Andy & Brian discuss the challenges of deciding which data to track, and how to use it to make better business decisions.

They offer 6 ways to make analytics more actionable:

1) Collect data in one place and with a holistic view
2) Compare data over time.
3) Know your KPIs – look across channels, not just eCommerce.
4) Set and measure against benchmarks
5) Set and measure against goals, then monitor.
6) Use testing to impact specific KPIs (bounce rate, conversion rate, AOV, etc).

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Brian Beck: Welcome to Friday 15 with Master B2B Brian Beck here with Andy my co-host in this thought leadership series and our weekly LinkedIn Live program and our podcast broadcast around the Galaxy.

Andy Hoar: The part of the Galaxy I’m in today is a little place called New York City, you may have heard of it heard of it. I came here for a round table which we’re gonna talk about here in a moment ,but yeah it’s the first time I think I’ve ever done the Friday 15 from the east coast and so you’re in LA, so we’re bi-coastal.

Brian: We had an we had an exciting week this week Andy. We were we were all over the country and at different events and getting people together. In fact, our breaking news today is going to be all about where we were. So first off Andy you were in New York for our Round Table Master B2B executive Round Table in Midtown Manhattan. It was a really looked like an awesome event tell us how it went ,

Andy: We had actually a new cadre of companies – We had pharma companies like Merck. We had Panasonic, Duracell, some fashion companies, companies that make Home Furnishings, we had lighting companies, who do a lot more B2B than than you might think. And in fact, a few came and said hey wasn’t quite sure if this was right for me but when they came they were like wow yes we didn’t realize we do 20-30% of our business in B2B. We didn’t realize how different it really is until we’re there. Then we had a fantastic conversation about testing your website – the theme of our Round Table Series this year has been about customer experience, and we spent 30 minutes talking about how to test, because one of the gentlemen that was there actually is a former data scientist at Amazon and he was talking about how they do testing and what works and what doesn’t work. So I just stepped back and let him talk and for a good 30 minutes they were peppering questions at one another. It was exactly why we do these roundtables.

Brian: We’ve got a bunch of these coming up, and we’ll share the schedule later in the episode. Andy, while you were in New York in the heart of Manhattan I was up in Seattle at Amazon Accelerate. I was incredible the number of people that were at this thing. There were 5,000 people there, and there were probably another 10,000 online watching virtually. This is an Amazon Seller conference where they get all the folks who are doing what’s called Seller Central together and talk about new features. I spent a lot of time with Amazon business and with the head of Amazon business. One of the one of the big announcements for the Amazon business world was that Amazon is now allowing advertising specifically to reach business customers which they had they had never allowed before. So they’re advancing their paid advertising components to allow for things like that. They continue to chop away at all the things that businesses need to meet those business buyers. It just continues to raise the bar for the rest of the industry. The other big thing they introduced is one hour delivery using drones. They had their head of delivery up there talking about continuing to advance their delivery product – they did two-day and then one-day and now it’s one hour. By the end of the year they’re going to have that capability in key markets. Think about what distribution is traditionally differentiated on – instant delivery or quick delivery.

Andy: The time frame between science fiction and reality is shrinking. Because I remember when they brought this up I don’t know six or seven years ago if I’m recalling correctly, I think there was even like a 60 Minutes discussion about it. They had Jeff Bezos on there and they said you’re goinog to use drones? Are we gonna be eating meat pellets and living on the moon too? It sounded so futuristic and here it is.

Brian: They continue to push the bar. Their whole culture is all about testing, and it’s fascinating because they also do things that are counter to their own core business. They’re doing a lot to enable other people to sell on their websites. There was a lot of talk about logistics and operations and extending Amazon shipping capabilities. They are legitimately competing with with FedEx and UPS – they’re buying market share now. They’re out there offering discounts on shipping rates.They’re enabling other off-Amazon transactions to occur, which is counterintuitive in some ways if you think about it. The CEO of retail got up there and said we believe in this. They do things that are outside the box which most traditional businesses don’t do.

Andy: Remember the Amazon Marketplace was exactly that. Amazon used to sell stuff themselves and they didn’t have this third-party Marketplace. And then they said well, if we can’t beat them join them because people were going to third parties and buying these things and they were using Amazon to we room their stuff (as supposed to showroom) and so Amazon decided they’re never going to win that game so let’s just get 15% of whatever. Let’s just bring them into the fold. And of course the Amazon people responsible for selling Amazon stuff said wait a minute, why are you inviting the competition in? And they said because it’s actually better for customers. This is where your mindset really matters. In fact, the other day in our discussion one of the things that the former Amazon gentleman said is that when they’re doing testing some of what was optimized for his group actually was not optimized for another group. They would have these knockdown, drag out fights internally where they say if we change the page to help us here it would actually hurt us there. So somebody had to make a decision. So not only do they compete with other people outside of Amazon, they compete with people inside of Amazon because ultimately what matters is what customers want and the data always wins.

