Certain key prerequisites need to be in place to scale a product management organization successfully.
Strong leadership and management The leadership team has defined and live their role role of management & leadership in the agile customer centric organization. Not less management is needed – just better, different.
Clear vision, strategy and principles An organization bigger than 10 teams needs a clear vision, strategy and principles for orientation.
Coordinated objectives The objectives within a team and the whole organization need to be aligned with the vision and strategy.
Scalable architecture A scalable architecture follows a good / clear product vision. Technical debt is typically a result of growth – a system designed for 5m users can not stand 50m users. Time to rethink! Addressing Technical debt is in the accountability of the CTO and typically referred to as a business continuity issue. 20% of engineering capacity usually goes in Tech debt prevention / reparation. If the backlog is empty, switch to Tech debt removal work. Resolve Tech debt step by step – rewriting is typically the worst answer to this issue. It normally takes twice as long than anticipated. Technical debt killed Netscape and Friendster lost the battle to Facebook due to technical issues.
Team structure The higher goal behind team structure is to create autonomous teams with minimized dependencies – make them work independently for maximum execution capabilities.
Delivery managers Delivery managers identify impediments and remove them. They are a mixture of project managers and SCRUM Masters. Typically, Accenture is a great source of talent for Delivery managers.
Constant evangelism Repeat, repeat, repeat. Constant repetition and sending the product management messages, beginning with the vision over the mission towards hypothesis driven and value orientated work processes, is critical to scale product – especially in big companies.
The role of Leadership
“The combination of curiosity, respect, and kindness combined with crazy work ethic will take you anywhere you want to go.”
Stacee Santi
Leaders in successful organizations are coaches. Their primary goal is to grow people, to support them in succeeding. Below listed are focus points of leadership roles within the product organization.
Product Leaders
‣ evangelism ‣ coaching
Design Leaders
‣ design language ‣ standards ‣ coaching
Engineering Leaders
‣ architecture ‣ standards ‣ coaching
Delivery Manager Leaders
‣ commitment ‣ dependencies
Data Leaders
‣ data tools ‣ common KPI language
At Spotify, any leader within the organization could deliver the coaching function. It’s every leaders’ goal to grow people. Marty mentioned that Christian Idiodi (https://www.linkedin.com/in/cidiodi/) is exceptionally good at growing product people.
Structuring
Product Teams
The Product and Technology leads need to come up with an answer to the question on how to structure product teams. Should the organization have vertical teams focusing on specific business topics? Horizontally layered, with frontend, backend and data teams? A mix of both?
A typical setup includes vertical focus teams mixed with Common Services Teams (CST). The CST take special care of architectural topics, scalability and performance and they develop shared services. The PM role is typically more technical focussed and the customers are usually other product teams. Usually, there is no design role needed. Discovery happens in conjunction with other product teams.
The overarching goal of this structuring exercise is to create a setup with minimized dependencies to create fully autonomous teams. Here are some questions to ask during the process:
How many product teams?
What is the scope of each team?
Dependencies between teams?
Compatibility with architecture?
What is the size of each team?
What expertise is required for each?
Level of autonomy of each?
Code ownership and dependencies
Setting up multiple teams with defined scopes raises another important question – who owns which part of the source code. Typically there are two models – a team owns their code (“request-model”) or the code is owned by everybody (“open-source model”). The request-model implies a higher degree of dependencies between teams needing to work cross-domain. If one team depends on changes done by another team the first team can no longer work fully autonomous. With the open-source model every team can change any part of the source code. This model needs higher standards when it comes to source code management and testing. Marty discusses this on his blog “Autonomy vs. Ownership”.
Amazon’s way to setting up teams
Set vision & goal
Define architecture
Form teams around architecture
Set clear accountability and ownership
This blog post is part of a series. It summarizes my personal notes of the workshop held by Marty Cagan “How to Create Tech Products Customers Love” from 5th to 6th of June in 2019 in San Francisco.
