Webinar: Build The Right Thing: A Feature Strategy Guide by Spotify Sr PM, Dejan Krstic

Product School
3 Jun 202216:55

Summary

TLDRビデオスクリプトは、製品戦略、機能の優先順位付け、リリース方法、影響の評価、そして長期的な製品強化に関するアイデアを共有しています。製品マネージャーであるDanは、機能を正しく構築し、リリースする方法と、リリース後の機能のパフォーマンスを評価する方法について詳しく説明しています。特に、実験的リリース、最小限の機能リリース、段階的リリースなど、様々な方法を紹介しています。また、機能が戦略的に重要であるかどうか、ターゲットユーザーの利用率、リテンションスコアなどを活用して、機能のパフォーマンスを評価する方法を示しています。この概要では、製品戦略の4つの主要な柱と、機能戦略に焦点を当てて説明しています。

Takeaways

  • 🌟 製品戦略の4つの柱: 特徴戦略、成長戦略、製品市場適合拡大戦略、スケーリング戦略
  • 🎯 特徴戦略の目的は価値創造と獲得能力の向上
  • 👨‍👩‍👧‍👦 新規ユーザー獲得、既存ユーザーの維持、収益化の3つの方法で特徴は価値を創出できる
  • 🔍 ユーザーインサイト(インタビューやユーザー調査などの質的なデータ)、データインサイト(実際の消費者行動のパターンなどの量的なデータ)を考慮する必要がある
  • 🧠 特徴マップを作成して、ターゲット層、ユーザー価値、ビジネス価値の3つの側面を評価する
  • 🚀 実験的リリース、最小機能リリース、段階的リリースの3つのリリース手法がある
  • 💡 実験的リリースでは、仮説検証を通じて特徴をモデル化する
  • ☑️ 最小機能リリースでは、コアの顧客価値提案を検証する
  • 🔧 段階的リリースでは、より堅牢な特徴を段階的にリリースする
  • 📊 リテンションスコアと特徴マトリックスを使って、リリース後の特徴のパフォーマンスを評価する

Q & A

  • 製品機能を優先順位付けする際の重要な要因は何ですか?

    -製品機能を優先順位付けする際には、ビジョンとチームの目標、ユーザーインサイト(インタビューや調査から得られる定性的な洞察)、データインサイト(実際の消費行動や行動パターンから得られる定量的な洞察)を考慮する必要があります。

  • ユーザーの問題の深刻度を評価する際の考慮事項は何ですか?

    -ユーザーの問題の深刻度を評価する際には、低い場合は簡単な回避策が見つかる、中程度の場合は複雑で痛みを伴う回避策になる、高い場合は製品の本来の価値提案を体験できなくなることを考慮する必要があります。

  • 機能やプロダクトを設計する際のユーザーカテゴリー分けについて説明してください。

    -設計時には、本来の対象とするコアユーザー、付随的に価値を得るアジャセントユーザー、全く価値を得られないノンアジャセントユーザーを区別して考える必要があります。

  • 機能を立ち上げる際に役立つ手法は何ですか?

    -機能立ち上げ前に実施すると有用な手法としては、予備死亡アナリシス(Pre-Mortem)があります。これは失敗した場合の結果を想像することで、リスクを事前に想定し準備することができます。

  • 機能をリリースする際の3つのモデルを説明してください。

    -3つのモデルは、実験的リリース(主要な仮説を検証するための実験的な機能)、最小限の機能リリース(コア価値提案を検証するための最小限の機能)、フェーズリリース(機能を段階的に丸ごとリリースする方法)です。

  • 実験的リリースの手順は何ですか?

    -実験的リリースの手順は、(1)仮説を特定する (2)仮説を洗練させランク付けする (3)適切な実験を設計する (4)実験を実施する (5)学びをまとめる (6)行動を起こす、というサイクルを回すことです。

  • リテンションスコアは何を測定する指標ですか?

