MEDDIC Sales Methodology: What VSL Marketers Can Steal From Enterprise Sales

Most marketers writing video sales letters are leaving serious money on the table, and they do not even know it. They obsess over hooks, storytelling frameworks, and emotional triggers while ignoring a proven qualification system that enterprise sales teams have used for decades to close seven and eight-figure deals consistently.

That system is the MEDDIC sales methodology, and it is packed with insights that translate directly into higher-converting VSLs.

Originally developed by PTC sales leaders in the 1990s, MEDDIC stands for Metrics, Economic Buyer, Decision Criteria, Decision Process, Identify Pain, and Champion. Enterprise reps use it to qualify prospects and navigate complex sales cycles. But here is the thing: the psychological principles underneath this framework are identical to what makes a VSL persuade a cold viewer to pull out their credit card.

In this analysis, you will learn exactly which MEDDIC components map to VSL structure, why understanding your prospect's internal buying process makes your copy dramatically more precise, and how borrowing from enterprise sales thinking can sharpen every script you write going forward.

What MEDDIC Actually Is

MEDDIC is a B2B sales qualification framework developed at PTC (Parametric Technology Corporation) in the 1990s, pioneered by Jack Napoli to bring repeatable structure to enterprise sales cycles where deals are large, timelines are long, and multiple stakeholders control the outcome. The acronym breaks down into six components: Metrics, Economic Buyer, Decision Criteria, Decision Process, Identify Pain, and Champion. Each element represents a specific question a rep must answer with evidence before a deal earns the right to consume your time and resources.

That last point is the whole idea. MEDDIC is not a conversation guide or a rapport-building script. It is a qualification gate. If you cannot quantify the value your solution delivers (Metrics), identify who actually controls the budget (Economic Buyer), understand how the prospect will formally evaluate options (Decision Criteria), map the internal approval chain (Decision Process), articulate the urgent business problem driving the purchase (Identify Pain), and name the internal advocate actively pushing for your solution (Champion), the deal is not qualified. You are guessing, and guessing is expensive. Per the MEDDIC framework breakdown from Atlassian, the framework's core function is replacing gut-feel deal assessment with structured, evidence-based qualification.

Now, some useful context on how widely this framework actually shows up in practice. A 2026 analysis of 1,298 executive sales job postings found that 9.0% explicitly mention MEDDIC or MEDDPICC. That is real employer demand, not just conference-circuit noise. But 90.2% of postings in the same sample mention no specific methodology at all, and Consultative Selling leads every framework in the data at 13.2% of postings, beating MEDDIC by nearly 47%. According to Salesforce's guide to MEDDIC, the framework works best in complex enterprise environments, but it is not a universal mandate across every sales context.

That nuance matters. MEDDIC is a thinking discipline, not a religion. Its value comes from forcing structured, honest deal assessment before you commit resources. The acronym is just a memory device. Treat it as a checklist to tick and you will get exactly what checklist-tickers always get: bad data and false confidence. Apply it as a genuine qualification lens and it changes how you allocate time, attention, and pipeline resources across every deal you touch.

The Core Principle Worth Stealing

The single most transferable idea from MEDDIC is this: replace gut-feel with documented, quantified evidence. Every other nuance in the framework flows from that one principle. It is the reason MEDDIC has outlasted dozens of competing frameworks since the 1990s, and it is precisely why the same logic applies to how you run your VSL funnel.

In enterprise sales, gut-feel looks like a rep who tells their manager "this deal feels strong" without ever confirming who controls the budget, what the decision timeline actually is, or whether there is a verified business case on paper. The rep is comfortable with their contact, the conversations have been warm, and they log the opportunity at 80% confidence. Then the deal goes dark. That is not bad luck. That is the cost of assumption replacing evidence.

The VSL equivalent is scaling your Meta spend based on average watch time. Average watch time is a gut-feel metric. It tells you that viewers watched some portion of your video. It tells you nothing about where the script lost them, whether they bailed before the offer, or whether your hook is holding attention past the 30-second mark where purchase intent starts to form. You are making a capital allocation decision on the same quality of information as the rep who "felt good" about the deal.