In 2014, Facebook changed its mantra for developers from “Move Fast and Break Things” to “Move Fast with Stable Infra”. Only moving fast doesn’t bring the business benefit home. At a certain tipping point the engineering organization introduces more “broken things” than they’re able to fix in the long run – speed of the organization stalls or declines.
The new mantra relies on two cornerstone principles Release Quickly and Often and Release with Confidence. Releasing quickly and often leads to small and quick iteration cycles. These foster quicker learning and fuel innovation. Releasing with confidence keeps damage away from your brand, your revenue streams, your customer base and your employees. Confidence refers to accurate software, reliable and well-performing releases, scalable software and released software without privacy or security concerns.
A good example of a well performing product organization is Google Search. They run a total of 15 product teams. They work on over 2.000 product ideas – no product optimizations – in Discovery with less than 500 built in Delivery. The ratio is 4:1 – 4 ideas in Discovery, 1 get build. They aim for 10.000 ideas in Discovery with less than 500 being built. See “Rigorous testing” from Google for further information. Google mentions in this article they did 595.429 Search quality tests, 44.155 Side-by-side experiments, 15.096 LIve traffic experiments resulting in 3.234 Launches – in 2018 alone!
The industry benchmark in well operated digital product development organizations is to kill 75% of ideas in Discovery. If not a minimum of 50% of the ideas are killed then it’s Design, not Discovery and a clear signal of a malfunctioning organization.
Issues of Conventional Agile
“Agile is all about building and delivering software, but it says almost nothing about how to come up with a valuable product backlog.”
Marty Cagan
Agile software development helps a lot, but doesn’t say anything about building a valuable backlog. It is completely blind on the value side, answering the what should be built. Marty discusses a solution to this issue – dual-track agile – on his blog.
Issues of Conventional Agile:
Thanks to agile, teams move faster. But agile doesn’t provide teams with a valuable goal.
Moving fast without a goal leads to feature chasing. The goal of agile is to produce more output, not necessarily more business results or more value. This is especially tricky with mature products where removing features might provide more value to the business and customer (use A/B testing to understand the value). Also in mobile products more features most often fail – they distract more since there is too less inventory – and finally don’t have an impact.
Engineers are most time only coders and don’t contribute further more to the overall value generation process.
In agile it’s very hard to predict any dates and make commitments since it’s based on user stories only.
UX Design suffers because there is simply no place for UX in agile. The sprint iteration doesn’t reserve time slots for UX to work as part of the iteration. If engineering is ready to go UX should be ready as well but usually start right now to work as well.
The architecture of the system suffers as well. Fast moving teams produce more technical debt. Without really knowing good ways to solve an issue, suboptimal long-term solutions are implemented and increase the need to repair architectural decisions.
The rest of the company – marketing, sales, customer support … are brought in too late into the process.
Salesforce rolled out agile by the book about 15 years ago. As a consequence the whole UX & Design team threatened to quit because conventional agile didn’t include UX & Design at all. So, after some discussion Salesforce included UX & Design in their agile working model and pivoted from the Conventional Agile approach.
Continuous Discovery and Delivery – Dual Track Agile
“Discovery, by definition, means you don’t know the answer when you start.”
Ed Catmull, Co-founder of Pixar
Parallel Product Discovery and Delivery – Dual-Track Agile
“Dual-Track Agile” emphasizes the parallelism of Product Discovery and Product Delivery. They happen all the time in parallel. The Discovery track is all about fast learning, building a validated product backlog. Delivery is all about creating software that could be released with confidence, shippable software. The Discovery track is led by Product, Delivery by Engineering.
Product Discovery provides missionaries with purpose. In Discovery, the key risks are addressed and meaningful answers to these questions give guidance to the team. The given answers are all validated, there is enough evidence proving it will work.
Will they buy it? (Value)
Can they use it? (Usability)
Can we build it? (Feasibility)
Can our stakeholders support it?