    -リテンションスコアは、対象ユーザー数に対して製品を一度使用した人数とリテンションされた継続利用者数を割った指標で、機能の評価に使われます。

  • 機能マトリックスで特に注目すべき機能の種類は何ですか?

    -機能マトリックスでは、高い戦略的重要性があるにもかかわらずユーザー満足度が低い「負債」機能に特に注目する必要があります。

  • コアとオーバーパフォーマンスの機能についてはどのような対応が必要ですか?

    -コア機能とオーバーパフォーマンスの機能については、価値の最大化と対象ユーザー層の拡大に注力する必要があります。

  • プロジェクト機能についてはどのような考え方が必要ですか?

    -プロジェクト機能については、投資継続の価値があるか、製品の全体的な健全性のために廃止すべきか検討する必要があります。

Outlines

00:00

👋 自己紹介と機能戦略の概要

この段落では、話者であるDanが自身の経歴を簡単に紹介し、機能戦略について説明することの重要性を強調しています。機能戦略は、製品価値を創造し、獲得することに焦点を当てています。その目的は、新規ユーザーの獲得、既存ユーザーの維持、収益化を通じて価値を生み出すことです。また、機能戦略は製品戦略の柱の1つであり、成長戦略、製品マーケットフィット拡大戦略、そして拡張戦略と並んで重要であることを説明しています。

05:01

🔍 インサイトと特徴マップ

この段落では、機能戦略における重要なインサイトと特徴マップについて説明しています。戦略的インサイト、ユーザーインサイト、データインサイト、定性的評価がそれぞれ重要であることを強調しています。さらに、ユーザーの問題の深刻度、ユーザーカテゴリーを特定することの重要性も説明しています。これらのインサイトとデータを基に、定性的な特徴マップを作成することができ、ターゲット人口、ユーザー価値、ビジネス価値の3つの柱を持つ特徴マップができると説明しています。

10:03

🚀 リリースモデル

この段落では、機能をリリースする際の3つのモデルについて説明しています。実験的リリース、最小機能リリース、フェーズリリースがそれぞれ違った目的を持ち、機能の不確実性の程度に応じて使い分けられることが述べられています。実験的リリースでは、仮説を検証し学習することに重点が置かれ、最小機能リリースでは主要なユーザー価値を検証することが目的となります。フェーズリリースでは、機能をさらに細分化して、段階的にリリースしていくことが説明されています。

15:04

📊 リテンションスコアと特徴マトリクス

この段落では、機能をリリースした後の評価方法について説明しています。リテンションスコアを計算することで、ターゲットとなるユーザーのうち、実際に機能を使用し続けているユーザーの割合を評価できます。さらに、リテンションスコアを戦略的重要度に対してプロットすることで、特徴マトリクスを作成できます。この特徴マトリクスにより、機能を4つのカテゴリーに分類し、さらなる投資の必要性や、サンセットすべき機能かどうかを判断することができると説明しています。

Mindmap

Keywords

💡製品戦略

企業や組織が製品やサービスを開発し、それらの価値を創造し獲得する能力を改善するための戦略的な取り組みを指します。本ビデオでは、製品戦略は4つの柱に分けられており、特に「フィーチャー戦略」に焦点が当てられています。フィーチャー戦略は、製品やサービスの機能開発を優先順位付けし、収益化と影響度を評価し、長期的に製品を改良していく重要な取り組みであると説明されています。

💡最小機能製品 (Minimum Viable Feature)

最小機能製品(MVF)は、製品やサービスの主要な価値提案を検証するために、最小限の機能や設計のみを備えた完全に機能するバージョンのことです。本ビデオでは、MVFを使ってユーザーの反応を検証し、核となる価値提案が有効かどうかを評価し、フルスケールの製品開発に進むかどうかを判断するリリース手法として紹介されています。

