How to Maximize ROI on Data Products

Having a lot of data isn’t the same thing as having high-value data. If you’re having a hard time explaining that to executive leaders, try a different approach. Teach them how to put a dollar value on the business’s data. Every curated dataset creates new opportunities for the business, and that’s the connection between data and profit. The simplest data valuation method is called ‘With & Without’. The business thinks that every dataset creates the same value, so I run an early experiment to disprove that assumption. I turn off access to datasets that stakeholders believe are high value and wait for the complaints to roll in. In most cases, no one notices. Three months later, I propose putting the dataset into cold storage. Business leaders push back, saying their teams would grind to a halt without access to those datasets. I tell them about the experiment. Now I can start a rational conversation about connecting data to use cases and putting a dollar value on each dataset. Data doesn’t create value for two reasons: 1️⃣ It’s incomplete. The data required to support the use case isn’t being gathered holistically. Sometimes that’s an accessibility issue. Other times, the use case, workflow, and outcomes aren’t understood well enough to know what data is necessary. 2️⃣ It lacks context. Data points aren’t enough to support use cases. Context about the process, product, person, intent, and outcome is required. Until data is gathered contextually, its value creation is limited. Connecting datasets with opportunities creates the justification for changing how the business gathers and leverages data. Putting a dollar value on contextual datasets quantifies the ROI of information architecture and engineering initiatives. That’s the shortest path to getting budget and buy-in. Quantify value in terms that business leaders care about and show them a clear connection with outcomes they believe are essential.

What happens when you align product performance with sessions, conversion rate, advertising spend, stock on hand and sell-through date? You stop guessing and start making commercial decisions with real clarity. The best merchandise planners and marketers already know this: no metric in isolation tells the full story. The strongest teams are combining traditional planning metrics with ecommerce performance data to understand not just what is happening, but why. For DTC brands, bringing these data points together turns a messy performance picture into a simple set of actions: 🔍 1. Decide what to advertise more When a product has strong conversion, healthy margins and enough stock to support demand, but low sessions, it’s usually a sign that it needs more visibility. This is the sweet spot for scaling paid spend: the product already proves it can sell — it just needs more traffic. 💸 2. Identify what to mark down If you’re holding too much stock and the sell-through date is creeping up, yet conversion is weak even with steady sessions, discounting becomes a strategic lever. Markdowns help clear inventory without wasting ad spend on products the customer clearly isn’t choosing at full price. ✋ 3. Know when to pull back advertising High ad spend + plenty of sessions but poor conversion = a red flag. This is where you pause or reduce spend, diagnose the issue (price, positioning, creative, customer reviews), and redirect budget to products with stronger unit economics. Sometimes the best ROI comes from simply stopping the leak. When metrics live in silos, teams argue. When metrics connect, teams act. This is how modern DTC brands protect margin, improve cash flow and scale the right products at the right time.

