Latest / Partnerships Unraveled / 006 - How to create a data-informed channel culture – 3 key questions

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006 - How to create a data-informed channel culture – 3 key questions · Partnerships Unraveled

• 0:07
  Welcome back to Partnerships Unraveled, the podcast where we unravel the mysteries of Channel and partnerships on a weekly basis.
• 0:15
  My name is Rick van den Bosch, and I'm the CEO and founder of Channext.
• 0:18
  And I'm here together with our VP partners, Alex Whitford.
• 0:22
  Alex, how are you doing?
• 0:23
  Yeah, really well Rick, how are you?
• 0:25
  Very excited for today, I think a topic that both you and I really like.
• 0:31
  Yeah, you can tell we were nerds when we were kids because data is one of my favorite things to talk about.
• 0:35
  So it's going to be a good one.
• 0:37
  Fully agree there.
• 0:38
  And I think indeed data is a topic we're going to dive in very deep today.
• 0:42
  But more specifically data in the Channel, of course.
• 0:45
  And I think what would be interesting to start with, of course, there's been a lot of things around, especially in the last decade, like data as the new oil.
• 0:52
  How important is it to have data?
• 0:55
  All the successful companies we see Google, Facebook, Microsoft, Amazon, they are fully driven by data.
• 1:03
  So I think maybe before we dive into Channel, maybe it's interesting to dive a little bit deeper into why data is so important in today's economy.
• 1:12
  And maybe Alex, could you take us away a little bit there?
• 1:15
  Sure.
• 1:15
  So I think there are two different ways you can talk about data.
• 1:18
  So the examples you just gave Facebook or Google and those entities, they're very much using data as identity tracking, right?
• 1:28
  So they're leveraging that resource to be able to market very strategically, very accurately, and ultimately generate sales.
• 1:36
  So those businesses have built a huge resource of information and they effectively are all in one large competitive game around.
• 1:46
  Is our marketing algorithm better than our competitors'?
• 1:49
  And do we have the biggest pool of people so that we can run advertising?
• 1:54
  So that's sort of one umbrella term for data.
• 1:57
  But then I think there's another form of data.
• 1:59
  When we talk about business, it's not just that resource, but it's more data-driven decision making, which is to understand real breakdowns of process flow so that you can allocate resource very effectively, be very agile and make very accurate decisions.
• 2:16
  When you're a small business, you can afford in some ways because you're so agile.
• 2:20
  And typically you do things very cheaply to make ineffective decisions because you can move fast.
• 2:26
  But if you're Amazon or if you're Google, or if you're a large enterprise business and you decide to strategically go after a new market, you want to be as accurate in that decision making process as possible.
• 2:38
  And data is a real key element to making accurate decisions.
• 2:42
  Yes, I think when I look at those companies and of course the one I didn't mention just now was Apple, but those are like the five companies with the I think a lot of people look at them as the most successful companies of the last decade.
• 2:54
  I think what's very interesting is that you see where they especially have a lot of data is they are really close to the customer, to the end user.
• 3:03
  And I think if you look at it, they are like obsessed about how can we know as much as possible from the end-user.
• 3:10
  And I think that's so interesting to look at that because that's in my opinion how they won if you know your end-users so well and you know what they desire and know what they are longing for.
• 3:20
  Maybe I think there's also a trend the other way around, of course, or running in the other direction that maybe has gone too far.
• 3:28
  But I think that's thinking about that from a company level, how do I know as much as possible from my end-users so I can serve them in the best way?
• 3:37
  That's crucial in your data collection and setting up your processes to get the right data to really make that decision in your business.
• 3:46
  Yeah, you talk about it, maybe it's straying too far the right way.
• 3:49
  We're actually seeing Apple believe that and they're almost now trying to brand themselves in like the most secure technology way, right.
• 3:58
  If you open up an app now, they ask you, do you want to share your data?
• 4:01
  Do you want to share your data while you're using the app. Once?
• 4:04
  Never at all?
• 4:05
  And they're almost repositioning themselves now of those big tech companies as the tech company that's most likely to protect your data.
• 4:14
  Because I think we're now going to have almost that sort of counterculture where yeah, I'm just always going to tick terms and conditions.
• 4:20
  I never really cared and now suddenly I'm getting emails from people that I've never heard of because someone sold my data.
• 4:26
  Now we're actually stepping back the other way and we don't want that to happen.
• 4:31
  What's interesting is how other businesses can learn from that practice and go, okay, well, we're never going to be an Apple, but what can we do to really make informed decisions?
• 4:42
  And you sort of highlighted the key thing.
• 4:44
  There is accuracy, right?
• 4:46
  If I know lots about your buying habits, if I know lots about your technology that you own today, when to refresh, I can make better decisions.
• 4:56
  And there are ways that normal businesses, not just these top 0.0001% businesses, can do similar things.
• 5:04
  Maybe not the scale or the penetration that these businesses can do, but they make very, very informed decisions and that allows them to service end-users in a much better way.
• 5:13
  And I think that's where data is headed. Is it's becoming less about, oh, we're going to take your data because then we can market to you more effectively and sell you more things.
