How to Develop New Products by Financial Times Head of Product
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Main takeaways: - Find your north star to develop great products - How that single metric matters in an overwhelming data informed world - Product development process across cultures and territories
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How to Develop New Products by Financial Times Head of Product
- 1. www.productschool.com How to Develop New Products by Financial Times Head of Product
- 2. FREE INVITE Join 30,000+ Product Managers on
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- 7. COURSES Blockchain for Managers Learn how to trade cryptocurrencies and build products using the blockchain
- 9. Claire Parker Senior Product Manager FInancial Times How to develop new products
- 10. How to develop new products - To measure is to know - Our wider product approach - Taking our product approach across cultures
- 11. About the FT - 930,000 paid for customers - 730,000 digital only subscribers - 1.9 million daily readership - We operate with a strict paywall - Subscription based digital company
- 12. To measure is to know
- 13. 1st Pitfall: Metrics are proxies
- 14. Metric: total time spent
- 15. Real value: viewing interesting content
- 17. Real value: connect with people you care
- 18. 2nd Pitfall: Many truths
- 19. 2nd Pitfall: Many truths Email Alerts Attending Events App Downloads Reader comments Facebook Likes Sharing Stories Time Spent Reading Twitter Followers and Retweets Multichannel Consumption
- 20. 3rd Pitfall: overwhelming information
- 21. Too many data points = lack of focus
- 22. A north star - A single metric - Simple to Understand - With consistently proven correlation with your real business goal
- 24. Switch from advertising to subscriptions
- 25. 50% of digital news readers consume via social media
- 26. And 2/3s of people don’t remember their news source!
- 27. Acquiring subscribers is really hard!
- 28. Which is why we used our data intelligence! purchase renew
- 29. Subscription is all about habits Reminder Routine Habit
- 30. We kept on finding... Revenue vs. Engagement Conversion Rate vs. Engagement Cancellation Rate vs. Engagement
- 31. Correlates with renewal rates B2C B2B
- 32. Our “North Star” Engagement Score: RFV Recency | Frequency | Volume
- 33. Not everyone is a digital news junkie Recency Frequency Best customers Engagement opportunities
- 34. How it guides product development
- 35. Our product vision Help our readers make better business decisions to advance their career/business by providing them the most relevant information to them, without obscuring the FT view and saving them time
- 38. myFT | Impact on RFV Engagement increased 86% compared to the control group
- 40. Speed | Impact on RFV Even though our beta site had fewer features + content, 5% of our loyal customers became immediately engaged….Why?
- 41. Speed | Impact on RFV Hypothesis: Every second counts, speed has a direct correlation to engagement
- 42. Speed | Impact on RFV - A series of tests: - Control: Fastest site - Variant A: 1 second slower - Variant B: 2 seconds slower - Variant C: 3 Seconds slower
- 43. Speed | Impact on RFV Speed matters as it has a direct correlation with revenue 1 second = £$millions
- 44. Speed | Impact on RFV
- 46. App | Impact on RFV
- 47. App | Impact on RFV Optimising notifications
- 48. 3 takeaways
- 49. Find A North Star within your org - A single metric - Simple to Understand - With consistently proven correlation with your real goal
- 50. That is helping you to - Focus on the most important thing - Have one single version of the truth - Consolidate effort and conversation
- 51. Challenge it - Have your users gained value? - Have your numbers gone up as expected? - Is the North Star correlation valid and accurate?