📌 The True ROI of Business Intelligence Every company wants to be data-driven. They invest in modern data stacks, hire analysts, launch dashboards. And then… nothing really changes. Decisions are still made based on gut. Insights are acknowledged but not acted on. Dashboards are checked but not really used. Here’s the truth no one wants to admit: Being “data-driven” doesn’t mean collecting data. It means consistently taking better actions because of it. And that’s where most companies fall short. The real ROI of data analytics doesn’t happen when the report is delivered. It happens when a business process improves because of it. Let’s break it down with some examples to better understand my point: 1) A churn report doesn’t create value. → But an ops team that launches a new retention workflow based on that report? That’s ROI. 2) A marketing dashboard doesn’t grow revenue. → But reallocating ad spend based on performance patterns? That’s ROI. 3) A sales funnel visualization doesn’t close deals. → But identifying and removing a drop-off bottleneck? That’s definitely ROI. Do you see my point? So the question now becomes: How do you ensure your analytics actually lead to action? Here’s a playbook I would recommend: 1️⃣ 𝐓𝐢𝐞 𝐄𝐯𝐞𝐫𝐲 𝐈𝐧𝐬𝐢𝐠𝐡𝐭 𝐭𝐨 𝐚 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 Before you publish a report, ask yourself: “What is someone supposed to do with this?” If the answer isn’t obvious, the insight isn’t useful yet. Make it actionable and not just interesting. 2️⃣ 𝐀𝐬𝐬𝐢𝐠𝐧 𝐎𝐰𝐧𝐞𝐫𝐬𝐡𝐢𝐩 If a KPI has no owner, it has no future. → Every critical metric should have a name next to it. Not to blame, but to empower. Because action requires accountability. This is the easiest way to make people adopt your dashboards. 3️⃣ 𝐌𝐚𝐤𝐞 𝐭𝐡𝐞 𝐍𝐞𝐱𝐭 𝐒𝐭𝐞𝐩 𝐂𝐥𝐞𝐚𝐫 Your dashboard isn’t an outcome. It’s a means to make better decisions. And you should definitely make it easy for your end user. → Schedule recurring check-ins for feedback → Create a simple action log linked to KPIs → Use alerts to notify the right person when a critical KPI changes Most organizations don’t fail because they don’t have insights. They fail because they don’t have systems for what happens next. The bottom line is: A lot of companies say they want to be data-driven. But in practice? If your BI initiative doesn’t lead to action, it’s not complete. The ROI of analytics lives in the next step. Design everything you build to make that step easier, clearer, and faster. #BusinessIntelligence #DataAnalytics

Many companies think they're set if they have product usage metrics and can track user engagement. But unfortunately, that's only part of the picture. The real value comes from connecting that usage data to actual business impact. The best product ops teams create the vision and ability to connect those data points. They help relate user behavior metrics to critical business outcomes like revenue, churn, and more. Imagine seeing a feature with rising usage month-over-month. Seems great, right? But what if you found that the usage spike was mainly from a customer segment you're looking to phase out... while adoption from your strategic focus segment had dropped 20%? Yikes. Having that analytical power to map product metrics to business metrics is the secret sauce. With product ops, you can scale those capabilities across the entire product org and executive team, guiding decision-making in the right direction. As Aniel Sud, CTO of Optimizely, puts it: "Product ops becomes data-driven over time, turning data into actual value." And according to Joe Peake of Featurespace, the goal is analyzing each product's revenue opportunity and ROI - not just relying on gut feelings about the market. True product insight means bringing all data together - from product usage to customer feedback to financial impacts. As Shira Bauman of Zapier notes, "Learning about the data that people care about, and partnering across data teams, is so important." With product ops connecting those dots, we get out of the "build trap" and can optimize for real outcomes. The path to successful products lies in combining engagement metrics with business performance. What's your experience been in tying product usage data to business metrics? Share your insights and lessons learned in the comments!

Here's how to check if a CDP is worth your cost. CDPs (customer data platforms) can be very expensive. Most brands assume they'll get the magical 5-10X ROI claimed in a CDP vendor pitch deck. ...but most fail to break even. Here's how you can calculate ROI for your own brand. - Pick 1-2 use cases (playbooks) with the highest opportunity - Manually run them - Extrapolate the value if automated Example 1: Paid Ads or Discount Suppression Goal: Suppress ads from any customers who have purchased in the last X days. - Export a list of all customers who purchased in the last X days - Import that suppression list into your media platforms (Meta, Google, TikTok) - Monitor Cost, CPM, CPC over that period Did you see significant cost declines? CAC improvements? (since you're not wasting ad budget on repeat customers) Example 2: Targeting by LTV, pLTV Goal: Increase retention for high-value customers - Export your transactional data into a warehouse - Calculate your customer LTV - Group customers into buckets (deciles, low/medium/high) - Import the customer list into your media and messaging platforms - Execute differentiated campaigns against high LTV segments Did your repeat purchase rate increase? LTV? Decrease in churn? Additional notes... 1. This manual exercise also builds your team's data maturity. It is likely helpful as a test even if you are not considering a CDP. 2. Some CDPs will allow you to run these no-cost POCs during the onboard cycle. Do it. 3. Layering multiple use cases has a compounding effect, which can be challenging to execute and measure manually. 4. ROI from operational efficiency is real, especially in large, multi-channel orgs 5. There is an intangible ROI to governance improvements from CDPs 6. This manual effort will take buy-in, planning, and patience. It's worth it if done right. I'm curious, have you run these types of ROI "tests" either before/after implementing a CDP? Any learnings to share? #cdp #roi #marketing