Brian: So let’s get into our topic today – making analytics actionable in B2B e-commerce. So this is a question, Andy. I was a VP of eCommerce for 15, 16 years. This is really key. This is a key part of being successful in your function as a leader. So again, here, Andy, we went to the authority on all things now, Chat GPT. We asked ChatGPT for a definition of eCommerce analytics. And I won’t read the whole thing here. But essentially, it said it refers to the process of collecting, analyzing, interpreting data. And also understanding various metrics and KPIs that reflect customer behavior, sales performance, effectiveness of marketing, and overall business health for eCommerce. And so in my history, Andy, and the history of many of the folks that listen into our podcasts and our sessions here, how we’ve traditionally looked at eCommerce is really in a silo. I’m showing an example of a Google Analytics page that shows things like visits and new versus repeat visitors, number of page views, conversion rate, revenue, average order value, what we call bounce rate, which is the number of people that hit the site and leave it immediately. This is how we traditionally looked at it. In some ways this is good. It gives us a lot of good actionable data because, hey, we could say people are bouncing on the website. Where are they bouncing? Why are they leaving immediately and go fix that page? But the problem is it doesn’t tell a full story. So I wanted to share some data here I found from BCG research that shows that on average two thirds of purchases were influenced by digital, but they happened offline. These are offline transactions being influenced by online transactions. And Andy, we found this in our own research too. And this is B2B specific. The data I’m showing here has to do with industrial machinery, industrial supplies, packaging. And these are B2B categories that are demonstrating that digital is really a significant influence. So your data, the KPIs that we look at as an eCommerce operator, don’t reflect this. They’re just reflecting what’s happening on the website, which is important, but it’s not the full story. So this can get even more complex when we think about different scenarios. Andy, you found some data here from Harvard Business Review. You want to talk about this?

Andy: There are basically what they describe as three states in which analytics operates. One is kind of a descriptive model. And other ones a predictive model. And the last one is a prescriptive model. The difference is, I think, like the weather. So let’s say you want to describe what happened yesterday. Oh, it rained yesterday. The model said it rained yesterday. Well, you need that in order to inform the future model. So you have to understand the past. That’s kind of a description of what happened. But there’s nothing predictive, prescriptive about it. Then there’s kind of the predictive one, which is, oh, in the next three days, we’re expecting rain, which is a model that will tell you that there’s rain coming. But it’s not the ultimate sort of nirvana around analytics, which is – help me understand when it’s going to rain and where it’s going to rain and how much it’s going to rain. And that’s what everybody’s aiming for, which is the prescriptive decision-making that analytics can deliver. And I think they did a nice job here of talking about all three of them. Because when we talk about analytics, we don’t always talk about the differences here. They also stress that you need to have a data culture. This sounds so much like what we said many thousands of times about having a digital mindset or a digital culture. You need a data culture that appreciates data and sees it as an opportunity for differentiation and not something to be managed or costed out. And then I think the other thing is it’s important to know that the analytics have to be aligned with your business goals, which is what we really talk about on the next slide, which is that this is fascinating, actually. This is very complex, but I’m going to try and simplify or oversimplify so we can get it done here. Think of a two by two matrix where you have aligned companies and misaligned companies and then low digital maturity companies and high digital maturity companies. What they wanted to know was what does analytics fit into each of those four categories? I’ll take the two extremes. The aligned companies, meaning internally aligned, the C-suite and the rank and file agree on what we’re doing here as a business and the analytics is measuring the same thing that are digitally mature. Those people when they get the analytics right, their research showed that the analytics has a multiplier effect on the business. In contrast, the companies of this sort are misaligned – the way they did this is they interviewed the CEO and somebody in charge of analytics and they asked them both the same set of questions, then they saw if there was a difference in the answer. If there was, they called that misaligned. Misaligned companies that are digitally immature, not surprisingly, the analytics didn’t have much of an effect because they’re misaligned and the digital maturity is low. They’re just at 2%, and they don’t quite know what they’re doing. They’re still feeling around in the dark. What they said, the analytics really underperform is for misaligned companies that are digitally mature. That sounds almost like an oxymoron, but if you think about a company where the C-suite is thinking one thing and the analytics team is thinking something else, but they’re selling 50-60% of their stuff online. They said, “Where do you get the analytics wrong in that scenario?” It actually has a negative effect. Misaligned digitally immature companies where the growth KPIs underperform, the financial KPIs underperform, and the customer KPIs all underperform. Think about it.

