Climbing the Analytics Maturity Curve

In this chapter, I’ll introduce you to one of the most essential frameworks in this book – the Analytics Maturity Curve. Although relatively simple, it has the power to orientate us at every level of our journey. We can agree on where we are now, where we want to be, and how determined we are to get there, but why bother? Do we just want unF**ked data, or will we do something more impressive?

We’ll go through level 1 and what it means to be looking forward to good data and why bad data is such a prevalent problem. We’ll look at the importance of an objective audit. I’ll give you the must-have information you should consider for any audit or reconciliation, starting with the new Google Analytics 4 as our example and what you should expect from any new implementation or audit.

1.1 – A Path to Somewhere

In this book, you will find many frameworks, images, diagrams, glossaries, and workable templates to take into your role. I’ll present you with questions to consider in each chapter to bring this all back to your own uniquely F**ked situation so you can finally understand why your data is F**ked and what to do about it. Let’s start with one of the most essential and valuable frameworks.

Source: McKtui Consulting

There are many like it, but this one is mine. It is my life. I must master it as I must master my life. Without it, my analysis would be useless. Full Metal Jacket – probably, if the war was about data.

There are lots of marketing curves out there, typically to explain the various stages of development. My favourite was Bill Gassman’s ‘Tool Maturity’. It was this model and conversations with clients and colleagues that led to the ‘Analytics Maturity Curve’ stages that every company could go through to achieve whatever outcome they set their sights on.

Our graph shows us time and expected investment going from left to right, starting from level 1, eventuating at level 5. The real value to the business increases as we make our way through, and the curve bends up gradually before leaping up from level 3 to level 4 and then to level 5. Between level 3 and level 4, there is an obvious GAP, but don’t worry about that yet; let’s walk before we run and look at the reasons for this later.

So how mature is your organisation in digital and web analytics and why does that matter, and how do we make sure we don’t keep falling off?

‘Maturity describes how deeply and effectively your organisation uses tools, people, processes, and strategy to manage and analyses data to inform business decisions.’ - Król, Karol and Zdonek, Dariusz. "Analytics Maturity Models: An Overview."

Most importantly, it’s a great place to begin and define our problem. Your company may not know a lot about analytics. Still, given a simple description of each stage, most people will immediately have an opinion on where they think the company is and where they feel they should be, and that conversation is a great place to start.

The stages break down like this and are not necessarily always sequential:

Level Zero – just starting, or you do not trust your data. You may have some version of Google Analytics (GA) added to your website, or you’re using a Content Management System (CMS) analytics package, but you don’t really understand what you’re looking at, why it matters, or whether it’s even accurate.

Level One – is about ensuring you have a basic level of accurate web metric reporting. You have set the foundations for actionable insights with reliable basic web metrics. You have limited marketing and single-source web data but trust its accuracy. You’ve got – Basic, Accurate Web Metrics.

Level Two – is about using your data to act and continuing to work on metrics, accuracy, and process. You are optimising behaviour and traffic based on known previously-agreed key performance indicators (KPIs) and agreed targets using basic self-service dashboards or custom-built reports. You are doing – Performance Driven Marketing.

Level Three – is about laying the groundwork for greater personalisation and uncovering detailed insights and wisdom. You have established vital and detailed target segments and specific user journeys. You are starting to integrate more sources of customer data. You’re building your way to – Actionable Insights.

Level Four – is about personalisation and taking a 330° view of a customer (30° because of privacy). You have built models on Customer Lifetime Value (CLV) and are automating either content or marketing. You likely have a Customer Relationship Management (CRM) system integrated with the rest of your marketing business technology stack. You may use a Customer Data Platform (CDP) and an integrated data warehouse. There are various degrees to success, but holy shit, you’re doing it – Omni-channel Marketing through Automated Personalisation.

Level Five – is taking the foundation of all the work completed and applying data science-powered analysis (often with artificial intelligence (AI)), and implementation over the top. You are planning to use or are already using AI and move from ‘Descriptive’ to ‘Predictive’ and ‘Prescriptive’ analytics. Hello, great sage; you’re using Predictive Analytics.

The first questions to ask regarding your organisation are:

·       What level would you say you are now?

·       What level do you hope to be in the next six months?

·       What level do you hope to be in the next 12 months?

·       What level do you hope to be eventually?

Knowing where you are and where you want to be is not only the start of the conversation but will help you to start assessing how much investment and time may be required to get there. Just getting some level of agreement around where you are and where you want to be is step one. For some companies, if you are small, or can’t readily see the possible benefits for you, then stages 1–2 may be good enough; but make sure you at least ask the question because if your competitors or customers decide it’s not, you might be in for some trouble.

We will go through the details of each stage, but here is an example of what could feature in a possible project plan for your organisation. This example is for a medium- to corporate-sized business. Throughout the book, where we don’t have an appropriate real-life example, we’ll use an up-and-coming, ready-to-take-industry-by-storm and entirely imaginary business: Shoe-in.com.

The Analytics Maturity Curve – Example

Following our overall strategic priorities and project goal, we are working to enable Shoe-in.com to progress from level 1 (as assessed) to level 3 in 12 months through the Analytics Maturity Curve led by the following key activities. This is how they relate to each stage:

Level 1–3 activities (looking back):

·       Establish Technical Foundations – Implement a CDP to standardise existing data collection and serve as a foundation for later Level 4–5 activities, such as CRM integration and predictive analytics. The initial focus will be Web Analytics tools.

·       Implementation of Core Web Analytics – Key groundwork completed regarding existing solution design, basic tracking of page views, events, and measurement planning. More testing is also recommended to validate existing tracking.

·       Self-Service Dashboards; Tactical and Strategic Reporting – Empowering the team at Shoe-in.com to self-serve and focus on business decision-making with up-to-date data and insights at their fingertips.

·       Segmentation and User Journey Mapping – There are gaps in coverage of engagement tracking for all assets/value streams. Work must be done to ensure that personas or segments are set up correctly and usable across all current and future sources and destinations.

Level 4–5 activities:

·       Data Integration (chasm) – Current priority. Integration of other existing MarTech tools, such as CRM.

·       Data Science and Performance Management (looking forward) – Development of further personalisation and statistical analysis-driven segmentation such as propensity to covert (high engagement). Plan to review database connections.

A list like this, although requiring much more detail in practice, allows us to plan and prioritise the next steps, mapping our trajectory through the Analytics Maturity Curve, preparing for potential difficulties, and identifying lack of knowledge in certain areas.

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