Insightly_Ep19
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Jordan Walker: [00:00:00] Welcome back, everyone. If you are one of our amazing repeat listeners, welcome back to the Insightly podcast. If you are brand new to Insightly, welcome, welcome. We are so glad to have you. You probably stumbled upon our podcast because you have interest in learning more about data and how to utilize it at either within the role that you play in your organization.

Maybe [00:01:00] you're exploring a data career. Maybe you're a business owner and you understand that data is a competitive advantage and you want to have better, foundations to guide your team to implementing. And so if you're in any of those categories, you're in the right place, this is exactly for you.

If, like I said, if this is your first time, Alyssa and I really started this podcast so that we could take some of our conference room conversations into a format and share it with others. So hopefully you find it valuable. Today though, we have talked about a lot of different areas within business data and how you can activate it, questions you should ask your data.

We've interviewed some really cool people who work within data and cybersecurity and analytics. But today, I want to kind of kick it back to some data terminology 101. As companies really are looking to implement more of [00:02:00] a data first or using data to help them make better business decisions,

Like with anything, this realm comes with a lot of new terms, it comes with acronyms, and sometimes these acronyms can kind of turn into buzzwords where we're not actually using them correctly.

And so my goal for today's episode is to break down some of those most commonly used terms that we tend to hear and that we also use when we're talking to people. Just to kind of give like a good baseline so that if you're having conversations with your team members that you can start getting into the practice of using the same terms in the right way so that it's not, okay, well, what do you mean by that kind of a thing?

I want to act, before we get into some like really foundational terms though, I actually want to start out with two acronyms that I think get thrown around a whole lot. in our worlds, [00:03:00] and one of these acronyms we each deal with a lot more than the other.

Alyssa McGinn: That's perfect.

Jordan Walker: So, the two terms that everyone probably hears a lot, first one, key performance indicators, or KPIs. We all want to know, what's your KPI? How are we measuring this? What's the KPI, right? The other one that I actually, like, really love, because it's just, I don't know, the acronym's funny to me. But it's Objectives and Key Results, or OKRs, and a lot of sales organizations especially have OKRs for, you know, revenue goals and whatnot, and so we hear KPI and OKR pretty frequently.

Let me break down what these are first. Key Performance Indicators. This is something that I deal with a lot more in my business because Key Performance Indicators are really measuring. progress toward a specific activity or a specific goal. KPI is really [00:04:00] supposed to be used for what are the indicators to let us know if this activity or this goal is heading in the right direction or we're heading in the right direction.

Alyssa McGinn: Hence the word indicator.

Jordan Walker: Indicator, yes, like little pulse check, right? Objectives and key results, OKRs, those are the bigger business goals.

So the difference is OKR is really the big umbrella. As a company, what do we need to achieve? What are the big milestones that we're really looking at?

Then within that, we're gonna have activities and tactics and strategies in order to help achieve those goals, but within those tactics and strategies, we have KPIs to know how those are performing.

Alyssa McGinn: performing.

Yeah, I have a visual in my head

Jordan Walker: Okay, share.

Alyssa McGinn: So the way that I think about it is just like with anything, it's a funnel, right? So it's like, we think about objectives and actually just the O and OKR, the [00:05:00] objectives as the North

Jordan Walker: North Star,

Alyssa McGinn: like where is the business trying to go?

And like, I heard someone say recently, priority is a single word.

Like what is your priority? It's not your, okay, you have a list of priorities. What is the priority?

Jordan Walker: Yeah, what's the one thing that you're not going to be happy about if it doesn't happen?

The

Alyssa McGinn: And so it's like the objective is the North Star of the business. It is the priority and so you go from that and then Then you go to key results.

I think that's honestly those are together, but it's the next level So what are gonna be the the things that are gonna be moving to get you to the North

Jordan Walker: Star? Yeah, so like, increase in sales revenue, increase in net promoter score, customer satisfaction.

Which

Alyssa McGinn: would be, probably, that would roll

Jordan Walker: Whatever the objective is.

Grow Grow this segment of the

Alyssa McGinn: business,

Yeah.