- 52. Product approach
- 53. We start with focusing on business outcomes
- 54. A strong product vision setting direction
- 55. Using measurements to always learn
- 56. Working lean to deliver value quickly
- 57. Product approach
- 58. Nikkei acquired the FT in 2015 58
- 59. Nikkei’s global business publication
- 60. NAR & FT formed a partnership
- 62. To accelerate digital change across all of NAR
- 63. By improving NAR’s products
- 64. Product approach was not being followed
- 65. 1st Pitfall: no clear direction
- 66. We were delivering things
- 67. 2nd Pitfall: abandoned lean principles
- 68. We had slipped into waterfall ways of working
- 69. 3rd Pitfall: long distance relationship
- 70. Multiple locations was tricky
- 72. One team “Nikkei Asian Review provides original, quality insight that enables professionals with an interest in Asian business to make informed decisions and gain competitive advantage” We needed a common purpose = product vision
- 73. One team Japan + UK Nikkei + FT Newsroom, Commercial + Product
- 74. One team From a client & supplier relationship to a partner
- 75. Lean Focusing on the problem we are trying to solve for the customer/ business
- 77. Lean Using data to learn from our work & adopt a more continuous development approach
- 78. Group consensus Group harmony is important, particularly when making decisions
- 79. Group consensus Which was slowing us down
- 80. Group consensus So we created empowered sub groups Leadership = prioritisation Experts = specifics Other stakeholders = FYI
- 81. 3 takeaways
- 82. A product approach across cultures - A product development approach can work across cultures - Tailor how you work together as you implement that approach - It is hard...try, learn & repeat
- 84. www.productschool.com Part-time Product Management, Coding, Data, Digital Marketing and Blockchain courses in San Francisco, Silicon Valley, New York, Santa Monica, Los Angeles, Austin, Boston, Boulder, Chicago, Denver, Orange County, Seattle, Bellevue, Toronto, London and Online
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- This statement is not rocket science but you would be surprised as to how many times product managers have gotten hung up on the wrong metrics when defining success of their products Simply put - if you cant measure something - you cannot learn from it or improve upon iteration However, Its hugely important to measure the right things
- Lets talk about some common pitfalls that face product managers Metrics are proxies - what you measure needs to tie to success Its the difference between numbers and numbers that matter Base your metrics on your goals and you are off to a flying start Lets look at some examples
- Netflix measure total time spent However, their real value is viewing interesting content - with their metric what if someone falls asleep whilst watching their content? Ive done that many times - does this mean my total time spent was valuable!?
- What they are really trying to measure is the real value - are we as customers viewing interesting content
- Facebook’s metric was time on newsfeed and interaction. Their real value was bringing people together - they didn’t catch the problem with viewing content that upsets you still engages you
- the real value they were trying to measure was connecting with people you care about
- The second pitfall is the sheer number of possibilities for metrics
- Engagement is highly important at the FT - but its a buzz word - how do we measure it? These are all examples of evidence touchpoints that someone has engaged with us It doesn't tell us the full story - we want to understand what drives habitual use + loyalty and retention
- The 3rd pitfall is an overwhelming amount of data
- Collecting data is no longer an issue - we have qual and quant data at our fingertips There are no shortages of tools out there to just grab every metric desired The real issue is the LACK of Focus
- Identify metrics that matter the most Keep it simple for you to be able to succeed in your product development Ensure they correlate to your goals & your product vision + customer needs
- For a long time news organisations thought they would give their news away for free and benefit from advertising and scale of global distribution. The scale argument - unfortunately - never quite worked. There are other players out there - 2 particularly big - who will always have significantly more scale. And digital ad revenues simply plummetted. It is now clearer than ever that there is no future in ad supported news media. The other scary trend - which I could ask crowdsource your perspectives on - but will take you out of your misery - is about changing trends in how people read the news. 50% of digital news readers now consume news via social platforms. Publishers use them as a way of raising brand awareness. Yet research suggests that 2/3rds of readers here in the UK do not remember the source of the news they read. So publishers give away their news for free, hoping for brand equity. In most cases they don’t get it, and don’t make much ad revenue along the way either. It’s not a hugely rosy picture. Which is why we ask people to pay. But it’s hard. REALLY hard to convince a free reader to become a paid reader. Especially when your product is relatively expensive. Which is where our data intelligence comes to bear. Where we understand what drives purchase and renewal behaviour.. And how we use this data to drive improvements. Some news orgs give news away for free and they sought to generate revenue through advertising + scale of distribution Scale never worked Digital ads revenue plummeted Another scary trend is readership = 50% of users consumer digital news through social media, when asked they dont even remember who they read the news with Its not a pretty picture but thats why we are a paid for news publisher Its hard to convince free readers to turn into paid readers but we have to do it. Its costs 5 times as much to acquire new users as it does to retain ones you already have This is where our data intelligence comes to the forefront Through it we are able to understand drivers for purchasing as well as renewal of our subscriptions, we use this to help in product development
