シャドーイング練習: Product Analytics in 100 Seconds - YouTubeで英語スピーキングを学ぶ
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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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このビデオで話す練習をする理由は？
この「Product Analytics in 100 Seconds」というビデオは、製品開発や市場適合についての深い洞察を提供しています。動画内で語られる内容は、ビジネスやテクノロジーの分野に関心がある英語学習者にとって非常に役立つものです。
英語のリスニングとスピーキング技能を同時に向上させるためには、こうしたビデオを使って英語シャドーイング（shadowspeak）を行うことが非常に効果的です。実際の会話の文脈を通して学ぶことで、発音や表現力、そして自信を高めることができます。また、このような専門的な内容に触れることは、IELTS スピーキング対策としても非常に有益です。
文法と表現の文脈
このビデオにはいくつかの重要な文法構造や表現が含まれています：
- 「We’re on a mission to…」 - 目的を示す表現で、何かを達成しようとする意志を強調しています。
- 「Every interaction is a learning opportunity.」 - 教訓を得ることを強調する文で、重要な経験から学ぶ姿勢を示しています。
- 「We might want to dig deeper into the data…」 - 意見や提案をする際の柔らかい表現方法です。
- 「This lets the receiving server identify events…」 - 技術的なプロセスを説明するのに役立つ構文です。
これらの表現を使用して、日常的な会話の中でも積極的に応用してみましょう。例えば、自分の意見や経験を述べる際に簡単に使えます。
一般的な発音のトラップ
ビデオの中で注意すべき発音やアクセントのポイントは以下の通りです：
- 「analytics」 - この単語は、特に「a」が強調される点に注意が必要です。
- 「interaction」 - 強調の位置が重要で、特に「in」にアクセントを置いて練習しましょう。
- 「cohort」 - 珍しい音節の区切りに気を付けてください。
これらの単語を何度も繰り返して発音練習を行うことで、日常会話における自信を高められます。YouTubeで英語学習をする際には、こうしたビデオを活用して発音矯正やリスニング力を磨きましょう。
シャドーイングとは？英語上達に効果的な理由
シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。