💡フィーチャーマトリクス

フィーチャーマトリクスは、リリース後の製品やサービスの各機能のパフォーマンスを評価するための分析ツールです。フィーチャーの戦略的重要度と留保率(定期的に使われ続けている割合)をプロットすることで、機能をコア、プロジェクト、賢明な判断が必要な機能などに分類することができます。本ビデオでは、このツールを使って適切に機能をカテゴリー化し、投資の優先順位付けやサンセット化の判断などの意思決定に活用することが提案されています。

💡バリデーションループ

バリデーションループは、実験的リリース手法に関連する概念で、仮説を立て、最小限のMVPを構築し、ユーザーのデータと行動を観察し、結果から学びながらさらに新しい仮説を作り、そのプロセスを繰り返し行うことで、最終的なソリューションに対する確信を高めていく手法のことです。本ビデオでは、バリデーションループが「構築 - 測定 - 学習」のサイクルを通して、より高速で深い洞察を得られることが説明されています。

💡フェーズリリース

フェーズリリースは、複雑な機能を分割して段階的に価値を提供し続けるリリース手法です。フィーチャーやMVFによって主要な価値提案を検証できた後、機能を個々のフェーズに分けてリリースすることで、必要な機能を適宜提供していけるアプローチです。本ビデオでは、シンプルな機能であればフル機能を一度にリリースできますが、複雑な機能ではフェーズリリースが適切であると説明されています。

💡質的特徴マップ

質的特徴マップは、製品の機能やサービスの目標ユーザー、ユーザーにもたらす価値、ビジネス価値を特徴づけるためのフレームワークです。本ビデオでは、ターゲット人口、ユーザー価値、ビジネス価値の3つの柱から成り、新しいフィーチャーを企画する際の枠組みとなる重要な手法と解説されています。質的特徴マップを用いることで、機能設計の準備ができ、ビジネスと顧客に対するインパクトを予測できるようになります。

💡プロブレム重大性

プロブレム重大性とは、ユーザーが製品やサービスから受ける価値を妨げる問題の深刻さを示す概念です。本ビデオでは、低、中、高の3段階で問題の障害度合いが説明されています。問題が重大である程、ユーザーは製品の本来の価値を享受しづらくなるため、機能設計においてユーザーの問題の重大性を把握することが重要となります。企画したフィーチャーがプロブレム重大性に合致していなければ、ユーザーニーズを満たせない可能性があります。

💡ユーザーカテゴリー

ユーザーカテゴリーは、フィーチャーやサービスを設計する際に考慮すべき異なるタイプのユーザーグループを指します。本ビデオでは、コア・ユーザー(機能が設計された主なターゲット)、隣接ユーザー(設計時には考慮されていなかったが利用価値を得ているユーザー)、非隣接ユーザー(機能から全く価値を得られていないユーザー)の3つのカテゴリーが紹介されています。ユーザーカテゴリーを考慮し、各グループのニーズを理解することで、より適切な機能設計が可能になります。

💡事前モーテム

事前モーテムは、プロジェクトの前段階で、起こりうる良い結果、悪い結果を想定することでリスクを特定し、対応策を検討する手法です。本ビデオでは、フィーチャーリリース前にこの手法を活用し、アウトカムを想定し、次のステップを事前に検討しておくことが提案されています。起こりうるさまざまな結果を考えることで、計画を成功に導き、学びにつながる可能性を高められるのが、事前モーテムの利点とされています。

💡留保率

留保率は、製品やサービスの目標ユーザーのうち、どの程度がその機能を定期的に使い続けているかを示す指標です。本ビデオでは、総目標ユーザー数に対する留保率を計算することで、フィーチャー評価の第一歩となることが説明されています。留保率が高ければ、機能が適切に設計され、ユーザーの価値観に合致したものと評価できますが、低い場合はユーザーニーズを再検討する必要があります。

Highlights

I'm Dan, a product manager for more than 12 years now. I came from business, where I was keen to improve processes and make our teams work more efficiently.