Brian: I guess maybe it’s a little bit of, “Hey, we’re digitally mature. We’ve arrived. Maybe they don’t need to think about this, or put effort into alignment because they feel like they’re there or at least I don’t know.” What’s interesting, Andy – It can almost tell whatever story you wanted to tell, or you can use data as an operator and a silo to tell your story and ignore other data. This does, I think, come down a lot to culture. Getting back to our earlier discussion, Amazon – Harrington, again, who’s the CEO, Doug Harrington of the retail business, was talking about data and how they’re using AI, to do some of the predictive things you were talking about. What’s interesting about Amazon’s culture is they’re very much about the data and analytics and allowing it to tell a story and they demand that the data be viewed objectively. I think that’s a key piece of this. We got some comments when we talked about this on LinkedIn, Marc Vasquez, who’s the Global Director of eCommerce at Ideal Triton products, weighed in on this. I think he highlighted a really interesting side of this. He said, “I think the problem can come from a proliferation of data and changing KPIs.” He’s saying, “Too much data. We don’t always track the right things, but we may not know it until we’ve been tracking it for too long, and it stays on the dashboard and it becomes a mile long.” This is part of the problem. There can be too much data, right? As an operator, you don’t know what to focus on.

Andy: There was an interesting study that Gartner did. In 2018 where they asked digital leaders and CDOs, rank the top three activities that you’re measuring for data and analytics success. They were all internal in 2018. Four years later, they asked the same people, the same set of questions, and all of the analytics were focused on external things. Which is, by the way, better, I think, generally, but look at that shift in four years from highly focused internally to highly focused externally. To Marc’s point, I think you can find that analytics, because they’re reflecting the business strategy, can be a moving target. For companies that are not honest, intellectually honest, about what their business is doing from a digital perspective (to your point earlier – it’s not just about selling online, it’s about cross-channel influence as well.) If they’re not intellectually honest and not measuring that stuff, then surprise, surprise, the analytics are going to be off because the business doesn’t reflect the analytics. The analytics don’t reflect the business.

Brian: The question we started with this all with is, how do we make analytics actionable? How do we distill it down to the point where it can be… What are the most important metrics to be measuring? Again, on the alignment side, too, not just in a silo. This is some data that I found, Andy. I looked at a number of different sources… I saw collecting data in one place, having a holistic view, omnichannel view. It’s one of the challenges that we talk a lot about data at our roundtables of the fact that if we’ve got it in a lot of different places, it’s hard to analyze it. It’s hard to pull it together into a cohesive picture. Comparing data over time is the best practice. Again, it sounds motherhood, and apple pie, but how are we doing if we standardize the data and how we’re collecting it? How are we performing? Know your KPIs and looking across channels. My favorite metric is share of wallet. How are we doing from a transactional standpoint across all channels in meeting customers’ needs?

Andy: The collecting data in one place is… Having it all in one place is an enormous challenge for B2B companies. Comparing data over time. Once you’ve got it there that’s oftentimes simply a report. But that first one, these are not all made the same. That first one I found is by far the most difficult one. Number two, I find is actually the Harvard Business Review story talked about aligning the business performance and the key goals with the KPIs because oftentimes they’re not aligned and people are narrowly measuring one thing or the business challenges change every year. It’s always about profit, right? But one year, it can be about growth and next year, it can be about cost reduction. And all of a sudden you’re getting lurching back and forth all over the place around what you’re measuring. So, analytics is kind of the tail that gets wagged by the dog.

Brian: A couple of other things of note here. One is measuring benchmarks and also goals. Setting up and understanding where you’re going with your metrics and then highlighting those. And then the last thing I wanted to mention here was around testing. We talked about this at the roundtable this week, figuring out which metrics. And there was a really interesting figure that was highlighted during your roundtable – only 12% of tests actually result in something actionable. The other 88% tell you nothing. Is that true?

Andy: That’s right. That’s exactly right. And I think people’s mouths were agape when they heard this. And we asked the people in the roundtable, confidentially, we had them fill out a form. What are your top priorities for 2025? The number one answer was analytics. A head of customer experience. So when we decided to do this topic, I’m like, Brian, you’re going to be shocked to hear that. That’s exactly what we heard in New York. Now, not everybody feels that way necessarily, but I think it was like 78%, which was overwhelmingly in New York the number one answer.

Brian: That’s amazing. So we did ask the LinkedIn community also. Do they feel like they have enough information on their analytics dashboard, not enough, or just the right amount to make actionable decisions? That’s the key. That’s the key word here, actionable decisions. 56% said, not enough information. So unlike Marc, who highlighted the issue with regards to too much information, not knowing what to do with it, only 22% said that. 56% said they didn’t have enough info to act on, and only 22% said they had to write the right amount of information. So we have work to do here, folks, in making this information actionable. And it puts all the way back to the foundation of getting the right data into one place so you can build it.