Metrics That Matter: A Lifecycle Framework for Learning Businesses

Leading Learning Podcast co-hosts Celisa Steele and Jeff Cobb

Many learning businesses are data-rich and insight-poor. It’s easy to track what’s easy to count—e-mail opens, registrations, completions—but harder to know if the products are doing what they were created to do.

In this episode of the Leading Learning Podcast, Jeff Cobb and Celisa Steele discuss what learning businesses should be measuring and why. They identify the most common metric traps—including surrogation and vanity metrics—and introduce a five-lens framework for thinking about which metrics matter at which stage of a product’s lifecycle: Validate, Reach & Convert, Engage, Retain & Reactivate, and Prove Impact. They also make the case that a metric without a target is just a number and that the most valuable feedback often comes not from dashboards but from conversations with learners and employers.

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Metrics That Matter: A Lifecycle Framework for Learning Businesses

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Celisa Steele: [00:00:03] If you want to grow the reach, revenue, and impact of your learning business, you’re in the right place. I’m Celisa Steele.

Jeff Cobb: [00:00:10] I’m Jeff Cobb, and this is the Leading Learning Podcast.

Jeff Cobb: [00:00:18] Talk to enough learning business leaders, as we do, and a common worry crops up: Is this program we offer working and having an impact, or have we just gotten used to running it?

Celisa Steele: [00:00:30] And that worry isn’t contained to a single offering. Most learning businesses, of course, run a whole portfolio—a conference or two, some on‑demand courses, maybe a certification—and the honest answer to “Is this working?” might be different for each product.

Jeff Cobb: [00:00:47] Which raises another question: How do we know if a product is working?

Celisa Steele: [00:00:51] Elizabeth Engel touched on this back in episode 470, when she walked through the build-measure-learn cycle of the Lean Startup methodology. She said that figuring out which metrics matter could be a whole podcast in itself.

Jeff Cobb: [00:01:06] So we’re taking her up on that especially because the importance of metrics is a theme that keeps resurfacing.

Celisa Steele: [00:01:13] Steven Khraiss talked about lagging versus predictive indicators in episode 485. And, in episode 479, Eric Ries (of Lean Startup fame) talked about how metrics can become a substitute for the thing they’re supposed to measure. Metrics are essential to our own focus on reach, revenue, and impact and making sure that learning businesses are achieving all three of those.

Jeff Cobb: [00:01:39] Metrics are something everyone agrees matter and very few people feel settled about. So today we want to dig into what learning businesses should pay attention to, when, and why. We’ll name some of the traps that make metrics less useful than they should be, and we’ll offer a set of lenses for thinking about which metrics matter at which point in an offering’s lifecycle.

Celisa Steele: [00:02:05] We’ll also make the case that a metric needs a partner—a target, an expectation, a reason you think you’ll see what you’re hoping to see. Because a number on its own can be misleading.

Traps That Make Metrics Less Useful Than They Should Be

Jeff Cobb: [00:02:18] We mentioned that there can be some traps with metrics, things you can fall into that aren’t the behavior that you want in using metrics. So let’s talk about the traps that make metrics less useful than they should be.

Celisa Steele: [00:02:31] A very common problem is that any specific learning business has a ton of data, but there’s no real plan for what to do with that data. Back in episode 436, Alaina Szlachta said, if you don’t have a plan for what you’re going to do with data, then don’t even collect it in the first place. Because once you have it, it can be a little bit of, “What do we do with it now?”

Jeff Cobb: [00:02:55] Another problem can be categorized as surrogation, or metrics becoming a surrogate. Eric Ries uses that term in episode 479 for when a metric becomes a stand-in for the thing it was supposed to measure. His example is customer service, and the concept of average hold time became a big goal instead of the proxy that it was meant to be. Companies found ways to get people off the phone without solving their problem. Maybe you have experienced this—I know I have—and customer service got roughly twice as bad over the course of 50 years.

Celisa Steele: [00:03:32] And then there’s also the trap of vanity metrics. Amy Michalski and Shelly Strickland of AIIM in episode 482 talk about this. They believe that engagement and the quality of engagement matter a lot more than things like open rates, which Shelly characterized as a vanity metric because it’s not really telling you a whole lot about what your learners or your audience values or needs. It’s the attendance, the engagement, the genuine engagement that tells you what your learners are looking for.

