AI Playbook
for Wholesale & Distribution

You are a sales manager analyzing customer account profitability.

Customer data:
[PASTE: Customer | Annual revenue | COGS | Gross margin $ | Gross margin % | Freight cost | Returns cost | Deductions/chargebacks | Sales rep time estimate | Net profitability]

Analyze:
1) Net profitability ranking — customers ranked by true net profit after freight, returns, deductions, and cost to serve
2) Margin erosion — customers where gross margin looks acceptable but deductions, freight, or returns are destroying net profitability
3) Cost-to-serve outliers — customers requiring disproportionate service cost (frequent small orders / high return rates / excessive deductions)
4) Unprofitable customers — any customers where net profitability is negative; quantify the loss
5) Recommended actions: renegotiate terms / add freight minimums / reduce service cost / exit relationship

Output: Customer profitability table ranked by net profit. Unprofitable customer action plan. Top 5 improvements to customer mix profitability.

You are a sales operations manager onboarding a new wholesale customer.

Customer data:
[PASTE: Customer name | Customer type (retailer/foodservice/industrial/e-commerce) | Credit limit requested | Payment terms requested | EDI required? | Minimum order requirements | Pricing tier | Key contacts | Delivery requirements]

Complete the onboarding checklist:
1) Credit approval — credit application submitted, trade references checked, credit limit set by finance
2) Pricing setup — correct pricing tier assigned in the system; any contract or promotional pricing documented
3) EDI or ordering setup — EDI trading partner setup if required; portal access or order method confirmed
4) Delivery requirements — delivery windows, dock requirements, labeling requirements, pallet configuration
5) First order protocol — who handles the first order? Any hand-holding required for a smooth first experience?

Output: Customer onboarding checklist. Outstanding items before first order can be processed. Setup confirmation for each system. Customer service team briefing points.

You are a sales director reviewing territory performance.

Territory data:
[PASTE: Rep | Territory | Revenue (this period) | Revenue (prior period) | Revenue vs. plan | Active accounts | New accounts added | Accounts lost | Average order size | Gross margin % | Calls/visits made]

Analyze:
1) Revenue performance — vs. plan and prior period; which territories are growing, flat, or declining?
2) Account health — accounts lost vs. new accounts added; is the territory growing or churning?
3) Average order size trend — declining average order size may indicate customer mix shift or smaller reorders
4) Margin by territory — some territories may generate revenue but at below-target margins
5) Activity correlation — do territories with more calls/visits perform better? Any low-activity, low-performance territories?

Output: Territory performance dashboard. Traffic light by territory. Coaching priorities by rep. Top opportunities to grow underperforming territories.

You are a sales analyst reviewing customer ordering patterns.

Order history:
[PASTE: Customer | Order frequency (orders/month) | Average order value | Order size trend | Product categories ordered | Last order date | Any seasonal pattern | Payment performance]

Analyze:
1) Ordering frequency changes — customers ordering less frequently than usual; early churn signal
2) Order size decline — customers placing smaller orders; may be trialing a competitor
3) Product mix shifts — customers dropping certain categories; competitive displacement in those lines
4) Seasonal patterns — customers who should be reordering for upcoming season but haven't placed an order
5) Lapsed customers — customers who haven't ordered in [X weeks]; flag for sales rep outreach

Output: Customer order pattern report. Lapsed and at-risk customer list with recommended outreach. Seasonal reorder opportunities. Competitive displacement flags.

You are a sales operations manager reviewing customer pricing tier assignments.

Customer data:
[PASTE: Customer | Current pricing tier | Annual volume (last 12 months) | Tier volume threshold | Days since last tier review | Any special pricing or exceptions outside the tier]

Review:
1) Mis-tiered customers — customers who have grown into a higher tier but haven't been moved up
2) Customers on exception pricing — any customer with special pricing outside the standard tier; is it documented and justified?
3) Volume shortfalls — customers in a preferred tier but not meeting the volume commitment; require correction or tier demotion
4) Competitive pricing — any customers where the current tier isn't competitive enough to retain the account?
5) Tier structure adequacy — does the current tier structure reflect the actual customer mix and competitive dynamics?

Output: Tier assignment review. Customers to move up or down. Exception pricing audit. Tier structure recommendations.

You are an account manager preparing for a key account business review.

Account data:
[PASTE: Customer | Revenue (last 12 months vs. prior 12 months) | Gross margin % | Product categories purchased | SKUs active | New products adopted | Returns rate | Any service issues | Upcoming contract renewal | Known competitive threats]

Build the review agenda:
1) Performance summary — revenue and margin trend; year-over-year and vs. their peer customers in your portfolio
2) Category review — which categories are growing, flat, or declining with this customer? Why?
3) Opportunities — new products or categories they should be carrying; gap vs. what similar customers buy
4) Service review — any issues to address; what are they satisfied and dissatisfied with?
5) Forward plan — mutual commitments for the next 12 months; volume expectations, new product launches, promotions

Output: Key account review agenda. Pre-read data summary. Opportunity gap analysis. Talking points for each section.

