Shadowing-Übung: Product Analytics in 100 Seconds - Englisch Sprechen Lernen mit YouTube
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Product Analytics in 100 seconds.
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Product Analytics in 100 seconds.
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As founders and product builders, we're on a mission to produce product market fit.
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Along the way, we talk to our users and collect qualitative feedback to improve the product.
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There's a quantitative approach too.
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Enter product analytics, creating user insight by surfacing usage data.
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In product analytics, every interaction is a learning opportunity.
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In our app, we capture those interactions, we transfer them into an analytics application, and create data artifacts that inform a new product change.
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Four data scopes are important.
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The user, who interacts with the product across multiple sessions as tracked by individual events and further described by event properties.
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Within this online shop, let's track the click of a product card to understand browsing patterns of our users.
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We first include an analytics snippet within our code that lets us use methods to communicate with our analytics tool.
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Then we listen to a click event on the product card used to track method, define an event name and add details via properties, like the product category or the product's position on the page.
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The event triggers a simple API call to the analytics server, submitting our custom properties along with information about the page it was fired on and device details.
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Every event also includes an ID stored as a cookie on the user's device.
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This lets the receiving server identify events from the same user and continuously build up an event stream.
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When users provide additional information about themselves we can capture that using the identify method, attach user traits and build up a detailed user profile on our analytics platform.
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We've been craving to answer why are landing page visitors not buying our products?
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Spoiler, analytics won't tell us why.
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Instead, a funnel analysis can tell us what is happening across our core conversion events and serve as input to decide what to do about the drop-off.
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This is why providing access to the data and sharing it with our team is crucial.
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From there on, we might want to dig deeper into the data, or we might want to pull in qualitative feedback and understand why users behave a certain way.
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This way, we are not being entirely data-driven, but data-informed.
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After all, data being a deep sibling to qualitative feedback tells us what problems to solve.
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And then you basically use intuition to figure out what solutions to those problems might be.
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We go from observation to insight to our next product hypothesis.
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Product analytics can also help us to segment our users, for example, into acquisition channels, or we build so-called user cohorts along behavioral traits.
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Here, we are assigning all users who set a goal within our fitness app to the goal setters cohort.
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Let's use this cohort in a retention analysis.
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We could ask what percentage of new subscribers keeps completing at least one workout per week?
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And what's the difference between those that set goals and those that don't?
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Such a retention curve falling to zero would not indicate product market fit.
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This one much more showing less immediate drop-off and higher persistent usage in subsequent weeks.
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The ability to segment users also helps us running A-B tests.
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Before users receive our app content, we randomly assign a product variant and then direct users to this variant showing either A or B.
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We also set a cookie to track what variant they're seeing.
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In our analytics tool, we can then group users by this experiment property and understand how the introduced change impacts user behavior.
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What gets measured gets managed, so focus on the quality of your metrics.
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A North Star metric provides this clarity, but make sure to break it down.
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Like the learning app Duolingo, optimizing for daily active users, but breaking that down into user activity states, managed by teams, optimizing a more movable metric that is informed by lower level product events.
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With the Object Action Naming Convention, we keep our events set up consistent.
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With an accessible tracking plan, we keep it transparent.
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Our analytics needs are growing?
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Consider using a customer data platform to connect multiple data sources to multiple destinations such as a data warehouse for joining analytics with business data, a marketing platform for delivering personalized experiences, or a webhook triggering custom workflows based on incoming event data.
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Product analytics can be evaluative helping us to understand how did this particular product change impact user behavior.
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It can also be generative enabling us to identify events that drive desired outcomes, helping us to prioritize new strategic opportunities.
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Take Bourbon, a location app designed to make plans, check in at locations and share photos.
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By looking at behavioral data, they uncovered that a small group of users wasn't actually using most of their features, but heavily sharing photos.
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Bourbon decided to focus solely on photo sharing and launch with a new logo and name.
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Ultimately, whether we use our measurements to inform strategy or evaluate features, the measure of who we are is what we do with what we have.
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So as product builders on this beautiful iterative journey of chasing our vision, it is imperative to understand which ideas to cut short and which ones to take further.
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Power your next iterations with product analytics.
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And while doing so, don't forget, stay product-led.
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