• 5:24
  It's going to become far more about, well no, how can you service me better?
• 5:29
  Which is a different proposition, right?
• 5:32
  It's not just I'm now going to pepper you with products that you don't need or like, and hopefully you're going to buy one.
• 5:37
  It's going to remind you, hang on, you need this.
• 5:40
  Actually, we can tie these products together.
• 5:42
  If you buy this, this and this, this may also support you.
• 5:46
  That understanding of being very, very end-user focused and capturing that information for the highest level of quality.
• 5:53
  Really that's what data is about.
• 5:55
  Yeah.
• 5:56
  So your entire company is focused around the customer, around the end-user, and from there on, you have to get the right data to make the right informed decisions towards the end-user.
• 6:07
  And so to steer your company in the right level, what type of data are you thinking of when you think of decision making?
• 6:14
  Maybe for yourself or in the enterprises where you've worked?
• 6:16
  Yeah, so I think when I look at sort of data-driven decisions, it's more understanding.
• 6:22
  First we're going to look at where we want to go.
• 6:25
  Right?
• 6:26
  We've talked a lot about planning, and for me, a plan comprises three things.
• 6:31
  It's where are we going?
• 6:32
  How are we going to get there and when are we going to get there by?
• 6:35
  If you have those three things, you've got the formation of a pretty good plan.
• 6:41
  Data is what then underpins that decision-making process to say, okay, what is going to make the how are we going to get there very, very, very precise.
• 6:52
  When I do sort of training, when I do management training for people in this business or for my consulting practice, it's one of the things I talk about a lot, which is to say, how are we going to get there?
• 7:01
  It's really easy these days.
• 7:02
  If I want to go from London to Edinburgh, I just put it in Apple Maps and it just works.
• 7:07
  It does the precise calculation for me.
• 7:10
  In fact, it'll tell you how long it takes to get there.
• 7:12
  That's because it's got the data to be able to predict that flow.
• 7:15
  30 years ago, it would be a map.
• 7:16
  I'm not really sure.
• 7:17
  And that's just a key indication on a very simple metaphor of, okay, I now know it's going to take 6 hours.
• 7:24
  This is the exact route I'm going to take.
• 7:26
  These are where I'm able to stop before I'd be unfolding.
• 7:29
  A huge map. Get lost, get stuck.
• 7:31
  That's an example of how data can help with travel.
• 7:34
  But that level of data and predictability is how you do any decent long term planning.
• 7:40
  So if we would split those three questions you were just asking, the first one was where are we going?
• 7:45
  Right, absolutely.
• 7:46
  I think as a company, that's your North Star metric, so it really set a metric where you can align your entire company, where, you know, if we do that well, we are bringing the most value to the market, to the end-user, and therefore we will become successful.
• 8:00
  What do you think about that?
• 8:02
  Yeah, exactly.
• 8:02
  And I think sometimes that North Star metric can be broken down into team goals, individual goals.
• 8:10
  But I very much believe you want individuals to have a number, a target, a goal, and even teams of people all have the same goal because then you get a very, very efficient business.
• 8:26
  We work a lot at Channext with OKRs.
• 8:28
  It's something I'm super passionate about because for me, being able to tie the strands of business together so that each individual knows that they're part of a well-functioning machine, but not just that they're contributing and they are valuable to that growth.
• 8:42
  Not just that the management teams running around making decisions and everyone else sort of just follows, how do I, how does my North Star, my individual North Star metric all roll up together to achieve greatness?
• 8:54
  And then the second question was, how do we get there?
• 8:57
  Right?
• 8:57
  What type of metrics?
• 8:59
  Like, it doesn't have to be specific metrics, but what type of metrics are you looking to measure that?
• 9:05
  Yeah, so I think maybe it's worth talking about lead and lag measures.
• 9:10
  Right?
• 9:10
  So there's a really good book called 4DX and it basically breaks down how you do sort of enterprise business planning, and they break down their goals into two major buckets.
• 9:21
  You've got your lag measure, which is basically your North Star metric.
• 9:24
  It's what are we aiming for?
• 9:26
  And your lead measures are what are the smaller objectives which get me towards that goal?
• 9:33
  So a very typical one might be revenues.
• 9:35
  My lag measure, I want to grow 10 million in the next twelve months.
• 9:38
  Fine.
• 9:38
  My lead measure might be, well, I need to onboard this many customers, I need to expand this many customers.
• 9:44
  And you can start to see, okay, hang on, we've now got a slightly smaller objective that's easier to digest.
• 9:48
  Something like 10 million growth, that's a very hard thing to wrap your head around.
• 9:54
  Then you start to go, well, okay, if I need to recruit ten new customers who do a million each, and our conversions at 30%, well, actually I need to reach out to 30 new customers, right?
• 10:05
  Who's going to put the list together?
• 10:06
  How are we going to reach out to them?
• 10:06
  Is it sales?
• 10:08
  Is it marketing?
• 10:08
  Is it a combination?
• 10:10
  And suddenly you start to break down this long journey from London into Edinburgh into sort of mile-long jumps, that's much easier to manage, not deviate off the path too much.