(Viability)
Bonus question: Should we build it?
(Ethics)
When all critical questions have good and validated answers, describe what to build in Delivery. Therefore, use e.g. JIRA stories with prototype and UX design (prototype-as-spec).
It is, however, not needed to validate all risks all the time in discovery before putting anything into the backlog. The trio of people (PM, PD, TL) does the risk assessment (takes 5 minutes talking). Pick and choose the techniques to validate if needed. Don’t be religious about the process.
Example: At ebay, one base assumption is: create a safe place for transactions. This assumption is the root cause for the overall reputation system within ebay.
Discovery and prioritized backlogs
“Don’t prioritize your backlog – simply try thousands of ideas!“
Marty Cagan
Follow only those ideas in favor of the overall business objectives. Don’t spend too much time thinking about them, just try them. One iteration in Discovery means: One new idea or another approach to an older idea.
Put the ideas that work into the product backlog. If the product backlog is empty switch to “feed-the-beast” mode and allow the teams to work on e.g. technical debt or other meaningful tasks. If the product backlog is too full – means more than 2 weeks of work – you have a rotten backlog. Here, ideas will no longer be valid or well memorized if too much time has gone before going into Delivery.
Good example for a Product Discovery phase and how fast they were collecting evidence and pivoting: BMC Business Modell Competition – First Place Winner: Owlet (https://www.youtube.com/watch?v=f-8v_RgwGe0)
This blog post is part of a series. It summarizes my personal notes of the workshop held by Marty Cagan “How to Create Tech Products Customers Love” from 5th to 6th of June in 2019 in San Francisco.
“The most important thing is to now what you can’t know.”
Marc Andreessen
1) We know
we can’t count on our customers to tell us what to build
Avoid focus groups. Customers don’t tell you what to build because they simply don’t know what is possible with latest technologies in place. Furthermore, they don’t know what they actually want until they see it – hence use prototypes during your customer feedback sessions. Customer-facing product teams go 3 hours per week to see the customers. “Customer inspired – customer enabled”.
The iPhone wouldn’t happen with focus groups. Skype hired Nokia, Motorola and Apple people and they compared the different approaches to innovative products. It took 3 ½ years to build the iPhone. Nokia and Motorola were doing focus groups when Apple started the iPhone. Palm has just released the Treo with Touch Screen and people didn’t like it. The focus group gave clear indication to skip the touch screen. So, at the time, Motorola and Nokia decided not to build phones based on touch screens. Apple hat a vision – the iPhone vision – “Build a touch screen powered phone.”
2) The most
important thing is to establish value
Value for customers, creating value for customers. Typical product roadmaps line up features. The underlying assumption of feature roadmaps is that there’s value in the feature and business viability is granted. But that is typically not true.
3) We
recognize that engineering is hard, but the user experience is often even more
difficult, and more critical to success
This is especially true at B2C companies – 50 engineers and 2 visual designers don’t work out. The experience and value perceptions created at the customer is vital – UX is more critical to success than technology.
4) We
recognize that functionality, design, and technology are inherently intertwined
5) We
expect that most of our ideas won’t work, and those that do will require
several iterations.
Many
iterations never make it beyond us – the team; they’re simply stopped
internally in early stages.
How many
iterations should one follow before skipping the idea?
Depends on the importance of task
Ask yourself: “Are you still learning with every iteration?”
Change the approach to the problem (e.g. churn)
Timebox: 2 days for one approach
Avoid the “fall-in-love”-pitfall with design: use less than 2 days for design before result is shown to users.
Example: Ceramic class separated into two groups with separate goals. The first group should build one high quality pot. The other group is instructed to build as many pots as possible – it’s only the weight that counts. The second group won the quality challenge because of practice! The first group wanted to produce the “perfect” pot but failed with just 1 mediocre pot.