We quickly realized that we need to change our approach and apply agile principles to deliver incremental value, quickly and get insights about most pressing problem spaces.

The feature strategy focuses on improving our ability to create and capture value.

The growth strategy on the other hand, largely focuses on maximizing products, existing value proposition.

The product market fit expansion strategy tries to add value in two ways: one, is to adapt products to new complementary markets, the other on adding new complementary products with the goal to overcome saturations.

The last product strategy pillar is scaling, where you invest in supporting processes, infrastructure, and strategies which support the three previous mentioned strategy layers.

Features can create value in three ways: through acquiring new users, retaining existing ones, and monetizing.

There are strategic insights you need to consider like, what is our company vision, what are our group or team objectives, and how will this feature help achieving them.

Then we have user insights, these are our qualitative insights, together from interviews, user research or surveys, to fully understand the problems our users have.

In addition we have data insights, and here we track the actual consumption, or behavior to understand patterns, identify and validate hypothesis we have.

Qualitative evaluations are very important as they help you with questions like, how many users is this feature or product designed for, how does the feature add value to the users, and how does the feature add value to our business.

It's also crucial to think about user problem severity, if the user has low disruptions usually they found easy workarounds, with moderate disruptions these workarounds become more complex and painful, high disruptions on the other hand, prevent the users experiencing the value proposition which is very critical.

Another aspect is identifying user categories, who are my core users those for whom the feature is designed for, who are my adjacent users, who are getting some value from the feature but they haven't been considered why designing it, and non-adjacent users these aren't getting any value from the feature or product.

A great methodology that you can use to help you be better prepared for the upcoming release is the pre mortem, it helps you think about what could happen good or bad, so that you can plan before it starts.

Before you roll up your sleeves you should make sure to pressure test your insights, did you use the right data, have you avoided biases, make sure the right feature has been prioritized and sense check the launch plan in order to minimize the costs, and maximize the value.

Transcripts

play00:00

hi everyone

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hope you're well

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and thank you for joining me to talk a

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bit about feature strategy

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today

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i'd like to share some thoughts around

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how to prioritize features and products

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how to launch them

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and evaluate the impact to be able to

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enhance the product over the long term

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but before we start i like to share a

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little bit about myself

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i'm dan

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i'm a product manager for more than 12

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years now i came from business

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where i was keen to improve processes

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and make our teams work more efficient

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due to my curiosity i slowly

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transitioned to product management which

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back then was writing huge

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specifications and praying for six

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months that the feature will be

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delivered as expected

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but what shall i say

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they never did

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we quickly realized that we need to

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change our approach and apply agile

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principles to deliver incremental value

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quickly and get insights about most

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pressing problem spaces

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i then joined soundcloud where we built

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insights and analytics platform from

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scratch with the mission to empower

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emerging artists through actionable

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recommendation to understand build and

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connect with their audience and grow

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their careers

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in addition we launched fan powered

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royalties also known as the user-centric

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model

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a new way for artists to earn money from

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streaming services

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i recently joined spotify to help

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achieving the mission to unlock the

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potential of human creativity by giving

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a million of creative artists the

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opportunity to live of the art

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and billions of fans the opportunity to

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enjoy and be inspired by it

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but enough about me let's jump into the

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topic

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the feature strategy is one pillar of

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the wider product strategy work

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the feature strategy focuses on

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improving our ability to create and

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capture value

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the growth strategy on the other hand

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largely focuses on maximizing products

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existing value proposition

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effective growth strategies connect

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acquisition retention and monetization

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it's important to move away from the

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final thinking towards growth loops

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where we have an input an action and an

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output which will feed the next loop

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and so on

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the product market fit expansion

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strategy tries to add value in two ways

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one

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is to adapt products to new

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complementary markets the other on

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adding new complementary products with

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the goal to overcome saturations like

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market saturations market capture

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reaches the natural ceiling or product