• 10:22
  And suddenly you get a very complex journey over twelve months, 24 months, 36 months broken down into I know what I'm doing on a daily basis, an hourly basis, a monthly basis, and that allows for much more focus.
• 10:37
  But also if you get a bit lost, you're going to notice very, very quickly.
• 10:42
  And that's what's critical to that.
• 10:44
  How are we going to get there?
• 10:46
  Is breaking a much more complex thing into its small parts.
• 10:50
  Yeah, I think from that perspective.
• 10:52
  Indeed, if I look at lead and lag measures, let's take the revenue as an example.
• 10:58
  Usually we look at lead measures and then we say, oh, let's do pipeline.
• 11:02
  But if I look at that, that's also actually also a lag measure because there's so much you have to do before you get to pipeline.
• 11:10
  And I think that's super interesting what you just said.
• 11:12
  If you think about lead measures, you really want to look at lead measures.
• 11:15
  What can I measure on a daily basis that I already know?
• 11:18
  At the end of a week, in a quarter, where am I at with my end goal?
• 11:23
  And if you look at it from that perspective with your lead measures, then you get so much more control or you feel so much more in control over where you're going and by when you will be getting there.
• 11:33
  I think that's a very strong thing.
• 11:33
  Absolutely.
• 11:36
  It's actually why I'm such a fan of OKRs, which is a sort of another sort of management framework to help strategize and build goals effectively.
• 11:46
  Effectively what we do there is we set a lag measure, we set a goal and we break a yearly goal into quarterly goals, into monthly goals, into weekly goals, into daily goals, to the point now where personally I know if I do all the actions every day, I will hit my goal.
• 12:04
  And you want that almost guaranteed performance metrics.
• 12:08
  And this is why data management in your business is so critical.
• 12:11
  Because if we all do our actions, we guarantee success.
• 12:16
  That's a big statement that most companies can't make and it's something we work very, very hard here to build that framework into people's structure so that we go, okay, I know how this works now.
• 12:27
  I've got X and Y criteria to hit on a daily basis, weekly basis or monthly basis.
• 12:31
  And as long as I'm continuing to make progress on those, we are going to get where we need to be.
• 12:36
  It's never going to be perfect.
• 12:38
  We're always going to take a left turn when we should have taken a right.
• 12:40
  But we're so aware of what those smaller goals are.
• 12:43
  We're able to divert course and get back on track very quickly.
• 12:47
  Yes, I fully agree there.
• 12:48
  And then I think then coming back to your third question by when are we getting there?
• 12:53
  I think that's where you hit the final important part of having data.
• 12:57
  The more data you have, the more you learn, the better you can actually predict by when are we getting there?
• 13:03
  And I think that's crucial if you get the data.
• 13:06
  And I think that's why so many people are looking at how can I get to a more predictable model?
• 13:11
  I think that's your final question, right?
• 13:13
  Like by when are we getting there?
• 13:14
  The better you are at answering that question, the more success you'll have because you can shift your resources of your business in such a good way.
• 13:22
  Absolutely.
• 13:22
  The when I really like because it's actually a metric of ambition which I think most people miss.
• 13:30
  Let's just do the London to Edinburgh journey, let's say that takes 6 hours.
• 13:33
  Well if I said we're going to get there in 5 hours, is that ambitious?
• 13:36
  Yes.
• 13:36
  If I said we're going to get there in 9 hours, is that ambitious?
• 13:40
  No.
• 13:40
  Well I know that because I've got the data.
• 13:43
  Apple Maps tells me how long it should take and yet lots of times businesses arbitrarily set goals and we're going to generate 20% growth.
• 13:52
  Well is that good?
• 13:53
  Well, I don't know because we don't know what we're doing to deliver that plan.
• 13:57
  So better data informs you of how ambitious you can be and so that's where that predictable growth and understanding business performance criteria.
• 14:08
  If we all hit our OKRs this quarter and we do it in ten weeks, well, I know next quarter we actually can set the target slightly further because twelve weeks and a quarter we did it in ten, we've done pretty well.
• 14:22
  And so that allows that data and predictability for me to get a very clear understanding of not just how our top-line number is looking, not just how our North Star metric, but actually all the little actions that all brought together.
• 14:35
  And that's why data running through a business delivers predictable growth, but also delivers the right level ambition so that you can stretch but not straight too far.
• 14:46
  To summarize, where are we going?
• 14:48
  Your North Star metric or your lack metric also like revenue, then how are we getting there?
• 14:54
  So your lead measures like what are we doing on a daily basis to make sure we eventually reach or achieve our North Star metric and then finally, when are we getting there?
• 15:04
  Is getting your business into a predictable model and make it more and more predictable.
• 15:08
  And I think if you have all three of those, that's where you really drive your culture to a data driven.
• 15:14
  Culture 100% and that's where you minimize operational lag, that's where you can get agility at scale and all of those things are polar opposites.
• 15:25
  Right?
• 15:25
  It's very, very rare to hear oh yeah, we've got 100,000 employees and we are agile.
