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Group B2B purchases as new tasks, modified rebuys or straight rebuys using documented buying evidence. Compare conversion, won deal size and subsequent order frequency without guessing a buyer's situation from invoices.
Buying situation segmentation groups purchases by whether the buyer is undertaking a new task, changing a previous purchase or making a routine rebuy. Tag the purchase decision from documented evidence, then compare conversion, won deal size and order frequency. An invoice alone cannot tell you whether the customer's requirement is new.
The unit is the purchase decision, usually recorded as a CRM opportunity. An established account can make a new-task purchase in an unfamiliar category while continuing straight rebuys for routine supplies. A new customer can already know the category well and simply be changing suppliers.
OpenStax's business-buying textbook distinguishes the situations by familiarity and the extent of change: routine purchases without modifications, repeat purchases with meaningful differences, and first-time purchases of a product or service. Turn those definitions into an approved tagging guide.
| Approved tag | Evidence required | Example at a US industrial supplier |
|---|---|---|
| New task | Buyer confirms this is a first purchase for the requirement | A manufacturer specifying its first automated packaging station |
| Modified rebuy | Prior purchase exists, with a documented material change | A familiar component order with a new material specification |
| Straight rebuy | Familiar purchase continues without material change | Replenishing an established component to the same specification |
| Unclassified | Evidence is missing or contradictory | An imported deal with no discovery notes |
Do not treat a higher order value as automatic evidence of a modified rebuy. A routine order may simply have a larger quantity. The sales team must agree on what counts as a meaningful change and retain the evidence supporting the tag.
This differs from behavioral segmentation, which groups observed purchasing patterns. It also differs from the company attributes and economic measures used in B2B customer segmentation from the ledger.
Start with opportunity ID, account ID, close date, closed outcome, approved buying-situation tag, tag evidence and won contract value. Join a separate order export through the opportunity ID. Before joining, summarize order lines to distinct orders and then to opportunities; repeating a deal value on every order line will inflate it.
Keep one analytical row per closed opportunity. Put open opportunities in a separate pipeline view so a young deal is not counted as a loss. Freeze the tag as it stood at the purchase decision; later evidence corrections should be documented rather than silently replacing historical classifications.
Choose a consistent observation window for subsequent orders. A deal won yesterday has had less opportunity to generate repeat orders than one won months earlier. The example uses each win's first 90 days, with only fully observed wins included.
A fictional US industrial supplier reviews nine opportunities closed in the first quarter of 2026. Every win has a complete 90-day order window. The won values are contract amounts, not recognized revenue, and the order counts refer only to orders linked to those opportunities.
| Opportunity | Buying situation | Closed outcome | Won value (USD) | Orders in first 90 days |
|---|---|---|---|---|
| D101 | New task | Lost | 0 | 0 |
| D102 | New task | Won | 60,000 | 1 |
| D103 | New task | Lost | 0 | 0 |
| D104 | Modified rebuy | Won | 30,000 | 2 |
| D105 | Modified rebuy | Won | 45,000 | 3 |
| D106 | Modified rebuy | Lost | 0 | 0 |
| D107 | Straight rebuy | Won | 24,000 | 4 |
| D108 | Straight rebuy | Won | 18,000 | 3 |
| D109 | Straight rebuy | Won | 12,000 | 2 |
| Total | 189,000 | 15 |
Use three separate measures:
Closed-deal conversion = Won opportunities / All closed opportunities
Average won deal size = Total won value / Won opportunities
90-day order frequency = Orders linked to won opportunities in their first 90 days / Won opportunities
Order frequency here means orders per won opportunity over a fixed period. It is not orders per account, because an account may appear in several situations.
| Situation | Closed deals | Wins | Conversion | Won value (USD) | Average won deal (USD) | 90-day orders | Orders per win |
|---|---|---|---|---|---|---|---|
| New task | 3 | 1 | 33.3% | 60,000 | 60,000 | 1 | 1.0 |
| Modified rebuy | 3 | 2 | 66.7% | 75,000 | 37,500 | 5 | 2.5 |
| Straight rebuy | 3 | 3 | 100.0% | 54,000 | 18,000 | 9 | 3.0 |
| Total | 9 | 6 | 66.7% | 189,000 | 31,500 | 15 | 2.5 |
For modified rebuys, conversion is 2 / 3 = 66.7%, average won value is (30,000 + 45,000) / 2 = $37,500, and order frequency is (2 + 3) / 2 = 2.5. Percentages and frequency are rounded to one decimal place; dollar values are exact in this example.
The total is computed from the underlying counts and amounts: 6 / 9 = 66.7%, 189,000 / 6 = $31,500, and 15 / 6 = 2.5 orders per win. Do not average the segment deal sizes to calculate the overall deal size.
The detail table uses columns A:E, with records in rows 2:10. Put a situation tag in G2. Closed deals and wins can be counted with:
=COUNTIF($B$2:$B$10,G2)
=COUNTIFS($B$2:$B$10,G2,$C$2:$C$10,"Won")
The Google Sheets COUNTIFS documentation explains counting records that meet multiple criteria. Here those criteria are the situation tag and Won outcome. Covirage is not affiliated with Google.
Sum won values and orders for each tag, divide by its wins, and retain a blank rather than a fabricated average when wins are zero. The segment counts must add to nine closed opportunities and six wins; won values must sum to $189,000 and order counts to 15. Match those totals to the filtered source exports before interpreting the table.
The example's new-task deals have the largest won value and the lowest observed conversion. That supports reviewing discovery, requirements and evaluation support. It does not prove that new tasks will always be more profitable or that more sales effort will improve conversion.
Straight rebuys show more orders per win in this cohort. The operations question is whether repeat ordering is easy and fulfillment is dependable. Modified rebuys call for reviewing specification changes, approvals and the commercial handoff. Product category and contract structure may explain some of the differences, so compare similar purchases before changing staffing.
Sales operations owns the tagging guide and export checks. Account teams supply evidence. Finance validates won values, and operations verifies order counts. If the next question is which accounts repeat most often regardless of buying situation, use RFM analysis separately.
Bring your opportunity export with approved buying-situation tags and an order file with matching IDs. Covirage maps the columns, its tools compute and check the comparisons, and the external AI model explains the results with every figure traced to its rows. Create a free Covirage account, which takes about a minute, then set up your business and bring a sample of your own data.
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A new task involves a purchase the buyer has not made before. A modified rebuy repeats a purchase with a significant change, such as specifications or supplier. A straight rebuy repeats a familiar purchase without a material change. Classify from the buyer's perspective.
No. A customer new to your company may already buy the same category elsewhere and be considering a supplier change. That can be a modified rebuy. Ask about the purchase and its history instead of assigning the tag from your account creation date.
Invoice history can show purchase dates, values and repetition, but it usually cannot establish whether the buyer is handling a new requirement or changing an existing specification. Use supplied CRM tags supported by discovery notes, customer requirements or procurement documents.
Assign them to opportunities or purchase decisions. An account may place routine replenishment orders while evaluating a first purchase in another category. An account-level summary can describe its mix of situations, but a permanent single tag would hide those differences.