Built for SDR and AE teams

The qualification question bank that lives inside the CRM, not on page 14 of a Google Doc nobody updates.

Every B2B sales team runs qualification by one of five frameworks. The problem is never the framework; it is that the questions sit in a Notion page while the discovery call runs, and the answers never become a reportable field. Strkr ships the question bank wired into the deal object.

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What this audience is actually dealing with

The pains that bring buyers here.

Qualification questions are one of those sales artifacts that every team claims to use and almost nobody actually uses in the shape the enablement team wrote them. The gap is structural. The enablement lead publishes a 40-question discovery guide inside a Google Doc. The SDR runs the call with a sticky note of five questions they remember. The AE inherits the opportunity three days later and asks the same five again because the SDR did not log the answers anywhere a human can read. The playbook exists in theory. The playbook does not exist in the data model. Five patterns show up on every call we take with a sales leader who is trying to fix this, and they repeat whether the team is 8 SDRs or 80.

The sticky-note problem

The question guide lives in Notion while the call runs in Zoom.

The enablement team puts the discovery guide in a shared document that is 12 scrolls deep. The SDR has it open on a second monitor for the first week, then forgets about it by week three. The AE picks three familiar questions from memory and skips the five that matter most for the forecast. The qualification framework exists on paper, not in the actual conversation.

Free-text answers

Qualification answers land in the notes field as a wall of text.

An SDR hangs up and types a paragraph into the activity notes. The paragraph says the prospect has budget and a timeline. Nobody can filter by that. The forecast report cannot slice by MEDDIC completeness because the data is prose. The RevOps lead ends up building a weekly manual spot-check sheet that samples 20 deals and reads notes to grade qualification depth.

Framework fragmentation

Three managers run three different frameworks on the same pipeline.

The VP of sales mandated MEDDIC last offsite. The two new managers run BANT because that is what they used at the last company. The enablement team wrote a hybrid called CHAMP. The reps hear three different qualification vocabularies in three different team meetings. The pipeline reports split qualification completeness along manager lines and nobody can tell if the framework is working or just the manager who runs it.

Pass-off friction

SDR-to-AE handoff loses the qualification answers.

The SDR qualified the lead with eight questions. The handoff to the AE happens in Slack with a two-line summary. The AE opens the opportunity cold and runs discovery from scratch. The prospect notices. The prospect is annoyed. The deal starts in a hole. A structured qualification record on the lead that promotes with the lead to the opportunity fixes this, which is a question of data model not a question of coaching.

Framework drift over time

The questions that mattered a year ago do not match this quarter.

The ICP shifts. The product shifts. The win-loss patterns from Q1 should rewrite the qualification question bank by Q4, but nobody has a mechanism to tie the two together. The question bank goes stale. The reps start asking questions that match the old product and the new prospects feel like the sales motion is five steps behind the pitch deck.

Reporting on qualification quality

The leader cannot answer "which questions correlate with close rate."

A VP of sales looking at the funnel wants to know which qualification answers predict a closed-won. If the answers are free text, you cannot do that analysis. If the answers are structured fields on the deal, the win-loss report writes itself. Most teams never get the second shape and so the qualification framework stays a belief system instead of a measurable practice.

Frameworks and when each one fits

Pick the framework; the question bank follows.

Every B2B qualification framework is a take on five questions: who, why, when, how much, and what blocks it. The frameworks differ on emphasis and vocabulary rather than on the underlying signals. Strkr ships default question banks for each of the five most common frameworks, which an admin can edit, fork, or combine. The cards below are the fit calls across common team shapes.

BANT

Budget, Authority, Need, Timeline. The classic.

BANT is IBM-era qualification that still works for transactional B2B motions under 50,000 ACV. Budget first, authority second, need third, timeline fourth. Strkr ships 12 default questions mapped to the four BANT pillars, each of which captures to a structured field on the deal. BANT is a strong default for an SDR team that needs a floor rather than a complex meta-game.