So, you have the North Star, you have the key results, then you have the key performance indicators under the key [00:06:00] results, and then you have the metrics that roll up to the

KPIs,~ ~

and then you have the raw data, what metrics do we need to pull from that?

How do we create KPIs out of multiple metrics? How does that lead to our key results to our North Star

Jordan Walker: Oh my gosh, yes well that's really perfect because we're going to get into like what is data actually and what is a metric here in a second So, ho pin that thought but I love the funnel aspect so like if we were thinking of what are what would be some typical just like objectives at the top of the funnel.

Alyssa McGinn: Create a new product go into a new

territory.

Jordan Walker: So then key results could be things like?

Alyssa McGinn: Could be like

identify a headquarters in the new territory that we want. Could be something like we want to go to this new business line. We're going to need to start the business line.

Jordan Walker: So if the [00:07:00] objective is okay we'ree gonna launch a new

product. Yeah.

Then, in the key results, you might have some of those tactics outlined, but then maybe some of the key results are. We're actually going to kind of measure this against, this is the budget that we're going to invest in launching this new product. Then you get into, okay, well, as we're launching the product, what are the strategies that we're going to use to launch it?

And then that's where you get into those key performance indicators of, okay.

Alyssa McGinn: okay. Tracking the strategy's progress, right. Yeah. It's, and I feel like key results is tricky because that can be very

Jordan Walker: Yeah. that's where I see the SMART goals, that format coming into play. It's not like just a data point that you're trying to reach. Like we're going to invest 10 percent of our marketing budget to launch this new product line.

That 10 percent of the marketing budget is something within the key results that we're going to [00:08:00] look at, but you have to have like that SMART goal

Alyssa McGinn: like that

Jordan Walker: point in

the

Alyssa McGinn: should tell everyone what that stands for.

Jordan Walker: Specific, measurable, attainable,

Alyssa McGinn: I thought it was actionable. Is it attainable?

Jordan Walker: Well, I use

attainable. Like,

actionable also work, yeah, because like you have attainable and actionable I think can kind of work in both ways because it's can't, is this actually something that you can do?

Can you take action on it and what are, what's the action that you're gonna take on it? Relevant, is it, is this even a goal that we need to be going

after and timely?

Alyssa McGinn: Nice. Smart. Smart. Love it.

Jordan Walker: So KPIs, OKRs, I think we've got a good foundation there. Now let's take it back to like some of those like really basic levels and you hit on it in the funnel of we have metrics and then we have raw data.

Let's talk about data first because I think data gets utilized as like this big, I mean we [00:09:00] use it a lot as like a general term. We're here talking about data.

Alyssa McGinn: Right.

Right

Jordan Walker: In reality though, we are talking more about like metrics and analytics and business intelligence and things like that because data at the end of the day is really just the raw facts and figures.

One of the points that I think is important to note in this is that sometimes we think that data is only numbers. Data is text, it's reviews, it's testimonials, and that's where we start getting into that breakdown of. Is it qualitative

Alyssa McGinn: data,

or

is

Jordan Walker: it quantitative data?

But

Alyssa McGinn: also quantitative data isn't always only numbers

Is it a A blurb? Is it a review? I would say that's more qualitative. Is it a name, even a location? Is it something that is like singular and factual that can be organized?

Jordan Walker: That

Alyssa McGinn: is more

quantitative.

But I think that is a good point because we,

I don't know, data

[00:10:00] does just include so much, but I think a lot of times people that don't deal with the raw data don't, associate data with KPIs or with metrics

Jordan Walker: I think the thing within, like when you're thinking about what can data be, it's really, if you can process and organize it to find

trends, it's a data point. That can be

used. And then you kind of get into like the breakdown of is it structured data versus unstructured data? And the only difference between that is Structured can be organized in a predefined format.

