This article comes from Robert Solby’s talk, ‘Building a Business Case for Customer Marketing: Communicating Metrics That Matter’, at our 2022 Customer Marketing Summit, check out his full presentation here. 

As customer marketers, we've all been there - trying to prove the value of our programs to leadership but struggling to find the right data to back it up. I know I have!

For years, metrics and reporting felt intimidating and daunting. But I've come to realize that having data fluency is actually key to communicating customer marketing's impact across an organization.

My journey at Adobe has shown me the power of using data as a language to showcase that customer marketing isn't just a cost center, but an indispensable, top-line-producing revenue driver. 

By building a strong metrics and reporting practice, I've been able to influence stakeholders to view our team as a strategic partner, not just a group handling customer references and reviews.

In this article, I'll share the framework I followed to construct a metrics program that proves customer marketing's value to the business. Get ready to go from metrics-phobic to a data-driven pro!

Why customer marketing metrics matter

At the end of the day, quantitative data and hard numbers are what leadership cares about most. 

We can wax poetic about the benefits of customer engagement all we want, but without tying those efforts to revenue, retention, or other key business goals, it's an uphill battle to get buy-in and resources.

That's why building data literacy and a solid metrics foundation is so crucial for customer marketers. It allows us to communicate in a language that executive teams understand and respect. 

When you can show clearly how your customer marketing programs are impacting the business pipeline, reducing churn, or increasing sales cycle efficiency, you suddenly have a seat at the strategic table.

I like to think of customer marketing as an ecosystem of different tactics and programs - customer advisory boards, communities, advocacy initiatives, and more. 

Data is the bridge that connects that ecosystem to quantifiable business impact. 

Leverage the right metrics, and you can definitively prove customer marketing isn't just a nice-to-have, but an essential function that top-performing companies can't do without.

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My framework: Plan, execute, drive insights

Of course, saying you need to build a metrics practice is a lot easier than actually doing it. When I first started down this path at Adobe, I'll admit I was pretty lost and overwhelmed. 

But I knew I had to be bold, take some risks, and form a methodology to do it right, that's what led me to this three-phase framework:

Planning

They say failing to plan is planning to fail, and that's certainly true for metrics projects. My first step was defining the end goals and metrics that truly matter for demonstrating customer marketing's value.

At Adobe, two KPIs rise above all others: revenue impact and customer retention. Those were the numbers I needed to focus on tying back to our programs. 

Your priorities may be different depending on your company's maturity and other factors.

Next, I identified all the key internal stakeholders and partners I'd need to collaborate with to pull this off. I knew I had to get alignment and buy-in from teams like:

  • Marketing analytics
  • Data science 
  • Marketing operations
  • Sales operations
  • Finance

From there, I documented project roles and responsibilities, making sure everyone knew what track they owned. I'm a firm believer in a divide-and-conquer approach for initiatives this big.

Finally, I spent a ton of time understanding our existing tech stack, data sources, and data flows. What data did we already have access to? What were the gaps we needed to fill? How did all the data move between systems? 

This planning phase culminated in a detailed project plan that served as our guiding light and a high-level executive presentation to get stakeholders on the same page with the mission and timelines.

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Execution

With a solid plan in place, it was time to execute. This is where the rubber met the road in actually building out that strong data foundation.

One of the biggest challenges was simply coordinating all the different teams and striking a collaboration cadence. 

We had to set a regular meeting rhythm and designate someone on point to track and hold people accountable as priorities inevitably shifted.

From a technical side, my teammates worked hard on developing the timely data architecture required. Using SQL, Python, and other tools, they made sure all our data sources were integrated and talking to each other properly to feed into centralized reporting and dashboards.

Speaking of which, documentation wasn't an area I could neglect either. Referring back to my process flowcharts and documenting every step of our data journey in detail proved invaluable when we inevitably ran into snags along the way. 

It meant I could quickly pinpoint where issues were occurring and course-correct.

Finally, data validation through QA/QC processes was essential before we could start sharing metrics more broadly. You'd be amazed how often I found data duplication issues, parsing mistakes, or other gremlins in there. 

In the end, we had robust reporting dashboards combining metrics across programs, slicing and dicing the data in impactful ways like:

🌍 Customer advocacy program penetration by region. 

🤝 Retention rates for engaged advocates versus the average customer.

💸 Community membership conversions to new revenue opportunities.

Being able to visualize the data opened so many new doors to derive insights and tailor program strategies.

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Driving actionable insights

However, metrics don't actually drive business impact on their own. That's why the most important (and fun!) phase was taking those data outputs and truly doing something with them.

First and foremost, you have to critically digest what the data is telling you. Never just take the numbers at face value. I made sure to analyze the findings from multiple lenses:

- What new trends or patterns was I seeing emerge over time?

- Were there any surprises that contradicted my original hypotheses? 

- How did the data compare across different segments or regions?

Once I had a full grasp of the story the data was telling, I could start developing actionable strategies and tactics to improve performance based on those insights. 

My core principles were using the data to be innovative, intentional, and influential in three key ways:

1. Innovating our customer marketing programs, refining them in ways we'd never considered, to boost engagement and impact.

2. Being more intentional and targeted in our outreach, segmentation, and nurturing using account and behavioral data to guide our plays.

3. Influencing internal teams by providing quantitative evidence to sway skeptics and get more allies for customer marketing as a priority.

Of course, crafting data-driven strategies was only half the battle. I then had to evangelize those insights internally to raise awareness and get buy-in from all our cross-functional partners. 

To do this, I tailored how I communicated and visualized the data based on the audience:

  • For customer success teams hyper-focused on retention, I highlighted metrics around reduced churn for engaged advocates.
  • For sales, I reframed the data around faster cycle times and pipeline acceleration.  
  • For marketing, I showed how our programs directly attributed to new revenue opportunities.

Finally, even after implementing fresh tactics based on the data, I knew measurement couldn't stop there. 

We had to set up continuous processes to track the impact of those new efforts over time. 

Key takeaways

Looking back at my journey over the last few years at Adobe, I’ve learned so many valuable lessons about building effective metrics and reporting practices for customer marketing:

1. Plan for success upfront. Don't try to rush or cut corners. Define your goals and the metrics that matter most ahead of time. Map out your data landscape and get full stakeholder alignment. 

2. Don't bite off more than you can chew. Start by just focusing on one customer marketing program or subset of metrics/data sources you can realistically take on at first. 

3. Embrace the journey and view iteration as a good thing. You will inevitably hit roadblocks and have to adapt on the fly. Just make sure to thoroughly document everything so you can continually refine your work.

At the end of the day, you don't need to become a data scientist. But you do need to get comfortable leveraging both qualitative insights and quantitative metrics in a balanced way to champion your customer marketing programs.

Follow a systematic approach to planning, execution, and deriving insights, and those numbers will become your most powerful weapons for influence within your organization. 

Don't let a lack of data hold you back from getting the resources and recognition your team deserves. 

Start building that strong metrics foundation today, and you'll never look back!