• 15:30
  Well, we know that's true businesses like Amazon managed to pivot incredibly fast through COVID because they are agile at scale.
• 15:38
  And they do that because they are able to drive individual performance metrics that all aggregate to top line performance.
• 15:46
  So now I think we've got a pretty clear understanding of why data is so important and how to shift your company into a data driven mindset, or as I like to say, a data informed mindset.
• 15:56
  But we'll get back to that later.
• 15:58
  But then we've got the channel and I think if there's one thing we know about the channel is that the data part is actually one of the hardest things in general because the data is so scattered.
• 16:08
  We are working with so many partners, so many stakeholders, everybody using their own systems.
• 16:14
  Everything is like manual.
• 16:15
  We need to input it everywhere.
• 16:17
  So where do you start from a general perspective? Or maybe let me rephrase, what do you see as the biggest challenge there?
• 16:23
  And from there let's dive in together.
• 16:25
  Like where do you start to bring it to a more data-driven or data-informed channel?
• 16:32
  Exactly.
• 16:32
  We've touched on Agility and data helps provide Agility because you are aware of barriers much faster if you're really tracking leading indicators closely and suddenly you see, hang on, we're not hitting our leading indicators.
• 16:45
  Not just pipeline, but indicators even like click-through rates on marketing campaigns, you're able to hang on, we might not hit target, but you might notice that in the first or second week of the quarter and you're able to pivot accordingly. That through the channel is really difficult, really hard.
• 17:01
  And in fact, most channel organizations monitor deal reg and that's their first leading indicator.
• 17:07
  Well, frankly, there's a whole load of stuff ahead of deal reg that goes into scaling.
• 17:12
  And so Agility through the channext is very, very hard to manage effectively because you don't have that leading indicator of growth.
• 17:22
  Now, the question I'd like to ask you, and you're very much focused on this data-driven approach, is how can we drive better Agility through the channel given that there's such a black box of data?
• 17:35
  I think the most crucial part there is that there is such a black box of data.
• 17:40
  But why is there such a black box?
• 17:42
  That's what I've been thinking about so much.
• 17:44
  And I think if you look at it from that perspective, all those partners, they are working with so many vendors and there is no integration with each other.
• 17:53
  So everything is manual in the channel.
• 17:55
  So you need to go to a portal where you fill in the data.
• 17:58
  From there on, the vendor has the data, they can do some analysis.
• 18:02
  But there's such a lag in time from when it actually happened until it's like reported.
• 18:08
  And I think that's why we are always running behind on data in the channel.
• 18:13
  And I think if I look at it from that perspective, that's a thing we really need to change.
• 18:17
  Like if you don't have real-time data, that's the strength of having a direct sales approach.
• 18:22
  You have everything in your own systems.
• 18:24
  You know where you're at with marketing, you know where you're at with your pipeline, you know where you're at with customer success.
• 18:30
  And that's the mindset we should have in channel as well.
• 18:33
  Like how do we make sure that we get all those data in a central place so we can actually measure our entire funnel from marketing to sales, to implementation, technical, to customer success and from there and really bring it to the next level and make sure you get everything in one central hub.
• 18:52
  You can do the analysis, but also you really know on a daily basis, like we said at the beginning with your lead indicators, how are we running as a channel organization towards our North Star metric?
• 19:04
  Yeah, exactly.
• 19:04
  And I think we both agree that channel is the hyper reach, right, that allows you to touch the most amount of end-users.
• 19:14
  If we look at Microsoft, 400,000 Global partners, they've got the biggest reach of basically any B2B business.
• 19:21
  Now, if we agree that that has the hyper reach, then if you want to achieve that level of scale and reach, you need to use the channel.
• 19:29
  But then we also agree that the data as a direct model is better and that will help you be more agile and more accurate.
• 19:36
  So the blend of both is somehow deliver that level of data through the channel.
• 19:41
  And it's one of the things that we're working very hard at Channext to help deliver from a marketing perspective.
• 19:46
  But it's how all channel organizations should be thinking.
• 19:48
  How do we get a foot in each camp?
• 19:50
  How do we get the best of both worlds where I'm getting the agility and the focus of a direct organization, but I'm getting it through my channel and also getting that reach.
• 20:00
  And I really think there's such a benefit for all stakeholders in the channel because partners really hate all the manual data input, but they know they need to do it.
• 20:09
  They know they need to do deal registration to get their discounts.
• 20:11
  They know they need to register their leads or follow up on their leads on a regular basis when they receive them.
• 20:17
  They know they need to input their marketing data to get their market development funds on their own bank account, so to say.
• 20:24
  And I think they generally don't like it.
• 20:27
  I've spoken to a partner sales manager and he said, Rick, it's always so hard for me to have my salespeople update our own CRM, let alone have them go to 20 different portals and do deal registration lead management over there.
• 20:43
  So I think there's also a big desire on the partner side to actually make it more easy to have it integrated.
• 20:50
  But how do you do that?
• 20:52
  I think that's a very important thing to think about.
• 20:54
  How can you make it easier for your partners by sharing the data automatically?
