Imagine this scene: a video call full of product, marketing, and analytics folks. They all look at a slide full of data from last week. Think of a table with 50 different data points. “Metric A is not correct; we had problems with the pipeline, and we are in touch with Alex to figure out a fix”, says Jordan, the marketing manager. “Same with metric B”, Avery, the product manager, replies, “we had tracking issues last week”. At the end of the weekly meeting, we spent most of our time discussing the issues instead of what the data tells us and how we should respond.

Just two hours later, the weekly experiment review takes place. Once again, Jamie, one of the growth product managers, announces that they must delay the experiment. Last week, the user bucketing failed, and they assigned too many users to the control group. Yesterday, when the team finally released the fix, they discovered that a colleague started an experiment on the same page using 100% of the traffic for six weeks without previous alignment.

These scenarios aren't isolated incidents—they're symptoms of a systemic problem plaguing organisations that have invested heavily in data infrastructure but struggle with actual execution. According to research from Wavestone and NewVantage Partners1, nearly 80% of data leaders cite culture, people, and process, not technology, as the top barrier to becoming data-driven. Despite massive investments in data infrastructure, only 20.6% of organisations say they've established a data culture, the same report states.

The misalignment between product vision and data reality costs companies millions in wasted investment and missed opportunities. This disconnect isn't just inefficient—it harms business growth and innovation.

The Four Horsemen of Data-Product Dysfunction

THE TRUST GAP

At the heart of data-product misalignment lies a fundamental issue of trust. According to research by Precisely and Drexel LeBow2, 67% of business leaders report that they don't fully trust the data their organisation is using. Similarly, a BARC study on AI Observability3 found that only 59% of respondents trust their model inputs and outputs.

These stats and the trust deficits are unsurprising and have several root causes.

• Inconsistent implementation: When tracking is implemented differently across platforms or products, conflicting numbers emerge. Often, this comes from organisational design. If a company has a mobile app team and a web team working on the same feature on different platforms but not cross-functionally and without proper tracking definitions, you’ll likely end up with two different names for the same user action.

• Poor data governance: Data integrity suffers without clear ownership and quality standards. Finding the accountable person can be difficult, especially with high-level metrics like users and sessions, which result from many teams’ efforts.

• Siloed collection systems: Disconnected data sources create multiple versions of the "truth." App installs are a great example. While product teams rely on their analytics system or the raw data of the app stores, marketing often depends on their mobile measurement partner tool (MMP), creating two entirely different views on the same KPI.

The psychological impact is profound: when teams stop believing in data, they stop using it. When two dashboards show conflicting numbers for the same metric, both become suspect. Organisations develop competing data narratives, each department wielding its preferred numbers in territorial battles over resources and priorities.

Without a single source of truth, the very foundation of data-driven decision making crumbles.

THE TRANSLATION BARRIER

A particularly stubborn problem is the divide between technical and product teams. According to research by Qlik and Accenture4, only 27% of organisations said their data and analytics projects produce actionable insights. This isn't surprising when data teams focus on what can be measured while product teams need what should be measured.

I came across data science teams that build interesting models but are entirely disconnected from the needs of the product teams. It’s like creating features without doing user research upfront. It is a time investment without prior knowledge of its potential return on investment.

McKinsey's Analytics Translator Report5 highlights this issue: "Translators help ensure that deep insights... translate into impact at scale." Without these translators, the expertise divide between data analysts lacking product context and product managers lacking data literacy becomes unbridgeable.

This results in:

• Technical terminology that obscures rather than clarifies business objectives

• Metrics that track activity but fail to connect to meaningful business impact

• Data collection that misses crucial context needed for product decisions

When a product manager asks, "Are users enjoying the new feature?" and receives an answer about "session duration variance by segment," the translation has failed. The data might be accurate, but it doesn't answer the question in a way that drives action.

We know similar gaps between product and engineering or design, which lead to the creation of the product trio to overcome barriers and improve mutual knowledge and understanding. As I laid out in a previous article, it is about time to add the analyst and form a quartet. When analysts and data scientists are part of a team with a specific mission and target, they will learn the business context, the user problems and start to come up with valuable insights and solutions.

THE IMPLEMENTATION CANYON

The journey from data strategy to execution often resembles crossing a treacherous canyon, with many falling into the abyss before reaching the other side.

According to Precisely & Drexel LeBow research6, only 46% of organisations report having a comprehensive data strategy to begin with. But even among those with strategies, many fail to implement them effectively. This reveals a systemic disconnect between strategic planning and actual execution.

This canyon forms through several common patterns:

• Strategic frameworks created without awareness of operational realities

• Technical implementations that prioritise architectural elegance over user needs

• Handoffs between strategy and implementation teams with inadequate knowledge transfer

A particularly illuminating example of this canyon occurred in an organisation where the data team created a strategy internally but without sufficiently involving the operators from marketing and product. This led to a new custom-built data lake made by the data engineering team with an architecture that only some parts of the company used. Meanwhile, analysts from product and marketing, as well as product managers, continued using a competing infrastructure.

When the organisation finally created a new unified data model to consolidate efforts, everyone was forced to move to the custom-made data lake, only to discover extremely slow query performance and the need to rewrite queries entirely as the new system couldn't handle existing workflows. The solution had been built not with all end-user needs in mind.

This scenario perfectly exemplifies the Implementation Canyon: technically sound strategies fail to deliver value because they're disconnected from the practical realities of those who need to use them. The result is the "shelf-ware" phenomenon, expensive but poorly implemented analytics frameworks that fail to serve their purpose while teams struggle with the real-world complexities of execution.

