Podcast

Front-end analytics: three must-haves for success

Front-end analytics means using the data you have, to drive decisions when planning. It’s about predicting the areas of marketing with the highest potential. You have access to the data, but how do you make it actionable? Jan-Eric Anderson, president at Callahan, and Zack Pike, head of data at Callahan, explain three important components a marketer must have to make data actionable, and discuss the benefits to implementing front-end analytics.

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Welcome to Callahan’s Uncovering Aha! podcast. We talk about a range of topics for marketing decision-makers, with a special focus on how to uncover insights in data to drive brand strategy and inspire creativity. Featuring Zack Pike and Jan-Eric Anderson.

 

Jan-Eric:
Hi, I’m Jan-Eric Anderson, president of Callahan.

Zack:
And I’m Zack Pike, head of data at Callahan.

Jan-Eric:
Hey, Zack. It’s good to see you.

Jan-Eric:
Hey, I was reading an article the other day and it made me recall some conversations we’ve had here on the podcast. And I actually thought it might be good to revisit a topic that we’ve talked about in various forms here on this podcast.

Zack:
Okay.

Jan-Eric:
So, the basis of the article I was reading was talking about how one of the biggest challenges facing CMOs and marketing leaders today is how to make data actionable. It was a recurring theme here that CMOs and marketing leaders, they don’t have a problem with access to data. There’s plenty of data that they have at their fingertips, but they don’t really know what to do with it. That becomes a recurring challenge or a recurring problem, is what do we do to make this data actionable?

Zack:
Right.

Jan-Eric:
And it definitely reminded me of so many conversations that we’ve been having here on the podcast as it relates to data and that very issue. And so, I thought it might be a good time to revisit the topic of front-end data analytics and what it means and why it matters and how it’s helpful. So, if you’re up for it, maybe we can just start with a definition. When we talk about front-end data analytics in the marketing analytics world, what do you mean by that when you say that?

Zack:
Well, if we think about a new objective a company has, so we’re planning for 2021, or we are getting ready to launch this new product, or we’re going to start spending more marketing dollars that we’ve spent in the past, some type of decision point where there’s a planning exercise happening and if we just talk about that in terms of marketing, since that’s what we do, a company could hire us. And with our background and our smart people and our history, we could probably put together a pretty smart plan just kind of off the cuff for them. Right? We’d have some conversations about what they’re trying to accomplish and what they’ve done in the past and we can put together a reasonable plan. And most business people could do that. So, that’s one way to do it.

Zack:
The second way, which is this front-end analytics piece is we have been collecting all of this data for so long, right? Every company has years and years of data sitting in all types of different places that they’ve either used a little bit or maybe have never used. So, front-end analytics, really, at its core, is just grabbing all that data and using it to drive decisions. And really, the decision we’re going for with the way we talk about front-end analytics is we’re trying to predict the areas of highest potential. So, it doesn’t matter how big your marketing budget is, it’s probably not big enough to do everything that you want it to do. So, you have to make decisions about what you’re going to do and what you’re not going to do.

Zack:
And we believe that aligning those decisions to my areas where I have the most opportunity to impact results… There’s lots of ways to define that, client by client. But if we can align our dollars and our effort and our resources to those areas, we’re going to have a bigger impact on the business versus taking kind of a shotgun approach or a peanut butter spread approach, which is what happens most of the time, because it’s sometimes really difficult to use data to plan on the front-end. Usually we use data to measure on the back-end. Our approach is to use it before we ever spend a dollar.

Jan-Eric:
Yeah. So, I was going to ask you about that. You coined the term front-end analytics that suggests that maybe there’s a back-end analytics. So, the back-end analytics, as I understand it, is a little bit more of, maybe, the more common version of analytics in the marketing world, which is post-mortem. And now that we’ve done a bunch of stuff or spent a bunch of money, what happened?

Zack:
Yep.

Jan-Eric:
Right? And that becomes the rear end view, if you will, on the back-end of it, to where it’s an opportunity to say, “We did this, and here’s what happened.” But it’s all after the fact. Right?

Zack:
Yeah. The other thing I’ll mention about front-end analytics is we’re often using data that you wouldn’t expect to use, right? So, if we talk about traditional back-end analytics, if I run a media campaign or a media test, I’m usually using the data I would expect, I’m using all my media data, maybe a little bit of sales data. On the front-end side, the more creative you are with your selection of data, the better your results is going to be. So, it’s going to include all your marketing history and sales history, but it’s also going to include other things that impact the business. The whole point is to figure out what levers impact the business so I can figure out what I think I can control and then align my budget to that opportunity.

Zack:
So, we could include even data that’s not yours. So, if your business is impacted by weather, we want to include all of that. If your business is impacted by certain economic trends that are changing week-to-week or month-to-month, we want to include that type of stuff as well. So, the front-end side is typically much more involved than the back-end. And it takes a more creative mind to think through what all we should be including, and frankly, a better analyst to be able to think critically about the business.