MEDDIC

Metrics, Economic buyer, Decision criteria, Decision process, Identify pain, Champion.

MEDDIC is the mid-market and enterprise standard for complex deals. The six pillars capture the full picture of a committee-driven buying motion. Strkr ships 24 default MEDDIC questions, with each answer captured as a structured field. The forecast report then slices by MEDDIC completeness, which is the single most useful filter a VP of sales can look at on a Monday morning.

MEDDPICC

MEDDIC plus Paper process and Competition.

MEDDPICC adds Paper (procurement, legal, security review) and Competition (which other vendor is in the account) to MEDDIC. The two extra pillars show up as separate pain in deals over 100,000 ACV. Strkr ships MEDDPICC as a flipped-on variant of MEDDIC, so teams that outgrow MEDDIC can add the two extra sections without rebuilding their qualification taxonomy.

CHAMP

Challenges, Authority, Money, Prioritization.

CHAMP flips BANT by leading with pain instead of budget. It fits teams selling to buyers who do not control budget directly, especially internal champions. The question order matters here because starting with budget on the first call feels transactional, while starting with the pain feels consultative. Strkr ships CHAMP with 10 default questions and a reorder-first conversation guide.

GPCTBA/C&I

Goals, Plans, Challenges, Timing, Budget, Authority, Consequences, Implications.

GPCTBA/C&I is the HubSpot-era inbound qualification framework. The eight pillars are longer than BANT and sometimes feel heavy, but the Consequences and Implications questions earn their place in deals where the buyer is still deciding whether to act at all. Strkr ships 16 default GPCTBA/C&I questions for inbound-led teams. The data model is identical to MEDDIC, so swap-outs are painless.

Custom hybrid

Fork the default bank and keep what works.

Most mature teams end up on a custom hybrid. Strkr lets an admin fork any default framework, drop questions that do not apply, add industry-specific questions, and change field types. The forked bank is still a structured object, so reports and forecast slices still work. The custom bank is where the qualification framework stops being a one-size-fits-all template and starts matching the real motion.

Framework switch mid-deal

Promote a BANT lead into a MEDDIC opportunity.

Common pattern: SDRs qualify with BANT on cold leads, AEs run MEDDIC on opportunities. Strkr maps field answers across frameworks on promotion so the BANT budget answer carries into the MEDDIC metrics pillar, authority carries into economic buyer, timeline into decision process. The mapping is editable and the AE sees the SDR's work on the first opportunity view.

How the question bank lives in the CRM

Questions as structured fields, not Notion pages.

The structural move that changes everything is this: every qualification question is a field on the deal object. Not a text blob in notes. Not a sub-record. A proper typed field with validation, with lookup values where relevant, and with the question text rendered inline on the deal page so the rep sees it the moment they open the record. The cards below are the mechanics.

Field types

Short text, long text, picklist, number, date, user, boolean.

The qualification builder lets an admin pick a field type for every question. Short text for named champion. Long text for the pain description. Picklist for decision criteria. Number for budget band. Date for decision deadline. User for the economic buyer contact lookup. Boolean for champion identified yes or no. The result: answers that filter cleanly on the forecast report.

Inline on the deal view

The questions sit on the right-rail panel.

The AE opens the deal and sees the qualification panel on the right rail with every question rendered. Unanswered questions have an empty state that nudges the rep to log the answer. Answered questions show the rep who answered and when, so the AE can tell what the SDR captured and what still needs discovery. The panel collapses when the rep wants more canvas room.

Capture on call

The question panel is open while the Zoom runs.

The rep floats the Strkr qualification panel next to the Zoom window. As the prospect talks, the rep types answers inline. Strkr AI listens via the call recorder and suggests answers in a side column so the rep can accept with one click instead of transcribing. The call hang-up to logged qualification is under 60 seconds in the common case.