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saturations where the product becomes

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fully optimized for its use case

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the last product strategy pillar is

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scaling

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where you invest in supporting processes

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infrastructure

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and strategies which support the three

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previous mentioned strategy layers

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key pillars here are

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tech scaling

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platformization

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technical dap management modernization

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of ux

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process scaling

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process improvement evaluations value

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stream mapping

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and user scaling

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value added use cases underserved user

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segments or identifying bad behavior

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so with our feature strategy work

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our goal is to improve the ability to

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create and capture value

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features can create value in three ways

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through acquiring new users

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retaining existing ones

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and monetizing

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it's crucial to evaluate and enhance

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existing features

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as this will inform your future build

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strategy and help you develop new

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features

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and continue the loop of evaluating new

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features performances post launch

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with the goal to achieve feature product

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fit

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there are strategic insights you need to

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consider like

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what is our company vision

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what are our group or team objectives

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and how will this feature help achieving

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them

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then we have user insights

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these are our qualitative insights

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together from interviews

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user research or surveys

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to fully understand the problems our

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users have

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in addition we have data insights

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and here we track the actual consumption

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or behavior to understand patterns

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identify and validate hypothesis we have

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qualitative evaluations are very

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important as they help you with

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questions like

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how many users is this feature or

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product designed for

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how does the feature add value to the

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users

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and how does the feature add value to

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our business

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it's also crucial to think about user

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problem severity

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if the user has low disruptions usually

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they found easy workarounds

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with moderate disruptions these

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workarounds become more complex and

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painful

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high disruptions on the other hand

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prevent the users experiencing the value

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proposition which is very critical

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another aspect is identifying user

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categories

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who are my core users those for whom the

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feature is designed for who are my

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adjacent users

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who are getting some value from the

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feature but they haven't been considered

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why designing it

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and non-adjacent users these aren't

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getting any value from the feature or

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product

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based on all previous mentioned areas

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you're able to create this qualitative

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feature map

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this map has three pillars

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the first

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is the target population in which you

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describe the target group

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the segment your features is designed

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for and the target size

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the estimated percentage of users or

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server segment represents

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the second pillar is the user value

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here you describe the user problem the

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feature of product tries to solve

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the problem frequency so how often the

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user is experiencing it

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and the problem severity how painful is

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it for our users

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the last pillar is the business value

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what impact will this feature bring and

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how strategically important is it

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a great mythology that you can use

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to help you be better prepared for the

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upcoming release is the pre mortem

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it helps you think about

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what could happen good or bad

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so that you can plan before it starts

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some questions you can ask are

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how does the feature add value to a

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product

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what target segment

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are the primary set of users

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how much confidence do you have in the

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success of the feature

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how will we launch this feature what

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metrics will indicate the success

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what outcomes are possible for the

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feature performance

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what would each outcome teach us about

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the performance and what would the next

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step be after each of those outcomes

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these are only of course recommendations

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and you should identify outcomes and

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metrics tied to your company and product

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this framework is great

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as you're able to validate and compare

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your assumptions with the actual results

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post implementation

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and it will also help you strengthen

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your product sense

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and before you roll up your sleeves you

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should make sure to pressure test your

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insights

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did you use the right data

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have you avoided biases

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make sure

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the right feature

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has been prioritized and sense check the

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launch plan in order to minimize the

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costs

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and maximize the value

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now

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we should be well prepared to release

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our features but how shall we do it

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which model is appropriate

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we have the experimental release which

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focuses on running experiments to

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validate key assumptions we have for our

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feature in order to learn and shape it

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over the time

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then we have the minimum viable feature

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release

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a fully functioning version of the

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feature

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but with a minimum functionality as our

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goal is to validate the core value

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proposition

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and lastly

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the face release

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which delivers a more robust version of

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the feature but broken down into faces

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in order to continuously release a value

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when ready

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these methods don't compete with each

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other it's actually the opposite

