Digital transformation has reached near-universal adoption — but success remains elusive. Most large organizations have spent the last decade modernizing systems, embedding data teams and rethinking customer journeys. Despite this progress, failure rates remain stubbornly high — estimates range from 70% to 88%.
The reasons are well-documented, but the solution lies in something more specific: shifting how transformation is delivered in practice, especially within marketing, data, martech and digital teams.
Let’s unpack practical, often overlooked fixes that sit within our control — the kind that can turn a high-risk program into a measurable success.
Why success remains out of reach — despite all the effort
You’d be hard-pressed to find a large multinational that hasn’t invested heavily in digital transformation over the past decade. New teams, new platforms, new roadmaps — the ambition has been there. The investment was, too.
By now, you’d expect these processes and frameworks are refined, well-tested and even routine. Yet the results suggest otherwise.
The high failure rates mean that for every five companies undergoing this level of change, only one is likely to come out ahead. The rest? Burnt out, over budget or stuck in endless cycles of reinvention — often with consultants still in the room.
At this point, there’s little need to rehash why these programs fail. The cultural challenges, the strategy gaps, the organizational resistance — they’ve all been covered. The consequences are equally well understood: lost revenue, eroded trust, shareholder pressure.
So let’s look at a more productive space — the parts of transformation that are within reach for those working across marketing, martech, data and digital. The focus isn’t on fixing everything but building momentum through targeted, meaningful action.
What follows are four common pitfalls — and practical ways to avoid them.
Pitfall 1: New customer data models lack practical business application
You assembled the team. You built a state-of-the-art data capability. You connected your customer 360 to your new data warehouse. It’s perfect. It’s AI-ready. The dashboards show market data worldwide and can be queried via a conversational UI.
It’s taken three years. Three users open the dashboard more than once a month. The marketing team still uses the Meta segments for targeting, as apparently your dataset does not include in-app browsing behaviors.
Solution: Purpose-built data and dedicated data engineering
Customer 360, a customer-centric data model, is a holy grail for many organizations. And something is to be said for its sheer power — when you see your actual customer emerge and you know what interests them, how often they buy and what messaging will resonate.
Yet proving the value of that data is, understandably, a challenge without activation. You can build the most comprehensive data model in the world, but unless practical activation use cases drive it, it can likely become a sunk cost.
Building data for marketing is one of the lowest-hanging fruit, as these teams already use data and ML models for their targeting — it’s just currently outsourced.
Ground your customer 360 with practical data — event aggregations, attributes and segments that will be easy for marketers to use. Build what is required and what will gain traction, generate momentum and prove ROI on your data investment.
Doing this will secure your early adopters and advocates while giving you time to develop more advanced AI and ML models.
Pitfall 2: Siloed and underutilized proprietary data science initiatives
The marketing team should be the primary target for partnering with data science teams. It’s the path of least resistance — data-driven optimization is ingrained in performance marketing DNA and the teams have an acute awareness of what data will deliver business impact and how.
Yet marketing and data science are often completely disconnected, restricting your ability to leverage your data science investment. This is an opportunity to change that.
Solution: Embedded data science resources
You could create a transformational capability by embedding data science and engineering teams alongside your marketers. Common pain points in marketing transformations include:
- Lack of analytics resources.
- Inability to access, integrate, transform and activate customer data.
Marketing teams often look to external partners because internal teams and datasets are not designed to support this. Involving data scientists and engineers in end-to-end use case development — from creating audience segments and sizing them, to analyzing the results to establish the winning pilots — accelerates performance growth with real-time experimentation and feedback.
It also develops a robust data science capability with data scientists who understand how the data they create is activated and contributes to business performance.
It’s applied, it’s practical and pragmatic — it can prove the ROI on data science investment and create motivated, cross-functional teams. It moves quickly, reducing time from ideation to execution and stripping away organizational barriers.
Dig deeper: How to safeguard your brand during a digital transformation
Pitfall 3: Success measurement is disconnected from business performance
Informed decision-making at scale and the ability to anticipate problems ahead and course-correct in time rely on an impeccable flow of information, communication and tracking the right KPIs.
You might think the transformation is going well if all your metrics are green. However, that relies on everyone using the same set of comparable KPIs that are relevant to the individual initiatives and align with business performance goals.