Gartner research found that 77% of B2B buyers rated their purchase experience as extremely complex or difficult. Structured frameworks exist because buying decisions are not linear or simple. The viewer's path from cold traffic to checkout is just as nonlinear. Objections surface at specific seconds. Attention breaks at the transition from problem-agitation to offer. Trust either builds or collapses during the credibility segment. None of that shows up in a single aggregate metric.

The failure mode of MEDDIC is well documented: reps treat field completion as administrative overhead, skip the documentation, and the pipeline looks healthy right up until deals collapse without warning. The VSL parallel is identical. When browser pixels miss up to 30% of conversion events due to ad blockers and iOS privacy restrictions, you are scaling spend on an incomplete picture. You are guessing at script fixes because you cannot see the exact second viewers leave.

As sales trainer Rob Ferber puts it, MEDDIC is not a selling methodology, it is an inspection tool: "Methodologies equal the motion. MEDDIC equals the inspection." That framing maps cleanly onto VSL optimization. Your script framework is the motion. Second-by-second engagement heatmaps are the inspection layer. Verified MEDDIC fields tell a sales team whether a deal is real. Verified engagement data tells you whether your funnel is working or just burning ad spend on viewers who exit before they ever see your price.

Metrics: Revenue Attribution and the Pixel Gap

MEDDIC's Metrics component is the harshest filter in the framework. "We improve efficiency" fails it. "We reduced onboarding time by 40%" passes. The distinction matters because Metrics forces you to express value as a number that a buyer can verify, defend to their CFO, and tie directly to business outcomes. Vague claims get disqualified. According to MEDDICC, Metrics is defined as "the quantified value of your solution" — language that leaves zero room for approximation.

Now apply that same standard to your own VSL funnel. If someone asked you for the measurable outcome of your video, could you answer with a specific number? Not "our funnel converts well" but "viewers who reach the 12-minute mark generate $4.20 in revenue per viewer, and viewers who drop before minute 8 generate almost nothing." That is the VSL equivalent of a MEDDIC Metrics answer: revenue per engaged viewer tied to a specific watch depth.

Without second-by-second engagement data, you cannot calculate that number. You are back to gut feel, which is exactly what MEDDIC was built to eliminate.

Here is where the problem compounds. Even if you have a video analytics setup, up to 30% of your conversion data is invisible to browser-based pixels. Ad blockers and iOS privacy settings suppress those events before they ever reach Meta or Google. You are not working with a complete dataset; you are working with roughly 70 cents of every dollar of evidence. As Outreach notes, MEDDIC's power depends on information quality: "the more you dig, the more information you find." Corrupted attribution data undermines that principle at the foundation.

Server-side pixel forwarding closes that gap. Instead of relying on a browser to fire a tracking event, conversion data routes directly from the server to Meta and Google, bypassing blockers entirely. The result is attribution that reflects what actually happened, not what the browser was permitted to report.

This matters specifically for your Metrics answer. If your revenue attribution runs through a pixel that misses nearly a third of conversions, you cannot accurately calculate which watch depth drives purchases. You cannot identify the moment in your script where buyers commit. You are scaling ad spend on an incomplete picture and calling it data.

Accurate Metrics in your funnel requires two things working together: second-by-second engagement tracking that shows exactly where viewers drop, rewind, or convert, and server-side tracking that ensures every purchase gets attributed back to the correct viewer and watch depth. Without both, you fail your own MEDDIC qualification test before a single buyer ever sees your offer.

Economic Buyer: Identifying Which Viewer Segment Actually Converts

In B2B sales, the Economic Buyer is the person who can actually sign off on the deal. Not the champion who loves your product. Not the end user who sits through every demo. The person who controls the budget and can say yes without asking permission. MEDDIC forces reps to find that person deliberately, because time spent building rapport with the wrong stakeholder is time that never converts into revenue. The discipline is simple: identify who owns the decision, qualify everything else against that standard.

Your VSL funnel has the same problem. Not every viewer is your economic buyer. Some watch out of curiosity. Some click from a cold audience and drop in 20 seconds. Some make it halfway through, consume the content, and still never buy. The viewer segment that actually converts, whether segmented by traffic source, device type, or watch depth, is your economic buyer equivalent. Identifying that segment is not a nice-to-have. It is where your optimization budget should concentrate.

MEDDIC as a qualification framework asks: who controls the budget? The direct-response version of that question is: among all the viewer segments generating watch data right now, which segment owns the conversion metric? Revenue attribution tied to individual viewers and watch depth answers that question directly. When you can see revenue per viewer broken out by audience segment and watch depth, you stop guessing which traffic source is producing buyers and start scaling the one that is.