Without an intentional bridge across this implementation canyon, organisations waste resources building systems that fail to serve their intended purpose, and the gap between strategic intent and operational reality continues to widen.​​​​​​​​​​​​​​​​

THE CAPABILITY VACUUM

The fourth dysfunction extends beyond dependency on external consultants to encompass a more fundamental challenge: the failure to build cross-functional capabilities within the organisation. According to Qlik and Accenture's research7 on data literacy, only 21% of the global workforce is fully confident in their data literacy skills.

This capability vacuum manifests in various ways:

• Knowledge silos that prevent cross-functional understanding

• Centralised "service provider" models that fail to build distributed expertise

• Documentation deficits that make knowledge transfer nearly impossible

• Role boundaries that limit skill development beyond immediate responsibilities

I've seen this dynamic play out in many companies where tracking is defined by analysts, often from a central team, who are not fully knowledgeable of what the product team is trying to learn. Instead of training product managers to build efficient tracking while maintaining consistency, organisations fall back on providing analytics as a service. This approach leads to a continuous, siloed understanding and limits the mindset and capabilities development of product managers.

The consequences are significant: when product requirements change, teams lack the knowledge to adapt their tracking appropriately. When new questions arise, they must wait for specialised resources rather than exploring data independently. And when analysts move on, they take critical context with them that isn't documented or transferred.

As the Qlik and Accenture report8 notes: "Organizations must provide employees with the tools, processes and methodologies that enable them to use data." This isn't just about technical training, it's about breaking down the artificial boundaries between those who "do data" and those who "use data."

Without intentional capability building across functions, companies remain trapped in inefficient service models where data expertise is concentrated in a few individuals or teams rather than embedded throughout the organisation as a shared capability. This creates bottlenecks and dependencies and ultimately limits the organisation’s ability to be truly data-driven.​​​​​​​​​​​​​​​​

The True Business Impact

The consequences of these dysfunctions go far beyond frustration and inefficiency, they directly impact business performance.

DIRECT FINANCIAL COSTS

The financial burden of data-product misalignment is substantial:

• Wasted infrastructure investment: Companies invest heavily in data systems that remain underutilised or untrusted.

• Lost productivity: According to Qlik and Accenture9, "Companies lose an average of more than five working days (43 hours) per employee each year due to data-related stress and procrastination."

• Quality issues: Wavestone/NewVantage Partners10 notes that only 37% of organisations say they have been able to improve data quality, meaning most companies continue to pay for suboptimal data.

• Maintenance costs: Organisations spend significant resources maintaining systems that don't deliver actionable insights.

Beyond these direct costs, teams waste countless hours reconciling conflicting data sources, creating parallel reporting systems, and engaging in "data detective work" to verify basic information.

DECISION QUALITY COSTS

Perhaps most concerning is the impact on decision quality:

• Reverting to gut feeling: Qlik and Accenture11 found that only 37% of employees trust their decisions more when based on data, while 48% defer to gut feeling.

• HiPPO-driven decisions: As IDC and Heap Analytics note12, "Decisions are often driven by the HIPPO (highest paid person's opinion) without regard for data."

• False negatives: Potentially successful initiatives are killed due to measurement errors or misinterpretation.

• False positives: Failed strategies continue to receive investment because data doesn't effectively capture their shortcomings.

When data systems fail to provide clear, trusted insights, organisations fall back on intuition, often disguised with post-hoc data justifications. The compounding effect of consecutive misguided decisions can derail product strategy and market positioning.

GROWTH AND INNOVATION COSTS

The impact on growth and innovation is equally significant:

• Velocity gaps: IDC and Heap Analytics13 found that data “Leaders can answer product performance questions in minutes or hours; lagging orgs take days or longer."

• Experimentation failures: Without reliable data, experimentation programs cannot confidently validate or invalidate hypotheses.

• Innovation barriers: Precisely and Drexel LeBow14 note that "Only 12% say their organisation's data is of sufficient quality and accessibility for AI," hampering innovation initiatives.

• Competitive disadvantage: Organisations with mature data practices can identify growth opportunities and optimise experiences faster than competitors struggling with data basics.

In rapidly evolving markets, these velocity and capability gaps can mean the difference between market leadership and obsolescence.

ORGANISATIONAL AND CULTURAL COSTS

Perhaps most insidious are the long-term cultural costs:

• Cross-functional friction: Tension grows between technical and product teams when collaboration repeatedly fails.

• Talent retention challenges: High-performing data-literal professionals leave organisations where their work doesn't translate to impact.

• Cynicism cycle: Failed data initiatives breed cynicism about future initiatives, creating a downward spiral of decreasing engagement.

Cultural damage compounds over time, making each subsequent attempt at building data capabilities more difficult.

Recognising Misalignment Signals

Before organisations can address these challenges, they must recognise the warning signs.

• Data is being used primarily to justify decisions rather than to inform them

• Product roadmaps disconnected from analytics insights

• Multiple, conflicting sources of "truth" for key metrics

• Regular debates about basic definitions and measurement approaches

• Data teams focused on collection rather than activation

• Extensive time spent on manual reporting rather than insight generation

Looking Forward: Principles for Better Alignment

While the challenges are substantial, they are not insurmountable. Organisations must recognise, as IDC and Heap Analytics15 note, "the need for both data access and data confidence to empower decision making." For that, we need to start building bridges between technical experts and operational leaders.

The path forward requires:

1. Shared objectives between data and product teams

2. Communication frameworks that bridge technical and business understanding

3. Governance models that maintain data quality while serving business needs

4. Knowledge transfer that builds internal capabilities systematically

The Paradox of Modern Organisations

We face a striking paradox: organisations have more data than ever before, yet many have less clarity on what actions to take. The companies that will thrive in the coming years are those that recognise these challenges as strategic rather than merely technical. They will invest not just in data infrastructure, but also in the human systems and organisational bridges that translate data into action.