So you're thinking, okay I've got a spreadsheet full of information or I can download a table or You know something like that to then upload and connect to an analytics

Alyssa McGinn: platform

Yeah, it is what it sounds like, you know, like if it's structured, it's normally going to be in like a database format. So it's like in a, in tables, if it's unstructured, it's like, imagine like pulling data off of a PDF. And it's just, there's [00:11:00] no Rhyme or Reason or Structure to it, it's just like

Jordan Walker: Typically that's where you see unstructured data aligning with like images, audio, text, like some of those kind of formats that Yes, you can download a transcript, but it's not in zeros and ones, in that numerical format. And so you're looking at it more from a thematic level of things, like what am I trying to actually analyze here with words and images, and am I trying to pull sentiment out of this?

Am I trying to find messaging themes within this? It's just a different way of how can you actually organize it to then,

Alyssa McGinn: And now there's like cool, we don't have to get into this, but there's cool AI tools that will, scan

Jordan Walker: data. Uh huh. Oh, I have a tool that I absolutely love using for competitive analysis and messaging analysis.

You can also use it for, like, user experience studies and product launches and things like that, but what I've been doing with it [00:12:00] is pulling in a lot of, testimonials and reviews about products to then pull out themes of Well, what actions are people indicating that they're taking in this review?

Like typically what we hear is people first call and then they peer review and then, you know, so you're kind of getting an overview of, okay, what are common steps in the user journey based on hundreds of different reviews and tests. It's super cool, but it's very, it's unstructured data that I am theming.

Alyssa McGinn: Theming! Love that.

Jordan Walker: AI. Okay, so we have data. Then you get metrics and Metrics are the specific measurements and calculations that actually come from that data So maybe you have the sales transactions by company Let's say the metric would maybe be your overall sales percentage, or your revenue growth, or conversion rate based on a set of data.

Yeah, Yeah,

Alyssa McGinn: and I [00:13:00] think there's a confusing point here because in a previous episode we talked about metric mapping.

Jordan Walker: Mmm

Alyssa McGinn: so if you want to know

revenue growth

there's another layer down where it's like when I think about metrics it's like if the KPI is an increased revenue growth what we have to go down to How are we calculating revenue growth?

And then what are the data points? So maybe data point is like

Jordan Walker: points,

Alyssa McGinn: metric before raw data.

Jordan Walker: is

Alyssa McGinn: that slots in but.

Jordan Walker: Well, like I think that's. But yeah so like metric is still the calculation.

Alyssa McGinn: Sure,

Jordan Walker: all of those, you know, so like think of, okay, I'm trying to see who are my most profitable customers, right?

In a matrix, let's say, well, that is pulling a calculation based off of a set of indicators that we're measuring essentially. So, okay, well what would we label as a most profitable or most valuable customer? [00:14:00] Not just revenue, but maybe it's like longevity of being a customer, repeat or frequency of orders or touch points, you know, so you've got a lot of like data that you can pull in to then help you understand what is the most valuable customer to us within our organization.

And that might look different for different businesses, like, cause it could be maybe those that are more engaged with us. in a non sales way actually are more profitable for us in the long run, but that's where if you listen to episode 19 where we're talking about asking questions of your data, this is kind of that process of where if you are trying to figure out what, how do we identify what that looks like, this is when you start asking those questions to figure out what are those indicators for you, and maybe you go in a couple of different directions to see what that actually means.

Alyssa McGinn: I think that's fair. And I mean, I think it's just clear as we're talking about this that it's somewhat business specific and somewhat

convoluted.

And like, there really [00:15:00] has to be, like, clear definitions going into a conversation about these things, even between the two of us around that.

Because it's like, even some context where you would say metric, I might think of, you know, Something more granular than that, and you're thinking of it as, and in some cases I'd be like thinking about it in terms of a calculation to get to a, you know, higher level indicator,

Jordan Walker: sometimes it's confusing because some of these like calculations are just normal now like average order or order value that to me like we could call it a data point because it's just normal like okay what's

our average order value but technically based on definition it's a metric.

Alyssa McGinn: And my brain goes, you hear average order value and you go to, okay, literally what columns in the table do you have to be able to pull to be able to present average order

value? So it's like, okay, we need to

be able to

Jordan Walker: are going to be the data.

Alyssa McGinn: right? So

Jordan Walker: [00:16:00] So this episode is just as much for us as it is for everyone else. Let's talk about another like big episode. Elusive one that gets thrown around a lot. Analytics.