The cost of misidentification is real on both sides. In enterprise sales, chasing the wrong stakeholder wastes months. In a VSL funnel, misidentifying your converting viewer segment wastes ad spend at whatever daily budget you are running, compounded every single day you do not fix it.

Here is the practical diagnostic: pull your viewer data and ask two questions. Among viewers who watch past the 50% mark of your VSL, what percentage convert? Among viewers who drop before 30 seconds, what percentage convert? That gap is not just a retention problem. It tells you where your economic buyer actually lives in the video. Deep watchers who convert at a meaningfully higher rate than shallow watchers are your qualified segment. Everything upstream of that, your audience targeting, your ad creative, your landing page, should be engineered to get more of those viewers to the 50% mark, and to filter out the segments that consume without buying. That is qualification logic applied to a video funnel, and it is exactly what second-by-second engagement heatmaps paired with revenue attribution make possible.

Decision Criteria: Engineering Your VSL to Match How Buyers Evaluate

In MEDDIC, Decision Criteria answers one specific question: what standards is the buyer actually using to evaluate solutions? Not what you assume they care about. What they are actively measuring. Price relative to alternatives, integration complexity, vendor track record, ROI timeline. A rep who maps their pitch to those exact criteria wins deals that better-funded competitors lose. A rep who ignores them loses deals they should have closed.

Your VSL viewer is running the same internal checklist, in real time, whether your script accounts for it or not.

Every person who clicks play brings a parallel evaluation running underneath the surface: Is this person credible enough to take seriously? Does this solution actually apply to my situation, or is it built for someone else? Is the price proportionate to the outcome being promised? Your script either speaks directly to those criteria or it leaves them unresolved. Unresolved criteria become drop-offs.

This is where MEDDIC as a diagnostic language becomes genuinely useful for VSL marketers. Engagement heatmaps give you a second-by-second record of exactly where viewers paused, rewound, or stopped watching. A sharp drop at the 4-minute mark is not a data point to average away. It is a signal that something in those seconds failed the viewer's evaluation: a price anchor that felt disconnected from the value built up to that point, a claim that sounded unsubstantiated, a use case framing that did not match their actual situation. The heatmap tells you the where. Script analysis tells you the what.

Second-by-second script analysis maps individual lines in your VSL to the viewer behavior data underneath them. That precision matters because a script is not one unit. It is dozens of micro-commitments, and one line that breaks trust or misses the viewer's criteria can undo everything that landed before it. This is the VSL equivalent of a sales rep discovering, after losing a deal, that they never actually understood how the buyer was evaluating vendors.

A/B split testing closes the loop. Once you identify sections where decision criteria are not landing, you can test a direct fix: a different proof structure, a reframed price anchor, an alternate outcome claim positioned for a different viewer segment. As MEDDIC research consistently shows, aligning your positioning to the buyer's actual evaluative framework rather than your assumptions about it produces measurable lifts. In VSL terms, that lift shows up as conversion rate improvement you can trace back to a specific script change, validated with real traffic data.

Decision Process: Mapping the Viewer's Path from Cold Traffic to Purchase

In MEDDIC, Decision Process documents the exact sequence of steps a buying committee moves through before a purchase gets approved. Miss one step and you get blindsided: a procurement team surfaces at the last minute, a legal review stalls the deal, a stakeholder you never met kills it on day 89. The framework forces you to map buyer reality, not seller preference. That distinction is what makes it transferable beyond enterprise sales.

Your VSL viewer has a decision process too. It is faster and entirely internal, but it follows a real sequence. They arrive skeptical because they came from a paid ad. They need a hook that earns 30 more seconds of attention before they decide whether to stay. They need an agitation section that confirms you actually understand their specific problem, not a generic version of it. They need a credibility moment that gives them permission to trust you. And they need a concrete, time-anchored reason to act now rather than closing the tab and telling themselves they will come back later. Skip any of those steps and you do not get a second chance to reintroduce them.

The problem is that most marketers never see where that internal process breaks down. They look at view counts and revenue and try to connect the two with guesswork. Engagement heatmaps replace that guesswork with second-level evidence. You can see exactly where viewers lose momentum, which sections they replay before buying, and where the drop-off cliff sits in your credibility sequence. That data tells you whether your script is pacing with the viewer's decision timeline or fighting against it.