• 21:00
  And I think that's what I find really cool also with this ecosystem movement is that we think of it as, hey, we should be closer together, we should be more integrated.
• 21:09
  And I think that's very true for the channel.
• 21:11
  The closer we are getting together, the better we can actually cooperate, and the more value we can provide to the end user and we will get more data, you will get more control, more predictability.
• 21:24
  So really getting that ecosystem, but also a little bit of the direct-sales perspective from a data perspective into channel, I think that's crucial.
• 21:33
  Yeah, and it's putting a partner at the core of that, right, which is a slightly different approach to what you see with lots of vendors.
• 21:43
  The vendors put themselves at the core and they go, right, this is our partner portal or this is our MDF process, or this is our deal registration process.
• 21:51
  And like you said, the partnerships got, sells 20 to 30 different vendors on average.
• 21:56
  They are not going to do that.
• 21:57
  And then that's when you get a lack of data and a lack of predictable growth.
• 22:01
  A really good example is if a partner doesn't do a deal wrench, okay, well that's fine if they close the deal, but if they have been closing a deal a month and then they suddenly decide that they're going to switch and they're going to start selling a competitive brand, well they're just not going to do the deal regs anymore.
• 22:17
  And that's such an important data point, which if you're not tracking it consistently because they never want to do it because it's too complicated, you're not going to have that frontline visibility.
• 22:28
  If that happens at a macro scale, that's the reason that you miss target because you don't have those lead measures.
• 22:34
  And so what's so important and why data is so critical is if you put it at the core of your decision making process, you have to put the partners facilitation mechanism.
• 22:45
  And in my mind that only comes from third-party integrations.
• 22:47
  Right, because you need a software and tool that allows multiple vendors to work with one partner through the same tool to make that life easier for them.
• 22:59
  Because then they're much more likely to do everything you need and everyone wins.
• 23:03
  Yes, I think that everyone wins is so crucial here.
• 23:06
  Like for the partnerships, their life gets easier, the data is shared automatically, they don't have to do it manually anymore.
• 23:14
  And on the vendor side, you will get so much data throughout your entire funnel that you can actually start looking at it and making those data informed decisions that you want to make within your channel.
• 23:25
  And I think one of the hardest things in general that I see is that we are looking at, in my opinion, one of the biggest lack measures there is revenue.
• 23:34
  We take that as a lead measure because it's the only metric we have, the only metric we really have, like the sales-out lists.
• 23:41
  Of course we are generating some metrics when partners are inputting it, but that's only a very small percentage of partners who are actually doing that.
• 23:50
  So all the rest of it is like a complete black box because we only can look at the revenue.
• 23:55
  So we know that we are doing a good job at the end of the week, at the end of the month, or at the end of the quarter, but it's already too late to iterate.
• 24:05
  And sometimes even worse than that.
• 24:06
  I know lots of vendors, right, who maybe have stuck slightly in the past and they're only getting POS reports – point of sale reports – once a quarter.
• 24:14
  So they can see how much revenue they're achieving because the distribution is placing that on the business.
• 24:20
  But they can't see that broken down by partner.
• 24:22
  So at absolute best, they're getting quarterly performance they're getting partner performance reports on a quarterly basis.
• 24:31
  So you're only getting it four times a year.
• 24:33
  Well, how inaccurate a decision process can you make?
• 24:36
  There you go.
• 24:37
  Well they didn't hit this quarter, okay.
• 24:39
  They didn't hit last quarter, okay, we're going to stop prioritizing them.
• 24:42
  Whereas if you had the report on a daily basis, or at least if you were tracking other metrics, you'd be able to say, hang on a second, they stopped selling our product.
• 24:52
  We're able to track that far more cohesively and maybe do something about it.
• 24:56
  Whereas if you only notice after six months they've churned, they're out, they've stopped caring about you as a vendor and they've moved on.
• 25:04
  And that's where that lack of data means that you're so much slower than competitive markets and you're never going to have full control of the channel and full support of the channel in the.
• 25:16
  Way you want it's.
• 25:18
  One of the things I've learned from my father, he always used to say to me, like, Rick, if there's one thing that's really hard in the channel, it's that partners never tell you when they are leaving, so they leave silently.
• 25:31
  That's how he used to say to me.
• 25:33
  And I think that's the thing I've been thinking about a lot from a data perspective.
• 25:37
  Like maybe in revenue was still good that quarter, but in their heart they are leaving.
• 25:42
  It's getting away.
• 25:43
  It's getting away and what's happening there, like what if many of those partners would have that at the same time you don't know that they are leaving.
• 25:49
  There is zero data that alarms you.
• 25:52
  Your partner might be leaving.
• 25:54
  And I think if you get more data, I think that's the next topic that might be interesting to zoom in on a little bit.
• 26:00
  Like what type of metrics do you want to measure?
• 26:03
  What type of metrics could actually give you insight into where a partner is heading?
• 26:09
  Where is the revenue of this partner heading?
• 26:11
  Well, exactly.
• 26:12
  And I think some vendors are trying to put that together.
• 26:18
  Some are doing it very well and some, if not most, are doing it actually quite poorly because they put a lead measure in place.