Jeff Cobb: [00:04:06] I had something like this happen recently on a survey an organization was running, and the marketing team came back and said, “Very high open rate. A 90-percent open rate on the survey e‑mail.” But nobody had taken the survey. It didn’t really matter that they opened the e‑mail, so vanity metrics are definitely a danger. Another is the danger of metric fixation, or an over‑reliance on a measure rather than the goal you’re after. It can get easy to game the numbers. We see this happen all the time with things like the Balanced Scorecard, for example, which can be so valuable, but, if it becomes all about the numbers on the scorecard, and you forget what the scorecard was there for in the first place, then that value erodes. Make sure that the real goal, the real mission behind the metric, is not lost.

Celisa Steele: [00:04:54] One other trap or danger to watch out for when you’re thinking about metrics is that no single metric is going to tell the whole story. This came up in that episode that I did with Amy and Shelly from AIIM. It might be that a Webinar doesn’t convert to a course sale, and that could be seen as a failure. But maybe it’s bringing in highly engaged prospects who ultimately convert to membership six months later. If you’re just looking at a single metric—did someone come to this free Webinar and sign up for the paid course immediately after?—that’s not going to tell you that whole story of that cultivation that you might be doing as part of that free Webinar to ultimately bring people into the fold, into membership, and into your learning portfolio. Again, it can be hard to rely on any single metric to tell you the whole story.

Jeff Cobb: [00:05:47] Lots of potential traps. There are others that we haven’t even named here. You need to be careful about them, but we’ll stress that none of this is an argument against metrics. We are big fans of metrics. It’s just an argument for being very deliberate about which ones and why and being intentional about how you’re using those metrics.

Pair Every Metric with a Target or Hypothesis

Celisa Steele: [00:06:07] What we want to talk about next is the idea of pairing your metrics with a target or a hypothesis. I want to go back to something that Alaina Szlachta said in her interview that we did with her. She very elegantly and succinctly described measurement and evaluation as answering this question: Did we do what we said we would do? And that means you need to have said what you were trying to do. This is her idea of an “impact hypothesis”—you need to be clear on what difference you’re trying to make. What are you trying to do? And then you can start measuring whether you’re having that impact, if you’re making that difference that you set out to do. You have to have this hypothesis, this idea of what kind of impact you want to have before you get going because that’s then what you’re going to measure against.

Jeff Cobb: [00:06:56] An example might be “We should see higher open rates because of this segmentation.” And then you test that. Do you get higher open rates because of the segmentation that you’re doing? Note that AIIM sets targets around registration, lead generation, and membership campaigns but stays flexible because audience behavior shifts year to year.

Celisa Steele: [00:07:17] The point there is that you could have this impact hypothesis about what you’re expecting to see, but you don’t have to treat that as being set in stone. You can learn from what data is coming in. It might help you tweak your impact hypothesis, get a little bit more specific, as long as it’s based in your audience’s behavior and what you know about them. That ties into the idea that a missed target isn’t necessarily a failure. You want to have the metric. You want to have that target. Maybe you don’t hit that target, but treat that as a reason to ask why rather than treat it as an outright failure. And, by asking why, you can begin to think about what you might need to do differently to help you achieve that impact that you were hoping to achieve in the first place.

Jeff Cobb: [00:08:03] A practical, concrete tip might be that, before you launch a product or before you pull a report, write the sentence “We expect to see _____________ because _______________.”

Celisa Steele: [00:08:16] Once you know what you expect and why, the next question is “What counts as a reasonable baseline for comparison?” This is why we want to talk about having that right expectation that is specific to your audience and to your learning business.

The Right Expectation Is Audience-Specific

Jeff Cobb: [00:08:35] Related to the last point, your target—or, if you’ve been collecting numbers for a while, the baseline you’re comparing against—needs to reflect how a specific audience or segment behaves, not a generic assumption. We’ve had a client, for example, that had many of its audience who couldn’t get to its annual event every year because someone had to stay and keep the practice open. This was an industry in which people are running health‑related practices. So, for a lot of attendees, the real cycle is closer to every third year, not every year. And a generic year-over-year repeat attendance target would read that as a retention problem when it really isn’t. It would be a case of a wrong target, not behavior that indicates a problem with the offering.

Celisa Steele: [00:09:26] Yes, and we’ve heard that story from other organizations as well—other types of professionals who either can’t afford in terms of dollars or can’t afford in terms of time out of the office and away from work to attend that annual conference every year. In at least one case—and there are many, many more—part of what the learning business did was add a virtual option for the years when people can’t attend in person. That’s a simple solution of building an offering around the real cadence for a specific audience rather than trying to fight that natural cadence and deal with, in some ways, a non-existent problem, as you were saying, Jeff.