Alyssa McGinn: Oh yeah.

Jordan Walker: Oh my gosh. Like, especially in the marketing world when, data started to become a sales tool. Like I remember this as a part of my earlier career when I was really starting to get into this, We would use analytics as a sales point, but not in the correct way at all.

And so analytics, like I think if you're in the marketing world, your first thing that you might think of is like a Google analytics because that's become the branded thing, right? But analytics is really just the process of examining data to uncover patterns. So we have the data, we have these metric calculations that get used, but then analytics is that process of really looking at it in trend [00:17:00] lines, in matrixes, in heat maps, in whatever that visualization should be to pull things together, to then play around and see, okay, well, what happens if?

Or how does this affect that? And so, like in my world, I'm looking at a lot of customer experience metrics to see how does their customer experience lead to greater lead generating outcomes? Does it create a qualified lead? Does it create, general awareness? Those are the kinds of things that I'm analyzing to then optimize sales and marketing tactics.

Alyssa McGinn: I think just as metrics and KPIs and OKRs maybe get confused, there's a, subset is data visualization, analytics, and business

intelligence are kind of like inter,

interwoven

and used synonymously, in most cases, you can use it somewhat synonymously, and that's fine, [00:18:00] but I was thinking data visualization is truly just visualizing data, like

putting it in a format where you can see it and look at it,

Jordan Walker: And understand it.

Right.

Alyssa McGinn: Like,

I guess it's more than just looking at raw data, but like visualizing it in charts and graphs.

But then the analytics is, like it's the process of

analyzing

Jordan Walker: visualization doesn't

Alyssa McGinn: data visualization doesn't necessarily include that.

Jordan Walker: Well, so the example that I wrote down in here is, okay, let's say that you're trying to understand the customer's journey across all channels online and offline. So you're gonna have to take numerous data sets like sales transactions, lead source, maybe you're pulling in some like customer satisfaction, like anecdotes different marketing channel data, website, app, social media, what have you.

But then through the process of analytics, your

You're [00:19:00] visualizing those trends across the customer sales funnel and then you're using those metrics to maybe establish what are benchmarks in between each stage, what are areas where maybe we're causing more tension in the process. So then that's where you can start getting into some of those additional questions of if I'm trying to better understand the customer's journey, here's how I can use data to help me visualize that journey and uncover.

gaps that we don't have the answers to or areas of improvement and so that thinking about it through the customer journey helped me kind of like tie or connect the dots between how analytics and visualization applies to something that we all look at I think a lot more frequently. If you're not used to like pulling up a dashboard or something.

Alyssa McGinn: Right. I mean, "dashboards" is another like kind of buzzy word.

Jordan Walker: Yeah that is

Alyssa McGinn: is

Jordan Walker: But it's just analytics. It's visualized [00:20:00] analytics.

Alyssa McGinn: The data visualization analytics dashboard.

Jordan Walker: Oh my gosh. Well, you hit on this one a second ago with business intelligence. So I think the brand of Power BI has made business intelligence seem like it's only about data visualization. And it's not, like, business intelligence is really making decisions based off of data for your business.

Alyssa McGinn: Yeah. It's more, I think it umbrellas, like, over, it includes data visualization, it includes analytics. I mean, it includes everything we've talked about, is like business intelligence, like all the way up to like, I guess if you take the funnel, like business intelligence is like,

Jordan Walker: yeah.

Alyssa McGinn: everything else kind of like funnels down as ways to have

Jordan Walker: Increase your intelligence.

Alyssa McGinn: Around your business.

Yeah.

Right. But I do think Power BI, it's one of those things where it could easily become What are the, what's the terminology [00:21:00] where you use a brand name for like, what is actually the product?

Jordan Walker: ~yeah. ~I don't know the word that we're looking for, but it's, I use this example a lot when I'm talking about, like, keyword search, and it's, you know, do you search for Kleenex or do you search for tissue? Right.

you

know, like, Or like,

Alyssa McGinn: you say Zoom, but you really mean

video

call.

Jordan Walker: I'm gonna go Google it, you know, like, but maybe you use Bing, you know?