Play gates give you a practical tool for viewers who are engaged but not yet at the purchase decision. Placing a lead capture gate at a high-retention moment in the video, after the pain agitation but before the offer reveal, captures prospects who are mid-process rather than forcing a premature close. It is the VSL equivalent of qualifying a warm prospect instead of walking away from the deal entirely.

Script pacing is where this falls apart most often. Marketers build VSLs on their preferred timeline: hook, problem, solution, offer, close. Viewers operate on their own timeline, and according to Salesforce's breakdown of the MEDDIC sales process, effective qualification always maps to buyer reality first. The same principle applies here. A VSL with strong view counts and weak purchase rates is almost always a pacing mismatch. The data to fix it exists in your heatmap. The question is whether you are looking at it.

Identify Pain: The VSL Hook and Agitation Structure

MEDDIC's Identify Pain step doesn't accept vague answers. "They're struggling with lead generation" fails. "They're spending $8,000 a month on paid traffic and converting below 1.2% because their pixel is missing 30% of post-click events" passes. The framework demands the specific, quantified consequence of inaction, because that precision is what separates a motivated buyer from someone who acknowledges a problem and does nothing about it. Generic pain identification produces generic pipeline. Specific pain identification produces urgency.

Your VSL operates on identical logic. The first 90 seconds of your script are your discovery call. The hook must name the pain with enough precision that a qualified viewer stops scrolling and thinks, "that's exactly what's happening to me." A hook like "are you struggling to grow your business?" fails the MEDDIC pain test and it fails your conversion rate simultaneously. A hook like "if you're running paid traffic to a VSL and your cost per acquisition keeps climbing even when the creative looks strong, this is why" passes both. The qualified viewer recognizes themselves. The unqualified viewer leaves early, which is exactly what you want.

When engagement heatmaps show sustained drop-off before the 30-second mark, that is almost always a pain identification failure. The opening didn't connect with the viewer's actual problem, so they made the rational decision to leave before you got to the solution. That's not a media buying problem. That's a script problem. Specifically, it's a hook precision problem, and second-by-second engagement data makes it diagnosable rather than theoretical.

The agitation phase of your script, the section where you dwell on consequences before introducing the solution, is the direct-response equivalent of MEDDIC's "Implicate" stage. You're not just acknowledging pain; you're ensuring the viewer fully understands and emotionally registers the cost of staying in that situation. A prospect who understands the cost moves. A viewer who understands the cost keeps watching. The agitation phase exists to create that registered urgency before you pitch.

One component most marketers overlook: a significant portion of paid traffic arrives on mobile with audio off. A viewer who can't hear your hook still needs to receive the full pain identification sequence. AI-generated captions aren't a compliance feature. They're a conversion tool. Every word of your hook and agitation structure needs to land in text form for the muted viewer, with the same precision you'd demand from the audio. A truncated or auto-generated caption track that loses the specific pain claim in the first 30 seconds produces the same early drop-off as a vague hook spoken out loud.

Champion: The Behavioral Champion in Your VSL Analytics

In traditional enterprise sales, the Champion is the person inside the buying organization who believes in your solution so strongly that they sell it upward when you are not in the room. They pull internal strings, pre-answer objections, and keep your deal alive through budget reviews and committee meetings. Without a Champion, deals stall. No one is advocating for you when procurement starts asking hard questions.

Your VSL funnel has the exact same dynamic, but it plays out in viewer behavior instead of boardroom politics.

Behavioral champions are viewers who rewind specific sections, re-watch the full video more than once, or pause at high-stakes moments before hitting the buy button. These are not passive viewers. They are actively processing your argument, re-checking your claims, and stress-testing your offer before they commit. That pattern of behavior is the direct-response equivalent of an internal advocate internalizing a solution before going to bat for it.

The key is identifying exactly which sections trigger that behavior, and that is where second-by-second engagement heatmaps do work that aggregate metrics cannot. When you see a cluster of rewinds around a specific guarantee statement, a concrete outcome claim, or a pricing frame, that is your script doing its hardest work. Those are the sections where viewers are re-processing before committing. They heard something that moved them, and they needed to hear it again to feel certain.