Jeff Cobb: [00:10:09] Right. A practical, concrete tip would be, before treating a number as a problem, ask what the true, reasonable rhythm is for the specific audience that the number applies to and whether your target reflects that.

Celisa Steele: [00:10:26] This is very much in line with a caution that Steven Khraiss raised in episode 485 about benchmarking (episode 485). It may sound obvious, but comparisons only mean something if you’re comparing apples to apples. What might be the cadence or the expectation for one particular audience may not apply to another. Make sure that you’re benchmarking against the appropriate measures and data.

A Lifecycle Lenses for Metrics

Jeff Cobb: [00:10:55] Now let’s move to looking at different lenses to view metrics through, a lifecycle of lenses for metrics.

Celisa Steele: [00:11:05] We have five that we want to offer, and these five can apply to any offering. Which of the five lenses matters most is going to depend on where that offering is, how mature it is, or where a particular cohort is within it across its lifecycle. As a preview—we’re going to talk about these more—in order across an offering’s lifecycle, the five lenses are (1) Validate, (2) Reach & Convert, (3) Engage, (4) Retain & Reactivate, and (5) Prove Impact.

Jeff Cobb: [00:11:38] If you’re talking about a brand-new offering, there’s only going to be one cohort initially, so the lenses would unfold sequentially in that case—there simply hasn’t been time for later lenses to apply. For a more mature, ongoing offering, the lenses are concurrent because cohorts overlap. Take the example of an in‑person workshop. This year’s new registrants are in Reach & Convert, last year’s attendees are in Retain & Reactivate, and the cohort from three years ago is old enough to assess in Prove Impact.

Celisa Steele: [00:12:14] As you’re saying, Jeff, the lenses can be both sequential and concurrent. Whether this looks sequential or also looks concurrent depends on how much history the offering has. Again, a brand-new offering, one cohort, so you’re going to watch it move through those lenses one at a time. An offering that’s been around for years is going to have those multiple cohorts stacked up at once, so multiple lenses are going to be in play simultaneously.

Validate

Jeff Cobb: [00:12:39] Let’s talk about each of these lenses a little bit more. We’ll start with Validate. The purpose of this lens is to test assumptions before committing significant resources. This is the beginning of the cure against “Build it, and they will come.” You want to do some testing first to see if it’s something that your audience is interested in.

Celisa Steele: [00:13:02] This might be where you’re building a minimum viable product and measuring with a real call to action. You’re not just doing some hypothetical “Would you sign up for this?” or “What would you think of an offering like this?” You’re asking learners or prospective purchasers to take an action, and then, based on that call to action, you’re going to then decide, “Okay, yes, we’re going to persevere with it. Nope, we’re going to pivot it. It’s not quite landing right. Or, you know what, it’s really not gotten any traction at all, so we’re going to kill it.”

Jeff Cobb: [00:13:33] And this can be simple. A classic here is asking people to submit their name and e-mail to get more information about something. If they’re willing to take that small action, that starts to show you some interest. But you want that call to action, that concrete action, to be a test. It’s not a product launch yet. You may not be asking them to purchase something or, at least, not purchase the full thing that you want to build. You’re, at this point, willing to walk away from the whole idea depending on what you learn. If nobody’s clicking anything, if nobody’s putting in their e-mail address, if nobody’s willing to make a micropayment, then you’ve got good information about whether you should proceed or not.

Celisa Steele: [00:14:09] This Validate lens is clear Lean Startup territory. It’s what we talk about with Elizabeth Engel and Jamie Notter in episode 470. It’s what we talk about with Eric Ries himself in episode 479. Again, for a brand-new offering, this is going to be the only live lens. That said, it’s still worth setting a soft impact hypothesis even here. You still want to begin thinking about at this point what does this need to be? What does it need to do if it’s going to work? What are we trying to do here? For a mature offering, this Validate lens is going to resurface when elements or components are shifted, and that’s going to happen not necessarily on any set schedule but when you vary an element or component of an offering. For example, let’s say you have an existing annual conference, but you’re going to try out a new conference session format. You’re going to need to validate that and spend a little bit of time in some of these lenses with that new element or component. It might mean adding that virtual option along with the in-person conference attendance option. Even something as simple as swapping out a Webinar title to see what helps move conversion—that’s something that came up with Shelly and Amy from AIIM in episode 482. And then occasionally Validate will resurface at the whole offering level too.

