In this episode of “Earley On…”, Seth Earley, CEO of Earley Information Science, discusses the topic of how to make sense of metrics. Topics explored include how to deal with the tools, technologies and data with which we are collecting and receiving information about our customers’ interactions, and that comprise the analytics ecosystems in modern marketing organizations.
Welcome to our podcast. My name is Seth Earley. And I am going to talk to you today about Making Sense of Metrics. How do we make sense of all this data that we’re getting from all of the systems that are indicating the effectiveness of our interactions with customers which really looking at customer analytics as looking marketing and analytics? And when you think about this ecosystem of tools and technologies that we’re using to engage with customers or somehow served them to meet their needs, to attract their loyalty. We have 100 of systems potentially, at least dozens. I know there’s an enormous ecosystem of thousands of vendors out there and there’s so much money going into this. It’s ridiculous. Something like 3,000 marketing technology vendors in the market place. But let’s imagine we have a handful, we've decided what we are going to use for our technology stock with a handful. Now, every customer interaction leaves a vapor trail of data throughout the systems, through these processes. And the question is how do we interpret this data? How do we track this data? How do we make decisions about what to do differently, right? What does data give you? It gives you an indicator or something. And what will you do with that data? You have to make a decision and if it's meaningful if you can act on it, if you just have the data and you look at it, “Okay, great” well nothing changed. They are not doing anything with it. We might want to change your promotion. You might want to improve performance of the website in some way perhaps search or retrieval. We might want to change the user experience. We might want to modify a product or offering and metrics really provides the signals to the organization about what to do and when to do it. And when you think about how we are trying to go to market and how we’re trying to determine on what metrics and most important it really depends on what kind of strategy we have for engaging with the market place. Are we looking at our customer behavior? Or are we trying to get them to buying more stuff? Or maybe that’s what everyone wants. Maybe we’re looking at recommendations or abandonment. Perhaps we are looking at a customer feedback and your dissatisfaction or the complaints or social media. We could be looking at an employee behavior. We want to understand something about how our employees are interacting or their feedback. What are they’re telling us? What kinds of suggestions are they making? What processes might they want to improve? Or we could be looking in process performance. We could be looking at the cause to serve across different domains. Or we could look at product performance. So all of these different areas are leverage in different ways depending upon what kind of strategy we have in terms of our global market. We might want to really have a great customer intimacy in which case we’re going to try to invest in tools to a sense and acquire and analyze or respond to customer feedback and intelligence of behaviors. And maybe we’re looking at sales in customer engagement. We might look at ecommerce sales. We could look at customer relationship management. We could look at strategy marketing. We also could be looking across in the next generation of our journey management. We’re trying to makes sense of these vapor trails of data because every interaction leaves traces of information throughout different systems and it tells us what to do. We need to do something with that data. We need to change an interaction or behavior or a promotion or an experience of some sort. And so when we start considering this investment that we’re making in marketing technologies many organizations just do not have the fluency in order to understand how the leverage is most effectively. We’re really trying to drive the operationalization of marketing analytics deeper into the organization so that we can act on those analytics. And the challenge is, how do we get the business and there’s a loadable school marketers out there. How do we get the business to build their fluency when it comes to analytics? If we need specialized knowledge to perform the analysis, it’s not going to be part of people’s day to day work. And if we need a very highly skilled people to do this and that skill is not shared then it’s going to have a problem. And when you start looking at marketers and how they’re assessing themselves, according to Forster, less than one and five analytics professionals say that their efficiency effectiveness or acquisition of metrics are completely effective. So one of the challenges around marketing analytics in metrics is that the metrics determining what the signals are telling us. So let’s imagine that we have a high bounce rate on the website. There can be multiple root causes for that. The content might not meet the needs of the user. The path to the content might not be clearer, perhaps the user where’s in the wrong place in the first place. The use experience led them down the wrong path. Maybe the navigational labeling was misleading or perhaps the search engine returning incorrect results in the bounce doubt. Or it could be that the user was simply not clear with what they wanted. And so we need to start looking at these signals in context, different audiences in our marketing ecosystem or marketing processes who have different interpretations and different needs. And we have stakeholders that require metrics across that entire application ecosystem, perhaps to track a single process they might be hitting different systems. In different systems are going to be describing products in content, in data, in customers that have to be normalized. The attributes has to be normalized. I heard people talk about this from the perspective of a marketing