Ask yourself: Are the reports you’re producing and reading showing the metrics that will drive {insert revenue/sales/cost savings target here}? If not, you might be merrily marching along to join the 70%–88% failure group.
Solution: Built-in measurement and streamlined KPIs
Like any transformation, a business case is produced, budgets developed and work begins. In two, three or five years, you can reap the rewards: double-digit revenue growth, cost reductions, etc.
“Start with the end in mind” is a great mantra. “Carry on as if the end is near” is arguably better.
The exponential growth curve will only materialize if the foundations placed today work toward that big end goal. And you are gathering empirical evidence to continuously validate that this is still achievable and moving forward methodically.
This is when deploying data-driven use-case activations with built-in measurement of your core business KPIs can make a material difference.
If you create and deliver this data to your marketing team, will they drive incremental revenue? Suppose your data engineers and data scientists target the same incremental revenue figure. In that case, the chances are they’ll be more acutely aware of when this data needs to be delivered and what work to prioritize.
Note: You must have frameworks to ensure that those initial pilots ultimately build toward a more cohesive strategic piece. Otherwise, they could accumulate too much technical debt and need constant rework.
However, having access to immediate test opportunities and their results will help you make practical choices based on business KPI delivery — discarding the unsuccessful trials and building out the ones that work. Iteratively validating and enhancing your frameworks will also shape that transformation growth curve.
It will help gain traction, build momentum, and secure stakeholder buy-in. If communicated consistently, it will cement a data-driven culture where measurement is ingrained in everything you do. When the two, three or five years end, you’ll know exactly where you’ve landed compared to that original business case.
Pitfall 4: Transformation initiatives disconnected from short-term, immediate trading
Given the scale of change and the ramp-up required, a delayed yet accelerating revenue realization over several years makes perfect sense in a comprehensive digital transformation roadmap. The potential disruption to BAU revenue delivery is often omitted in these initial transformational forecasts.
Large programs usually place additional pressure on the same high-performing teams and individuals, inevitably causing strain and distraction. Investment and focus shift to new systems, data, organizational and process changes, recruitment, procurement, etc.
And especially if you’ve succumbed to Pitfall 3, your teams could lose sight of what’s happening here and now.
Solution: Balance interim business performance vs. long-term transformational goals
Design your long-term digital transformation roadmap to purposefully target quick wins and provide interim trading support.
Plan to incur some short-term impact on BAU trading and work to mitigate it. For example, most companies will anticipate temporary transformation resource increases. However, you might need to bring in “surge” resources to supplement your day-to-day trading.
You want your best people to work on your transformation for obvious reasons — but those are the same people who have helped your business deliver results. Make sure they are supported. You will only succeed and sustain that success with them on board.
Your transformation plans should also carry enough flexibility to pivot and support immediate business trading needs. Align pilots with key trading seasons — ideally landing in time to test and scale the most successful use cases ahead of your peak season. This way, every achievement and win is amplified.
Embedded teams, focused on core business KPIs, iteratively working toward a long-term goal
Digital transformations are incredibly challenging. Change of this scale over several years is hard to navigate. Maintaining conviction in your direction requires a sustained flow of evidence or an immense amount of trust. The former is much easier to manage.
A digital transformation can also be a ground to build, test and empower — new teams and ways of working, new opportunities to make an impact, see the results and make active choices. And if the choices you make are guided by the principles of:
- Purpose-built, commercially driven data—informed by use-case needs.
- Integrated data science and engineering — powering your marketing activations.
- Joint core business KPIs — aligned, tracked, measured and communicated.
- Iterative, practical activation pilots — measured, assessed and refined — continuously accumulate toward your end goal.
- Active balance between long-term ambition and interim business performance, with trackable quick wins.
- A well-supported team, able to see interim results and make adjustments with direct, tangible and timely feedback.
Then you will be much more likely to join the coveted 30–12% club. Or, at the very least, see the evidence and performance KPIs pointing toward trouble early enough to course-correct in time.
Dig deeper: Why CMOs must be the company’s biggest advocates for digitalization
The post 4 pitfalls of digital transformations and how to avoid them appeared first on MarTech.