- For a long time news organisations thought they would give their news away for free and benefit from advertising and scale of global distribution. The scale argument - unfortunately - never quite worked. There are other players out there - 2 particularly big - who will always have significantly more scale. And digital ad revenues simply plummetted. It is now clearer than ever that there is no future in ad supported news media. The other scary trend - which I could ask crowdsource your perspectives on - but will take you out of your misery - is about changing trends in how people read the news. 50% of digital news readers now consume news via social platforms. Publishers use them as a way of raising brand awareness. Yet research suggests that 2/3rds of readers here in the UK do not remember the source of the news they read. So publishers give away their news for free, hoping for brand equity. In most cases they don’t get it, and don’t make much ad revenue along the way either. It’s not a hugely rosy picture. Which is why we ask people to pay. But it’s hard. REALLY hard to convince a free reader to become a paid reader. Especially when your product is relatively expensive. Which is where our data intelligence comes to bear. Where we understand what drives purchase and renewal behaviour.. And how we use this data to drive improvements. Some news orgs give news away for free and they sought to generate revenue through advertising + scale of distribution Scale never worked Digital ads revenue plummeted Another scary trend is readership = 50% of users consumer digital news through social media, when asked they dont even remember who they read the news with Its not a pretty picture but thats why we are a paid for news publisher Its hard to convince free readers to turn into paid readers but we have to do it. Its costs 5 times as much to acquire new users as it does to retain ones you already have This is where our data intelligence comes to the forefront Through it we are able to understand drivers for purchasing as well as renewal of our subscriptions, we use this to help in product development
- For a long time news organisations thought they would give their news away for free and benefit from advertising and scale of global distribution. The scale argument - unfortunately - never quite worked. There are other players out there - 2 particularly big - who will always have significantly more scale. And digital ad revenues simply plummetted. It is now clearer than ever that there is no future in ad supported news media. The other scary trend - which I could ask crowdsource your perspectives on - but will take you out of your misery - is about changing trends in how people read the news. 50% of digital news readers now consume news via social platforms. Publishers use them as a way of raising brand awareness. Yet research suggests that 2/3rds of readers here in the UK do not remember the source of the news they read. So publishers give away their news for free, hoping for brand equity. In most cases they don’t get it, and don’t make much ad revenue along the way either. It’s not a hugely rosy picture. Which is why we ask people to pay. But it’s hard. REALLY hard to convince a free reader to become a paid reader. Especially when your product is relatively expensive. Which is where our data intelligence comes to bear. Where we understand what drives purchase and renewal behaviour.. And how we use this data to drive improvements. Some news orgs give news away for free and they sought to generate revenue through advertising + scale of distribution Scale never worked Digital ads revenue plummeted Another scary trend is readership = 50% of users consumer digital news through social media, when asked they dont even remember who they read the news with Its not a pretty picture but thats why we are a paid for news publisher Its hard to convince free readers to turn into paid readers but we have to do it. Its costs 5 times as much to acquire new users as it does to retain ones you already have This is where our data intelligence comes to the forefront Through it we are able to understand drivers for purchasing as well as renewal of our subscriptions, we use this to help in product development
- For a long time news organisations thought they would give their news away for free and benefit from advertising and scale of global distribution. The scale argument - unfortunately - never quite worked. There are other players out there - 2 particularly big - who will always have significantly more scale. And digital ad revenues simply plummetted. It is now clearer than ever that there is no future in ad supported news media. The other scary trend - which I could ask crowdsource your perspectives on - but will take you out of your misery - is about changing trends in how people read the news. 50% of digital news readers now consume news via social platforms. Publishers use them as a way of raising brand awareness. Yet research suggests that 2/3rds of readers here in the UK do not remember the source of the news they read. So publishers give away their news for free, hoping for brand equity. In most cases they don’t get it, and don’t make much ad revenue along the way either. It’s not a hugely rosy picture. Which is why we ask people to pay. But it’s hard. REALLY hard to convince a free reader to become a paid reader. Especially when your product is relatively expensive. Which is where our data intelligence comes to bear. Where we understand what drives purchase and renewal behaviour.. And how we use this data to drive improvements. Some news orgs give news away for free and they sought to generate revenue through advertising + scale of distribution Scale never worked Digital ads revenue plummeted Another scary trend is readership = 50% of users consumer digital news through social media, when asked they dont even remember who they read the news with Its not a pretty picture but thats why we are a paid for news publisher Its hard to convince free readers to turn into paid readers but we have to do it. Its costs 5 times as much to acquire new users as it does to retain ones you already have This is where our data intelligence comes to the forefront Through it we are able to understand drivers for purchasing as well as renewal of our subscriptions, we use this to help in product development