For leaders navigating these challenges, the first step is honest assessment. Where do your organisation's data and product teams most disconnect? Which of the four dysfunctions causes the most significant impact in your context? And most importantly, what bridges can you begin building to close these gaps?

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Jakob Gehring is a product growth leader with 20+ years of experience in Europe's largest marketplaces. He specialises in bridging the gap between data analytics and product management through practical, implementation-focused approaches.

This article dives into the hidden costs of broken user journeys between the web and the app.

Disconnected Experiences: The Web-to-App Problem

Have you ever come across broken journeys? Think about the last time you used a company’s website and app and experienced one of the following challenges.

• You click on a marketing link and find yourself on a generic app store page.

• A link you click opens the homepage instead of the specific content you were looking for.

• The installed app does not open; instead, you land on the webpage, and you find yourself manually searching for the product in the app.

Those incidents happen continuously and are symptoms of a widespread problem affecting businesses across industries. According to a report by Mobot, “only 28.6% of attempted deep links on Facebook’s app succeed”¹, which poses a direct financial impact in light of more than a third of digital ad spending going to Facebook in the US in the first half of 2024².

The Triple Threat: How Broken User Flows Damage Your Business

Broken user journeys have consequences beyond users’ frustration. Let’s examine three areas in which your business will be impacted.

1. Wasted Marketing Spend

We spend more and more on digital ads to drive traffic to our digital properties -13 % more year over year alone in Germany, according to Sensor Tower³. With digital ads targeting acquisition and re-engagement of users, smooth deep linking becomes crucial for the success of your marketing program and the efficiency of the budget used. Besides wasting real money due to failed attempts of connecting users to your content and offerings, you lose out long-term by reduced user retention. According to AppsFlyer, “when comparing apps that run re-engagement campaigns vs. apps that do not, we can see that the first group is enjoying a staggering 85% uplift in week 12 retention rates”⁴.

2. Lost Conversions

Failing to bring users to the content you advertised not only drains your marketing budget. You are losing out on conversions, too. According to Think with Google, “advertisers who have implemented deep linking see over 2X uplift in conversion rate”⁵. Seamless web-to-app flows can help to compensate for the eight percentage points higher cart abandonment rates we observe on mobile devices compared to the desktop web.

Looking at data from February 2024 to January 2025, the average car abandonment rate on mobile devices is 78% compared to 70% on the desktop web.⁶ With an average of 76% of eCommerce traffic⁷ coming from mobile devices, you need to provide frictionless entry to your app as 88% of the time people spend on their mobile device is in apps⁸.

3. Missed User Trust

More time spent in your app means higher engagement and increased loyalty. Especially if you try to attract a younger audience, you want to build journeys that lead to your mobile app. According to a report by Tapcart from 2024, younger generations (Gen Z and Millennials, who were born after 1981) show a growing preference for mobile apps, with about a third in favour of apps⁹.

But how do you attract those users? With a trustworthy brand which provides a good user experience. If 32% of users are ready to stop using a brand they love after just having one bad experience, based on a 2018 PwC survey¹⁰, you want to make sure your acquisition journeys are well constructed. Your mobile app is even increasing your brand’s trustworthiness according to 53% overall and a staggering 71% of Gen Z survey respondents¹¹.

The Potential of Getting It Right

How does the grass look on the other side for companies that successfully addressed cross-platform user experience challenges? Branch data suggests that converting mobile web users to loyal app users is 82% less expensive than running app install ads¹². Think with Google states that “according to a recent global study [they] performed with Kantar, 87% of retailers agree that their app users are more loyal and have a higher lifetime value than nonusers”¹³. So companies experience less cost and increased loyalty, and with functioning and well-designed deep linking, you can nearly double your conversion rates compared to paid media, according to AppsFlyer data from 2024¹⁴.

Understanding the Challenge

Let’s not talk about solutions right now but try to understand the full scope of the challenges of cross-platform journeys.

Technical Complexity

Our users are present on multiple devices, operating systems, and browsers. In combination with the various marketing channels for paid and owned media, we have complex journey scenarios that require careful planning, implementation, and thorough testing.

User Expectations

Your users expect seamless experience and working transitions between desktop, mobile web and apps. Friction or even failure leads to lower trust and brand loyalty, with the competition stepping in.

Looking Ahead

Ignoring broken cross-platform journeys will increase costs and further impact business metrics. We need to stay on top of continuously changing consumer behaviour and treat those journeys as strategic benefits rather than technical issues. It is not about user frustration but how we impact marketing effectiveness, conversion rates, user loyalty and brand perception.

In my next article, I will explore why cross-platform user experience matters more than ever.