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a feature needs to run through each of

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those release methods based on the

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ambiguity spectrum

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if you have high ambiguity

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and not enough insights from our already

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mentioned sources like strategic user or

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data insights

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then the experimental release is your

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choice

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as you need to figure out what the right

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feature product is

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to solve the biggest problem for users

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if you have high confidence and you

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validated your hypotheses through the

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other releases

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then the face release is your choice

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as this focus more on building the

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feature or product right in order to

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deliver incremental value

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let me dig deeper into each of those

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methods

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with the experimental release our goal

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is to learn and shape the feature

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continuously with user behavior

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we are able to do it when we list all

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assumptions we believe in the success of

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the feature

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turn these into experiments

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that can be validated with users

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and refine our features with every

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experimentational outcome

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hypothesis driven validations are key to

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success as clearly defined incremental

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experimentations

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lead to faster learnings and deeper

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insights

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they're much more valuable than writing

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detailed specifications

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as i have done previously

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here you can see a step-to-step guide

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first you identify your assumptions

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then you refrain your assumptions and

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hypotheses

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rank them in order of importance

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design appropriate experiments

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conduct these

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synthesize your learnings and act

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the validated learning loop is really

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powerful you start to build your minimum

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viable product experiment based on your

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hypothesis you have

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then you measure the qualitative and

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quantitative user data

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learn from the results

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and create newly improved hypotheses and

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start the process again

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until you gain enough evidence and

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confidence in your solution

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i receive a lot of questions around what

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i consider to be an experiment

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and the answer is whatever helps you

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validate your assumptions as fast as

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possible

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it could be a spreadsheet

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with which you validate a business logic

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a ui sketch on paper a user flow a low

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no code prototype so basically anything

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that makes you learn

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so once we have enough confidence that

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we have identified the right feature we

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can move to the minimum viable feature

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release method

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since here our goal is to validate the

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core user value proposition while using

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minimal design and functionality

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we can achieve that by minimizing the

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number of platforms the feature is built

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for surface for example

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only releasing it on ios and build

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desktop or android versions later

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or by minimizing the number of

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integrations the feature needs

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or

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minimizing design and

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engineering resources needed

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now that we have validated the core user

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value proposition

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we are very confident that this is the

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right thing to build

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we can use the phase release method now

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for smaller features this means that

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that we can fully build the entire

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feature and release it

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for more complex features

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we will be breaking down the feature

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into faces

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which can be released independently by

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continuously delivering value

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we did it

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we have successfully released our

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feature

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but here our job doesn't end

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we need to evaluate the performance of

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the feature post launch

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a great tool

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that is doing that is the so-called

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retention score

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based on our pre-work creating the

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quality feature map we know what our

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target users are

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then we check how many of those users

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have tried the product once so adopt it

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and how many use the feature regularly

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now

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so are retained

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we then need to divide the retained

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users with our target audience

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and have the retention score

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this exercise helps us evaluate our

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features

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as calculating the retention score is

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the first step which is followed by

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plotting the score against the strategic

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importance

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with this approach we are able to

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evaluate our features

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and the great tool for that is the

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so-called feature matrix

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on the y-axis you have the retention

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score and on the x-axis the strategic

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importance

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we're able to categorize our features in

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four areas

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the core

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project and liability features

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we as product folks need to have a

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special eye on the liability features

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as they have a very high strategic

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importance but are not satisfying our

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users yet

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for core and over-performing features

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it's important to maximize the value

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capture

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and try to expand the target audience

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for our project features you should ask

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yourself if it's worth investing or if

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we should sunset them

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as it's important to the overall health

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and success of our product

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if you let too many features creep into

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your product the core value proposition

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and vision can easily get diluted

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i hope you enjoyed and found this

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webinar useful

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please start experimenting with some of

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the frameworks and ideas i mentioned

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i look forward to hearing your feedback

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thank you and all the best

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[Music]

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you

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