– Marie Fenner, Global SVP, Analytics
Executive sponsor and evangelist for Piano Analytics, leading global go-to-market initiatives across EMEA, the Americas, and APAC. With 25+ years in digital analytics and audience measurement, Marie helps organizations turn data into sustainable growth by connecting insights, technology, and customer experience. 

Analytics tools are often viewed as a cost rather than an investment. That framing alone tells you a lot. A "cost" suggests operational overhead. An investment is capital deployed to generate future returns. 

If you intend to use data to predict, optimize, and actively shape outcomes, you are making an investment – and investments deserve proper calculations. So I prefer to think in terms of Total Investment of Ownership. The real question is: what is the full investment required to own and operate an analytics solution properly? 

There are tangible costs such as software fees, migration, and maintenance. But there are also less visible and often more dangerous costs: regulatory exposure, technical debt, and vendor lock-in.  

Below is how I think about these, based on many migrations we’ve supported and what those projects have taught us. 

• Software costs 

• Migration & adoption costs 

• Maintenance costs 

Analytics software pricing: what you're really paying for  

Software fees are typically based on traffic volume – either number of events or number of unique users. Key considerations: 

• Pricing model fit: If your site generates a high volume of events from a small number of users, a user-based pricing model may be more attractive. The opposite is also true: a site with fewer events, but a large number of unique users may prefer event-based pricing. 

• Feature mapping: Cost every feature you realistically need upfront. Surprises are rarely pleasant in procurement. 

• Data mining costs: Some vendors offer native exploration within the platform; others require exporting raw data to environments like Google BigQuery – adding infrastructure costs, usage fees, and engineering overhead. 

• Contract structure: Longer-term agreements (typically 3-5 years) can provide cost predictability and protection against inflationary increases. 

3 steps to plan your analytics migration 

The primary objective during migration is simple: get to value quickly. Ask yourself: 

• Is the software straightforward to implement, and does it integrate with my existing tag management system? 

• How long will we run two systems in parallel? 

• How will dashboards be migrated and users onboarded? 

A pragmatic way to approach the transition is to focus on generating value as quickly as possible. Start with a minimum viable setup:  

• Replicate the essentials: if your current implementation is reasonably solid, port your existing data layer, removing outdated elements. 

• Enrich: Use the new platform's capabilities to add context and depth to your data. 

• Adopt: Involve teams early, build excitement, and establish a culture of continuous improvement. 

Who should handle implementation – in-house, agency, or vendor? 

In our experience, the most effective approach is a collaboration: the client's agency partner leading the project, supported by the vendor team. All roles should be documented in a clear SoW with a defined RACI model. 

The hidden costs of maintaining an analytics platform 

Maintenance is often underestimated – until something breaks. Evaluate: 

• Platform intuitiveness and self-service capability 

• Support tiers and SLA guarantees for uptime and service quality 

• Scalability during traffic spikes – and whether scaling incurs additional cost

Answering these questions surfaces real costs: agency support, internal resource allocation, vendor professional services, training, integration work, and temporary duplication of systems.  

The return on investment multiplies with active usage. Rather than focusing on the number of accounts created, we work with our clients to track monthly active users (MAU). It gives a clearer indication if people are genuinely using data to make more informed decisions. 

Compliance, vendor risk, and other costs you can't ignore

• Regulatory compliance. Fines under GDPR, HIPAA, or CCPA can reach 4% of global annual revenue – but reputational damage and loss of consumer trust cost far more. 

• Financial stability. Assess your vendor's financial health and any M&A activity that could alter your commercial relationship. Vendor instability is a business continuity risk. 

• Innovation culture. The right vendor should evolve as your needs evolve, with a clear roadmap and the capability to execute it. 

Final words 

For senior leaders evaluating an analytics platform, the decision often comes down to four critical questions: 

1. Are we paying for the right pricing model? 
Does the vendor’s pricing structure (events vs. users, feature tiers, data exports) align with our actual traffic and analytics needs? 

2. How quickly will this investment deliver value? 
Do we have a clear migration plan, ownership model (internal, agency, vendor), and a realistic timeline to reach meaningful insights? 

3. Will the organization actually use it? 
Are we measuring success through active usage (e.g., Monthly Active Users)? 

4. Is this vendor a long-term partner? 
Are they financially stable, compliant with relevant regulations, and investing in innovation that aligns with our future needs? 

It doesn't have to be a hefty investment – but it does need to be a deliberate one. Revisit your business goals, reassess your data ambitions, and evaluate whether your current analytics platform is genuinely fit for purpose. 

If the gap between where you are and where you need to be is widening, the real risk is staying still.