"ROI for data is overrated." I hear this from smart data leaders, and they're wrong. Randy Bean's annual CDO survey tells the real story: CDO tenures average under three years, and some executives have given their data leaders as little as 60 days to prove value before pulling the plug. The inability to tie data investments to business outcomes is the single biggest career risk in the role. The problem is not that data ROI can't be measured. Measuring it requires real work, and most teams would rather avoid the conversation entirely. At a previous employer, we made a simple rule: every data product needed a value hypothesis before we built it. Not a vague promise that it would "help the business," but a specific projection tied to revenue, cost reduction, or risk mitigation, signed off jointly by finance and the business partner. In one case, we used data to identify untapped growth in a key revenue-generating division. The projections told us how much the business could expand if we targeted the right segments and regions, and product penetration in the target market ended up far exceeding what anyone had originally projected. The data products were specific enough that the business and data teams jointly identified opportunities that general reporting had never surfaced. When we first introduced the value hypothesis requirement, the pushback came from everywhere, but especially from business partners who felt we were putting them on the hook for outcomes they couldn't fully control. "We can't measure that," "This slows us down," "You're setting us up to fail." The breakthrough came when we made one thing clear: we weren't putting them on the hook. We were getting on the hook with them. The data team carried the same value enablement objectives in our OKRs, tied to the same performance reviews. If the business missed its target, we missed ours. That alignment changed the dynamic from "here's your data, good luck" to genuine partnership where both teams had a stake in the result. You know the partnership is real when the business starts telling your story for you. When they present results to leadership and say "we did this with the help of the data team" or "we were enabled by the data team," that's not a courtesy mention. That's proof that data is embedded in how the business operates. Randy Bean's latest CDO survey shows that 25.8% of CDOs now have tenures over five years, and the pattern among them is consistent: they stopped trying to prove the value of data in the abstract and started tying every initiative to a specific business outcome, focusing on the highest-value problems first. If your data team cannot articulate its value in terms of business outcomes to the organization, that is the first problem to solve. #CDO #data #ROI #businessvalue

The missing data link: 5 practical lessons to scale your data product. From Dr. Asin Tavakoli, Holger Harreis , Kayvaun Rowshankish, Avinash Javaji, Klemens Hjartar" at McKinsey & Company. "5 key lessons: 1. It’s about more value, not better data. The goal of developing data products isn’t to generate better data; it’s to generate value. No data product program should begin until leadership has a firm grasp of the value that each use case can generate and prioritized the biggest opportunities. 2.Understand the economics of data products. A data product’s effectiveness is based on the “flywheel effect” of accelerating value capture and reducing costs with each additional business case that it enables. 3.Build data products that can power the flywheel effect. Harnessing the flywheel effect of ever-lowering costs and -rising value requires building a capability that maximizes reuse and reduces rework. 4. Find people who can run data products like a business. Put in place empowered data product owners (DPOs) and senior data leaders who understand what matters to the business, from articulating the value in business terms to building support. 5. Integrate gen AI into the data product program. Gen AI is already proving that it can help develop better data products faster (as much as three times faster) and cheaper than other methods."