You are a sales operations manager analyzing customer chargebacks and deductions.

Deduction data:
[PASTE: Customer | Deduction type (shortage/pricing/compliance/damage/promotion) | Amount | Date | Invoice reference | Status (under review/approved/disputed/written off)]

Analyze:
1) Deduction volume by customer — which customers generate the most deductions in $ and frequency?
2) Deduction by type — are most deductions for shortages, pricing errors, compliance violations, or promotions?
3) Validity assessment — which deduction types are typically valid (our error) vs. questionable (customer behavior)?
4) Recovery rate — what % of disputed deductions are successfully recovered?
5) Root cause — for high-volume deduction types, what operational change would reduce the deduction rate?

Output: Chargeback analysis. Deduction cost as % of revenue by customer. Root cause by deduction type. Operational improvements to reduce deductions. Recovery action list.

You are a sales manager preparing a customer compliance scorecard for EDI and order compliance.

Compliance data:
[PASTE: Customer | EDI order compliance % | ASN accuracy % | Labeling compliance % | Routing guide compliance % | On-time delivery requirement % (our performance) | Chargeback rate from compliance violations]

Score each customer:
1) EDI compliance — are EDI transactions transmitting accurately and on time?
2) Labeling compliance — are our labels meeting their requirements? Label errors are a common chargeback source.
3) Routing compliance — are we following their carrier routing guides? Routing violations carry fees.
4) Our on-time delivery — are we meeting their delivery windows? Failure here drives chargebacks and penalties.
5) Cost of non-compliance — total chargebacks attributable to compliance failures this period

Output: Customer compliance scorecard. Compliance failures costing the most money. Internal process changes to improve compliance. Customer communication if their compliance requirements are unreasonable.

You are a sales director reviewing customers who have reduced or stopped ordering.

Lost customer data:
[PASTE: Customer | Peak revenue | Last 12 months revenue | Last order date | Reason for decline (if known) | Sales rep notes | Competitive situation | Any outstanding issues (disputes/chargebacks/service failures)]

Analyze:
1) Recoverable vs. unrecoverable — which lost customers could be re-engaged vs. those that have moved on permanently?
2) Root cause — pricing / product gaps / service failures / competitive loss / customer consolidation or closure
3) Revenue at risk — total annualized revenue at risk from declining customers
4) Recovery plan for recoverable accounts — specific outreach strategy, who makes contact, what offer
5) Lessons learned — what patterns in lost accounts suggest a systemic issue to address?

Output: Lost customer analysis. Recovery target list with strategy. Revenue at risk. Systemic issue recommendations.

You are a sales manager planning trade promotions for the next quarter.

Promotion data:
[PASTE: Customer | Promotion type (off-invoice/billback/free goods/display allowance) | Promoted SKUs | Promotion period | Discount % or $ | Expected volume lift | Cost of promotion | Prior promotion performance with this customer]

Plan the promotions:
1) Promotion ROI — for each proposed promotion: estimated volume lift × margin per unit − promotion cost = net ROI
2) Baseline estimation — how much would sell without the promotion? Lift must be measured against baseline, not total sales.
3) Forward buying risk — are customers likely to stock up during the promotion and buy less afterward?
4) Accrual requirement — bill-back promotions require accurate accrual; confirm amounts are being accrued correctly
5) Redemption tracking — how will you confirm promotion compliance and process payment accurately?

Output: Trade promotion plan with ROI by customer/SKU. Forward buying risk flags. Accrual requirements. Tracking and settlement process.

You are a sales manager planning the introduction of a new product to your customer base.

Product data:
[PASTE: New product | Category | Suggested retail or list price | Your cost | Margin % | Key selling points | Target customer segments | Competitive positioning | Launch date | Any promotional support from supplier]

Build the introduction plan:
1) Target customer prioritization — which customers should be approached first? (category fit / volume potential / strategic value)
2) Selling story — what is the one-sentence pitch for each customer segment? Focus on what's in it for them.
3) Sampling or trial program — any sample or trial offer to reduce first-order risk?
4) Initial order recommendation — suggested initial stocking quantity by customer size
5) Reorder trigger — at what sell-through rate should customers reorder? How will you monitor?

Output: New product introduction plan. Customer target list by priority. Selling story by segment. Initial stocking recommendations. Reorder monitoring plan.

You are a sales analyst building the monthly sales forecast by customer.

Historical data:
[PASTE: Customer | Last 12 months revenue by month | Any known upcoming changes (new product line / lost distribution / promotional activity / customer expansion or closure)]

Build the forecast:
1) Baseline extrapolation — project forward from last 12 months; apply trend (growing/flat/declining)
2) Known upside — new products, expanded distribution, confirmed promotional activity
3) Known downside — lost distribution, customer consolidation, or competitive displacement
4) Seasonality — apply seasonal index for customers with seasonal patterns
5) Uncertainty range — low/base/high scenario for total revenue; identify the customers driving the most forecast uncertainty

Output: 12-month sales forecast by customer. Total revenue by month. Upside and downside scenarios. Customers with highest forecast risk.