Everyone, welcome back to another episode of Stacking Growth. Your host here at Eben Hughes, VP of the Man to Refine Labs, joined by my kick-ass co-host, Ashley. Hi, everyone. My name is Ashley Lewin, and I'm a senior director of the Man's Gen here at Refine Labs, and I'm happy to have a conversation with you all.
Yeah, I'm excited to have this one. A national and I have spent a lot of time behind the scenes talking about this topic, and we decided that it was one that can help a lot of marketers, and even if you aren't a marketer out there, really think about data. In this episode, we'll really go into why data can be so overwhelming, what are some of the scenarios that create this overwhelmingness of the data, what are some actionable steps to take back control, how we can use those steps to tell a story, how we can just get more control of the data that's in front of us, because it's marketers. The word data is terrifying.
We either love it, we hate it, but it's never a positive thing because we just have too much of our disposal. We just thought this would be an awesome time to have a conversation riff back and forth, Ashley, just to learn a little bit more about how you handled some of this, data challenges and just go from there. I'd say with that, let's dive in. All right, let's go.
The biggest piece is I feel like as marketers, we feel overwhelmed with data, especially with just the number of data points available to us, knowing which one's the most important or why it matters if we should be opinionated about it. There's this concept with data around a signal and a noise. So noise, if you think of a frequency with an old TV, it's all the static. And then the signal is a strong line that tells what we're looking for is the story.
This is the key thing we're looking for. But often as marketers, we're trying to grab on to all the noise and all the data points because we're trying to tell a story. So I think one thing, let's take off this conversation really well, Evan. I would love to know a little bit about some scenarios you've seen where we almost get data analysis for Alice's from having too much data points.
What's been some experiences that you've seen and talking through it? Yeah, some experiences where there's been too much data and it's been overwhelming. Yeah. Yeah, I've mentioned that, hey, to admit, this happens more often than not, because I just get, I love data, kind of a data nerd in a sense.
I'm getting all the insights, but what I find myself is like one clear, I was putting together a QBR recently for a client and I was trying to get to an end goal, but I didn't know where I wanted to go. That makes sense. So like I knew that I had to tell something or I had some sort of like information to share, but I realized I didn't have like a cohesive outline. So I was aggregating data, pivot tables, charts, all of this stuff, and like this awesome, essentially this document, but I took a step back and I looked at it and I was like, what the hell is on this?
Right? Like I didn't least what I was trying to do through what I was digging for, because I was just kind of down those rabbit holes of data telling. So I was just one example recently where I was just like, gosh, being it, I'm going to save a lot of time. And I think that's what sparked this conversation behind the scenes is like, how do we get in these ruts where us as marketers actually jeopardize our time available to do too much analysis versus proactive strategic guidance?
I totally empathize with this and I come across it more than I care to admit. So you're definitely not alone there. And I have a hunch. Most of us are in the same boat together.
I've had an experience too. I think we get this a lot too when we're asking questions. Like, I've been in-house where a C-suite is like, what happened this month? Or why was it up or why was it down?
So I feel like a lot of times marketers get asked to go down these data rabbit holes too. And you're like, oh, gosh, what do I look for? Like, how do I tell this story? So I remember just going and trying to, I think I put like every single filter imaginable, just trying to get any data because you're trying to answer that question.
And I think when you're answering that question without a strong process of like a hypothesis, which we'll get into a little bit because you have a really great way of really kind of dissecting this and creating a structure to it. But without having that hypothesis, you're just stretching for anything. And you're like, I don't know, I'm going to look for everything. Maybe it was this lead source.
Maybe we weren't spending as much money. Maybe the ACV was down or whatever it is instead of being very streamlined with it. So that's kind of a story that I've had with it. I think that we've all come across of trying to figure out why a month was up or down, especially from other leaders in the company.
So I think we all could go on for hours on kind of our experiences with getting lost in data or the requests that we receive. I would also love to hear from your point of view, Evan, of how you storytellers, I feel like you do a really good job of this. I think this is the next critical point of it is like, how do we storytellers because numbers by themselves are graphs by themselves mean nothing. And it doesn't connect with anybody.