Jan-Eric:
So, let’s talk a little bit about that. What are the requirements to do front-end analytics? What does it take? What do you have to have? Is it just about access to data? Is it people? Is it technology or tools that do the math? What do you have to have? What are the requirements?

Zack:
Yeah. So, you kind of touched on, there’s three chunks of requirements, there’s technology, data and people. On the technology… Well, let me talk about data first. So, we kind of touched on that, the whole creativity around data. What often is paired with being creative with data is lots of data. Okay. So, you’ll have a lot of data coming from a lot of different sources, sales data, economic information, stuff that government puts out, all of your sales and marketing data, all needs to be included, which will, and if you’re doing it right, be way more than Excel can handle. Okay.

Zack:
So, the first step is data, but it’s going to create this set of data that’s too large for basic tools that someone might be using. So, that’s where we get to technology. You’re going to need some type of, I’ll just say, big data technology to be able to handle this stuff. That’s going to be some form of database, some form of analysis tool, or some form of visualization tool so you can communicate the results to the people who are going to make the decisions on this stuff. On our end, we’ve taken several different pieces of technology and stitched them together. Usually that’s what’s going to happen in this scenario. There are a lot of pieces of technology that will say they can handle everything, start to finish, but none of them really do it that great. So, the best thing to do is pick specific technologies for what you’re trying to accomplish and then stitch all those together into one kind of cohesive package, which is what we’ve done with the intelligence platform three and a half years ago or whatever.

Zack:
And then, the last piece. So, once I’ve got my data, I’ve got it all in a spot where I can ask a lot of questions of it quickly, it’s clean, it’s organized, now it’s on to the people. And this is the most critical portion of this equation is… So, if you start exploring this stuff, there are people who will try to sell you that machines can make all the decisions for you. And we’re still a ways away from that. When you’re talking about an exploratory type approach to figuring out where you should spend money, there’s not a clear answer. And in most cases, there’s no playbook on how to get there. Machines right now don’t do well in that type of environment. So, when we think about machine learning doing all this automated analysis, this isn’t a great application for that, which is why the people are so important.

Zack:
And so, you want a cross-functional group of people. You’ll probably want someone who’s got some background in marketing if we’re talking about a marketing type objective. Probably want someone who’s got a background in some finance or sales type of analysis. And both of those people need to be able to handle all that data that they’re going to be handling. So, that includes cleaning it up, getting it organized, technically, how to stitch all that data together because your historical media data will not match up with their historical sales data automatically. So, they’re going to have to do some work to do that behind the scenes before you can actually get to all of the predictive work around what correlates to better sales performance, is that something I can control, all of that type of stuff. So, those three chunks are critical to this equation.

Jan-Eric:
Right. And so, you had mentioned data and then the technology to be able to process data and then people to man the effort and oversee everything. I would imagine that can add up from a cost standpoint. Not that it has to be over the top, but you’ve got a highly specialized and highly trained people, technology that’s built to scale, there are going to be costs associated with that.

Zack:
Definitely.

Jan-Eric:
So, help articulate a bit why it would be worth it to consider an investment like this. What are the biggest benefits and clearest benefits that come? And then, what’s the expectation from a timing standpoint? Is it an immediate return? Is it a longterm slow burn kind of a return? Why should I care about this? Why should I consider this investment? What’s the benefit?

Zack:
Yeah. So, the benefits are really wide and far reaching. I’ll just talk about a few specific examples, but it’s really contingent on what the objective is we’re trying to accomplish. We’ve seen clients, where we were working on a big media plan for them for the following year, gain efficiencies in how that media was spent, where your focus in that front-end analysis is really about reducing waste in the media spend. And I mean, we all know that it’s really easy to waste money in media. But if you’re taking the strategic approach, you’re thinking very critically on the front-end, you’re using lots of data, you can greatly reduce that waste factor. And what that does is it also, typically, will drive up the ROI you see on the money you are spending because it’s being spent in the right places and probably at a higher level, which is probably going to produce a better result.

Zack:
So, that’s one example. We’ve also seen our retail clients get better relationships with their buyers at the retailers. So, if you’re a brand that sells through any big-box retailer, Walmart, Lowe’s Home Depot, PetSmart, Petco, whatever, you have a relationship with that buyer. And that buyer at the retailer, to an extent, decides your fate with the company as far as your sales levels. So, it’s a partnership, because you also decide their fate because you’re an important product in their store, hopefully. We’ve seen a lot of benefit in brands bringing this type of information to their buyer to help them plan their own business.

Zack:
So, if you’re working with a buyer at Walmart and you can tell that buyer, as an example, how varying levels of inventory at different times of the year, either help or hurt the sales velocity, and then how you’re going to match up your campaign strategy or promotion or even pricing strategy in line with those fluctuations in inventory, that is a level of detail that buyer, I guarantee you, does not have. Because these buyers are reliant on inventory analysts who are planning inventory for potentially thousands of products. So, if you can bring them data on your specific world, help them make your portion of their world as efficient as possible, it’s a well oiled machine, it only helps you. When it comes time to negotiate the deal for the next year, when it comes time to negotiate shelf placement during special events, it’s all a good thing. So, we’ve seen a lot of benefit in that realm as well.