Lookup to the Strkr AI assistant

Ask Strkr AI to pull the answer from the transcript.

Post-call, the AE can prompt Strkr AI with "pull the decision criteria answer from the Thursday call with Acme." Strkr AI retrieves the transcript, extracts the relevant exchange, writes the answer into the field, and cites the exact timestamp. The rep reviews, saves. The practice turns call recordings from an unused archive into a qualification data source.

Scoring and rollup

The qualification score rolls up as a single number.

Every answered question adds to a qualification score (0 to 100) that lives on the deal. The score is weighted by the admin: metrics worth 20, economic buyer worth 25, and so on. The forecast report groups deals by qualification band, which lets the sales leader read commit versus best case against actual qualification depth instead of rep gut feel.

Required-at-stage gating

Deals cannot advance past a stage without the required fields.

An admin can require specific qualification fields before a deal advances from one stage to the next. The economic buyer field is required at Discovery to Qualified. The decision criteria field is required at Qualified to Proposal. The gate is enforceable (block advance) or advisory (warn the rep). The gating is the mechanism that turns the qualification framework into a hygiene practice rather than a hope.

Per-industry variants

The ICP-specific questions sit under the generic ones.

A healthcare prospect needs different qualification than a manufacturing prospect. Strkr lets you set up industry-conditional questions that only render when the account industry matches. The healthcare rep sees HIPAA scope questions. The manufacturing rep sees integration-with-SAP questions. The question bank reads differently for different deals, which is what the frameworks should have been doing all along.

Historical answer timeline

Every answer edit lands in the activity timeline.

A qualification answer is not static. Budget might shift mid-cycle. Timeline might slip. Strkr logs every edit to a qualification field with who edited, when, and the prior value. The deal timeline reads like a qualification journal, which is useful on a stuck-deal review when the manager wants to see what shifted and when.

Reporting and win-loss on qualification

The questions that actually predict close.

The reason to turn qualification answers into structured fields is this: you can run a win-loss report on which qualification answers correlate with closed-won. That report is impossible when the answers live in notes. It is a two-click report when the answers are fields. The cards below are the reports Strkr ships and the decisions each one supports.

Qualification completeness report

What percent of deals have the required fields filled.

The weekly RevOps report groups active pipeline by qualification completeness band: 0-25 percent, 25-50 percent, 50-75 percent, 75-100 percent. The deals in the 0-25 band are the deals where the forecast is unreliable. The report drives a coaching conversation on the Monday pipeline call rather than a general exhortation to log more.

Win-rate by qualification depth

Deals with full MEDDIC close at 3x the rate.

The classic insight that every mature sales org eventually finds: deals that have every MEDDIC field answered close at roughly 2.5 to 3.5 times the rate of deals that have fewer than four of the six pillars filled. Strkr ships this report pre-built. The output is a direct answer to the "is MEDDIC worth the effort" question, which usually ends the debate inside the first quarter.

Field-level win correlation

Which single question matters most.

The report that teams always find most useful: a per-question win correlation that ranks which qualification answers predict close best. Economic buyer identified almost always ranks top-three. Decision criteria articulated almost always ranks top-five. The rank of each question is a direct insight into which parts of the framework earn their keep on this ICP.

Qualification coverage by rep

Who logs well and who leans on gut.

The per-rep qualification coverage report shows which reps consistently fill the fields and which reps leave gaps. The pattern is instructive: usually the top-quota reps are not the best at logging, which is a conversation about process adoption not about talent. The report is a coaching tool rather than a punishment tool.

Stuck-deal cause analysis

Why 60-day deals stall.

The stuck-deal report filters opportunities that have been in one stage for more than 30 days and looks at which qualification fields are empty. The common pattern: 70 percent of stuck deals have no economic buyer named. That specific insight becomes a unstick: an AE prompt to go find the economic buyer this week rather than a generic push on activity.

Win-loss interview tie-in

The qualification answers inform the post-mortem.