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Footnotes

1. The state of deep linking Report: Social App Review - MoBot app testing platform. (n.d.). Retrieved March 4, 2025, from https://www.mobot.io/blog/the-state-of-deep-linking-success-rates-across-major-social-channels

2. Sensor Tower. (n.d.). State of digital advertising. Retrieved March 4, 2025, from https://sensortower.com/state-of-digital-advertising-2024

3. Sensor Tower. (n.d.). State of digital advertising. Retrieved March 4, 2025, from https://sensortower.com/state-of-digital-advertising-2024

4. AppsFlyer. (2024, June 17). Re-engagement in the era of privacy – the definitive guide. AppsFlyer. Retrieved March 4, 2025, from https://www.appsflyer.com/resources/guides/re-engagement-privacy-era-2/

5. Jones, L. (2024, August 29). Marketers who prioritize apps are winning today — and tomorrow. Think With Google. Retrieved March 4, 2025, from https://www.thinkwithgoogle.com/marketing-strategies/app-and-mobile/marketing-apps/

6. Shopping cart abandonment rate for eCommerce. (n.d.). Retrieved March 5, 2025, from https://marketing.dynamicyield.com/benchmarks/cart-abandonment-rate/

7. eCommerce device usage statistics. (n.d.). Retrieved March 5, 2025, from https://marketing.dynamicyield.com/benchmarks/device-usage/

8. Wurmser, Y. (2020, July 9). The majority of Americans’ mobile time spent takes place in apps. EMARKETER. Retrieved March 5, 2025, from https://www.emarketer.com/content/the-majority-of-americans-mobile-time-spent-takes-place-in-apps

9. BFCM Must-Have Tips and Data-Backed Consumer Insights | TapCart. (n.d.). Retrieved March 5, 2025, from https://www.tapcart.com/report/bfcm-consumer-trends-2024

10. PricewaterhouseCoopers. (n.d.). Experience is everything: here’s how to get it right. PwC. Retrieved March 5, 2025, from https://www.pwc.com/us/en/services/consulting/library/consumer-intelligence-series/future-of-customer-experience.html

11. BFCM Must-Have Tips and Data-Backed Consumer Insights | TapCart. (n.d.). Retrieved March 5, 2025, from https://www.tapcart.com/report/bfcm-consumer-trends-2024

12. Singh, A. (2025, January 3). How to increase your mobile conversion rates in a privacy-led world. Branch. Retrieved March 5, 2025, from https://www.branch.io/resources/blog/how-to-increase-your-mobile-conversion-rates-in-a-privacy-led-world

13. Jones, L. (2024b, August 29). Marketers who prioritize apps are winning today — and tomorrow. Think With Google. https://www.thinkwithgoogle.com/marketing-strategies/app-and-mobile/marketing-apps/

14. Kleczewski, D. (2025, February 25). New data reveals: Owned media + deep linking reign supreme in 2025. AppsFlyer. Retrieved March 5, 2025, from https://www.appsflyer.com/blog/trends-insights/owned-media-deep-linking/

We have to ask ourselves, “How does one cohort of users lead to another cohort of users?”. Consequently, we should look at the entire journey of a user and not stop at the acquisition. In the world of growth loops, we can observe, for example, how we harness revenue or user-generated content to attract new users. Another powerful way of utilising users is word of mouth, like referrals, which benefits from our general trust towards peers and family.

Referral Basics

You can split referrals into two substantial types, pull and push. In the first type, the pull referral, someone approaches a user of your product and asks for a recommendation. For example, let’s assume your washing machine broke, and you talk about it with a family member. In that conversation, you could ask for more reliable washing machine brands. Your family member might respond by saying that her machine is reliable, and she can recommend the brand.

The second type, on the other hand, the push referral, is triggered because you gain something from sharing. For example, you played a game and finished a level with excellent results. When you share your results on social media, you receive some recognition through likes and comments in return.

Nurturing Sharing

While we cannot influence pull referrals directly, we can nurture users to push out recommendations. Within mobile apps, for example, it’s typical to ask our users to leave a rating in the app store.

Another common thing is a share button on content within a product. In e-commerce, it’s about the offered product; media products, in contrast, let you share their videos, audio, and articles. In games, you share achievements. All these referrals have different rewards for the user and the same benefit for the product, increasing reach to grow the user base.

As with all aspects of the product experience, you can influence the user’s behaviour. For example, you can add more engaging elements to a product that motivates users to share.

A Year Wrapped

The year ends, and a tradition from the media to showcase the past year’s highlights has made its way into apps as well. An example is “Wrapped” from Spotify. It’s a beautiful idea to reflect on the past months and highlight your top songs listened to on Spotify. While I was excited to play back my year, I quickly got a reality check. I recognised that Spotify included all the songs I played for my children. Subsequently, my number one artist was the Octonauts. On LinkedIn, I learned that I am not alone.

Can we please try for the algorithms to take out the songs we put on for our todlers? I’d love not to see Mumin and Baby Shark as my top songs for a third year in a row

LinkedIn User

Spotify built the feature with referrals in mind and split “Wrapped” into stories to share them. It is like achievements in games as one story highlights the number of minutes listened; another the top five artists. It is an excellent addition to sharing albums and songs. The user can feel proud or show off her exceptional music taste. But here is the thing, not all use cases equally work with that feature.

Importance of Use Cases

The fundament of every product is the use case. You define a target group, identify their problems, and build a product that solves them. Spotify has multiple use cases that have similarities but still have some distinctions. For example, we could define a use case, “Explore new Music”, where users usually go to a record store to listen to new releases. We can also think of “Playing audiobooks to my children” when parents typically play a Compact Disc.

Sometimes we can see only one-one relationships between the user and the use case. If you can create more use cases that one user can relate to, you improve retention by offering more value to your user. On top, you can transition users from one use case to the other and back. Spotify has this condition.

Imagine you start using Spotify to explore new music. Then, some months in, your niece stays over, and for the bedtime story, you are looking for some audiobook. As you have a habit of looking for new audio content on Spotify, you will also search for audiobooks. The vast selection of audiobooks will transition you to a second use case.

Andrew Chen describes this in his new book “The Cold Start Problem” as the Engagement Effect, he writes:

The Engagement Effect manifests itself by increased engagement as the network grows—this can be developed further by conceptually moving users up the “engagement ladder.” This is done by introducing people to new use cases via incentives, marketing/communications, and new product features.