Treat high-rewind sections as your strongest persuasion assets. Do not cut them for brevity in the next script iteration. Expand them. Reinforce the logic. Add a second proof element right after. The heatmap is telling you where your conversion leverage lives.

The reverse signal is equally important. Sections with unusually high drop-off rates mark where your behavioral champion gave up. That viewer was willing to engage, willing to consider, and something in that specific moment of the script lost them. Those sections are script liabilities, and they deserve to be rewritten before you touch anything else in the funnel.

Fix the drop-off sections first. Reinforce the rewind sections second. That priority order, driven by behavioral data instead of opinion, is exactly what separates systematic VSL optimization from guesswork.

Where Both Systems Break Down: The Data Integrity Problem

MEDDIC breaks down in one specific place, and it's not the framework itself. The failure happens at data entry. Under deal pressure, reps skip fields, write vague notes, or fill in what sounds plausible rather than what they actually know. The CRM shows a qualified pipeline. The forecast looks healthy. But the underlying data is fiction, and when deal reviews happen, the framework exists as a checkbox exercise rather than a genuine picture of deal health. The solution the enterprise sales world is moving toward is AI-enforced CRM hygiene: tools that pull MEDDIC fields automatically from call transcripts, removing the reliance on rep discipline entirely.

Your VSL funnel has the exact same problem, just one layer down.

Browser pixels miss up to 30% of conversion events because of ad blockers and iOS privacy restrictions. Every decision you make on that data, which audience to scale, which creative to keep running, which script to leave untouched, is built on a record with a third of the signal missing. You are not running on bad strategy. You are running on incomplete data and calling the results strategy.

This is not a tracking annoyance you work around. It is a structural accuracy problem that compounds every time you make a scaling decision. You cut a creative that was actually converting at full rate. You scale an audience that looked strong on 70% of the signal. You leave a script intact because the conversion drop-off was invisible at the browser level. Each individual decision seems reasonable. Cumulatively, they drag performance in a direction you cannot diagnose because your measurement baseline is wrong.

Server-side pixel forwarding is the architectural correction. Instead of relying on a browser to fire a conversion event, where an ad blocker or iOS restriction can intercept it, the event fires directly from the server layer to Meta and Google. Nothing in the browser can block it. This is not a workaround or a clever patch; it is simply the correct way to send conversion signals in a privacy-first tracking environment. The Conversions API from Meta and Enhanced Conversions from Google both exist for exactly this reason.

The compounding advantage of complete data is the same in both contexts. Enterprise sales teams with full MEDDIC field completion can accurately forecast, prioritize the right deals, and coach reps based on real patterns. VSL marketers with complete conversion data can make correct decisions on audience scaling, creative rotation, and script iteration without guessing at what the missing 30% would have told them. The strategy does not change. The accuracy of every decision built on top of it does.

MEDDPICC and What the Complexity Trend Tells Direct-Response Marketers

MEDDPICC extends the original six-element framework by adding two components: Paper Process and Competition. Paper Process covers the contracts, legal review, and procurement sign-off that happen after a buyer says yes verbally. Competition requires documenting which alternative solutions the buyer is actively evaluating and positioning explicitly against them. These additions exist because enterprise deals were dying at both stages with uncomfortable regularity, and practitioners needed a structured way to track and prevent those losses.

The Paper Process parallel for direct-response marketers is your order page. In enterprise sales, the deal collapses during legal review. In your funnel, it collapses between the click and the completed purchase: checkout abandonment, payment processing failures, and objections that never got answered in the VSL but surface at the moment of commitment. Your viewer already said yes in their head. The friction is procedural and psychological, inserted right at the point of execution. If you are not tracking where that drop-off happens at the second level in your video, and cross-referencing it with order page behavior, you are running blind through exactly the phase MEDDPICC was extended to address.

The Competition component maps directly onto offer positioning in your VSL. Your viewer is not evaluating your offer in isolation. They are mentally comparing it against whatever else they have seen: other programs, doing nothing, a cheaper option, a DIY approach. If your script never surfaces and neutralizes those alternatives, you are missing the functional equivalent of the Competition field entirely. The rep who does not document competing bids loses deals to solutions they never acknowledged. The VSL that ignores the alternatives in the viewer's head loses sales the same way.