- For a long time news organisations thought they would give their news away for free and benefit from advertising and scale of global distribution. The scale argument - unfortunately - never quite worked. There are other players out there - 2 particularly big - who will always have significantly more scale. And digital ad revenues simply plummetted. It is now clearer than ever that there is no future in ad supported news media. The other scary trend - which I could ask crowdsource your perspectives on - but will take you out of your misery - is about changing trends in how people read the news. 50% of digital news readers now consume news via social platforms. Publishers use them as a way of raising brand awareness. Yet research suggests that 2/3rds of readers here in the UK do not remember the source of the news they read. So publishers give away their news for free, hoping for brand equity. In most cases they don’t get it, and don’t make much ad revenue along the way either. It’s not a hugely rosy picture. Which is why we ask people to pay. But it’s hard. REALLY hard to convince a free reader to become a paid reader. Especially when your product is relatively expensive. Which is where our data intelligence comes to bear. Where we understand what drives purchase and renewal behaviour.. And how we use this data to drive improvements. Some news orgs give news away for free and they sought to generate revenue through advertising + scale of distribution Scale never worked Digital ads revenue plummeted Another scary trend is readership = 50% of users consumer digital news through social media, when asked they dont even remember who they read the news with Its not a pretty picture but thats why we are a paid for news publisher Its hard to convince free readers to turn into paid readers but we have to do it. Its costs 5 times as much to acquire new users as it does to retain ones you already have This is where our data intelligence comes to the forefront Through it we are able to understand drivers for purchasing as well as renewal of our subscriptions, we use this to help in product development
- For a long time news organisations thought they would give their news away for free and benefit from advertising and scale of global distribution. The scale argument - unfortunately - never quite worked. There are other players out there - 2 particularly big - who will always have significantly more scale. And digital ad revenues simply plummetted. It is now clearer than ever that there is no future in ad supported news media. The other scary trend - which I could ask crowdsource your perspectives on - but will take you out of your misery - is about changing trends in how people read the news. 50% of digital news readers now consume news via social platforms. Publishers use them as a way of raising brand awareness. Yet research suggests that 2/3rds of readers here in the UK do not remember the source of the news they read. So publishers give away their news for free, hoping for brand equity. In most cases they don’t get it, and don’t make much ad revenue along the way either. It’s not a hugely rosy picture. Which is why we ask people to pay. But it’s hard. REALLY hard to convince a free reader to become a paid reader. Especially when your product is relatively expensive. Which is where our data intelligence comes to bear. Where we understand what drives purchase and renewal behaviour.. And how we use this data to drive improvements. Some news orgs give news away for free and they sought to generate revenue through advertising + scale of distribution Scale never worked Digital ads revenue plummeted Another scary trend is readership = 50% of users consumer digital news through social media, when asked they dont even remember who they read the news with Its not a pretty picture but thats why we are a paid for news publisher Its hard to convince free readers to turn into paid readers but we have to do it. Its costs 5 times as much to acquire new users as it does to retain ones you already have This is where our data intelligence comes to the forefront Through it we are able to understand drivers for purchasing as well as renewal of our subscriptions, we use this to help in product development
- Are about creating habits I used the 3 R’s of habits here, except reward is replaced by habit The reminder is the cue or the trigger - it could be an email onboarding programme, my FT which we will speak about or push notifications The routine is about the action taken - we recommend onward journeys for our users and they consume more content with us The habit or reward is what the customers get from coming to the FT, It could be bringing you or I closer to in depth analysis of a topic - it could help me inform my company about mergers and acquisitions, it could be helping me inform my company about a lawsuit…..if this is positive the cycle repeats itself
- In all of the models we built - whether to drive customer acquisition or retention - one driver stood out more than any other Usage is the key to customer value - a great churn predictor, and of likelihood to buy in the first place