Jeff Cobb: [00:15:37] Yes, that’s right. When you’re getting a weak signal from one of your later lenses—so you might have a real drop in impact or engagement, not just a natural dip in your cadence—that can send you back to ask whether the whole offering still deserves to exist or whether you want to test some sort of key change that might drive better performance from the offering going forward. All of this also points to the topic of portfolio governance and sunsetting, which we covered in depth in episode 476.

Reach & Convert

Celisa Steele: [00:16:13] Validate is the first lens. The second lens is Reach & Convert. In this lens, you’re looking at making sure that people who would benefit from a specific product know that it exists—that’s about your reach—and that you’re turning some of those people into paid participants or, at least, registered participants (if it’s a free offering)—that’s where you’re converting.

Jeff Cobb: [00:16:37] This is different from the concrete action in Validate. To get to this Reach & Convert lens, you’ve already decided the offering is real because of your testing and that you’re going to proceed in offering it as a full-fledged item in your portfolio. Now, as part of that validation process, you’ve started to build the reach that you’re going to need for this; you’ve got some people interested in it. The action that people take here is going to be part of the ongoing funnel that you’re now developing to drive people into converting to and paying for the product. It’s not just going to be that one-off test of whether to proceed.

Celisa Steele: [00:17:18] In episode 482, one of the takeaways from Amy and Shelly at AIIM is this idea of, as you’re trying to Reach & Convert, you probably want to focus on outcomes, not necessarily the content. For AIIM, this took the format of saying, “Here is what this can help you solve—what this Webinar/conference can help you solve.” It’s not, “Hey, here’s our on-demand library, and you can access it 24/7” with no real connection to what those people care about in terms of the problems they’re grappling with on a day-to-day basis.

Jeff Cobb: [00:17:52] Yes, and it’s a nice connection because that outcome is the reason that you’re creating the product in the first place. It’s where you’re ultimately going to be showing impact, and that comes back into early in the process of convincing and converting people to participate in the product because they feel like they’re going to get that outcome. Now, we will give a caution here that Reach on its own or the metrics that go with Reach are really part of that classic vanity metric trap that we mentioned a little earlier—impressions and opens are easy to measure, easy to inflate, and easy to feel good about. Conversion is the real signal. Your validation, your testing, is already taking you towards that because you’re actually asking people to do it. But, obviously, when you get them to step up to the full product and convert and register or buy, that’s the real measure that you need here.

Engage

Celisa Steele: [00:18:47] We’ve talked about two lenses on metrics: Validate and then Reach & Convert. The third lens is Engage. This is where you’re tracking real-time signals of participation, and that’s then going to enable you to act while there’s still time to help someone who’s potentially struggling in an offering. This is about not just looking in the rearview mirror at what’s already happened; it’s about being a bit more predictive and forward‑facing or at least in the moment aware of what’s going on.

Jeff Cobb: [00:19:16] You want those real-time, behavioral signals that happen during participation, not just after the fact. You want those leading indicators, not just the lagging indicators that we often get in our evaluations and assessments and everything like that.

Celisa Steele: [00:19:30] Steven Khraiss mentioned in episode 485 the idea of an engagement score. They’ve seen learning businesses that put together something that rolls up an indication of engagement. It might be a composite that’s built from log-in frequency and how long that learner stay active during that session. How many modules did they make it through? Have they completed the practice exam or the test that’s associated with that content? Whether or not those would be components that would make sense for your learning business, the idea is to come up with some indicators that would help you predict who’s likely to finish and who’s potentially quietly slipping away. Because then you can help applaud those people who are making good progress, and you can also then hopefully reach out and provide some level of remediation for those folks who might be in danger of slipping away.

Jeff Cobb: [00:20:24] When you’re rolling up data into the dashboards that you might have in your learning management system or in any sort of business intelligence that your organization is generating, this is the kind of thing you want a real-time dashboard on, that you want to be looking at day in and day out. Again, contrast this with the traditional lagging indicators like completion, enrollment, and evaluation data—by the time you’ve got those and can look at them, it’s probably going to be too late to help a learner who’s struggling or to, in real time, be able to see that engagement issue happening and be able to address it

Retain & Reactivate

Celisa Steele: [00:21:00] The fourth lens is Retain & Reactivate. This is where you’re focused on getting those individuals to come back. You’re worried about staying sticky. You want those learners to come back to you when they have their next need.