6) We must validate our ideas with actual customers
Go out of the building, talk to real customers. It’s valuable to start with your team mates, colleagues, people working inside your company – but finally, you need to get the opinion of your real customers.
7) We
validate our ideas as quickly and cheaply as possible
Possible
even in hardware: 15 iterations per week. Google Glass team build an engagement
model prototype and improvised a futuristic user interface with simple
technologies. They quickly learned about a key problem: shoulder sourness and
skipped the key assumption for success.
8) We use
both quantitative and qualitative techniques
Quantitative:
What’s happening?
Qualitative:
Why is it happening?
Example: Etsy switched to endless scrolling from pagination. The A/B-Test showed people buying less. The qualitative analysis showed: there were simply too many cool things to buy and people couldn’t decide – paradox of choice
9) We must
validate both technical feasibility and business viability in product
discovery, not after
Include
engineers ways before planning. They need to be involved and see what to build
before sprint planning.
10) It’s
all about shared learning
Shared learning happens all the time in a co-located team. Discussions over coffee, exchange of opinions at the work-desk, discussions are all around. Furthermore, allow engineers 30 minutes playtime with the prototypes each day to let them express their concerns and start having good ideas on how this could be build.
With a distributed team in place, schedule a 30 minutes meeting every day to allow the exchange of ideas, to give engineers air time with the prototype and help them understand the ideas behind.
Big companies run innovation labs for product discovery. But good ideas never materialize due to the separation of Discovery and Delivery.
This blog post is part of a series. It summarizes my personal notes of the workshop held by Marty Cagan “How to Create Tech Products Customers Love” from 5th to 6th of June in 2019 in San Francisco.
Don’t use these discovery techniques for bug fixes or optimization. They’re meant for real product discovery work, identifying and foremost validating big new product ideas.
1) FRAMING
Framing is the activity where the problem space is defined and the relevance of the problem at hand gets better understood. Do not spend too much time in framing.
The Opportunity Assessment is enough 90% of the time.
What business objective are you focused on? typically one of the OKR objectives
How will you know if you have succeeded? typically one of the OKR key results
What problem are you solving for our customer? do you really know it’s a problem?
Who are you solving that problem for? typically a target market or persona from the strategy
Internal Press Release
The Internal Press Release is not intended to go public – it’s for internal use only. The anticipated audience is new product’s customers – actually it’s the team, management and stakeholders. It’s typically 1.5 pages maximum and written in “oprah-speak”, not “geek-speak”. Sometimes, 3-4 pages of FAQ for anticipated questions are added. The structure of the internal press release looks like this: heading, summary, problem, benefits, quote from you, customer quote, closing / call to action
Amazon uses Internal Press Releases for big ideas / efforts (e.g. site redesign or moving into new country).
An alternative to the Internal Press Release is the Happy Customer Letter describing the benefits for a customer written by the customer and the CEO-letter describing benefits for the company.
An alternative to the Lean Canvas is the Opportunity-Solution-Tree. It is introduced by Teresa Torres at her Mind The Product Talk 2017 in London “Critical Thinking For Product Teams“.
User Story Mapping helps to visualize and deconstruct the problem or solution space. It provides an holistic view and gives context. Through the collaborative process it encourages shared understanding, identifies holes in thinking and improves planning and estimates. Furthermore it heavily influences prototypes and actually helps to scope releases for the product.
The basic idea of the Customer Discovery Program is to discover and develop a set of reference customers in parallel with discovering and delivering the product. At the stage where the reference customer signs up for the program there is no product ready to be delivered. The Customer Discovery activity makes only sense for real big efforts, absolutely not for features. Serious enterprise customers are very likely to sign-up because they’re burned by the practices of Oracle, SAP and the likes – sell and run.