- Demonstrated the relationship between these components + acquisition + retention of new readers Means we linked engagement to revenue – gave us confidence this was the right thing
- Data model built and optimised over time Comprising of 3 metrics Recency - the lower the score the better Frequency - the higher the score the better Volume - the higher the score the better We boiled it down to 3 components Blend using maths Score each and every user All usage across all platforms and channels
- We started to segment across 3 dimensions We wanted to increase the volume of engaged users & understand this tipping point so we could scale sustainable audiences Segmentation allowed us to keep focussed on opportunities, after all telling product managers to simply focus on engagement is unworkable but if we ask them to look for segments of opportunities suddenly there is focus
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- •A Twitter-like follow topic feature •Daily email digest / on-site feed page to articlesw topic feature •Daily email digest / on-site feed page to articles MY FT - lots of investment and built over 3 years! Operational and development costs of over 1.1 million
- Why did we continue to invest in it Because it significantly drives engagement for us
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- Again we were able to then prioritise lots of investment in our infrastructure to make sure our site was as fast as possible
- And won an award in 2017 for doing so
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- We previously had a web app and relaunched our app into the i0s store We found the median rfv score to be increased and sustained over time for all these 3 groups of users This verified our decision to go back into the iOS store and again continued signalling to us that we needed to invest in our app
- Lastly on the app when we started experimenting with push notifications we were then able to use engagement to measure what number were most effective for our readers
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- Find your single simple metric that makes most sense to your business
- Let it help you focus your efforts in product development
- But continually challenge it Is it working - have you been able to change your numbers, does the correlation remain valid and true
- Now I have talked a lot in detail about how we use metrics at the FT as part of our product approach I wanted to use the last remaining time to talk a bit more in general about our wider product approach And then what I did when I joined a project that was not using it and how that played out
- So what we always do is focus on business outcomes We are always asking ourselves what business outcome is this piece of work going to drive - engagement, acquisition
- To us a strong product vision is incredibly important It creates a common goal - for your team, your stakeholders - basically everybody
- As I have previously explained measuring our success is very important Once we have defined the business outcomes we are trying to move we always ask ourselves how do we measure whether we have indeed made a difference
- And we always try to be as lean as possible We follow the learn, build, measure & learn cycle Try to be as agile as we can
- So what happens when a product approach is not followed
- Nikkei bought the FT in 2015
- They have an english speaking business publication
- Which the FT are partnering with
- To transfer what we have learned at the FT
- To nikkei to accelerate business growth
- As we look to improve their customer facing products, newsroom and marketing systems
- We were not following our product approach and it was making life diificult
- We were back finding pitfalls and the first was because we had no clear direction
- We had slipped into the pattern of delivery big things not several small things that built up to a vision or bigger picture we were trying to create
- We had abandoned all of our lean and agile ways of working
- The risk on the project was really high We were delivering huge things - a new website and a new CMS - all to tight deadlines so we had to go waterfall to manage the risk
- Lastly we were in a long distance relationship and they are hard!
- We are here and they are all the way over there There is an 8 hour time difference in the summer and a 9 hour difference in the winter This gives us little overlap All our meetings are done over hangout and written conversation over email and slack We were really struggling to make it work
- So I arrived s a product manager in April and the first thing I did was put in many of our product approach as this enabled the team to better deliver
- We needed to better operate as one team So we created a product vision and that gave us our common understanding and our shared purpose
- We did this collaboratively This helped us break down cultural boundaries It also acted as a mini reset - we had many reset moments over the summer but this was definitely one of them
- As it was really important that we moved from a client & supplier relationship to one that was more of a partnership
- We introduced a lean product development approach We would focus on the problem we are trying to solve for either the business or customers
- Build something to test our hypothesis
- We did this whilst being mindful of cultural differences So in Japan group consensus is highly important But we were at the point where we were trying to make tiny design decisions with group of 25 people on a google hangout
- WHich was really slowing us down
- So we took this cultural aspect but made it work for us
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- So i guess my three main takeaways for taking a product approach cross cultures
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