Jeff Cobb: [00:21:17] But that needs to be according to the right cadence. As we mentioned earlier, we had the organization where people were attending every three years. We had one where it switched off, and this year might not be somebody’s year, but the next year would be. You have to understand that about your audience. You’ve got to go out and get that data so you know what retention and reactivation needs to look like, and you’re not expending resources on trying to retain and reactivate people who really don’t have an issue. It’s just part of their cadence that they’re not showing up this year, but they might show up again next year or the year after.

Celisa Steele: [00:21:51] I want to highlight one of the things that came up in the conversation I had with Steven Khraiss because it’s such an important piece of retention and reactivation: the sense to which learners feel identified with the provider of that learning. Steven talks about it, and Holmes Corporation talks about it, as “membership-integrated learning.” And the finding that they have seen in their experience working with various clients is that members outperform nonmembers on success rates even before controlling for engagement—membership and learning reinforce each other.

Jeff Cobb: [00:22:28] This harks back to research that ASAE (the American Society of Association Executives) did probably more than a decade ago that we’ve mentioned a number of times, and I still don’t think it gets the level of attention that it should. They did a Decision to Learn study that showed the number one factor determining whether somebody is going to participate in an organization’s learning is their sense of affiliation with the organization. If they are a member, and if they feel that strong member affiliation, they’re likely to participate in the organization’s learning. The flip side of that too is, if they participate in the organization’s learning, that’s the way they’re engaging with the organization and feel more affiliated with the organization. You start to have this virtuous cycle that develops. As with all of this, you want to be measuring that. You want to be looking at that. Is that happening? How are you making that happen? It should be a major focus for organizations.

Prove Impact

Celisa Steele: [00:23:19] The fifth lens is Prove Impact. This is where you’re honestly assessing and determining whether the offering is making a real difference for stakeholders. In episode 474, Jeff, you and I talked about a four-perspective framework: Is the offering meaningful for the learner, employer, learning business, and broader field? You want to make sure that you’re thinking about all those different stakeholders. And then you also want to measure that at different points in time. Some impact you’re not going to see right at the end of a workshop or conference when you’re having them fill out that initial survey. You’re going to need to follow up later on to find out what stayed with them, what are they struggling with, what are they using. It might be three months out, six months out, twelve months out—whatever cadence makes sense based on your audience and the content. But you want to hopefully be able to see what is (or isn’t) being applied, what is (or isn’t) working in the workplace for those learners.

Jeff Cobb: [00:24:25] A major theme throughout this—and it’s the theme here again—is you want to plan for this from the beginning. You’re basically building in your impact plan from the initiation of the product. When you’ve got a new offering, impact obviously isn’t measurable yet. But you want to design for it. This goes back to Alaina’s “impact hypothesis” idea: Get clear on what difference you’re trying to make before you start measuring whether you made it. You should be building towards making that difference. That goes into the product design and deployment, and you build in the follow-up mechanism that’s going to prove it. You’ll measure it later, once enough time has passed, but you’ve got to have it built in from the beginning to be able to do that.

Celisa Steele: [00:25:11] We would encourage you to really try to validate the impact. What that means is, if you can help it, don’t rely solely on self-reported learner data. You want to also ask other stakeholders, especially the employer, about what changed and what is helping (or potentially not helping) once that learner is back and working on the job.

Jeff Cobb: [00:25:35] And, of course, get that employer input in the first place around what they want to see changed. Again, you’ve got the plan you’re designing to make that happen, and then you can go and check whether it did happen. The five lenses only work if you’re willing to go out and ask people, not just infer from the data. So let’s talk a little bit about going out and asking them.

Ask

Celisa Steele: [00:25:57] You want to get out in the field and talk to people directly. You want to talk to those learners, employers, other major stakeholders, whoever that might be for your audience or for that particular offering. This takes me back to the Elizabeth Engel and Jamie Notter episode, where they talk about design thinking and pairing it with some of the Lean Startup methodology and that build-measure-learn cycle. And one of the things they talk about in that episode, which is based on a white paper, is that empathy is so important. That’s a big part of why it is so valuable to talk to the learners, particularly out in the field, to see what is it that they’re dealing with, what are the barriers that they’re up against, what would help them learn and work in their environment. You can certainly make use of online surveys—they can be a good instrument for reaching many of your learners—but there’s also a whole lot to be said for going out and getting that close proximity in the field to a handful of your learners.