Unwrapping the Wrapped

Let’s go back to Spotify’s “Wrapped”. When crafting the feature, they did not consider all use cases, leading to a significant downside. Meaning, a certain percentage of users will not like the results of their “achievements” and will therefore restrain from sharing them. So instead, users start to discuss how the product failed them.

To identify the reason for the problem, we must look beyond “Wrapped”. I am logged in with my account and share a family subscription with my wife. I use Spotify during work to listen to my favourite music and turn on some audiobooks in the morning for my son before we take him to his kindergarten. Then, I tune in to some relaxing music in the evening while he falls asleep. I do this all with my account.

Now let’s look at Netflix. Before my son starts to watch his favoured show, he changes the profile on the Netflix app. First, he selects the built-in kid profile that limits the available content to his age. Second, he can see his top shows and starts to watch them. The great thing, Netflix does not include my children’s content in any recommendations in our main profile.

Such an option is missing from Spotify. Opening the app shows mixed up content. I can choose between kids audiobooks and chilled jazz. The recommender offers new music that matches my children’s good night sounds. As I learned to work around those flaws, I was genuinely disappointed about my stats for this year. I know that they do not reflect my actual listening habits, which kept me from sharing them.

We see a misalignment with the existing use cases, which in its fundamental nature shows implications across the experience of the product. It can even harm the potentials to grow the product.

When building products, it is crucial to align our experience and features to all use cases continuously. As soon as we add a new layer, we have to assess, review and adapt the product to match the user’s problems we try to solve.

Take a look at your current product metrics. Are you segmenting users by use case, or treating them as a monolithic group? Share one insight you've gained by considering different use cases, or a challenge you're facing in identifying them.

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Image Netflix: https://www.alphr.com/how-to-change-the-language-on-netflix/

Product teams traditionally revolve around three roles: the product manager who defines the 'what,' the designer who shapes the 'how,' and the engineer who builds the solution. But as products become increasingly data-driven, this triangular structure has a critical gap. Drawing from my experience leading growth teams at Europe's largest automotive marketplaces, I've seen firsthand how dedicated product analysts can transform decision-making. Here's why the 'magic triangle' of product management needs to become a square.

The product team

In modern product management, we describe a functional or ideal team consisting of a product manager, a designer, a tech lead and the developers needed depending on the product type. If we look at the familiar squad models, we also include marketing. One role we rarely find is the product analyst. If we look for a role description, we find answers like this one from comeet.com:

Product analysts perform market research and analyze market data, identifying consumer behaviors and trends. They make recommendations and provide launch strategies based on their analyses to increase firm profitability. Product analysts monitor product performance and recommend alterations to meet sales forecasts.

Cristina Burns, comeet.com

To me, this describes a role that is too far away from the action, the team. In my opinion, we need to adjust this definition a bit. The product analyst role should become part of every product team.

Data-driven vs basic product work

We believe that data-driven means to make decisions based on user research and A/B experiments. While this is true, it is also only half the truth. Running experiments is an essential task when we try to ship a new feature or improve an existing one. It gives us confidence in the results of our work, and we can show the impact accordingly in the company. In addition, experiments help the product team to learn. They create insights for the product manager and the designer to adapt the product.

Let's take a step back and look at what happens before the actual testing. Frameworks like the "Product Lifecycle" divide product work into specific phases. When using A/B experiments, we have a hypothesis that we want to prove or disprove. Data-driven in this phase means: we decide whether the feature works for the masses.

Running experiments and making decisions based on the resulting information is part of the essential product work, our daily bread and butter. We take users' opinions into account when doing qualitative research and use quantitative feedback like experiments to confirm our hypotheses. Is that already data-driven? Discovery precedes hypothesis. But what exactly is part of discovery, and who of us would claim actually to carry this out in our day-to-day business?

Am I data-driven?

With the ongoing debate about cookies and tracking user behaviour, we need to think about why we collect so much data in the first place. We tend to record every user action in our products. It seems essential to us to know every little thing about the user. However, are we using all this data and do we know how to use it to benefit our users? I don't think so.

Before I started my career in product management, I was a software developer for fifteen years. I worked a lot with databases, and I feel very comfortable writing SQL queries. Why am I sharing this? In my introduction, I wrote that I consider myself data-driven. On paper, I even have the skills to work with data. But that's not enough.

The boundaries

If you look at the role model of the product manager, you can't get past Marty Cagan:

In order to solve for value and viability, the competent product manager must skilled in the following four critical areas:
1. Deep Knowledge of the Users and Customers
2. Deep Knowledge of the Data
3. Deep Knowledge of the Business
4. Deep Knowledge of the Industry

https://svpg.com/the-product-manager-contribution/

And under "Deep Knowledge of the Data", it says:

The product manager needs to be expert on how the product is being used (from the user analytics tools), and also sales (from the sales analytics tools), and how this is changing over time (typically from the data warehousing tools). The product manager may have a data analyst or data scientist to assist, but this knowledge is not something that can be delegated.

https://svpg.com/the-product-manager-contribution/

We know that these four areas are vast and challenging to manage, especially when we think of the myriad of administrative tasks we all have to do. For example, we are writing tickets and preparing the team's sprints.

If we look at the reality in companies, we rarely have an optimal data pool. Not only the circumstance of the new data protection laws make analytics more and more difficult. Checking Google Analytics is also not enough in most cases, as we spread data across several systems many times. If we use mobile apps, the app stores contain data about installs and campaigns. CRM systems for emailing, push notifications, performance marketing tools, Google search and much more. Different data sources that we need to connect make it hard to get deep insights in a simple way. Do you have all your data in one place? Does your data warehouse offer a self-service tool for analysis?