Humans connect with stories. It's how we interpret things. It's how we digest it, memorize it, et cetera. So I would love to hear kind of how you go about this modata storytelling approach.
Yeah, I know. I think it's a good question. And I think you teed it up nicely too as you're example of just being overwhelmed with data because you're fielded with questions, right? So people only have a small portion of the insights when they typically are reaching out saying, hey, I noticed this trend or this looks off month over month.
And that really kind of usually is what unravels these rabbit holes. And it's the lack of a narrative that really guides people in the right direction. And I think this is one of those big pieces too, as we talk about this concept of storytelling with data. And it's absolutely like should be the framework or the beginning and end of like any sort of engagement, whether it's in-house or external clients.
What is the story you're trying to tell? But then also taking that step back and realizing that the best stories are told one chapter at a time. We can't tell the whole story in one meeting. We shouldn't ever tell the whole story in one meeting.
It's like, what is the chapter that we're going to focus on in this discussion? And then how do those chapters lace together to tell that whole story that strings the client along? And I think that that's one piece that we often miss, right? Because we get asked these questions and we just go to focus on that.
We don't zoom back out and kind of look at the storytelling component in the story. This idea of where we're trying to take the audience with us as marketers should be creative. Let's figure out a great way to get them to follow along and believe and trust in what we're helping them achieve. Oh, I love that.
Speaking it right to my ears there, your music, my ears, that's the right phrase right there. But yeah, I can't reiterate that enough is like the importance of the storytelling with it and not overwhelming your audience with data either. I think that's a huge mistake. I see a lot of times and I'm guilty of it still to the say of putting a bunch of numbers up on like a deck of what when you're presenting and trying to have a conversation and gosh, if you look at it, I can read clients, I can read the zoom room per se when I'm presenting something like that to clients and I see glossy eyes, which I probably would have the same glossy eyes on the other side.
It's just too much and it's not bite size. I don't understand it. I think as humans, we want to like read everything in front of us too. So we're trying to scan everything.
I think also not having like a benchmark of is this good? Is it bad? What's the takeaway here? If I just see a bunch of numbers next to it, that's not enough.
So I think as marketers, like it's our job to look at an array of data on a table and take the insights from it. So taking the science of the data that is available to us in the art, which is analyzing and storytelling from there. So I know, okay, so we have the data storytelling. We know it's so important.
I think we're all really jazzed about being able to tell stories a little bit better way. So before we can tell the story, we need to have a structure to how we talk about data or how we look for data. Otherwise, again, we'll end up in these rabbit holes, which whenever I say rabbit holes, I think of Allison and Wonderland. This whole conversation, I'm just thinking of us falling down the hole like Allison Wonderland, which is very true of how I feel with rabbit holes.
Good times. But I would love to hear your thoughts on how you go about this in a structured way, so keep yourself in line and make sure you don't keep falling like Alice. Yeah. That's a great analogy.
I think there's a couple of things that you just want to highlight there too is this isn't an instance where you shouldn't have the data available, right? It's just what data is mindful to share. So we should have as much as we can in terms of if it isn't parallel to the story we're trying to tell. It's just how are you delivering those messages?
What are you expecting the audience to engage with? Are you teasing up subsequent weeks of meetings or engagements with a specific member? So it's like having that when there's a lack of process to what you're spoke to, a lack of order of operations, that's where you really start to fumble and you catch yourself just regurgitating data vomit. And it's like this analysis for Alice is where you aren't sure where you're going.
And the next thing you know, you come up for it and you're like, wait, I just spoke 25 minutes at a word wall to my peers and they have no idea what I said and where do I? And then you think that that's something that, you know, I haven't mastered it, but I very much have tried to create like this order of operations flywheel for myself that I think is such a helpful approach to avoiding this data analysis for Alice is kind of taking that step back and being like, what's the what, the how, the why, and using that to kind of discontinue the evolution. So I'll just kind of give a quick example here is how I've always approached this is I established the what up front. I think that that to me is the most important.
What the hell am I trying to say? What the hell am I trying to do? Why am I trying to do it? But I write that down.