When a deal closes lost, the win-loss interview script pulls the qualification answers automatically and asks the interviewer to probe the gaps. The interview output then goes back into the qualification bank as new question candidates, which is how the question bank refreshes itself quarterly instead of going stale over three years.

Head-to-head

Strkr vs free-form notes for qualification capture.

The alternative to Strkr for most teams is not a competing qualification tool. The alternative is a Google Doc, a Notion page, and a notes field that nobody can filter. Here is the comparison on the job.

Feature Strkr Google Doc + notes field in generic CRM
Framework templates BANT, MEDDIC, MEDDPICC, CHAMP, GPCTBA/C&I shipped with 10 to 24 questions each Framework is prose in a shared doc; capture is manual notes
Capture surface Right-rail panel on the deal view with per-field inputs Rep types a paragraph into the notes field
Data shape Each question is a typed field (picklist, user lookup, number, date) Free-text, unfilterable
AI-assisted answer extraction Strkr AI pulls answers from call transcripts on request with timestamp citation Not available
Qualification score rollup Weighted score 0 to 100 on each deal with admin-defined weights Manually tallied in a spreadsheet when anyone bothers
Stage gating Required fields block or warn on stage advance No enforcement; reps advance by habit
Win-rate by qualification depth report Pre-built, slices by framework and completeness band Requires custom reporting work in a BI tool
Framework switching BANT fields map to MEDDIC on lead-to-opportunity promotion Reps re-ask the questions; prospect notices
Industry-specific question variants Conditional questions render by account industry One monolithic doc for every ICP
Audit trail on answer changes Every field edit logged to the deal timeline Last edit wins; prior answers lost
How teams use Strkr

How mature teams run qualification on Strkr.

The common pattern across teams that get qualification right: they treat the question bank as a living document, not a sacred artifact, and they tie the question weights to the win-loss report every quarter. The playbooks below are motions running on Strkr customer accounts today.

Series A SaaS, 14 SDR-AE pairs

BANT for the SDR, MEDDIC for the AE, mapped on promotion.

A Series A SaaS runs BANT on the SDR side with 12 questions. Promotion to opportunity maps the four BANT answers into the MEDDIC fields and the AE picks up with the metrics and decision-process questions filled in by context. The qualification score at opportunity creation is around 40 out of 100; the discovery call pushes it toward 70; the proposal stage requires 85-plus. The forecast groups by band and the VP of sales reads qualification depth as the leading indicator for the quarter.

Mid-market B2B, 40 AE team

MEDDPICC with stage gating at Proposal.

A 40-rep mid-market team runs MEDDPICC with 24 questions. Proposal stage requires economic buyer named, decision criteria articulated, paper process mapped, and competition identified. Deals that cannot clear those four requirements stay in Qualified. The gate pushed average deal cycle up 11 days in month one, then down 23 days by month four as reps started qualifying earlier. Win rate on proposal-stage deals climbed from 34 percent to 46 percent across two quarters.

Agency, 10 account managers

Custom hybrid for retainer renewals.

A 10-AM agency built a custom hybrid of CHAMP and MEDDIC for retainer renewals specifically. The question bank asks for budget cycle, approver, and specific pain. The qualification score drives the retention forecast, which is a different animal from the new-logo forecast. The custom bank was built in an afternoon by an admin without engineering help, which was the reason the team moved off a competitor that only shipped the five standard frameworks.

Enterprise, 60 AE team

MEDDPICC plus industry-conditional questions.

A 60-rep enterprise team runs MEDDPICC universally and layers industry-specific questions for healthcare, financial services, and manufacturing. The healthcare deals pick up HIPAA scope and BAA requirement fields. The financial services deals pick up regulatory scope. The manufacturing deals pick up SAP integration and plant count. The AE sees only the relevant subset, which keeps the panel readable and the data clean.

High-velocity SMB, 8 reps

Lightweight BANT, stage gating off, weekly review on completeness.