The opportunities

In recent years, digital product management has improved the inclusion of experience design. We do user research and rely on experimentation. So far, so good. It's time for another step, and I call for the inclusion of the product analyst.

We have an infinite amount of data that can provide us with countless insights. Often data analytics is used to validate existing user research. But we can also flip this around and use analytics as a starting point and validate it with qualitative user interviews.

For example, we started with a simple question in our recent engagement efforts and created a data analysis. In addition to data insights, we conducted user interviews and surveys to prove or disprove our information. The data tells us what is happening but not why.

We are now taking different approaches to developing our product. On the one hand, we work with user interviews, prototypes and surveys. The product designer plays an essential role in combination with user research. We can determine the how and why behind the behaviour.

On the other hand, we use data analysis to point out deeper patterns of behaviour and discover anomalies. We then use user research methods or run A/B experiments to incorporate our findings into the product.

For this, our product analyst provides the suitable methods and the necessary knowledge to analyse the data.

A product manager does not create the design, nor do we write the code. Let's get used to delegating the data analysis as well.

Here is my role description for our product analysts:

Product analysts bring together the various existing data sources and develop in-depth evaluations of user behaviour. With the help of their analyses, they introduce ideas into product development that support the product manager and product designer in deciding on changes and new developments. They help the product team understand their work and achieve their goals.

Does your product team include dedicated analytics talent? If so, how has it changed your decision-making process? If not, what's the biggest obstacle to adding this capability to your team?

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Product teams often struggle with the gap between high-level company objectives and day-to-day development tasks. This disconnect leads to short-term thinking and misaligned priorities that frustrate both leadership and engineers. Drawing from my experience leading product teams across major digital platforms, I've found that well-structured product goals create the critical bridge between strategic vision and tactical execution. In this article, I'll share a practical framework for defining product goals that align stakeholders, create focus, and allow for meaningful measurement of progress – without getting lost in quarterly planning cycles.

Just another layer?

About two years ago, a colleague introduced me to something called Product Goals. You can define the path you and your team want to walk over the next six to twelve months with them. You can use them to communicate your ambitions towards your leadership and measure your success. They also allow you to stay on track with your initiatives as it happens too easy to get lost during the year.

Do you start a new Product Team? Have a look at my article "Leading your new team to success with 3 vital aspects".

You might think about why to introduce another layer of planning into the existing ones. We work with Company Visions, Company Goals, the Business and Product Strategy and slice everything in OKR. So, where do Product Goals fit in?

Product Goals vs Product Strategy

Since its new release in November, the Scrum Guide includes Product Goals. They define them to sit just on top of the product strategy. The Product Samurai describes them as "Product Goals are measurable stepping stones designed to push the threshold of knowledge towards our Product Vision."1

In his newest book 'Empowered'2, Marty Cagan talks about the Product Strategy and its importance. In his definition, the product leader is supposed to define the product strategy. Unfortunately, the term "Strategy" is sometimes very misleading and raises, in some cases, debates about who owns the strategy, especially between the product management and business owner. Therefore, I prefer to use the term "Goal" and refer to what Cagan wrote about it, which is on point for me.

“Strategy” as a term is ambiguous as it exists at every level for just about everything—business strategy, go‐to‐market strategy, growth strategy, sales strategy, discovery strategy, delivery strategy, and so on. Whatever the goal is, your strategy is how you're planning to go about accomplishing that goal.‘

Four step to your Product Goals

Company setup and context

Company setups are very versatile. You have small startups with one, if any, product manager, where the company owner defines the company vision but often fails to provide all layers down to tangible product goals. On the other end of the spectrum, you find big corporations with dozens of product teams. With many groups working on the same company goals, you will find a second layer of complexity: teams' alignment.

In the end, the product manager needs to work on topics that will make a difference for your customer and impact the business. You might be the sole product manager, a group product manager for a specific business area, or the next unicorn startup founder. What I describe in the following is a way to lay a path out of how you and your team can create a storyline for your upcoming product work.

The leadership most likely sets a goal for the year. Example like "Increase number of subscriptions" or "Increase revenue by 10%" should sound familiar. Translate this to what you and your team should work on in the coming months. When you start the year, you want to know what you want to achieve by the end of the year:

1. Think about ideas on how to address the set goal. You do your research and discovery in the best condition and know the most pressing issues for your customers.

2. Formulate product goals out of your ideas.

3. Build a rough roadmap out of the product goals to get a tool to communicate your plans and ease alignment with stakeholders and other product teams.

Ideating Topics with the Team

One approach you can use is to gather the product team and ideate on initiatives.

You can download your workshop cheat sheet via Google Slides.

I recommend setting up a session of about two hours. In our new routine of working remotely, make use of online collaboration tools. Present the company goals, vision or any other details your leadership provided as the wanted outcome. That's your framework.

Depending on the structure and size of your organisation, you should split the sessions. For example, you can either have a session per product team or more high-level per business unit. I would not recommend mixing different customer personas, for instance, to create actionable goals.

Take about 15 minutes to present and answer questions. Then, you and your team will think inside this framework to come up with ideas. Raise the following question to your team.

“How can we improve our product for our customers to contribute to the set goal?”

You will see that your team and, in particular, your developers will come up with many ideas. Allow every participant about 20 minutes to write down their thoughts. After that, let everyone present their ideas, and together you group them at the end of this part. It will take about an hour, depending on the team size.

The resulting topics will be the basis for your product goals. Perform a dot voting on them afterwards to prioritise.

Defining your Product Goals

With having your prioritised list of topics, you are all set to define your product goals. You can either do it alone or better sit down with your product designer and tech lead. We aim to fill out the product goal template as good as possible, starting with the highest priority topic.

