Using Data to Inform Team Goal Setting

Bad goal setting can cripple your business (I know from firsthand experience). Here's how to set goals that propel your business forward. Step 1: Analyze last year’s performance. You can’t set the right goals without the correct information. So, take some time to gather data from the previous year to find areas of strength and weakness. Look at your: Revenue streams — what are your most profitable areas? Your biggest cost centers? Sales & marketing — can you spot trends in customer acquisition or marketing ROI? Operations — where is your business bottlenecked? Where might you be overstaffed? Employee performance — look at productivity and churn. Which direction are things going? — Step 2: Brainstorm areas for improvement. Write down all the possible things you could work on. This is a great group activity for your leadership team or even the whole company (depending on your size). The data you’ve collected in step 1 should give you some idea of opportunity areas. One tip: don’t discount an idea just because it’s hard. Often the biggest impact things are hard to do. But you should be realistic about the effort required to get something done, and its chances of success. — Step 3: Set SMART goals Specific: Define clear and precise goals. Instead of saying "increase sales," say "increase sales by 12% in the next 6 months." Measurable: Ensure each goal has quantifiable metrics. E.g. "Reduce customer acquisition costs by 15% by the end of the year." Achievable: Set realistic goals based on your resources, budget and other constraints. E.g. if you have limited cash, avoid goals that would severely impact your monthly cash flow. Relevant: Align goals with your overall business objectives. Ensure they address the key areas for improvement identified earlier. Time-bound: Set deadlines for each goal. E.g. "launch a new service by Q3." — Step 4: Develop an Action Plan For each goal, create an action plan that outlines: Steps and Milestones: Break down each goal into smaller, manageable tasks. Set milestones to track progress. Resources: Identify the resources needed (time, money, personnel) and ensure they are available. Responsibilities: Assign tasks to specific employees. Ensure everyone understands their role and what is expected of them. Timeline: Establish a timeline with deadlines for each task and milestone. Doubling down on one point there: always assign tasks to a single person. They can still bring in other people to contribute, but it’s one person’s responsibility to get it across the finish line. — Step 5: Monitor and Adjust Goals are not static. Regularly check your progress, and adjust based on new insights or changing circumstances. Schedule monthly and/or quarterly reviews to keep everything on track. Having a simple KPI tracker is a good way to keep tabs on things. Make sure you’re regularly checking in, and ask people to flag any roadblocks or necessary adjustments as soon as they identify them.

A lot of product teams yearn for a concept of "Isolated Bet Impact." The challenge lies in understanding how individual initiatives influence key metrics within a complex system where multiple factors—both internal and external—interact simultaneously. One of your bets might have a positive impact even though your KPIs are down overall. Conversely, you might have placed a bad bet that is obscured by overall growth in the business. Teams risk falling into "resulting," as the poker player Annie Duke describes, where they mistakenly judge the quality of their decisions solely by lagging business outcomes rather than understanding the true impact of their bets. Running multivariate experiments is part of the solution, but not the entire solution. While an experiment can isolate the impact of a product change on an input metric, the cumulative impact on lagging business KPIs remains unclear. Without a clear way to isolate the impact of bets, teams often give up on being metrics-driven and instead rely solely on intuition to prioritize bets or choose which experiments to run. However, there is a structured approach that radically improves how teams use data to make decisions: deterministic KPI trees, a tool for bringing clarity and rigor to bet evaluation. These trees define relationships between metrics mathematically, providing a framework to estimate the contribution of individual bets to overarching business goals. The attached screenshot showcases DoubleLoop's new Bet Simulator prototype (development just kicked off today!), which uses a deterministic KPI tree to estimate the impact of bets on a business's KPIs. Through the deterministic KPI tree, each factor can be analyzed in isolation. The relationships between metrics—such as Sales = Revenue per Visitor × Total Visitors—provide a structured way to attribute changes in the root metric (sales) to specific bets and external factors. While deterministic KPI trees offer clarity, there are limitations. Many metric relationships are probabilistic, not purely mathematical. That said, even an approximate sizing of bet opportunities with deterministic KPI trees is far superior to not estimating bet impact at all. Using deterministic models enables teams to: - Use data to discuss the relative importance of different bets - Avoid overestimating or underestimating impacts - Ensure assumptions align with realistic expectations and the nuances of the business