So I have Google She worked off whatever you use, just kind of scratch pad. What am I trying to accomplish for the next block of time? So let's say I have an open hour and I want to come up with, I want to understand why opportunities are declining month over month from an inbound web source or high-end to hand raisers for an example. The next is like I have to develop a hypothesis before I even start that.
So I want to be like, okay, I have to know that what I'm trying to change. So here's the what of what I'm trying to accomplish. But the hypothesis there. So let's say we're seeing a decline in opportunities month over month.
My hypothesis is they've had high turn on the sales side. So sales cycles are getting longer. We're seeing an exponential gap month over month. So then I know what data to go look for.
So then I started the top of the what and then one or two hypothesis to keep me laser focused on it and then I go and try to prove or disprove that. I think that that's like the one thing that we miss as marketers is we get so far down in that rabbit hole that we 12 other questions pop up and we forget our initial hypothesis and we don't go through that process of like, oh, I was wrong. Okay, stop reset. Here's the new what here's new hypothesis and so forth.
So long when to answer, but just kind of a process that's really helped me kind of keep organized and focused. No, I feel like that was absolutely beautiful. Like if you're listening to this episode, I would highly encourage you to pause right now if you didn't take notes on that section and grab a pen and paper, open your notes app and your phone and go back and write all those down because I find that so incredibly powerful and helpful even to this day of really slowing down and figuring out and going through that operations like how you laid it out right there and figuring out what the angle is, you know, what are you trying to, you know, prove? I think another thing that's really interesting in there too is do you find any like, do you find yourself trying to prove yourself right instead of being objective to the data to like, how do you overcome that?
Because I'm guilty of it. You know, I think we're all guilty of it. Like we worked really hard on this campaign. We want to see the results.
We start to manipulate the data to you instead of like, I think it's really powerful and someone can say like, Hey, we tried this. It didn't work. Here is why here's the lessons we learned. Here's what we're going to do to move forward.
I actually find that really powerful and inspiring and some of the best leaders can admit that. So I feel like that would be something that I want to like hear your thoughts on a little bit more. How do you avoid what is the confirmation bias when you're looking into data and you know, how do you prevent that? Yeah.
No, I think that's a fantastic question because we are all guilty of we know what levers we want to pull to tell the story or we start knowing the data well enough to be able to craft that narrative. There's two parts right there's crafting a true honest narrative based on what your findings are and there's crafting a subjective narrative based on like how you're interpreting it or what you're trying to tell. I think that that's why like when I have that three step framework for almost every data that I do, if that hypothesis component that I will not let myself deviate from. So I think that if I start to then that's where I catch myself like, oh, I'm kind of maybe pushing the envelope a little bit or thinking a bit differently about the data.
So I think holding yourself accountable to like the what and the why is going to help you continue to be a better, you know, kind of better data analysis too. Like you're able to kind of see both sides of the income present that to the client and prove yourself wrong. Sometimes those are the best conversations. Like I went in there with this assumption.
I was dead wrong. This is what I found. But I think that that shows like right our excitement of the data and how we can never guess, right? So that's based on signals that we should never just guess and point the data in direction we need to go.
I love it. Another kind of place that I have kind of ran into with like almost a roadblocks per say is distrust in data too, especially when you're presenting this story or the findings cross departmentally too. So that's something that I have learned in my past is like it may feel unnecessary at the time, but documentation around your data points and where you pull it and getting almost buy in from everyone of like, yes, when we say I'm going to boil this down to something very simple, but I've seen it before, revenue. What data point is true?
Do we all agree upon it? Whatever it may be. Even linking your reports I think is very important too. So if someone has a question in the data, they can click in and they can see I love to hyperlink my reports.
I save a ton in Salesforce in my folder so that anyone can go in and see it when I'm presenting a report. So I do say like that's something I also want to call out. I've seen this a little bit of a roadblock or two, especially with data and getting lost in data is this like, oh, that's not right or we need to pull it this way or whatever it may be. And you could be spending extra time because you're all pulling it differently or you're trying to prove that it's correct.