The reference customer bought the product without any side deals, runs the product in production and loves it enough to tell the world about it. In Customer Discovery, we’re looking for “Earlyvangelists”. An Earlyvangelist is best characterized by these criterion:
With the Customer Discovery Program it’s simple to tell if product / market-fit is reached: achieved if 6 reference customer for a single market segment (e.g. industry, geography, …) are found. The product / market-fit product is the smallest possible product that meets the needs of this group. If you find only 4 customers or less this means the product market fit is invalidated and a pivot is needed. Work with 5-6 companies – and not more than 8. Talk to as many as possible – e.g. 50. Select only the most attractive for the Discovery Program, the other customers go into the beta program. Agree with the selected companies to be a discovery partner and ensure the right level of access to people and input. Agree with them to become a public reference if they like the delivered product.
In Enterprise business the goal is to find a single product solution that fits all discovery partners. Again, it’s important that all partners are from one single market segment. In Consumer services you should identify 8-20 Earlyvangelists and agree with them on regular phone calls to synchronize.
Examples for customer discovery: OpenTable (SMB), Symantec (Enterprise), Bazaarvoie (B2B2C), xoopit (Platform Services), Apple (internal tools), BarkBox (Consumer Service)
Customer interviews are needed to understand your customer, to get rid of assumptions and start working with facts. Marty summarizes the value of interactions with customers in his post “Don’t talk to customers?“. Below are the key questions to answer:
Public API’s (let others innovate on your product) – be aware of bad usage: Cambridge Analytica + Facebook
Hack Days (directed and undirected)
Data Spelunking (Hackathon on data)
4. Prototyping
The prototype should minimize the time by factor 10 to provide something to look at. See more on prototypes at Marty’s blog “Flavors of Prototypes“.
User Prototypes
User prototypes are created very fast and lightweight by nature. The prototypes are used for value testing with a consumer or customer to quickly gather feedback on both, usability and value. Low fidelity user prototypes are used for team internal iterations. Use high fidelity prototypes to show-case internally to executive people. The prototype is usually created by the Product Designer with support from the Product Manager. Ideally, when finished, the prototype could be used as a specification for the Delivery process – “prototype-as-spec”.
Paper prototypes are too limited by nature, use wireframing tools (e.g. balsamiq, axureRP, proto.io, FLINTO, UXPin, marvel, invision, Adobe XD) instead. They allow interactions with the prototype and are no more effort to build.
Feasibility Prototypes
The feasibility prototype validates the solution approach. Usually, the prototype is build by engineering to gain further insights on the implementation and test technical (e.g. scalability, performance) risks. The prototype might not be more than a code fragment or a successful validation of a 3rd party software or API integration. It may also happen that product people are not involved in the prototype at all.
Live Data Prototypes
The purpose of the live data prototype is to collect further evidence pro or contra a product decision. This prototype is more expensive to build than the user prototype, but still far less than the actual product. The prototype is not the real product, it’s usually 5-10% of the real product. It includes quantitative a/b-testing but also qualitative testing and is based on real data. A small amount of real traffic could land on the prototype to collect data. Engineering is typically needed to create the live data prototype within 2 days up to 2 weeks.
A lot of people get excited when they see the live data prototype and tend to confuse the prototype with the real product. But there’s still a significant difference between production ready software and the live data prototype. The real product needs:
All required use cases
Instrumentation / analytics
Test automation
Scale and performance
SEO work
Maintainability
Internationalization / localization
A good example of a live data prototype is Amazon’s “Frequently bought together” feature. The idea of building the features was rejected by SVP Marketing. So, strong evidence was needed because it was simply too expensive and risky to productize the feature without further evidence. So, the team decided to build a live data prototype with a small amount of real traffic in a specific product category. The prototype was a/b-tested and the collected data provided a significant uplift in business KPI’s. This is a great example of a high-integrity business case.
Hybrid Prototypes
Hybrid prototypes mix those elements needed to tackle the specific risks at hand. They blend various techniques and are mainly limited by your own creativity.
