Webinar: Build The Right Thing: A Feature Strategy Guide by Spotify Sr PM, Dejan Krstic
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
👋 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。
🔍 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。
🚀 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。
📊 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
💡最小機能製品 (Minimum Viable Feature)
💡フィーチャーマトリクス
💡バリデーションループ
💡フェーズリリース
💡質的特徴マップ
💡プロブレム重大性
💡ユーザーカテゴリー
💡事前モーテム
💡留保率
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
hi everyone
hope you're well
and thank you for joining me to talk a
bit about feature strategy
today
i'd like to share some thoughts around
how to prioritize features and products
how to launch them
and evaluate the impact to be able to
enhance the product over the long term
but before we start i like to share a
little bit about myself
i'm dan
i'm 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 efficient
due to my curiosity i slowly
transitioned to product management which
back then was writing huge
specifications and praying for six
months that the feature will be
delivered as expected
but what shall i say
they never did
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
i then joined soundcloud where we built
insights and analytics platform from
scratch with the mission to empower
emerging artists through actionable
recommendation to understand build and
connect with their audience and grow
their careers
in addition we launched fan powered
royalties also known as the user-centric
model
a new way for artists to earn money from
streaming services
i recently joined spotify to help
achieving the mission to unlock the
potential of human creativity by giving
a million of creative artists the
opportunity to live of the art
and billions of fans the opportunity to
enjoy and be inspired by it
but enough about me let's jump into the
topic
the feature strategy is one pillar of
the wider product strategy work
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
effective growth strategies connect
acquisition retention and monetization
it's important to move away from the
final thinking towards growth loops
where we have an input an action and an
output which will feed the next loop
and so on
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 like
market saturations market capture
reaches the natural ceiling or product
saturations where the product becomes
fully optimized for its use case
the last product strategy pillar is
scaling
where you invest in supporting processes
infrastructure
and strategies which support the three
previous mentioned strategy layers
key pillars here are
tech scaling
platformization
technical dap management modernization
of ux
process scaling
process improvement evaluations value
stream mapping
and user scaling
value added use cases underserved user
segments or identifying bad behavior
so with our feature strategy work
our goal is to improve the ability to
create and capture value
features can create value in three ways
through acquiring new users
retaining existing ones
and monetizing
it's crucial to evaluate and enhance
existing features
as this will inform your future build
strategy and help you develop new
features
and continue the loop of evaluating new
features performances post launch
with the goal to achieve feature product
fit
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
based on all previous mentioned areas
you're able to create this qualitative
feature map
this map has three pillars
the first
is the target population in which you
describe the target group
the segment your features is designed
for and the target size
the estimated percentage of users or
server segment represents
the second pillar is the user value
here you describe the user problem the
feature of product tries to solve
the problem frequency so how often the
user is experiencing it
and the problem severity how painful is
it for our users
the last pillar is the business value
what impact will this feature bring and
how strategically important is it
a great mythology 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
some questions you can ask are
how does the feature add value to a
product
what target segment
are the primary set of users
how much confidence do you have in the
success of the feature
how will we launch this feature what
metrics will indicate the success
what outcomes are possible for the
feature performance
what would each outcome teach us about
the performance and what would the next
step be after each of those outcomes
these are only of course recommendations
and you should identify outcomes and
metrics tied to your company and product
this framework is great
as you're able to validate and compare
your assumptions with the actual results
post implementation
and it will also help you strengthen
your product sense
and 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
now
we should be well prepared to release
our features but how shall we do it
which model is appropriate
we have the experimental release which
focuses on running experiments to
validate key assumptions we have for our
feature in order to learn and shape it
over the time
then we have the minimum viable feature
release
a fully functioning version of the
feature
but with a minimum functionality as our
goal is to validate the core value
proposition
and lastly
the face release
which delivers a more robust version of
the feature but broken down into faces
in order to continuously release a value
when ready
these methods don't compete with each
other it's actually the opposite
a feature needs to run through each of
those release methods based on the
ambiguity spectrum
if you have high ambiguity
and not enough insights from our already
mentioned sources like strategic user or
data insights
then the experimental release is your
choice
as you need to figure out what the right
feature product is
to solve the biggest problem for users
if you have high confidence and you
validated your hypotheses through the
other releases
then the face release is your choice
as this focus more on building the
feature or product right in order to
deliver incremental value
let me dig deeper into each of those
methods
with the experimental release our goal
is to learn and shape the feature
continuously with user behavior
we are able to do it when we list all
assumptions we believe in the success of
the feature
turn these into experiments
that can be validated with users
and refine our features with every
experimentational outcome
hypothesis driven validations are key to
success as clearly defined incremental
experimentations
lead to faster learnings and deeper
insights
they're much more valuable than writing
detailed specifications
as i have done previously
here you can see a step-to-step guide
first you identify your assumptions
then you refrain your assumptions and
hypotheses
rank them in order of importance
design appropriate experiments
conduct these
synthesize your learnings and act
the validated learning loop is really
powerful you start to build your minimum
viable product experiment based on your
hypothesis you have
then you measure the qualitative and
quantitative user data
learn from the results
and create newly improved hypotheses and
start the process again
until you gain enough evidence and
confidence in your solution
i receive a lot of questions around what
i consider to be an experiment
and the answer is whatever helps you
validate your assumptions as fast as
possible
it could be a spreadsheet
with which you validate a business logic
a ui sketch on paper a user flow a low
no code prototype so basically anything
that makes you learn
so once we have enough confidence that
we have identified the right feature we
can move to the minimum viable feature
release method
since here our goal is to validate the
core user value proposition while using
minimal design and functionality
we can achieve that by minimizing the
number of platforms the feature is built
for surface for example
only releasing it on ios and build
desktop or android versions later
or by minimizing the number of
integrations the feature needs
or
minimizing design and
engineering resources needed
now that we have validated the core user
value proposition
we are very confident that this is the
right thing to build
we can use the phase release method now
for smaller features this means that
that we can fully build the entire
feature and release it
for more complex features
we will be breaking down the feature
into faces
which can be released independently by
continuously delivering value
we did it
we have successfully released our
feature
but here our job doesn't end
we need to evaluate the performance of
the feature post launch
a great tool
that is doing that is the so-called
retention score
based on our pre-work creating the
quality feature map we know what our
target users are
then we check how many of those users
have tried the product once so adopt it
and how many use the feature regularly
now
so are retained
we then need to divide the retained
users with our target audience
and have the retention score
this exercise helps us evaluate our
features
as calculating the retention score is
the first step which is followed by
plotting the score against the strategic
importance
with this approach we are able to
evaluate our features
and the great tool for that is the
so-called feature matrix
on the y-axis you have the retention
score and on the x-axis the strategic
importance
we're able to categorize our features in
four areas
the core
project and liability features
we as product folks need to have a
special eye on the liability features
as they have a very high strategic
importance but are not satisfying our
users yet
for core and over-performing features
it's important to maximize the value
capture
and try to expand the target audience
for our project features you should ask
yourself if it's worth investing or if
we should sunset them
as it's important to the overall health
and success of our product
if you let too many features creep into
your product the core value proposition
and vision can easily get diluted
i hope you enjoyed and found this
webinar useful
please start experimenting with some of
the frameworks and ideas i mentioned
i look forward to hearing your feedback
thank you and all the best
[Music]
you
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