Alyssa McGinn: I

Jordan Walker: could

Alyssa McGinn: this could easily become that, and I think it's kind of detrimental.

Honestly, because like Power BI is just one of many tools.

Jordan Walker: I use Looker Studio

because I deal more with

marketing

Alyssa McGinn: also use Tableau and Power BI and there's just multiple different platforms depending on what your needs are. I think that the definition and the idea of intelligence around your business could get watered down

super easily by just saying like, Oh, Power BI is business intelligence.

If we have Power BI, then we're good.

Jordan Walker: and that's, tools are tools. Right.

Alyssa McGinn: far from true.

Jordan Walker: [00:22:00] Exactly. in order for any of those tools to work well, and for you to really have business intelligence where you're using data to help inform business decisions, you have to understand what data do you have to work with, what are those raw facts and figures that you currently have, what are the ones that you need to start gaining,

What are we trying to measure against?

This goes back to our KPIs and our OKRs, so that applies to the metrics, and then you use analytics to help you visualize and ask questions to then uncover greater outcomes, new directions, aha moments, oh crap, we need to fix this

moment,

Alyssa McGinn: I've seen countless times where people have a Power BI or a Tableau, but that doesn't mean they're intelligent.

Like if you're, it's the same thing where it's like if you have a treadmill in your house, and you don't walk on it, or you have weights that you never pick up, like they don't make you get in [00:23:00] shape.

Just because you have these tools, or even if you have a few dashboards. If you don't use them, and if you're not, like, actively engaging with them to, you know, guide your strategy, I would say, why do you even have them?

Jordan Walker: I think maybe another conversation that we can have at another point, too, is like, just really understanding what some of the metrics and dimensions really are. Like something that you just made me think of is, you know, again, in my world, a lot of people have Google Analytics for, to measure web and app traffic, right?

For the longest time, people really put a lot of weight on a bounce rate. Like, what's the bounce rate of the website? Well, if you're running advertising campaigns, first of all, your bounce rate's always going to suck. A bounce rate is essentially like somebody came to the website, they did nothing, they left, they bounced.

They're like, hi, bye,

right? Sometimes that can give you indicators of high spam traffic. Sometimes it can give you indicators of, you know, fat thumb [00:24:00] syndrome of I meant to, I didn't mean to click that ad kind of a thing, but ultimately it's a distraction in my opinion. And actually with like Google Analytics 4.

The latest platform that came out a couple years ago, they don't even include bounce rate anymore. They, you can look at average time spent on site, but it's what is your engagement rate out of total visitors or users to your site? How many of them are actually active on your site? How many of them are actually engaged sessions?

Meaning they're clicking, scrolling, view, you know, they're actually doing things. That to me is a much more important thing to pay attention to than those who left you. Because you're not ever going to find out why they left you if they didn't do anything, at least from an engaged standpoint. So, I'm already going down a rabbit hole with this, but the point that I was going to make is maybe breaking down some of these, like, metrics and dimensions that we commonly see in CRM platforms, websites, social, that [00:25:00] sort of thing might be a benefit later on, because those are the kind of questions that I tend to get quite a bit.

Alyssa McGinn: Yeah, people putting a lot of weight on things that maybe aren't that important?

Jordan Walker: Or don't know how to, like,

use them.

Alyssa McGinn: You see this number and like, what should it tell

Jordan Walker: Yeah, like, I think it goes to a point that you and I kind of, talk about every now and then of,

you know, are we reporting just to report? Like, again, in the advertising and marketing world, we put a lot of stock on impressions, clicks, and whatnot, and to me, those are vanity metrics.

Like, okay, I'm gonna stop going down the rabbit hole. I think we should have a

vanity metric conversation

someday.

If you, too, are interested in vanity metrics, Or if you are one of those individuals that's like, well, I know that we have data. I know that we have things that we can look at in our CRM on our marketing channels, what have you, and you want to get more information on how to look at that data set or those metrics and what kind of questions you can maybe ask against those [00:26:00] specific metrics that are in there.

Shoot us an email. Hello at insightlypodcast. com.

Alyssa McGinn: We would love to hear from you. We'll see you next time.

Jordan Walker: See ya.