chart of accounts. What’s a marketing chart of accounts? It's consistency around naming conventions or promotions in campaigns, in products, in channels and mechanisms of interaction. Well, that’s the taxonomy, right. That’s a metadata schema. We also have different ownership and different clocks speeds or on processing tools and that can be a source of contention. So we always look at things according to the customer life cycle. We try to contextualize everything from that perspective and you could pick whatever life cycle you have learn, choose, purchase, use, maintain, recommend, that’s one life cycle. It could be different and when your customers learning about you when marketing is pushing stuff out. We have to profile customers. We have to evaluate each. We have to target the prospects with there’s a whole set of metrics around each of those. When we’re trying to support the choose stage of a life cycle or the customer is trying to choose what product they want, we have to look at on sight behaviors. We have to optimize search. We have to improve that experience in their metrics along those facets. When we are helping to purchase or need to purchase phase of the life cycle, we’re evaluating the promotions and we’re giving people cross-sell opportunities and upsell opportunities. We’re trying to personalize the offers, again, different metrics. I kind of brushed through this in terms of the learn choose, purchase used and maintain recommend but each of represents a stage in the customers purchasing and product acquisition life cycle. They have to learn about the product. They have to choose what product they want. They have to purchase it. They have to use it, if they maintain it. And hopefully you're going to recommend to their friends. But when we look at the use and maintain, I talked about the first three, we’re looking to use maintain. That’s really about self-service. That’s about knowledge retrieval that’s looking at product usage. And again, lots of different metrics in those areas, when we start looking at the recommendation phase or if we want to retain the customer after they're finished with their purchase but they're using the product. What we’re trying to do is we’re trying to analyze sentiment. We’re trying to measure community engagement. We’re trying to understand loyalty drivers. So this whole customer life cycle requires that we have metrics across each of these areas. And then in some cases the driver from one behavior and one stage of a life cycle, maybe in conflict with another stage, so, and we may want to reduce our customer support cause but that’s going to cause a reduction and satisfaction and reduce the recommendations, for example. So again there are limited resources and there are lots of different technologies or use different stakeholders. So when we’re trying to look at marketing metrics in customer analytics, we have to look across to this entire life cycle and we have to understand the needs of our different stakeholders so that they can make decisions and improve the process. Now, Forster suggests that the greatest opportunities for leveraging analytics are at later stages of the life cycle because a lot of organizations understand the first few stages, you know, they learn, choose, purchase now or they call or to discover explore by the other three including to Forster it’s used to ask and engage and he said that it was used to maintain and recommend are not well understood. And when you're starting to look at the components of that, you know, you're trying to reduce the caused to serve. You're trying to drive at merging. You're trying to improve usage or consumption. You're trying to focus on retention and loyalty and engagement. And we’re covering a customers if they defect. And there’s a lot of value in understanding those metrics. To us, it’s all about governance because if we start focusing on the customer life cycle and we look at the metrics that each of our systems are throwing out to the vapor trails or like the electronic body language. The data exhaust from these different systems. Each one of them are going to tell us a story about our effectiveness, about what we’re doing. What we’re doing right and what we’re doing wrong and we work in improve in where we can feel in gaps, in where can beat our competition. So those metrics tell a story. We have search metrics. We have behavior metrics. We have utilization metrics. We have content metrics. We have response metrics. And each one of those has classes of sub metric or other indicators. And so governance is about understanding the impact and the implication of each of those metrics and values that are outside of expected ranges because each time something is outside of an expected range, it needs to trigger something. How are we doing? Are we doing as we expected? Or we’re not doing as expected? Or, are we uncovering a problem? Do we know what to expect? Do we have our baselines? And so when you start looking at all of this life cycle, we’re sending out lots of information to try to get customers, you know, maybe we’re sending email marketing and traditional types of marketing vehicles and doing Webinars and conferences and then we have other types of outreach and we’re catering leads in sales force or another CRM or monitoring engagement and hotspot or Mercado [ph]. And then we’re trying to integrate this in such a way that we tie this to GPI’s and tie the behaviors and tie it to interventions. In designing that program requires a great deal of thoughtful analysis of the upstream processes and the ways that was different stakeholders interact. What they’re common needs are? Where they may have conflicting needs and then what the interventions are whenever we have something that tells us that we need to do something differently. I think that’s it for this podcast. This is Seth Earley. I'm CEO of Earley Information Science and please check out our website www.earley.com. That’s E-A-R-L-E-Y and we have executive roundtables. We have white papers. We have infographics with lots of great stuff on all these topics. Thank you and I'll talk to you next time.