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“Best customers for each dealer in Schedule 1” is one of those tasks that sounds simple until you hit the details—definitions, eligibility rules, capacity limits, and what happens when customers qualify for multiple dealers.
This guide shows a repeatable way to produce a defensible Schedule 1 mapping: customer eligibility first, then a weighted scoring model, then a clean assignment method that aligns with how your contract expects performance to be measured.
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No matter whether you’re doing this for an OEM-to-dealer program, a services channel, or a resale network, the same core problem appears: you need a customer list per dealer that is accurate, auditable, and hard to challenge.
What Schedule 1 usually covers (and what you should confirm)
Schedule 1 terms vary by contract, but most dealer agreements use it to define how customers are associated with dealers—often for eligibility, reporting, revenue attribution, or service responsibility.
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Before you score or assign anything, confirm these contract elements so your output matches what the agreement actually requires.
Definitions you must align to the contract
- Customer: Is it a legal entity, a location/site, a household, or a contract account?
- Dealer: Is it the legal entity, franchise location, territory entity, or sales channel?
- Best customers: Does the contract define “best” by revenue, profit, retention, service SLAs, growth, or something else?
- Eligibility: Are there exclusions (e.g., government accounts, resellers, legacy customers, accounts with chargebacks)?
- Timing: Is the performance window calendar year, trailing 12 months, or a specific date range?
- Attribution rules: If a customer buys from Dealer A and then Dealer B, which dealer gets credit for Schedule 1 purposes?
Rules that change how assignment should be done
Some agreements require one-to-one mapping (each customer belongs to only one dealer). Others allow multi-homing or fallback dealers.
- One-to-one vs. many-to-one: Decide whether customers can be eligible for multiple dealers.
- Territory constraints: Are dealers restricted by geography (postal codes, regions, counties)?
- Capacity limits: Does Schedule 1 impose maximum counts or quotas per dealer?
- Minimum thresholds: E.g., customer must have $X annual spend or at least Y completed service events.
- Dispute process: Who resolves mapping disputes and what evidence is accepted?
Prerequisites and data you need before picking best customers
You can’t produce a defensible Schedule 1 list from guesses. Build an evidence-backed dataset first, with consistent identifiers and a clear performance window.
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- Customer master: customer ID, legal name, billing entity, primary location, industry classification.
- Order/transaction history: order date, invoice amount, product/service type, dealer ID, SKU category.
- Service and support logs: tickets, SLA compliance, response/resolve times, warranty/returns.
- Renewals and retention: renewal dates, churn indicators, contract duration, reactivation events.
- Dealership performance data: dealer IDs, assigned territories, historical dealer-customer interaction.
- Compliance flags: credit risk, disputes, chargebacks, sanctions screening results (if applicable).
Data quality checks you should run
- De-duplicate customers: Merge records so one customer ID represents one account. Use a rule like exact tax ID match, then secondary matches (name + address).
- Confirm dealer mapping fields: Ensure every transaction has the correct dealer ID for attribution.
- Normalize amounts: Use invoice net revenue (exclude tax if your contract defines revenue net of tax).
- Time zone and date consistency: If your systems record in UTC, convert to contract-defined local time before cutting off date ranges.
- Missing values policy: Define what to do when SLA data is missing (e.g., treat as non-compliant, or exclude from SLA scoring with separate eligibility rules).
Define scoring criteria for best customers (use a weighted model)
“Best” needs a measurable definition. The most stable approach is a weighted score that mixes value, quality, and relationship health, while respecting contract eligibility rules.
A practical scoring framework
Use a 100-point scale so it’s easy to audit and explain to stakeholders. Adjust weights to match what Schedule 1 cares about.
| Category | Metric examples | Weight | How to compute |
|---|---|---|---|
| Customer value | Net revenue, contribution margin (if available) | 35% | Rank customers by net revenue in the performance window and scale 0–35 |
| Growth | YoY growth or trailing 12-month growth rate | 20% | Use growth rate with winsorization (cap extremes) |
| Retention & renewals | Renewal rate, churn probability proxy | 20% | Score based on renewal outcome or churn flags |
| Service quality | SLA hit rate, average resolution time, repeat issues | 15% | Scale 0–15 based on SLA compliance and stability |
| Compliance & risk | Chargebacks, disputes, credit holds, policy exceptions | 10% | Subtract or cap score when risk thresholds are breached |
Eligibility gates (before scoring)
Use hard filters so customers who clearly don’t qualify don’t consume scoring capacity.
- Active status: customer must have at least one qualifying transaction or service event during the performance window (or be explicitly included per contract).