Let's talk about prioritisation. If you start with a clean slate, you can use simple dot voting as described in the section before. However, if you already have data from your previous work and leverage a backlog of ideas, I recommend using a sizing method like ICE. I especially like the Confidence Meter from Itamar Gilad.

You can download your product goal template via Google Slides.

In the first step, take all the team's ideas for the topic you gathered during the workshop and write them into the section titled "Ideas".

Second, try to find a target group and the metrics you want to move and list them in the sections accordingly. You want to address a specific target group, "All users" does not create focus—the same counts for the metrics. Define one primary metric and only a couple of supporting health metrics. You might recognise that the ideas may be too widespread to accomplish this, and I recommend removing some ideas in that case.

By now, you have a clearer picture of what you want to achieve. Identify in the next step all the fuzzy things. Do you see dependencies with other teams to conclude your initiatives? Do you have questions that came up while you discussed the ideas, target group and metrics? Document all of this in the section "Open Questions".

With all the parts in mind, you can go ahead and define your product goal. Then, write down in a sentence or two what you want to achieve and give it a crisp headline.

Repeat this for all the topics you collected in the workshop. Please keep in mind that numerous product goals are not necessarily better. Create as much as you can handle, and that depends on many factors.

• What kind of team setup do you have?

• What is the role of your team (e.g. experience or platform team)?

• What kind of goals did you name?

It makes a difference if you want to improve the account creation flow compared to creating your first mobile app.

Establishing your Roadmap

You have done the most significant part of the work. It is now time to get ready and communicate your product goals for the upcoming period. When you create your goals at the beginning of the year, you most likely want to present your plans for the next six to twelve months.

Let us make a roadmap to showcase in what order we want to approach our product goals. We want to stay super high-level with our roadmap. In the case of a period of a year, we remain on a quarterly structure. A product goal can stretch over multiple quarters. Take your product goals and distribute them across the quarters. Just keep in mind to restrain yourself from committing to too many product goals. Depending on your team structure and the goals themselves, allow a focus with a max of two goals per quarter.

You can download your product goal roadmap template via Google Slides.

Communicate & Revise

With your roadmap in place, you can communicate with your management what initiatives you target to address their goals. You can explain how you prioritised your initiatives and share what ideas you see. You can also show what metrics and target group your initiatives will influence.

As with all the things in life and especially in digital product development, many parts change, and you need to adjust permanently. The product goals provide a high-level view of your plans. Adjust them as you move forward continuously.

What horizon do your product goals currently cover? Try implementing this four-step process with your team and share what changes you observe in your strategic alignment and ability to measure impact.

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For decades, marketing and product teams have operated with different goals, different metrics, and different data systems—creating organizational silos that hamper true business growth. The marketing-product divide creates blind spots throughout the user journey, with acquisition data disconnected from product engagement insights. The emergence of growth as a dedicated function isn't just an organizational trend; it's a necessary evolution that creates a shared data language between these traditionally separate domains. Drawing from my experience leading growth teams across major digital platforms, I've witnessed how unifying these perspectives transforms not just metrics, but entire business strategies.

Missing dots

Working on compounding growth gets more complex. Adding more and more users to a failing product experience does not work. At the same time, it does not create impact and might burn a massive budget to attract the wrong people to a nicely built product.

Over time, a fundamental misalignment grew in how we look at data. While product management relies on specific tools to improve the user experience, they are missing crucial information about where marketing acquired the user. Often, there is a lack of understanding of which channels perform well, what type of user, or what use case a channel can acquire. As a result, product management optimises their funnels without considering user acquisition.

On the other hand, marketing struggles to follow the user deep into the product. What does a user in two weeks do, whom I acquired today? What channel performs best, and how can we improve retention efforts to reduce acquisition costs? Crucial questions to drive the marketing strategy.

The lack of shared data and the resulting different views have technical and organisational reasons. Some tools available for marketing to run and track campaigns have limited compatibility with product analytics solutions. On the other hand, marketing attribution in product analytics plays an important role even though it is not readily available. Ultimately, marketing and product do not speak the same data language.

In addition, the increased specialisation led to an organisational separation of the user’s journey. However, even with new cross-functional measures, the challenge is the gap between marketing and product — the acquisition and the core product experience. As a result, we created a moat that the user has to overcome to experience the core value. Once inside the castle, it is difficult to cross to the outside to acquire new users, ultimately harming growth.

Building bridges

A new specialisation called growth emerged in recent years—powered by like-minded people from marketing and product management to help users jump over the moat. Growth “gets that target audience to experience the product value as fast as possible“.

It connects marketing and the core value proposition by leveraging data based on defined use cases. As described in a previous article, the importance of use cases for the product experience and growth is massive. Creating a shared view of the user and the underlying data helps to guide users from their first touchpoint to a lasting product experience. Furthermore, we can use the product to acquire and grow the user base with increased engagement and value creation.

Growth builds a holistic view of the product and its users to help identify the opportunities with the highest impact. But that’s not all. According to Lauren Schuman, VP Product Growth at Mural, Growth can influence in a broader way how a company moves forward.

“A Growth team should have a clear understanding of the business impact that is indicated by success for the team, usually through a North Star metric. But I do think it’s easy to over-index on moving a metric as success. From my experience, a Growth team really is successful when its value extends beyond the work that team physically does. It’s successful when it expands into influencing things like strategy, marketing, and product decisions across the company.”

Lauren Schuman, VP Product Growth at Mural (OV Build)

Storming becomes Norming

Whenever something new evolves, technology or new team members, we go through different phases. It is about time to accept that a new player has entered the game. We should get out of the storming and enter the norming phase to align better the three different functions of marketing, product and growth.