An 8-rep SMB team runs BANT with 8 questions, no stage gating, and a weekly Friday review of completeness bands. The informal process works because the team is small enough that the manager can see every active deal weekly. The qualification score is the shared language on the Friday review, which replaced a prior "walk me through your pipeline" meeting that ran 90 minutes with little structural output.

Qualification that lives in the data model, not the Notion doc.

Start a 14-day Strkr trial with BANT, MEDDIC, MEDDPICC, CHAMP, and GPCTBA/C&I question banks pre-loaded. Fork, edit, and reweight any framework. Capture answers inline on the deal view with Strkr AI drafting from the call transcript.

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Common questions

What buyers in this bucket ask most.

Which qualification framework should we pick, BANT or MEDDIC or CHAMP?

The framework matters less than whether the answers are structured fields that you can report on. Pick BANT for transactional deals under 50,000 ACV and SDR-led top-of-funnel. Pick MEDDIC for mid-market and enterprise deals with buying committees. Pick MEDDPICC when deals cross 100,000 ACV and procurement plus legal become their own friction. Pick CHAMP when selling to buyers who do not control budget directly. Pick GPCTBA/C&I for inbound-led motions where the buyer is still deciding whether to act. The frameworks are vocabulary; the underlying signals are the same five. Switch when the signals you need to capture change, not when a new book comes out.

Can we build a custom qualification framework instead of using a shipped one?

Yes. Strkr ships the five common frameworks as defaults, but every one is a fork target. An admin can fork any framework, drop questions, add questions, change field types, and reweight the scoring. The forked bank is still a structured object so the reports and forecast slices work identically. Most teams run a custom hybrid by year two, which is healthy. The default frameworks are a starting posture, not a final destination.

How does Strkr AI help with qualification capture?

Three ways. First, during the call, Strkr AI listens on the recording and suggests answers in a side column so the rep accepts with one click instead of transcribing. Second, post-call, the rep can prompt Strkr AI to pull a answer from a transcript, which Strkr AI retrieves with timestamp citation. Third, Strkr AI drafts the qualification summary on the deal timeline, so the SDR-to-AE handoff carries the structured context instead of a two-line Slack message. All of it is included on the paid tier with no per-call credit meter.

What if the rep lies in the qualification fields?

Qualification lies show up in the win-loss report. Reps who report every deal as fully qualified and then lose at the proposal stage repeatedly expose the pattern inside two quarters. Strkr also logs every answer edit to the deal timeline, so a manager spot-check will show if a rep flipped the economic buyer answer after a lost deal. The system does not catch every lie, but it exposes patterns much faster than free-text notes ever did, which is the realistic ceiling on an honesty-based process.

How does qualification tie into the forecast?

The forecast report groups active pipeline by qualification completeness band and by qualification score. The VP of sales reads commit versus best case against actual qualification depth, not against rep gut feel. The pattern that most mature teams land on: commit deals must be at a 75-plus qualification score, best-case deals at 50-plus, pipeline at 25-plus. The thresholds are tenant-configurable. The forecast then becomes a function of logged qualification rather than Monday-morning optimism, which is the single highest-leverage change a VP of sales can make in year one.

Is this a replacement for a dedicated conversation intelligence tool like Gong or Chorus?

Partially. Strkr ships call recording, transcription, and AI-assisted answer extraction on every paid tier. The surface overlaps meaningfully with Gong and Chorus for the qualification-capture job specifically. If the team wants deeper deal intelligence (sentiment analysis, competitor mention tracking, deal risk scoring from conversation), the specialist tools still have more depth. Many Strkr customers run Gong or Chorus alongside and pipe the transcripts into Strkr via the open API, which is a healthy pattern. The commodity capture job sits on Strkr; the specialist analysis stays specialist.

Also built for

Related audiences.

SDR teams Account executives RevOps leaders VP of sales B2B prospecting tools B2B sales intelligence

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