• 26:26
  Maybe something like partner recruitment as a good leading indicator of scale.
• 26:31
  It's a vanity metric because if you're not tracking all the other little steps from London to Edinburgh.
• 26:38
  If you're only tracking the first mile and the last mile, well, there's 200 miles in between that you're just not doing anything with.
• 26:45
  And that's the problem, is you're going, oh no, hang on, the first mile's gone really well.
• 26:49
  So my assumption is now the last mile will go well.
• 26:51
  It's a very long journey and you're tracking the first thing.
• 26:55
  And the problem is you need gates very, very often through the entire partner recruitment, partner enablement, partner certification, marketing, sales, operation, technical flow.
• 27:05
  You need gates at every juncture so that you can see this partner has been recruited, fully enabled, fully on board, is marketing and is selling and then very regular gates through that sales process.
• 27:17
  So, you know, they're not just closing one deal, but they've got repeatable business.
• 27:22
  And then if there's any deviation from that path, you're able to highlight, flag and drive not just data, but analysis.
• 27:30
  And that analysis drives action.
• 27:31
  So you can flag to a CAM, hang on, by the way, this partner is 15% down on deal registrations and those deal registrations are 15% down on a growing market.
• 27:42
  Something's wrong.
• 27:43
  Okay, we need to go and look at that.
• 27:44
  Or if I'm a sales leader, channel leader globally, or of a large territory.
• 27:50
  Hang on a second.
• 27:51
  These ten partners who all are with this one channel account manager, all of their deal regs are slightly down relative to everyone else.
• 27:59
  And that relative correlation is critical when you're doing data analysis.
• 28:03
  Hang on, does he need more support?
• 28:04
  How can we help him with training?
• 28:05
  How can we help them with development?
• 28:06
  What can we do to assist that channel account manager?
• 28:09
  Ensure that we're getting the right leading measures so that ultimately the lag measure is guaranteed.
• 28:16
  I think I would like to challenge our listeners a little bit.
• 28:18
  Like with every metric you have, think about is it actually making impact?
• 28:23
  I think if I look at it for us, how we are helping our customers, we first were really focused on recruited partners, but then we knew that you can recruit a partner, but if they are not active for you, what's the meaning of it?
• 28:37
  So the objective shifted for all our customers not to recruited partners, but connected partners, which means they integrate their socials, they integrate their website, because that's such a higher form, actually, of commitment from such a partner signing.
• 28:51
  You say it all the time, right?
• 28:52
  Signing a partner agreement is very easy and that's when you have a recruited partner.
• 28:56
  So you need to think, what type of actions do I know my partner needs to take so they will be a successful partner?
• 29:03
  And that's the lead metric you should take because then you're talking about impact instead of input.
• 29:09
  Absolutely.
• 29:09
  And maybe I'm just inherently lazy.
• 29:12
  I want to work with the least amount of partners possible to achieve my goal.
• 29:17
  One it's easier for me.
• 29:19
  Two, the partners get the right level of focus, and three, we're not going to dilute everyone's money because it's all focused through the same list of partnerships.
• 29:26
  I hear vendors talk often.
• 29:29
  We've got a 30% retention rate for partners and they're pleased with that because actually that's above market average.
• 29:36
  And I sit there and go, but that means 60% of your partners, sorry, 70% – can't do maths.
• 29:41
  70% of your partners you've put effort into and nothing has happened.
• 29:47
  Well, the operational lag of that activity is horrendous.
• 29:50
  And so to me that is a vanity metric because like you say, if you're just tracking that first mile and you're really pleased with the volume going through that first mile, well, it's a waste of time because what you actually care about is the 30%.
• 30:03
  And so rather than tracking the 70%, what you should be doing is how do we tighten up that number so we can recruit less partners but grow that percentage?
• 30:15
  And that's where you start to get the difference between input and impact, as you say, because the impactful number is the 30% and yet we're all tracking the 70%.
• 30:25
  Yeah, I think that's very accurate, actually.
• 30:28
  You could look at almost every metric there is and then think about how can you make it more impactful instead of certified partners?
• 30:35
  Maybe how many implementations did they actually do in the last six months and stuff like that.
• 30:41
  Of course it's a combination.
• 30:42
  Like, first you need to be certified before you can start implementing.
• 30:45
  But just doing the certification training means a little compared to actually doing the implementation for customers.
• 30:52
  And I think that's how we started this podcast a little bit.
• 30:55
  The more closer you are to your end-user, the more you know about them together with your partner.
• 31:01
  So what end-user bought what, which partner did the implementation, which contact, or which employee of the partner actually did the implementation?
• 31:11
  That's how you get so much more information and you can really drive your end-users towards the right partners, focus on the right partners and put your own energy into the right partners.
• 31:22
  I think that's crucial to think about with all the important lead measures there are in channel.
• 31:29
  Yeah, it's a data point conversation, right?
• 31:33
  So we're tracking seven data points.
• 31:35
  Okay, well that's nowhere near enough, right.
• 31:39
  I think in our platform alone, and we're a marketing automation tool, so we're just focused on partner recruitment, partner enablement, and then marketing consistency.