As a starter, we must accept growth with skills unique to improve user activation and retention. “Growth is a multi-disciplinary and data-driven approach to scaling products“. But, like any function, it cannot survive and strive alone. It needs help and support to show its impact. Organisations are not fluid, and we require clear responsibilities for teams to function well. OpenView created a pretty good overview of marketing, product and growth differences.

Growth is here to stay, not replacing marketing or product but supporting them with unique skills.

Where does your organisation stand in bridging marketing and product data? Share your biggest challenge in creating a unified view of the user journey, or a success story if you've managed to break down these silos.

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In today's product landscape, the gap between collecting data and actually using it to drive decisions remains surprisingly wide. Many teams struggle with turning metrics into meaningful action. After leading growth initiatives across major European digital marketplaces, I've found that successful product growth depends on three fundamental building blocks that bridge this gap. In this article, I'll share the framework that has helped my teams increase app downloads by double digits and significantly boost conversion rates through a systematic approach to product growth.

What is growth product management?

First, let’s shortly discuss and define what growth product management is. Most product managers should be thinking about growth, but for those in “growth product management”, this is their specific focus.

Keya Patel and Adam Fishman wrote an excellent article on the different types of product work and the increasing specialisation of product management.

In that article, they define “Growth Work” as the function that “creates and captures value by capturing more of the existing market”, which means that the growth product tries to expose as many customers as possible to the core value. But how can we do that?

A growth product manager focuses on the entire user journey from acquisition through retention to the user’s lifetime value. This goes hand in hand with the core product managers that take care of single features or areas within a product.

We can connect the two types of product work with the Jobs-To-Be-Done framework. While core product work handles the single steps that define the job as a whole, growth work overlooks the job or problem the user wants to solve.

Why is data so necessary for growth?

As we mentioned above, growth work looks at the user’s entire journey. It starts with the user’s first interaction with your product and all the following touchpoints over time. Data is essential to that work – it allows you to understand your users and what they do and helps you optimise your product and get more out of it. Without data, you do not know where your users came from and what parts of the product enabled them to solve their problems.

As Elena Verna puts it in one of her posts on LinkedIn, “Growth before data […] will not be a scientific, data-driven discovery of the growth model.” That’s precisely the crux. Without the data, you do not even bet. You gamble with your resources and your users. Instead of rolling the dice, you want to build knowledge on how your business can grow. Data helps you create the basis for your decision-making.

What metrics should you be tracking?

Metrics are highly dependent on the product you are working on. When it comes to growth work, there are a few areas that you should be paying extra attention to.

• Acquisition: The number of new users using your product and where they are coming from.

• Activation: The percentage of new users becoming “regulars”.

• Engagement: This is the depth or intensity with that your users interact with your product.

• Retention: This is the percentage of users returning to your product regularly over a certain period.

• Revenue: This is the money you bring in through your product.
  

The goal of the growth product manager has to identify the correct metrics for two purposes. One, they should inform you how your users solve their problems best with your product. Two, those metrics help you build the growth model, which enables you to prioritise and monitor your initiatives.

Metrics and data can be overwhelming at some point for the product manager. Please read up on my post about why we need more product analysts.

How do we keep the balance between qualitative and quantitative data?

Let’s be honest here – you’re not going to get all the data you need for your growth work from what you track. While you’re looking to get as much quantitative data as possible to answer the question on your growth model, you also need to remember that numbers tell only half of the story. That’s why you need to keep the balance between qualitative and quantitative data.

When it comes to qualitative data, you need to pay attention to people’s emotions while using your product and why they use it. Collecting feedback on your product and interviewing users helps to learn more about them and their motivation. Such user insights can spark ideas in many areas. For example, what landing pages do you need to create to acquire new users better? Direct user feedback can also help to understand what hinders users from being more engaged.

Mastering both types of data insights will give you a complete picture of your users and the product’s potential.

Closing the knowledge gap with Experimentation

The better you understand your users and their behaviour, the better you can serve them and grow your product. One crucial element of growth work is experimentation, and it’s the one where you’ll get the most value from data and insights. With experiments, you validate whether your ideas work, measure their impact, and iterate based on that data.

If you want to see results, you have to test and iterate. We all have ideas, but only some of them are worth pursuing. When you have an idea, start with small experiments to validate it and make sure it’s something that your customers want. This will help you avoid wasting time and effort on something that will never work. Running experiments doesn’t mean that the idea is terrible or you are not confident. It means closing the knowledge gap and being mindful of your business impact and resources.

No data, no growth?

You can’t be an effective growth product manager without data available to you and your team. But that doesn’t mean you cannot start your work as a growth product manager.

If you start from scratch, you have to build knowledge first. Talk to your users to find out who they are. Run interviews in your company to understand how you grow and where the challenges are. Look at the available data, identify gaps, and work with your team to close them. And finally, start to run experiments, try even to change the slightest thing to understand how your users react to change and how to build successful experiments.

Growth product management is exciting as it combines marketing with the product. It means understanding your users deeply and building vast knowledge by being able to read the data.

Over time you will base your growth product work on three pillars:

1. Insights: Do your user research and refine the company’s use cases based on the user problems you identify.

2. Data: Define, collect and analyse the data points you need to understand your users’ journey and how your business grows.

3. Experimentation: Create initiatives based on the data and insights and build ways to validate your assumptions.

How is your product team currently balancing these three pillars? Share your biggest challenge in connecting data to product decisions in the comments, or reply with an example of how you've successfully used insights, data, or experimentation to drive growth.

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