We have to show a Return on Investment to begin a data quality initiative. But how? By finding bad data that reduces the profitability. Many organizations only discuss the importance of data quality without taking the action to improve it. Endless meetings, complaining, or creating an impossible vision. The action is simple: we have to show the benefits of improving data quality. First, we need to identify a data quality issue that has a proven adverse effect on the organization. We can ask users for their pain points related to poor or missing data. We can review the list of recent incidents reported by users. The next step is to profile the data to confirm the existence of the issue. Next, we should propose how to handle it and estimate the time and cost to fix it. The next step is to get the business sponsors on our side. We have to show the Return on Investment. We know the cost required to fix the problem. We have to estimate the impact when the issue is not fixed and monetize it. For example, if each user spends 2 hours a week comparing data between systems because they cannot trust it, we can reduce that time. That is our net benefit. Finally, we can take that net benefit and the cost of implementing the solution to calculate the ROI. If the cost of solving the issue is just 20% of the annual cost, if we keep the issue, the benefits are obvious. There is only one more aspect to remember. If we invest in data quality, we will also have some annual costs, such as the salary of a dedicated data quality specialist or license fees. That is the annual cost for which we should also calculate the ROI. #dataquality #datagovernance #dataengineering

Let's calculate the ROI of your data 🧮 We all know data is valuable—but how do you actually quantify the business impact? It’s not just about tools and infrastructure. It’s about how data helps your business make better decisions, save money, and drive growth. So, how can you do it? 1. Tie Data to Revenue 💰 Identify current and past initiatives where data drove the business forwards. This is likely happening all the time, but it isn't well-documented and thus not communicated. This can be both an evergreen data product (ex: enhanced customer profiles that improve ad targeting by 10%) or tools used for point-in-time decisions (ex: insights uncovered by an analyst led to expansion in a new market that is extremely profitable). 2. Measure Cost Savings 💳 Data can streamline processes and reduce waste. Maybe more detailed, realtime inventory data allowed the business to optimize logistics and save money on shipping. Or what used to be done by a costly external vendor can now be done internally for a fraction of the cost. 3. Calculate Time Saved ⏳ Employee time is the most valuable time of all. Catalog all the tools and datasets that are saving other teams hours of time. Now that those folks aren't wasting time trying to corral files or wrangle spreadsheets, how are they spending their time doing higher value tasks? Don't be afraid to get specific and break it down by how much an hour of each person's time is worth. 4. Factor in Risk Mitigation 🚫 This can be the hardest to calculate, but is also likely the biggest value that isn't being considered. Did data help avoid costly mistakes or fines? Every time data is used to back up a hypothesis or justify a business decision, that should be counted as part of your ROI too. Tracking the ROI of data can feel tricky, but when you connect it to revenue, savings, and time, it’s easier to show the value. Remember, data isn’t just a tool — it’s an investment in your company’s growth 📈

More than 50% of data leaders cite "proving business value" as their top challenge. It is because they defined the target after they shot the arrow. Here’s how most data projects justify their ROI: We spend six months building a new data platform. After launch, we frantically search for any metric in the company that has gone up. We find one, draw a circle around it, and call it our bullseye. This isn't a strategy. It's storytelling. It's building a "credible narrative" after the fact to justify our existence. And our stakeholders can see right through it. The problem is, we treat success metrics as an afterthought. We're so focused on the technical execution that we forget to have the most important conversation before we start: "How will we know, with undeniable numbers, that we've won?" We need to co-create the scorecard with the business before the game begins. Define the exact, measurable business outcome you're aiming for. Agree on the proxy metrics, like time saved or errors reduced. Get it all signed off. Stop drawing bullseyes around your arrows. Start by agreeing on where the target is. 💬 What is your go-to "proxy metric" (e.g., time saved, clicks reduced) when you can't directly tie a project to revenue? 👉 Follow me, Dr. Markus, for more on defining and delivering data value. 🚀 Please repost this to help more teams define success before they start. #ROI #DataStrategy #BusinessValue #Measurement #DataLeadership #KPIs