Marketing Performance Measurement: A Maturity Model
I was recently invited to speak at a digital marketing and analytics event in Toronto. The agenda included an exploration of the current digital marketing landscape, best practices for using data and analytics to drive business growth, and a workshop component to assess how well these best practices were being followed in each of the attendees’ companies. The end goal was to provide practical advice that each person could implement in their business to drive a more effective digital marketing strategy.
The room was packed with Marketing VPs, Directors, Heads of Sales and a handful of Business Analysts, from some of Canada’s biggest businesses. I’ll be the first to admit that as a CEO, I’m not an in-the-trenches digital marketing expert. Fortunately for everyone, my two co-speakers were. My purpose for being at the event was to share my experiences, as well as our customers’ experiences, in applying data and analytics to measure and improve marketing performance.
My favorite part of the event came early on. Having done introductions and some initial scene-setting, we asked everyone in the room to honestly evaluate how far along they were on their digital marketing journey. We used what we call a “digital marketing maturity matrix” to help attendees identify their organization’s level of maturity in four specific areas of the marketing cycle; targeting prospects, capturing leads, nurturing leads, and measuring performance. Target, Capture, Nurture, Measure.
My job was to talk about that last area: Measure. Specifically, how to use analytics to measure marketing performance and then apply those insights to drive improvements across the other three areas. In this post, I’m going to explain the three maturity levels we discussed (Basic, Advanced, Optimized) and how to assess where on the maturity matrix your organization sits in terms of marketing performance measurement.
At the end of this post, I’ll share with you how most of the marketing leaders in Toronto rated themselves so you can benchmark yourself against some of Canada’s biggest brands.
Let’s talk about Measure.
Basic Analytics: Spreadsheets and “Gut Feel”
Spreadsheets still proliferate in the enterprise. Not just in marketing, but all over the shop. A Harvard Business Review last year reported that 92% of business managers still use spreadsheets for data analysis. This stat doesn’t surprise me. Despite the proliferation of BI and analytics tools on the market, many of us feel most comfortable and in control when using a spreadsheet, even when we know it’s not the best tool for the job.
We see it time and again when we talk to marketing teams about their data. Customer segmentation, prospect profiling, campaign tracking, spend reporting, LTV and CAC metrics, etc. are still commonly calculated and reported using spreadsheets. Sometimes several days per month are spent on pulling together the sales and marketing numbers for the Board from multiple different marketing systems. This is often supplemented by anecdotal feedback and “gut feel” projections to fill the gaps.
I’m sure every marketing leader reading this blog is familiar with the problems that ensue with this approach. Numbers that don’t add up month to month. Sales teams tracking results differently to Marketing teams which causes eternal, cross-department data wars. Spreadsheet errors leading to shaky spend decisions. Not to mention the sheer amount of time wasted on manually tracking and mashing together data from the same sources from across the business every month.
For me, the single biggest problem with spreadsheets though is their inherent lack of agility. They are slow to put together, slow to edit, and they only provide a one-dimensional snapshot of your data at a particular point in time. They are not a reliable or responsive way to track performance and make decisions on a critical part of your business.
So if you’re using spreadsheets and “gut-feeling” your way through the majority of your marketing performance measurement and reporting, you’re at level one on the maturity matrix. You’re using Basic analytics. It’s better than no analytics at all (which, believe me, I’ve seen plenty of too), and you’re definitely not alone. Still, there’s clearly room for improvement.
It’s not just small businesses with small budgets that are guilty of this either. It’s very common in large enterprises too, even where there are tools available to deliver more automated analytics. Which brings me to maturity level two…
Advanced Analytics: Embedded
Today, many marketing applications come with some form of analytics capability built-in. Think of the reporting capabilities available in your CRM, the engagement metrics in a social media dashboard, or the attribution analytics in your marketing automation tools. These are embedded application analytics.
Embedded analytics are great. They provide built-in reporting and dashboarding capabilities that are ready to go as soon as you start using the application. You never have to leave the application environment and there is very little manual data mashing involved.