I’ve talked before about Planning Season and the opportunity that analytics offer as a true force-multiplier for a company during the planning process. One thing that still surprises me is that many product and growth teams struggle in setting clear goals, such as defining a specific KPI value to aim to achieve by a certain date. In other cases, those who have actually set goals, they are defined in a somewhat random manner. 🤔 Clearly, it’s quite difficult to define success if you don’t set these goals. That’s why the best product companies in the world are investing significant efforts in the process - I know this first-hand from my days at Google. Defining goals should rely on two different methods that eventually converge: Top-Down and Bottom-Up: 👉 Top-Down - This method is based on company management setting goals for the company, for example achieving 30% year-over-year growth. The Product and Growth teams should then identify the biggest levers and define the KPIs that will enable the company to achieve this 30% growth, such as conversion, retention, engagement, revenue per user, etc. 👉 Bottom Up - Here, we map out all our initiatives and estimate the size of the impact. A good bottom-up will include: ☑️ The forecast of the KPI based on the current trend - aka the ”do nothing” scenario. ☑️ Then, adding the initiatives, the launch date(s), and estimated impact by the goal date. 💡 It may sound like a complex process, but assuming you have the right infrastructure, it’s quite straightforward. It also enables you to set your team up for success, gives you the ability to measure yourself and learn from the process, and helps you create a predictable and structured growth process. #ProductAnalytics #CausalML Loops

As organizations feverishly plan the next year, it presents a vital opportunity for data teams to shape and drive this process analytically. It is one of their key jobs-to-be-done. But, what does this look like? Let's consider a base financial model that outlines the desired direction for the business. The metrics of interest at this level are usually the highest-level outputs such as revenue and costs. 1) Breakdown Outputs: The first area where a data team can help is in breaking down these outputs into more granular and operational input pieces. How should we assess the contributions from various cohorts of users or accounts? From existing or new product lines? From new features? From different markets? By increasing supply? By driving engagement? By improving application performance? Or upgrading the operations? Data teams as one of very few teams with a holistic view of the business, can translate these top-line KPIs into targets for specific teams. 2) Resolve Conflicts: A second role for data teams is identifying and resolving conflicts. It is tempting to want all metrics to move up and to the right - but in reality, metrics are often in conflict. For instance, if you focus on driving traffic, you may see a drop in conversion rates. If you want to drive higher revenue per account, expect higher churn. If you want to improve margins, new acquisition efforts may slow down. Balancing these metric equations is vital for establishing metric goals, as failing to do so can demotivate even high performing teams who will struggle to connect their work to overall progress. 3) Inform Trade-offs: Data teams can help in making informed trade-offs. Drawing upon their experience of what’s worked, they can shape strategy discussions. A consequence of this is focus - deciding what to worry about, and what to de-prioritize can be liberating for operational teams. All these pieces of work are ultimately accomplished with a significant amount of data and code. Apart from spreadsheets or notebooks, which are both do-whatever-you-please tools, there aren’t many options for analytics or business teams. The flip side of having open-ended flexibility is that these operations are expensive - requiring experts to hand-craft queries, retrieve data, build models, and execute calculations. In practice, due to these productivity constraints, the planning process usually does not end up as analytically rigorous as desired. Worse yet, it can be half-baked where executives believe they are thorough, but the numbers are backed by false precision. All said, it is worth noting we are just getting started. Data teams are playing a greater role in shaping how organizations debate strategy, allocate capital, make bets, create plans, establish tactics, and set and monitor metric goals. I’m excited to see this elevate the visibility and ROI of data teams.