쉐도잉 연습: Product Analytics in 100 Seconds - YouTube로 영어 말하기 배우기

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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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왜 이 영상을 통해 말하기 연습을 해야 할까요?

이 비디오는 제품 분석에 대한 내용을 담고 있으며, 영어를 배우는 여러분에게 매우 유익한 Speaking 연습의 기회를 제공합니다. 강연자는 제품 개선을 위해 사용자 피드백을 수집하는 과정을 설명하며, 이는 실생활에서 자주 접하는 비즈니스 상황과 관련이 깊습니다. 이 영상을 따라 말하기 연습을 하면 실제 대화에서 사용할 수 있는 다양한 용어와 표현을 자연스럽게 익힐 수 있습니다. 긴밀한 피드백과 이해를 통해 자신의 영어 발음 교정에도 큰 도움이 될 것입니다. 이 영상은 영어 쉐도잉에 적합한 자료로, 유튜브 영어 공부에도 효과적입니다.

문법 및 표현 분석

영상에서 사용된 주요 문장 구조 몇 가지를 살펴보겠습니다:

  • “We talk to our users and collect qualitative feedback” - 이 문장은 현재 시제를 사용하여 지속적인 행동을 나타냅니다. ‘talk to’와 ‘collect’는 반복적인 행동을 설명합니다.
  • “Every interaction is a learning opportunity” - 이 문장은 간단한 현재형 구조로, 일반적인 진리를 표현합니다. ‘is’ 동사는 주어와 보어를 연결합니다.
  • “We could ask what percentage of new subscribers keeps completing at least one workout per week” - 여기에서 ‘could’는 가능성을 나타내며, 조건문을 구성하여 예측을 제시합니다. 문맥에서 이러한 구조는 시나리오를 가정하는 데 유용합니다.

이러한 표현들은 실제 비즈니스 상황에서도 자주 사용되며, 영어 대화에서 유용하게 활용할 수 있습니다.

일반적인 발음 함정

영어 발음에서 어려운 부분들은 다음과 같습니다:

  • “analytics” - 이 단어는 비슷한 발음을 가진 다른 단어들과 혼동될 수 있습니다. 'a-nal-y-tics'로 세분화해 발음하는 연습을 해보세요.
  • “interactions” - 이 단어는 자음과 모음이 연속적으로 나와 발음하기 어렵습니다. 특히 'in-ter'-actions 부분에서 적절한 억양을 유지하며 연습할 필요가 있습니다.
  • “retention analysis” - 이 두 단어는 함께 발음할 때 'retention'의 첫 음절이 강조되어야 합니다. 're-ten'-tion 분석이라고 생각하며 반복 연습해보세요.

이러한 발음 연습은 shadowspeak와 같은 기술을 사용하여 더욱 효과적으로 진행할 수 있으며, 원하는 발음을 정확하게 습득하는 데 도움이 될 것입니다.

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

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