- Exclusion list: remove customers with prohibited categories (e.g., reseller accounts, litigated accounts, sanctioned entities).
- Territory eligibility: if the contract ties customers to territories, ensure the customer’s primary location falls inside the dealer territory definition.
- Minimum spend/service threshold: e.g., annualized net revenue > $25,000 or at least 3 completed service events.
Methods to assign best customers to each dealer
Once you have scores, the assignment method decides who gets which customers. That’s where many disputes originate, so pick the method that best matches the contract language and your operational reality.
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For each dealer, pick the top N eligible customers by score, optionally applying capacity limits.
- Filter eligible customers for a dealer (territory/eligibility constraints).
- Sort by total score descending.
- Choose top N (or those above a score threshold) as “best customers.”
- If a customer qualifies for multiple dealers, assign it based on highest score among those dealers.
Method B: One-to-One Global Assignment (fair allocation)
Use this when Schedule 1 requires each customer to map to exactly one dealer and you must avoid double-counting.
- Build a candidate set: for every customer, list all dealers for which they’re eligible (territory/eligibility).
- Create a customer-dealer score matrix using the same scoring model.
- Assign each customer to the dealer with the highest score.
- Apply tie-breakers (see below) and capacity rules (max customers per dealer) if required.
Method C: Attribution-Based Assignment (last interaction / majority share)
This method follows actual dealer engagement. It’s common when contracts define “customer belongs to dealer based on purchase/service history.”
- Define an attribution rule: e.g., “last qualifying transaction dealer” or “dealer with highest spend share in the window.”
- For each customer, compute dealer spend share (or event share) by dealer.
- Assign the customer to the top dealer by share (or last interaction) if they meet eligibility.
- Then rank those assigned customers by score to determine the “best” list per dealer.
Method D: Territory First, Score Second (strict geo allocation)
If your contract is territory-driven, do geo assignment first, even if another dealer offers higher score.
- Assign each customer to the dealer responsible for the customer’s primary location.
- Apply eligibility gates to those assigned customers only.
- Rank by score and produce the top list per dealer.
Tie-breakers you should define (use the same order every time)
- Higher net revenue in the performance window
- Higher SLA hit rate / lower average resolution time
- More recent activity (e.g., last order date within 90 days)
- Fewer compliance flags (chargebacks/disputes/credit holds)
Step-by-step workflow to produce a Schedule 1 dealer-customer list
Here’s a workflow you can hand to analysts and expect consistent results across cycles (monthly, quarterly, or annual).
Step 1: Freeze the performance window
Pick a date range and document it in your output workbook. Example: trailing 12 months ending 2026-03-31 (or calendar year 2025-01-01 to 2025-12-31).
Step 2: Create an eligibility table
Generate a table with one row per customer (or customer-location), including eligibility flags: active status, territory match, exclusions, and minimum threshold pass/fail.
Step 3: Compute base metrics
For each eligible customer, compute values needed for the scoring model: net revenue, growth rate, renewal outcome, SLA compliance, and risk/dispute flags.
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Step 4: Convert metrics to a 0–100 score
Normalize metrics carefully so outliers don’t dominate. A common approach is percentile scaling (e.g., score based on customer’s rank among eligible customers) plus caps for extreme growth.
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Step 5: Apply the chosen assignment method
Pick one method (A, B, C, or D) based on contract rules. Then apply it consistently every time the Schedule 1 list updates.
Step 6: Produce dealer-specific outputs
For each dealer, output:
- Dealer name and dealer ID
- Top customers list (customer ID, customer legal name)
- Score breakdown (at least total score and the top 2 drivers)
- Evidence references (e.g., order IDs or aggregated metrics for the window)
Step 7: Add an audit trail
Stakeholders will ask “why is Customer X on Dealer Y’s list?” Without an audit trail, you’ll spend time re-running queries.
- Store the scoring inputs and eligibility flags used for that run
- Record the assignment method and tie-breaker order
- Log the dataset version and the ETL run ID (if applicable)
Quality controls to avoid disputes and contract breaches
“Best customers” lists are frequently disputed because outcomes look arbitrary. Quality controls prevent that by making the logic transparent and consistent.
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- Verify eligibility flags match the contract definition
- Confirm computed revenue and SLA metrics reconcile to source systems
- Check top-score drivers align with expected business behavior
Control 2: Validate distribution across dealers
If one dealer gets almost all customers, you likely have a territory mapping issue, dealer ID mismatch, or a scoring normalization error.