A good example of an hybrid prototype is Zappos. Zappos solved the problem of female shoppers to buy fashion shoes online. They defined and understood their personas and their key problems with shopping online: 1) returning goods 2) no timely delivery 3) not knowing the size 4) bad product images. Zappos prototyped a potential solution to the persona’s problems by mixing a variety of prototypes: user prototype (appealing front-end), live data prototype (product catalogue and images) and the “Wizard of Oz” (buying the shoes at the shop over the street and delivering to customer). Most important was: the users shouldn’t recognize the prototype character of the solution. Zappos controlled the amount of traffic via AdWords and made sure they could handle the manual part. So, with this mixture of prototypes – that for sure doesn’t scale – they could validate demand, value and usability.
Testing Product Ideas
“Prototype as if you know you’re right, but test as if you know you’re wrong.”
d.school
Marty writes in his blog about “Prototype Testing” more detailed on the various ways to testing.
5. Testing Usability
Usability testing includes interacting with customers, getting their feedback. For the test session, have the prototype ready – up and running and focus on the prepared questions to understand if users have issues using your product. The session may be conducted at your office, the customer’s office or at a mutual convenient location (e.g. Starbucks) or – if not possible – remote via video conference.
Recruiting users in B2B context is done via the customer discovery program, in B2C via AdWords. AdWords allow acquisition of users based on keywords and / or geo-targeting. It’s the most cost-effective solution and easy to stop and restart again. Payment is entirely based on performance.
6. Testing Value
Testing value focuses on three aspects: testing demand (is this really a problem?), testing efficacy (how well does the product solve the problem?) and testing response (how excited are the testers?).
Testing Demand
Testing demand answers the question if people are willing to use the product, if they understand the value and see the product solving a real problem they have. Marty talks more about Desirability Testing on his blog about “Product Validation“. Some techniques for demand testing are described below
Fake Door Test
A fake door test fakes a product feature for the customer. If the customer acts with the fake product a thank-you-message is displayed and sometimes contact information is collected. Furthermore, nothing happens. The goal of the test is to collect data, to measure the click-thru ratio. More information on the fake door test can be found here: http://learningloop.io/plays/fake-door-testing.
Landing Page Test
A landing page test pitches product features, products, product lines or other promises to the customer combined with an explicit call to action. Push traffic on the landing page via e.g. AdWords or other comparable methods. Now, measure the conversion – how many people do actually interact with the landing page and are interested enough to follow the call to action? With the click, nothing happens with the customer other than a friendly thank you message and sometimes the question for contact details. More information on the landing page test can be found here: http://learningloop.io/plays/spoof-landing-pages
Explainer Video
The explainer video shows a high fidelity prototype at work. It’s basically a video of a product demo. It is then distributed and measured like the landing page test above. The goal, again, is to measure demand for the demoed product. More on the explainer video: http://learningloop.io/plays/video-demo
Kickstarter Testing
A great way to test a product idea without jeopardizing your brand is to test demand on kickstarter.com. Just place the product idea at the crowdfunding platform as a “nobody”. If the idea creates enough buzz it’s worthwhile a further investment, if not it can be dropped silently without creating any noise. Read more on the idea from Mark Dwight “How to Kickstart Your Market – Why even established companies can use crowdfunding.“
Qualitative Value Testing
“Find everything that’s wrong with the product and fix it; Seek negative feedback.”
Elon Musk
Qualitative testing explains why it’s working or not, it gives insight why something is happening or isn’t. It doesn’t try to prove anything. You won’t get the answer from any one user test; every single test provides another piece of the puzzle. It’s important to test with real users and customers to judge the value.
Qualitative value testing is done with prototypes or the real product. It provides insights from usability and value perspective. On top it usually provides unexpected insights from the customer. It’s typically done fast and cheap. To really understand how much customers value the product, various questions or tests can be conducted:
Payment – will they pay for it? credit card information, pre-order form, letter of intent (in B2B)
Reputation – will they recommend it? NPS, introduction to peers or the boss, public reference
Time – will they meet again? Will they invest their time? agreement on follow-up meeting, non-trivial trial
Behavior – will they switch from their current solution?