So even like from an out, you know, zooming out from just this conversation, if you don't have an agreed upon documented data process where everyone can reference it and they can see how things are mapped in your CRM or where you pull it. Maybe like when you mean X, you pull it from Google Analytics when you say X is pulled from Salesforce, maybe it's more keto, how so whatever it may be. I think that is super important just to have that because it will have a lot of foundational work in order to be able to get that buy into. So that's just something I wanted to call out there.
I think that absolutely important component to all of this, right? Documentation from beginning to end, but also post the analysis, right? So we find ourselves as much as we hate to admit it, we're often trying to answer the same question in a different way that it was asked every time. So it's like, how do we create a structure behind that?
I think you nailed it right there, right? It's like documenting the key process. If there's a common theme and a question coming up, we're probably not explaining it too well or perhaps we're making assumptions that we shouldn't. So like outlining how you pull the data, why you pull the data, not only is it reference for you too, when you go back to your monthly business reviews or quarterly business reviews, just having that ability to just be like, okay, this is how we pull an opportunity analysis, this is how we pull the conversion funnel analysis, you're able to repeatable the scale that.
So then you're not spending hours in the data reminding the same conversations. Let's say something changes and then you catch yourself like another hour because you're trying to create the same data that you had in the last slide. It's just this snowball effect. So I think that's so important too.
I'm glad that brought that up. I'm sure the audience will appreciate that. For sure. A phrase on old mentor, one of my favorite mentors, Yisa Seidamy, is if you get the same question more than twice, that's on you because you're not communicating it correctly.
Something in your delivery or how you're communicating it isn't landing. I think that's very true to, you know, when we talk about data and the reports that we're answering. So if you're constantly getting a similar question of like, where did you find this or are we sure that's correct or how do you do that? I would take that on you as a leader to recognize that maybe something in the delivery is missing and look for ways to discuss it a little bit differently or documented.
Another favorite thing I like to do and this feels very simple and almost like elementary, I guess, is to document like in a word document or whatever you use, maybe use notion or whatever it may be of here's exactly how I go about finding an analysis on, you know, like revenue. Let's say whenever I am trying to solve for why revenue is up or down, here are the main data points I look for immediately. And then I'll see like a layer deeper on each one of them of like, okay, if that one is down and you want to know why, then you go into the lead source or you go into here, maybe look at the win rate or whatever it may be, I think having those in a top down approach is really, really critical as a marketing leader so that your team can pull these by themselves at any time. It's a really great teaching moment to upskill your team, even to keep yourself and check a little bit too.
So when you go into these questions of why is something up or down or I want to figure out more about this or what's the story here, you have a repeatable process that we've been chatting through here. It's just in a very laid out way, documented that, you know, you can reference it really fast. Oftentimes we have a million things on our minds so that leaves no room for air and you can almost like reframe and refocus from there too. So something helpful that I have found with leading teams.
I think that's great. Documentation should be in chapter one, right? Like document what you're trying to do, trying to tie it all back together as we think about this story, moving the story along and moving the audience along as they, you know, we're sharing data with them. So let's just make sure that we have that foundation and brand work up front and think that that's an important key.
This has been an awesome conversation. You know, I think that this is one that we're all guilty of getting caught in these rabbit holes and kind of going down these areas where we think we're finding the right data, but when we kind of come up and take that zoom out, that's not part of the story or that's not part of the narrative that we're trying to tell. So I really am glad that you know, kind of how important a process is to, you know, and having a process establishing a process and I think really that order of operations fly will like I encourage listeners to go try that bookmark that a little bit, just kind of establish the what, write it down. Don't just have it in your head because we can always change that story.
What are one or two hypothesis, prove or disprove and just repeat, keep repeating that in time blocks in your calendar until you get a good rhythm and process and I think that that'll really, really help save some time and get us to be more strategic about our data holes. Totally agree. And if you come across any other like tidbits, you know, that you have found works really well from your team, Evan and I would love to hear from you on LinkedIn too. So feel free to tag us or DM us, always love to have this conversation.
And yeah, this was fun. I could talk about data all day long. Yeah, I know. But actually, I'm definitely happy to hear what the viewers have to say.
Listeners have to say. So do reach out to us directly. I think that's great. Thank you for taking the time to chat.
Actually, always great and we'll do more of these to come. Thanks, everyone. All right. Okay.
Bye. Bye.