• 31:47
  We track 60 data points in that one small element.
• 31:51
  If I'm looking at a full channel journey, that includes partner recruitment, partner enablement, partner certification, sales process, marketing process, operational process renewals, upsells, customer success journeys, I need to be tracking hundreds of data points and a real data scientist to sit there and say, here's where the business is performing well, here's where the business is performing badly.
• 32:15
  I can't tell you the amount of times I've sat on sort of global QBRs and someone's put their hand up and said, guys, we're missing such an opportunity in this area.
• 32:24
  And it's just because no one had analyzed the data effectively to say, hang on, do you know in these ten customers, we've sold a customer success journey upsell, but we've only talked to ten customers about it.
• 32:35
  Oh, we should probably talk to more customers.
• 32:38
  Yeah, that seems blindingly obvious, but unless you've got that level of data, not just the input we've sold the business, but the impact, we've only spoken to ten.
• 32:48
  We've got the lead and the lag.
• 32:50
  It'd be really easy to pat yourself on the back for saying lag measures great, we got ten new deals came over.
• 32:56
  Well, what was the lead measure?
• 32:56
  It was ten.
• 32:57
  So you had 100% conversion, right?
• 33:00
  We should resource that effectively and do that at scale.
• 33:04
  And that's when I talk about data drives accuracy, because how do you talk to a thousand partners and only converted ten?
• 33:09
  That's not something to resource.
• 33:11
  And looking at things like conversion rates, looking at things like click through rates, that's the stuff that you start to really resource effectively and sort of close the loop on the whole journey.
• 33:21
  Why the businesses like Google, Apple, Facebook do so well is because they track clickthrough, they track conversion, they track ROI percentages to the decimal place so that you're able to say, I know exactly where to resource this effectively.
• 33:37
  I know exactly what campaign is performing.
• 33:40
  I'm doing A/B testing.
• 33:41
  I'm consistently iterating my process to constantly polish it.
• 33:45
  And that's what drives success.
• 33:48
  And I think while you were saying this is why I had to think about some conversations I had with channel people where they pushed back on it a little bit as well, like, yeah, but channel is a human to human business.
• 33:59
  It's about relationships.
• 34:01
  You can't put that into data.
• 34:03
  And I think that's why what we've been talking about a lot recently, why we love the word data-informed versus data-driven so much.
• 34:10
  Yes, you don't have to go like Google and so extreme, but what you really want is having the same availability of data and seeing it so you get the insights you need combined with the human knowledge you have and the human relationships you have to make the right decisions.
• 34:27
  And I think that's crucial in general.
• 34:28
  Get the data and the insights, and from there on, make sure you know the customer or sorry, the partner.
• 34:35
  In this case, you know them the best as a channel person.
• 34:39
  And from that perspective, if you can combine that with the right data, it will allow you to make the best decisions both for your channel team and the company as the partner combined.
• 34:50
  Yeah, look, individual talent is an amazing thing, right?
• 34:54
  And you win and lose by individual talent.
• 34:57
  But at a macro level, at a global level, you don't you want to lose by data and process?
• 35:03
  And so that's where if you really want to scale, the number one reasons that start ups fail at scale-up is because they've got three or four great people and they aren't able to take the skill set that is in those people and cascade it through a wider organization.
• 35:19
  And so they ultimately hit a ceiling that they can't grow past.
• 35:22
  And so yeah, I've spoken, I'm friends with some unbelievable salespeople.
• 35:28
  If I could dilute their talent into a drink and sell it, I'd be the richest man live.
• 35:32
  They are unbelievable.
• 35:34
  But you can never just hire those people.
• 35:36
  And so really what data helps you do is scale past individual talent.
• 35:42
  Now those people will always be the top performers, which is fantastic.
• 35:45
  And you always want to be on the hunt for those types of people.
• 35:48
  But if you want to have a channel organization of 700 people, you aren't recruiting 700 people of that caliber.
• 35:54
  And data and process help inform decisions and catch things very quickly so that maybe people that don't have that level of talent are still able to keep very close to them.
• 36:05
  Yeah, I fully agree there.
• 36:05
  I think if you look at it from that perspective, that's how you will win and succeed.
• 36:11
  And that's what we're all thinking about the entire day as channel people.
• 36:14
  Like how can we make everyone in our channel ecosystem as successful as possible?
• 36:19
  And that's what we need to do, I think to maybe round this podcast up a little bit.
• 36:25
  I think what would be interesting, we've spoken a lot about impact metrics as well, and lead and lag metrics.
• 36:33
  Sorry, from that perspective, might it be a nice idea that you tell us the last lead and left metric or impact metric from a sales perspective to take it away for customers.
• 36:46
  And I'll do it from a marketing perspective.
• 36:49
  And as usual, marketing comes before sales.
• 36:52
  Maybe I can start and then hand it over to you.
• 36:55
  And I think what's very interesting to think about is really, okay, how can I make my input metrics that I'm currently collecting from my partners when it comes to marketing, to impact metrics?