The challenge comes with data fragmentation. Most embedded analytics do not allow you to add in or analyze data beyond the data generated and used in that specific app. This causes data silo problems and information myopia. Does all of your marketing data live in just one application? Of course not. You probably have several, if not dozens, of marketing applications that are used regularly and are generating important data. This multi-app environment is particularly prominent in the marketing world.
Using only embedded analytics creates a piecemeal approach to tracking marketing performance and doing analysis. It’s like having all of the puzzle pieces to create the jigsaw but no way to put them all together. You can’t get a single view of your marketing organization, its performance and problems. You can’t get a single, consolidated view of a customer or prospect if the data points are spread across multiple systems. You cannot easily incorporate predictive models, and any whitespace analysis will be limited.
Lack of customization options can also be problematic. You may not have the flexibility to slice and dice your data however you want in an embedded analytics scenario. This gets frustrating very quickly when you know the data is available and are in the throes of a deep data discovery session.
The other issue with embedded analytics is that you are dealing with raw data. It likely hasn’t been “cleaned” for duplicates, or for missing or inaccurate information. To get the most accurate analytics and insights, you should ideally be analyzing data from a single, central data repository with some kind of data quality monitoring in place.
In many cases, the embedded analytics approach forces you to move in one direction or the other on the maturity matrix. You either end up going to an Optimized measurement approach, or revert back to spreadsheets—only this time, you’re mashing together data from various apps. Marketers typically still end up doing some spreadsheeting in order to get single, consolidated view of their world. Back to Square One. Hopefully I can convince anyone using an embedded use case to move in a positive direction (optimized data integration) not a negative direction (basic spreadsheets).
Optimized Analytics: Integrated Data & Continuous Analysis
The optimal marketing analytics strategy uses a single, composite analytics application to integrate your marketing data into a central data repository. Whether that data sits in your CRM, marketing automation tools, email marketing, social media management platform, budgeting application, google analytics, whatever. Your solution should automatically pull the data from those places on a regular basis and pump the very latest information into your reports and dashboards. This should happen automatically so can continuously analysis your data and use it as the basis for your marketing decision-making every day.
Perhaps the single most valuable thing about using a dedicated business intelligence and data analytics platform is flexibility. You can slice and dice your data any way you want. You can present it via dashboards that make sense for how you like to tell your marketing story. You can add in data from anywhere and model just about anything. And because you are accumulating and storing data in one place, you can easily see performance trends over time.
Ultimately, you have all of your data at your fingertips, rather than disparate pieces of information with some gut feel and assumptions thrown in.
A built-in data quality monitoring process can also be really useful in terms of ensuring accuracy of your underlying data. The Stytch Connector for Dun & Bradstreet is one such example of how marketers can cleanse and enrich their data in a more automated way as a part of their analytics process. If you have a hard time trusting the results of your analysis, or simply can’t get the information you need because of data gaps, then data quality processes and tools should be a critical piece of your analytics strategy.
Your Marketing Performance Measurement
When you have an optimized marketing analytics strategy, data becomes the heart of what you do.
Going back to that room in Toronto, we talked a lot about data as the foundation for success. About how to truly become data-driven and data-inspired marketers. It sounds like a difficult and lengthy process, and I think it scares many marketing teams. As data rapidly grows and fragments, it can be hard to know how to bring it all together and then take meaningful action from it. In fact, according to a recent McKinsey & Company survey, less than half of CMOs have qualitative metrics to show the impact of marketing spending on the business. We need to change that. I want marketers to feel empowered by their data, not overwhelmed by it. That’s core to what we believe at Stytch.
So how did the marketers in Toronto fare against the Maturity Matrix, in terms of measuring marketing performance? Not surprisingly, the room was split pretty evenly between levels one and two—Basic and Advanced. Most marketing organizations, regardless of size, are using a combination of spreadsheets and embedded analytics to track performance, diagnose problems and inform decision-making. So if you’re sitting somewhere between Basic and Advanced, you’re not alone. The good news is, it’s nowhere near as hard as it sounds to move to an optimized marketing analytics program.
I would love to hear your thoughts on the maturity matrix, and where your organization sits on it. To learn more about optimizing your marketing performance measurement get started with a personalized demo.