe, TRIM/LOWER text, bin company size). Add Days_Since_Last_Activity. Insert the dynamic percentile cutoffs and re-bucket.
2. Day 2: Add freshness penalty and value-aware points. Review 50 recent leads with one salesperson; sanity-check top 20%.
3. Day 3: Historical validation: compute conversion rate per bucket and revenue per 100 leads. Adjust weights ±1–2 points where needed.
4. Day 4: Define sales SLA (High leads contacted within 15 minutes). Set conditional formatting to flag High.
5. Day 5: Launch shadow routing: split High leads into A (fast-track) and B (business-as-usual). Start logging time-to-first-touch.
6. Day 6: Midweek check: Precision@Top20% and SLA compliance. Tweak only if extreme drift shows.
7. Day 7: Summarize week-one KPIs. Plan next week’s weight nudges and finalize automation (CRM or Zapier) for High leads.

Expectation to set with sales: the first pass must clearly surface the top 15–25% as genuinely better — not perfect. Aim for a 2–5x conversion multiple on High vs Low within 2–4 weeks; keep weekly iterations until you hit it.

Quick question to size the validation window: how many inbound leads do you get per month, and what’s your current win rate? I’ll tailor cutoffs and the shadow test split accordingly. Your move.

[Author]

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