• 37:06
  And the examples I always use are like the things we are measuring right now is how often was content downloaded, how many partners requested MDF and how much MDF, how many partners created co-branded collateral.
• 37:23
  And if I think about all those metrics, yes, they are nice metrics because you can see is a partner like, am I top of mind for my partner?
• 37:32
  But there is zero, there is zero chance where you actually know what was the effect of that action.
• 37:40
  And I think that's what's so interesting to think about from a marketing perspective.
• 37:43
  How can I shift content downloads to actual partner marketing participation?
• 37:49
  So how many partners not just downloaded it, but actually published it to their socials to their website, shared it with the end-user, or created co-branded collateral, not just how many created it, but how many end users that you actually reach.
• 38:05
  So by integrating with the systems of your partners, making sure that you know, how many end-users did we reach, what was the engagement?
• 38:12
  Or for example, with the MDF request, not just how much MDF was requested, but have real-time campaign results, being able as a true lead measure to see on a daily basis how are we performing with all our partners and our marketing activities.
• 38:27
  I think that's what really makes you successful in this and that's what you need to think about from a marketing perspective.
• 38:33
  How can I shift it into that?
• 38:35
  And then finally, of course, very important, how can I get those lead measures into a lag measure as a marketing team that actually correlates to revenue?
• 38:45
  So your lag measure is always revenue eventually, like the top North Star metric is revenue also as a marketer.
• 38:51
  But where do we shift that?
• 38:53
  And I think that's where we really believe, both of us, and we at Channext, that you need to drive towards a partner marketing engagement score.
• 38:59
  So how engaged is a partner, how good is their end user penetration?
• 39:04
  So how many endusers are they reaching?
• 39:06
  And that combined really tells you what the effect will be on the revenue level.
• 39:10
  And if you make that analysis, you can actually drive those lead metrics on a daily basis and monitor the lag metric all the time.
• 39:20
  How are we attributing to or contributing to revenue?
• 39:25
  What you've just described there, as you described, where are we going, how are we going to get there and when are we going to get there by?
• 39:31
  And how are we going to get there is broken down into each of its composite stages.
• 39:36
  So you're not just tracking downloads, you're tracking download...
• 39:39
  And then the utilization, and then the click through, and then the engagement and then how that drives up into revenue..
• 39:45
  You've actually gated every single part of the entire journey.
• 39:49
  And that's really what data-driven analysis does, is you start to analyze the whole journey, the whole way through the journey.
• 39:57
  And I think that's critical.
• 39:58
  And I would do the same thing from a sales perspective, right, so we can break channel sales into two key buckets, it's end-user and partners.
• 40:05
  So it's partner recruitment enablement certification.
• 40:08
  And I would put data points for each of those.
• 40:10
  It's not just recruited partners.
• 40:12
  Then we track the conversion into, right, have they signed to the partner portal?
• 40:16
  Have they downloaded the content?
• 40:17
  Have they done their certifications?
• 40:17
  Right, that gives you a base level of understanding.
• 40:21
  And then there's the engagement gap to go, right now we want to bring in a marketing strategy along the lines of what you've talked about and that's going to pivot us from a partner journey into an end user journey.
• 40:31
  So then we can start to say how many end-users have we actually reached out to from a marketing perspective and a sales perspective?
• 40:35
  Not just deal registrations.
• 40:39
  That's the bit before deal registrations.
• 40:41
  And that's where, you know, we're working very hard to drive a tighter alignment between vendors and partners to say, right, how do we track marketing and sales engagement pre deal registration?
• 40:52
  Because the journey doesn't start at deal registration.
• 40:55
  The journey starts at mapping, white space analysis, account plans, call outs, those processes that are going to get our vendor name in front of end-users, then track deal registration.
• 41:07
  How that goes through a forecast.
• 41:09
  Right now, most deal registrations are deal registration, closed one, closed last.
• 41:13
  That's that you're tracking the first mile, the last mile.
• 41:16
  What about all the stages in between?
• 41:17
  How's that sales cycle going?
• 41:19
  You would never let it run like that if it was your customer journey and you were direct.
• 41:24
  So work with the partner to do the same gating mechanism.
• 41:27
  What's the probability of closure?
• 41:28
  What are the steps to advance that progress?
• 41:30
  Those are the data points that you can start to track and then you see very quickly, right, we're not just tracking the first and last mile, we've gated the entire journey and we are working with our partners in conjunction to drive them through that journey for mutual success.
• 41:45
  Yeah, I really like the one.
• 41:47
  I think indeed, if you look at it like that, then that will really bring you towards success.
• 41:52
  And that's what we need, I think, just because I like your three questions so much.
• 41:57
  One more time to summarize it for the listener.
• 42:00
  Where are you going?
• 42:01
  How are you getting there?
• 42:03
  And by when will you get there?
• 42:05
  Focus on that in your channel team, in your channel organization, and that's how you will drive towards a data-informed channel culture.
• 42:12
  And I think thank you for listening again this time. Please share it with your colleagues as well if you liked it or let Alex or me know.
• 42:20
  We're very active on LinkedIn as well.
• 42:22
  What you think about it and hope to you see you next time.