Quantitative Value Testing
“Features are not inherently valuable. The value for our customers is only realized when a feature fulfills a need. It’s only realized for our business when we see the results of our work move the needle. That’s why we need to concentrate on the outcomes over the outputs.”
Quantitative value testing can provide evidence or even proof that something truly works – or isn’t. It generally can not explain why it’s so. It’s done to get a clearer picture on the impact on your revenue, your brand, your customers and also your employees.
Quantitative testing can be done with the existing product – or a live-data prototype – in an A/B testing setup on a certain amount of your traffic. Alternatively, it could be done with a limited amount of your customer through invitation. In a B2B scenario, you’d use your existing customer relation via the Customer Discovery Program to get exposure of the test to people.
A good example for a quantitative value test is Spotify’s “Discover Weekly” feature. Data collected in an A/B test was compelling enough to fully implement the feature. The launch-ready implementation included some big hurdles and a lot of effort on the data crunching side. So, it was well worth the effort to test – before – putting the feature in Delivery.
7. Testing Feasibility
Testing feasibility mainly addresses technical concerns – are we able to build this at all? To test feasibility it’s important to create prototypes that focus on the key areas of concern. Emphasize speed of learning over reuse of the written code. Your Tech people need to answer these questions:
Do we know how to build this?
Do we have the skills on the team?
Do we have enough time?
Do we have the right architecture or
components?
Do we understand the dependencies?
Will the performance and scale meet
our needs?
Do we have the infrastructure to
test and run this?
8. Testing Business Viability
Business viability – does this feature / product fit with our business? – needs to be addressed to be successful within the own organization. Your stakeholders (e.g. Senior Executives, Sales, Marketing, Finance, Legal, Security, Business Operations, …) need to be informed regularly, you need to earn their trust. Have discussions with them and make them feel you understand them – but remember: everyone has a voice, but not a vote! Try to engage individually with them, group meetings can cause a lot of damage and are harder to handle. When talking to your stakeholder, have your data ready – data always beats opinions. And read the signs during the meeting – differentiate between stop signs and yield signs.
Techniques you can use for a stakeholder meeting is typically a high fidelity user prototype and / or a product walkthrough.
This blog post is part of a series. It summarizes my personal notes of the workshop held by Marty Cagan “How to Create Tech Products Customers Love” from 5th to 6th of June in 2019 in San Francisco.
One transformation technique Marty recommends is the Discovery Sprint. He recommends to do a Discovery Sprint when a team struggles to learn how to do product discovery, when the team has something big and critically important to solve or if the team is just moving too slowly. Marty talks more about it on his blog https://svpg.com/discovery-sprints/ and refers to the Book “Sprint” by Jake Knapp et al.
Another is named Pilot Teams. The idea behind the Pilot Teams is to create success within a smaller protected environment and convince doubtful or fearful or lazy people to follow the change process. The principle is borrowed from the technology adoption curve (aka “Gartner Hype Cycle”) – some people are early adopters, others are less eager. Chris Jones from SVPG talks about this technique in “Pilot Teams“. With these pilot teams the idea of A/B testing – well known from product development – can be applied to organization development as well.
Outcome-based Roadmaps is yet another way to start the transformation process. Simply continue working with product roadmaps, however introduce two differences. First, annotate every roadmap item with its associated expected business result. Every time this item is discussed highlight the expected business result. Second, after the launch of an roadmap item report immediately the actual result vs. the expected result. So, during the next 3-12 month the opportunity assessment information will get its way into the roadmap. For prioritization try to move away from prioritizing ideas to problems.
Common Product Discovery Pitfalls
Marty mentions several pitfalls he experienced and saw teams struggle with. He talks a lot more in his blog post on “Product Discovery: Pitfalls and Anti-Patterns“. Here’s just a summary and some notes.