Jeff Cobb: [00:27:04] And this needs to be built in as a cultural thing for more learning businesses than it is. A huge part of the work we do at Tagoras is doing this asking for organizations. We will do surveys. We will do interviews. We will do focus groups. We will show up at events and talk with people close up. And that’s valuable. We enjoy doing that. We provide value to organizations in doing it. But, in most cases, we’re only going to be called in every two, three, four, five years to do something like that. Organizations need to be doing this on a regular basis and building it into how they approach learning. And it’s important when you’re doing this—the whole measure in build-measure-learn is a real call to action, not an opinion poll—to give people something and watch what they do to the extent that you’re able to do that. Don’t just ask about preferences or opinions. Really try to get at the behavior, not just what they say, but what they actually do or what they have done. And the only way you find that out is by engaging with them and asking and watching.

Celisa Steele: [00:28:06] Elizabeth shared that, when she worked with the Children’s Hospital Association, she spent the better part of a year going out and visiting every corporate partner in person to ask what they were trying to achieve and to see the barriers that they were up against and what it was like in their environments. That same kind of approach, that willingness to go out and have those conversations can apply to learning products and developing those as well. We have all sorts of organizations that collect survey data right after a Webinar finishes or someone finishes an online course. The important thing is then to make sure that you’re not just asking, but you are listening and taking action based on what you hear and changing things, stopping things, adding things.

Jeff Cobb: [00:28:54] And AIIM, you found out in the interview with them, they survey after every Webinar and event; more importantly, they act on what they hear—they’ve stopped doing things people said were too much. And, related to this, organizations collect a lot of this type of data, and it’s hard to act on it because there’s just so much data, and somebody’s got to be in charge of it, and you’ve got to figure out what the actions are from it. This is the type of area where AI can help so much now. Feeding that data that you’re getting from your Webinars and your events into AI and saying, “Based on our plan, based on what we’re trying to achieve”—going back to square A, you have to have a plan; you have to know where you’re trying to go—“what should we now do? What should we start? What should we stop? What should we change?” That’s the kind of thing that AI can dramatically expand an organization’s capacity around.

Celisa Steele: [00:29:41] We are arguing that, across these five lenses—for each of your products or at least your product lines—you’re going to want to have metrics that help you see how you’re doing when it comes to validation, when it comes to reaching and converting, when it comes to engagement, when it comes to retaining and reactivating, and then proving impact. We can’t tell you what those specific metrics are going to be for any of your learning products, but the main thing is, if you have that clear idea, as you were just saying, Jeff, of what this product is setting out to do, that’s going to tell you a lot about what metrics you need to be tracking to help you see, “Are we actually doing that?”

Bringing It Together

Jeff Cobb: [00:30:22] Let’s bring this all together and offer some practical ways that you can apply what we’ve been saying in your learning business. We propose that there are four questions that you might want to ask.

Celisa Steele: [00:30:37] First, does what we’re measuring match where this offering is in its lifecycle and what it was designed to do? Second, what did we expect for this offering, and why did we expect that? Third, do we care about this metric, or is it just the easiest thing to count? And, fourth and finally, have we recently asked the people this is for—learners, employers—directly about value and impact?

Jeff Cobb: [00:31:05] A few relatively straightforward questions. They’re not necessarily easy to answer, but, if you ask them and answer them honestly and objectively about a single offering, they’re going to tell you a lot about that product’s place in your portfolio.

Celisa Steele: [00:31:29] When metrics are paired with a real target, checked against the right baseline, and grounded in what learners and employers tell you, they stop being a scorecard and start being a genuine feedback loop, something meaningful and actionable.

Jeff Cobb: [00:31:45] Making that shift—from scorecard to feedback loop—is what we encourage learning businesses to do. Having those metrics in place and then making choices based on what they tell you is a key part of growing your reach, revenue, and impact.

Wrap-Up and Recap

Celisa Steele: [00:32:00] We’ve offered a handful of lenses—Validate, Reach & Convert, Engage, Retain & Reactivate, and Prove Impact—and we’ve offered some cautions that go with them. Pair metrics with a target, check your baseline against your audience’s real cadence, and don’t let an easy number stand in for the thing that you really care about.

Jeff Cobb: [00:32:22] And go ask people. Certainly learners but also employers. Enrollment numbers and dollars are only going to tell you so much.

Jeff Cobb: [00:32:37] And if you found this episode helpful, please share it with a colleague who might want some help in thinking through what to measure and why.

Celisa Steele: [00:32:44] Thanks again for listening—and see you next time on the Leading Learning Podcast.

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