- Compare dealer counts vs. prior cycle
- Identify sudden swings (e.g., > 30% increase in assigned top customers)
Control 3: Reconcile totals with business reporting
Your aggregated totals (net revenue, event counts) for the eligible set should align with your internal reporting totals for the same window—within a defined tolerance.
Common mistakes and how to fix them
These are the failure modes that show up in real programs.
Mistake 1: Scoring without eligibility gates
When an excluded customer slips into scoring, you’ll see disputes fast. Fix by enforcing hard filters before scoring and recording all filter results.
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If your contract treats a customer account as the unit, but your dataset treats each site as separate, your “best customers” list will be distorted. Fix by choosing the correct grain and aggregating accordingly.
Mistake 3: Incorrect dealer attribution field
Using a sales rep ID instead of dealer ID (or joining transactions to the wrong dealer table) can entirely change the mapping. Fix by validating dealer IDs on a sample of transactions and comparing against a “golden” report.
Mistake 4: Tie-breakers not documented
If two customers have the same score and you pick one arbitrarily, people notice. Fix by setting a deterministic tie-breaker sequence and keeping it consistent.
Mistake 5: Using raw metrics with extreme outliers
If one customer has an unusually high single purchase, it can dominate value scoring. Fix by winsorizing, using percentile ranks, or applying caps consistent with your scoring policy.
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Troubleshooting when results look wrong
When the list doesn’t match expectations, don’t guess—diagnose. Use these checks in order.
Problem: A dealer has too few best customers
- Check territory mapping coverage (are many customers outside the territory due to address normalization issues?).
- Verify minimum thresholds and exclusions aren’t overly strict.
- Confirm dealer ID alignment in transaction history.
Problem: A dealer gets customers they think don’t belong
- Confirm the contract unit of account (customer vs. location).
- Check assignment method (territory-first vs attribution-based).
- Validate the customer’s primary location or attribution rule inputs.
Problem: Scores don’t correlate with observable performance
- Confirm you’re using the correct performance window end date.
- Check SLA fields and whether missing SLA data is treated correctly.
- Recompute growth using the agreed formula (YoY vs trailing vs annualized).
Problem: Tie frequency is unusually high
- Scoring normalization may be too coarse (e.g., using bucketed values).
- Metrics may be missing for many customers, forcing identical scores.
- Fix by improving data completeness or using more granular normalization.
Alternatives and variants (when you can’t use the exact same criteria)
Contracts evolve and data availability varies. Here are common adaptations that still produce audit-ready results.
Variant 1: If margin data isn’t available
Replace contribution margin with net revenue and add a risk-adjusted penalty for returns/chargebacks. Keep weights the same but substitute metrics.
Variant 2: If SLA data isn’t complete
Either (a) exclude SLA from scoring and redistribute weight to value/growth, or (b) treat missing SLA as a separate “data quality penalty” so you don’t misrepresent service performance.
Variant 3: If the contract requires a fixed top count per dealer
Use Method A or D and enforce the top N rule exactly. When eligibility filters reduce the pool below N, apply a fallback rule explicitly (e.g., include next eligible tier by score).
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Variant 4: If customers can be reassigned over time
Some programs allow changes mid-cycle. Use an update cadence (monthly/quarterly) and keep an evidence trail for the reassignment decision, including “why the previous assignment no longer qualifies.”
FAQs
What does Best Customer mean if Schedule 1 is silent?
If the contract doesn’t define “best,” you should propose a measurable definition (usually value + growth + retention + service quality) and get written approval from the program owner. The goal is consistency and auditability, not perfection.
Can a customer appear in two dealers’ best-customer lists?
Only if the contract allows it. If the agreement requires one-to-one mapping, use the global assignment method (Method B) so each customer is assigned to exactly one dealer.
How do we handle customers that qualify for multiple dealers geographically?
Apply the contract’s tie-breakers. If none exist, pick a deterministic order such as: primary location territory match first, then highest score, then last interaction date.
What performance window should we use?
Use the one defined by Schedule 1 or the agreed program policy (common choices: trailing 12 months or calendar year). Don’t mix windows across dealers within the same run.
What if a customer’s transaction history is missing dealer IDs?
That’s a data integrity issue. You can’t assign fairly without reliable dealer attribution. Either backfill dealer IDs from a validated source or exclude those transactions from eligibility until corrected—document the decision.
Bottom Line
The best way to name the best customers for each dealer under Schedule 1 is to treat the process like a controlled program: define eligibility gates, calculate a weighted score on a fixed performance window, and assign customers using a deterministic method that matches your contract.
When you add an audit trail and clear tie-breakers, the list becomes explainable and defensible—exactly what Schedule 1 mapping is supposed to deliver.
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