Last year, in Zürich there was the Product Management Festival, a conference aimed at anyone involved with the management of (primarily digital) products.
What's intriguing is to see that many of the major digital players we know today (Facebook, Google, Flipkart, ...) have similar beliefs in how product teams should operate. I've collected some of my favourite take-aways.
When I look at the future, I strongly believe that a new disruptive force will occur which will affect companies globally.
Within tech, we see tremendous amount of innovation primarily because of organisations embracing to be product-driven. This means that unlike having a single product, like your traditional e-commerce or retail business, major players own a number of different products and verticals and continue investments in these and new products or services.
This to capture either more market value, for example AirBnB has now also included experiences and restaurants to their offering beyond just providing a place to stay.
Google is another great example of this. A lot of their original products built beyond their search and advertisement business were built to support their advertisement business by collecting more insightful data. For example: Using Google Maps for navigation-purposes means you're giving a lot of information about your own habits (and travel location) to Google, which improves their advertising business.
A Portfolio of Products
Hence, I strongly believe that organisations of the future will have a portfolio of different products. Either to support their main source(s) of revenue, or to partially prepare for waves of innovation. For example, for Apple Music it's totally fine that their subscription service cannibalises iTunes. It's preparing for the incoming wave of innovation which will sharply reduce the revenue from purchasing music anyhow.
With other words, investing to be a product-driven organisation will help you to become future-proof and tackle areas of growth heads-on.
How do I become a Product Organisation?
The Product Process
Luckily, within product (value) creation, most of the process is pretty clear:
- Data - What do we know today?
- Analyse - What do we conclude today?
- Hypothesis - What new product are we building?
- Experiment - What are the minimum viable product(s)?
- Iterate - How can we mature the minimum viable product?
The above process can be quite useful to internally communicate with your team how to approach building a new product.
Beliefs over Vision
One of the misconceptions within product management is that your product vision is crucial to guide your team. The reality is that your product vision comes only after your belief as a company is crystal clear.
A product vision often changes over time and that hurts your team and corresponding processes while scaling up. Especially as your organisation grows and you house multiple product teams.
Beliefs on the other hand is something shared company-wide and remains solid, despite which direction a product might take.
While your product vision might be to build a platform which connects people, portraying the belief that a connected world is a better world will build a grander perspective for employees (and can drive multiple ideas for products). Now, the best type of products don't just come from ideas though. They start with clear, validated people problems. Something to keep in mind!
Start with your belief, your product vision will follow (and evolve!) naturally.
Speed of Learning
Anyone who works within digital products will tell you that the creation of minimum viable products are important and that your assumptions should be tested with experiments.
What can be an incredible differentiator is speed. Upfront you know that 90% of your tests will fail (VWO, 2016). The question is how you can get to that 10% of learnings in the shortest amount of time?
Now, even smaller changes count for an experiment. Did you know that booking.com has 1000 running experiments at the same time? Continuously improving your product produces the best results, bottom-line.
This is why so many products see the light of day in tech organisations: Building a minimum viable product is quick, the success of a new product provides tremendous amount of value and the insuccess of a product provides value in the form of data and insight as well. With other words, there's little to lose when starting a new product experiment, as long as you can contain the resources invested (people, time, money).
An important lessons as well: Quantitative data is not a substitute for qualitative insight.
Data in Learning
There are many ways to collect and handle data while working with digital products. A common trap is to focus purely on revenue as KPI.
Metrics should be taken more broadly. It should be defined as something which has impact and is time-bound (eg. 7 days, a month, ...). This can go beyond the traditional revenue such as understanding your user's retention rates or general engagement rates. I like to think about value per user. Where the definition of value can change depending on the type of product.
Sometimes, it's also not the goal of a singular product to provide revenue, but rather to provide data and/or traffic to your main product(s). For example: Unsplash started as a free photography site, primarily to attract attention to the freelance marketplace Crew. Funny enough, Unsplash is it's own separate business now. Sometimes supporting products can grow larger than the product they were supposed to support.
When you're steering a product organisation which expands, autonomous teams are a necessity. This is why a belief-driven company is so crucial, this way the company can steer in the right direction and everybody shares the same beliefs.
Otherwise, it becomes tricky to set up multiple product teams which individually run different types of experiments. One of the core questions often posed by management teams in this type of environments is how you can achieve trust. This is primarily achieved by building accountability frameworks within your organisation.
Data Science Will Replace Human Processes
In a world where machine learning has become commercially viable and data science has become more critical within organisations to replace decision-taking processes, it's only a matter of time before human labour in regards to decision-taking processes will be replaced.
One of the missing pieces in the technology right now is empathy. This is where humans still will prove to be valuable in data science.
Hence: data is one aspect, and handling that date is a whole other aspect. In automising your organisation, do not forget the core value of empathy, otherwise automising your business will make your products worse.
How Do I Begin?
Now, if you read all of this and wonder - where should I start with my organisation? It boils down to something really simple: Start with a new minimum viable product. Make your small, insignificant idea a reality. Then, understand how it can run independently, affect your company vision and ultimately it's culture.
A successful minimum viable product, being run successfully from other processes within the organisation can be excellent lever to push for change. This results in effective stakeholder management by standardising the process in a way that minimum viable products are built and scaled and how these independent product teams work within the organisation. That's creating change bottom-up.
Of course, if your management team is hugely interested in making a shift to a product-driven organisation, setting out a company belief and building a roadmap is a great stepping stone to start building products.
Humble beginnings make for great endings!