Begin with a bounded question: can the available data support a replenishment proposal for one account segment? That is easier to evaluate than a general request to connect every system and automate sales.
Define the account level you are serving
A billing company, a buying group and an individual store may be different entities. If several locations share an account, aggregated orders can conceal local purchasing patterns.
Choose the commercial unit for the workflow and establish a stable identifier. Map contacts and orders to that unit. Document duplicates, account mergers and changes in legal or trading names.
A loose match between spreadsheets can send the right recommendation to the wrong establishment. Account identity should be resolved before interpreting buying cadence.
Agree a minimum data contract
| Data group | Useful fields | Reason to validate them |
|---|---|---|
| Accounts | Stable ID, location, status | Define the eligible population |
| Orders | Order ID, date, account, status | Reconstruct purchasing decisions |
| Order lines | Product, quantity, net value | Understand products and cadence |
| Products | SKU, category, pack size | Prepare a usable selection |
| Commercial terms | Applicable prices and rules | Avoid an offer the buyer cannot use |
| Relationship context | Contact, owner, service issues | Coordinate follow-up and exclusions |
Fields existing in a system do not automatically mean the proposed integration can access them. Confirm formats, meaning, permissions and the connection scope with the people responsible for each source.
Reconstruct comparable transactions
Choose the date that fits the purpose, such as confirmation or invoice date, and apply it consistently. Decide how canceled orders, refunds and split shipments affect the history so operational events do not create artificial purchasing frequency.
Check units. Twelve individual items and twelve cases represent different demand. Account for SKU replacements without merging products that are commercially different.
The history needed depends on purchase frequency and seasonality. There is no universal number of months or transactions that makes every individual forecast reliable. An account with little history may need qualification or onboarding rather than cadence-based outreach.
Specify how current the data must be
A dated export may be useful for an initial portfolio analysis. An operating workflow also needs a plan for recent orders, changed terms and new service issues.
Ask what happens if a buyer places an order after the latest extract, if a data sync fails or if a price changes. Define which actions are paused, who investigates and how the team recognizes stale information.
“Real time” is not a substitute for an agreed description of the required flows and acceptable delays. The right update pattern depends on the sales process and the consequences of acting on old data.
Review exceptions before contacting buyers
Test a multi-location customer, a canceled transaction, a return, a replaced SKU and an account with an open service issue. Include a newly placed order to check whether an obsolete reminder is stopped.
For each case, compare the source record, the interpreted signal and the proposed action. Ask someone familiar with the account to validate the commercial meaning. A technically successful connection can still produce a misleading recommendation.
Keep a record of unresolved data issues and their owners. That record helps establish whether the pilot is ready to operate or should remain an analysis exercise while specific fields are corrected.
Prepare the RAKOR scoping conversation without building the project alone
You do not need to turn this checklist into a completed data project before an initial discussion. It identifies what exists and the questions to examine together. A useful proposed starting point is one account segment, representative transactions and the systems containing them.
| What you bring to the conversation | What to examine together | What the agreed scope must specify |
|---|---|---|
| An account and representative order lines | Identity, units and history quality | Selected data and necessary access |
| An example of pricing and selection | Account rules and terms | Sources and update process |
| A reply or newly placed order scenario | Its effect on the next action | Flows, ownership and error handling |
This preparation makes it easier to review the available data and define the flows needed for your project. RAKOR defines integration scope with the business. The discussion should establish shared expectations for data and actions before the workflow operates.
Assign ongoing ownership
Name the people responsible for account quality, catalog data, commercial terms and integration support. Agree who can correct a recommendation and how errors reach the right owner.
RAKOR presents ERP and CRM connectivity with integration scope defined together. A logo on a product page is not a specification for a particular system connection. A usable project scope should identify data read, supported actions, any write-back requirements and the supervision needed after launch.
Sources and further reading
Scope the data needed for your account scenario
Discuss your accounts, catalog and existing tools through this concrete use case.
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