Post-sale revenue

My ERP does not show purchase frequency

A legacy ERP can hold the order history without turning it into a useful signal. The practical response is not to wait for a new dashboard: extract the transaction data, apply a clear rule outside the ERP, and send each account owner a list they can act on.

Data route

Export
Orders by customer
Clean
Returns and duplicates
Calculate
Interval per account
Review
On a fixed routine

The limitation is analytical, not a reason to stop

The ERP remains the record of orders, invoices and returns. It may not calculate the interval between purchases or identify an account whose pattern changed. Keep it as the transaction source and build the frequency view separately. That separation also makes the calculation easier to review when a commercial team questions a flag.

Export a dataset that can be checked

Export one row per order or invoice line, using a defined period and the same date basis throughout the file. Include the customer identifier, order or invoicing date, product or product line, quantity and value. Keep the document number and status when available so the team can trace a result back to its source.

  • Customer identifier: use the ERP code, not only the display name.
  • Date: choose order date or invoicing date according to the commercial question.
  • Product and line: preserve both when the ERP offers them.
  • Quantity and value: retain both, because a repeated date alone does not describe the account.

Normalize first, then calculate outside the ERP

Before calculating, define how cancellations and returns are represented. Exclude fully cancelled documents from the purchase sequence. For returns, use a documented rule: remove a reversed transaction, or retain the original purchase and record the return separately when it still matters to the commercial conversation. Apply the same rule to every customer.

Use a spreadsheet for a controlled first version or a database when the export needs joins, history or repeatable queries. Group the cleaned records by customer and, when useful, by product line. Order the dates, calculate the intervals between valid purchases, identify the most recent purchase and compare it with that customer’s own pattern. The result is a working hypothesis, not an automatic verdict that the customer has left.

Give account owners an action list, not a raw report

Each line in the returned list should name the customer, responsible owner, relevant product line, last valid purchase, observed purchase pattern, current exception and a proposed next action. The owner can then verify a known pause, an open quotation, a delivery issue or a change in the buyer before contacting the account.

Close the loop by recording the owner’s finding and decision in the same working view. This distinguishes a data issue from a commercial issue and improves the next export without turning the spreadsheet into a second ERP.

This is not a CRM replacement project

Changing CRM may be appropriate when the sales process, ownership, contact history or follow-up discipline needs redesign. That is a separate decision. This workflow starts with transaction data already held by the ERP and creates a dependable review routine before any system change is considered.

It is also distinct from the RD Station cluster. Those pages address CRM adoption, configuration and commercial process. Here, the question is narrower: how to turn historical ERP orders into a frequency signal and a responsible person’s next action.

Frequently asked questions

Why doesn't the ERP show purchase frequency? +
Because the ERP was designed to record transactions, issue invoices and control inventory, not to analyze each account's behavior over time. The data exists, but the ready-made report usually does not.
What data do I need to export from the ERP? +
At minimum, account code and name, order or invoice date, document number, product or line, quantity and value. Including salesperson and region helps distribute the action list later.
Is a spreadsheet enough for this analysis? +
To get started, yes. A well-organized spreadsheet can calculate intervals, recency and lines bought per account. When volume or update frequency grows, it is worth automating the extract.
What does normalizing the data mean? +
It means making the data comparable: merging duplicate records of the same company, handling returns and cancellations, grouping same-day orders and standardizing product names. Without it, the calculation measures data-entry errors.
How should I handle customers with several tax IDs? +
Decide whether the analysis is per site or per corporate group and apply that rule across the whole base. Often a company stops buying at one site and starts buying through another, which is not a loss.
What should the sales team receive after the analysis? +
A short list of accounts with owner, reason for the alert, last order, lines bought and suggested next action. A raw report with every account rarely turns into action.
Do I need to change ERP or implement a CRM? +
Not to do this reading. The problem is analytical and is solved by extracting and calculating outside the system. A system change is a separate decision, with its own cost and timeline.
How often should the extract be refreshed? +
Refresh it at the pace the team can act on the list. A frequent extract that nobody reads is worth less than a periodic one that feeds a decision meeting.

Is your ERP not giving you this read?

The diagnosis shows where the portfolio lost pace and which sales routine brings revenue back.

Schedule an ERP ReviewFree diagnosis, no commitment