Confirmation-biased Discovery The team and / or the stakeholders are not really interested in the results of Discovery, they just need affirmation.
Product as Prototype Discovery The team pretends working on a prototype implementation but it takes too long to actually get the prototype shipped (e.g. 4 month).
Partial Team Discovery Not Technology, UX and Product go see the customer, it’s only Product + UX.
One-Dimensional Discovery The team focusses only on quantitative or qualitative validation and draws wrong or incomplete conclusions.
Big Bang Discovery The team works on a single, big release shipped within a lengthy time frame. They don’t work in an iterative mode.
Outsourced Discovery The organization / stakeholders hired a “creative” agency to do the creative Discovery work. The implementation should then be picked up by the team.
Culture Baseline of successful companies
“If we get the culture right, most of the other stuff will happen naturally on its own.”
Tony Hiseh, CEO Zappos
Tackle Risks up Front
Value Risk – will they use / buy it?
Usability Risk – can they us it?
Feasibility Risk – can we build it?
Business Viability Risk – will our stakeholders support it?
Define Products Collaboratively, not Sequentially
Product Management
Product Design
Engineering
Focus on Business Results, not Output
Product teams exists to solve problems in ways that your customers love, yet work for your business.
This blog post is part of a series. It summarizes my personal notes of the workshop held by Marty Cagan “How to Create Tech Products Customers Love” from 5th to 6th of June in 2019 in San Francisco.
Anil Cheriyan shared his thoughts on where to focus to create a fast and better working IT environment in the financial services industry – to speed up the organization (see: https://enterprisersproject.com/article/2017/6/suntrust-cios-formula-speed-relies-cloud-devops). In his post he mentioned 5 areas you should look after to break with old habits and start creating a fast pacing environment.
Anil Cheriyan is Director/Deputy Commissioner, Technology Transformation Services for the U.S. Federal Government. Previously, he was managing partner of Phase IV Ventures, a consulting and advisory firm.
Cloud
Two important aspects associated with the terminology “Cloud”. First it’s important to understand the implications of the various cloud strategies (ranging from private cloud over hybrid constructs towards public clouds). Get your strategy clear on which areas to host where. Criteria to look at are: business value provided, business continuity, resilience, security. Second aspect is the organization. Get your people involved. They need to participate in the strategy definition. They will execute them actually. No time for information hiding and bimodal IT infrastructures.
Modular architecture
Getting towards a modular architecture introduces flexibility in decisions, eliminates bottle necks and allows a decentralized governance. Today’s architectures are still monoliths or more advanced SOA stacks or somewhere in between. A more modular architecture exposes API’s via micro services. This architecture allows distributed ownership models. Complex is actually the implementation of these architecture rewrites. A lot of business related activities and the re-architecture work is a hard effort to combine.
DevOps
DevOps is finally all about the mindset of people and the break-up of silo-ed organizations. People need to learn and understand the importance of collaboration and trust. This sounds simple, turns out to be a heavy change project. Anil started pilot projects and introduced the true DevOps mindset and collaboration through success cases. It’s not about adopting rules and processes from the DevOps movement “by the books” – it’s about training your talent to work closer together.
Agile development
Agile development in software development is quite wide spread and commonly used. The acceptance over waterfall models is – where appropriate – high. Issues occur if the agile software development processes get surrounded by traditional waterfall-oriented functions – control functions. The most challenging part is to get agility into release management, deployment and integration testing.
Design thinking
Most important aspect of design thinking is the customer centricity. Understanding the real problems of the user to be solved is at the core of the approach. Not hunting the 100% perfect solution with all nice and “useful” features. Going for the most valuable solution, ship it fast. This requires heavy re-thinking within the organization. It’s more about talent and collaboration models. Important is to get people together with a thorough understanding of the industry and processes to help solving the customer’s pain points.
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