Zack:
The other thing, and in my opinion, it’s one of the biggest benefits. So, I talked earlier about the peanut butter spread approach to planning or the shotgun approach where I’m kind of doing a similar thing across the entire country or across my entire system, whatever that is. The reason that happens is because typically the data is too much for a normal person to analyze. And so, it’s too hard so I just do the same thing everywhere. But if someone can show me a path to spending my resources in my most effective areas, either it could be areas that have the biggest opportunity for growth or it could also be the areas I need to protect the position I already have, because we can figure that out too in a front-end analysis, there’s a lot of benefit in that.

Zack:
Because now, the peanut butter approach, that’s like 50 years ago. This is… I know exactly why I’m spending dollars in certain portions of my system. And I know what I expect those dollars to do. Is it a focus of protecting what I had last year, making sure I don’t lose any ground to my competition? Or is it that I feel like my competition is weak in this area and I feel like I can grow another 10% next year? You’ve now got a strategic plan that is based in data and you’ve also got objectives around it. So, when it rolls around to the board meeting or you’re talking to the executive team next year, you can refer back to that plan and how you actually executed against it. That’s, in my opinion, one of the biggest benefits of this type of work.

Jan-Eric:
Well in embedded in that, what you just said, kind of hidden in there is actually… There’s an age old question. I cut my teeth in this industry as a media planner. And countless times, the media team gets asked for perspective on which markets should we have the up with media. And we would go to BDI or CDI, back in the day, as an indication of something to grasp to for some sort of indication of where we should go. Or we looking at media efficiency or if we really started getting after it, it was kind of what’s the potential for growth according to BDI, CDI? But then overlay, maybe, the cost of media as a variable in that market. And kind of how much is it costing me to pursue the growth opportunity?

Jan-Eric:
That’s almost laughable in terms of the type of analysis that could be done to identify where should priority geographies be, who are the priority customers or distribution channels or whatever partners or however that works depending on the category. And so, I think, Zach, actually, as you’re talking about this, it fundamentally challenges a traditional target where you ask that question to. Asking the question of where should I be having up is a good question to ask but the traditional or historic place where that question was directed, maybe to a media team, is not the right place. That’s not the team that should be answering it. Who should be answering is the front-end analytics team that’s able to take in a lot more information, has analyst capabilities that are well beyond what a media team is going to be trained to do, to answer that question.

Zack:
Yeah, they’re not equipped for it. I mean, that’s not their background. And yeah, yeah. It puts-

Jan-Eric:
Smarter data, smarter people and smart tools to be able to give better answers to the age old questions. Right?

Zack:
It puts you in a much stronger position of control over your environment. And I’ve said this before on the podcast, analytics is, at its core, about increasing confidence in decisions. Right? It’s rare that you just get a binary yes or no in the hard ones. Right? The hard ones, the big strategic changes, we are trying to increase confidence in a certain path. And the data will lead you in that direction. So, we’ve seen a lot of confidence increases in this type of approach because it’s just giving you a higher level of control.

Jan-Eric:
Yeah. Well, that’s a great note to kind of wrap up on. Is there anything else you want to add before we wrap up?

Zack:
The only other thing I would add is that this is… So, you mentioned CDI, BDI, you could also mention attribution modeling, which is a constant thing in the media space. But usually those things are one and done and no one ever has to prove or disprove what they said happened or was going to happen actually happened. If you can put this front-end analytics approach as part of your ongoing process, the back-end analytics becomes the next front-end analytics piece. And so, it’s a constant cycle.

Zack:
But it also forces you to put your money where your mouth is because when you come around to do your next planning period, you’re running through the analysis, you’re referring back to what you thought was going to happen last time. And you get much smarter by executing and being able to see, “Okay. Well, my prediction was right here. My prediction was wrong here. Why was that?” If I can figure out why, I become much smarter next time. So, if you do this for a few years, the level of smarts you get from just this being part of the organizational process is… I don’t know that there’s many of your competitors who would be doing this type of planning.

Jan-Eric:
Yeah. And along those lines, I’d offer up that, I heard a wise person say once that a corporations priorities are found in their budget sheet and their budget line items. And if an organization touts that they prioritize and value analytics and data to inform decisions and didn’t have some sort of front-end analytics line item in their expenses, it would raise a flag of concern for me that priorities and the end focus isn’t in the right spot. Well, this has been great. Again, I think it’s been a timely topic for a couple of years now and I thought it was good that we revisit this. So, I appreciate your time today.

 

You’ve been listening to the Uncovering Aha! podcast. Callahan provides data savvy strategy and inspired creativity for national consumer brands. Visit us at callahan.agency to learn more.