A/B split testing at the script and offer level is your Competition analysis. You are not running tests arbitrarily. You are systematically finding which positioning angle wins against the specific alternatives your viewer is already weighing. That requires structured iteration, not one-time guesses.

The fact that 9.0% of VP Sales and CRO job postings now mention MEDDPICC specifically signals where enterprise sales sophistication is heading: more variables, more data points, more structured interpretation. Direct-response analytics are on the same trajectory. Dashboard-glancing at average watch time is the old mode. Second-by-second engagement data, server-side conversion tracking, and script-level revenue attribution are the new standard, for the same underlying reason the framework kept growing.

Practical Takeaways: Apply MEDDIC Thinking to Your VSL Funnel This Week

Five concrete actions. Run all five before end of week.

Audit your revenue attribution data first. Pull your revenue attribution report and find the specific watch depth where your buyers cluster. If you cannot answer "at what second do my buyers commit?" with an actual number, your Metrics pillar is empty. You are qualifying on gut feel, which means every budget decision you make downstream is untethered from verified evidence. The MEDDIC framework demands quantified proof at every stage; your VSL funnel demands exactly the same standard from your analytics.

Check your pixel integrity before you scale another dollar. Open your conversion events and confirm whether they are being fired server-side or browser-only. If your tracking is browser-dependent, you are potentially missing up to 30% of conversion events due to ad blockers and iOS privacy restrictions. That gap does not just distort your reported ROAS; it means your Meta or Google algorithm is optimizing on an incomplete signal, and your scaling decisions are built on a foundation with a structural hole in it. Fix the data before you increase spend.

Let your engagement heatmaps tell you where to rewrite, not your instincts. Find the sections of your VSL with the highest rewind rates. Those are your behavioral champion moments, the exact frames where persuasion is landing hard enough that viewers replay it. Your script revisions should start there and expand outward, reinforcing what is already converting, not gutting sections that feel weak to you personally. Personal intuition about script quality is the MEDDIC equivalent of a rep writing "strong interest" in a CRM field with zero supporting evidence.

Map your 30-second drop-off rate directly to your hook's pain identification. If a meaningful percentage of viewers exit before the 30-second mark, your hook is not agitating a specific enough pain for your target viewer to recognize themselves and stay. That is a qualification failure in the first half-minute.

Run one targeted A/B test this week, not a full redesign. Isolate the exact timestamp where your heatmap shows the steepest engagement drop. Test a rewritten version of that specific section. That drop is where your Decision Criteria or pain identification is failing your viewer. One precise test beats a full-page overhaul every time.

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The Framework Is the Same. The Data Source Is Different.

MEDDIC was built to fix one specific failure mode: sales teams advancing deals on assumed information rather than verified facts. The framework's original insight, developed at PTC in the 1990s, was that deals don't fail because reps are incompetent. They fail because critical information is missing. Replace assumption with evidence, and win rates follow.

That is exactly the problem running your VSL funnel on browser-pixel data and average watch time creates. You are making budget decisions on incomplete information. Scaling a losing ad because iOS blocked 30% of your conversion signals is the same category of error as advancing an unqualified deal because a rep assumed the economic buyer was on board.

The six MEDDIC components map directly onto the six questions your VSL funnel should answer with verified data: what are my measurable revenue outcomes, which viewer segment actually converts, what are they evaluating before they buy, how do they move from viewer to purchaser, what pain does my script solve, and who are my behavioral champions signaling intent before conversion.

None of that analysis is possible without two data inputs: complete conversion data from server-side tracking, and second-by-second engagement data from heatmaps. Without both, you are filling in the framework with guesses. That is not MEDDIC thinking. That is the problem MEDDIC was designed to eliminate.

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Conclusion

The gap between a VSL that converts and one that falls flat often comes down to qualification, not copywriting tricks. MEDDIC gives you the framework to close that gap. Remember the core lessons: quantify your prospect's pain with real metrics, speak directly to the economic buyer holding the budget, align your offer with their existing decision criteria, and position yourself as the obvious solution before they even reach your price point.

These are not theoretical concepts. They are battle-tested principles that have driven billions in enterprise revenue, and they work just as powerfully inside a 20-minute video sales letter.

Now it is your turn. Audit your current VSL against each MEDDIC component and identify where prospects are mentally checking out. One strategic revision could be the difference between a campaign that breaks even and one that scales.