You can't be a PM or business owner without a deep understanding of machine learning: establishing the business value and then rallying a team to deliver to that value without falling into the many unique pitfalls of ML programs. Here is a "cheat sheet" compilation of how to deliver machine learning products and programs, highlighting practical tips from someone who's been on the journey.
Strategic planning is hard, but everyone thinks they can do it themselves.
It's never a great sign when the Product Manager is handed a technology with an assignment of "figuring out the use cases". Great products should be a labor of love, the result of an entrepreneurial person who felt the pain of a problem first-hand, or had great empathy for someone else's problem, and then passionately invested time and energy to solve that problem.
Talking to customers to uncover unmet needs is the most critical part of product strategy. The key to growth is a deep understanding of your target customers. Simply relying on internal ideas is not enough, nor is it enough to just look at google analytics and product usage data. You actually have to talk to the market.
Read my article The Complete Guide to Customer Interviews That Drive Product-Market Fit. When you get to the customer interview, take heed of these crucial guidelines!
Product leaders know they need to tailor their roadmap to customer demand. They base these decisions on market intelligence from the usual sources:
These sources are important, but are often indirect and lagging information, not to mention other biases. For those that just want to appease their boss, this may be enough. What you release next may or may not be successful, but at least you can show that you based your roadmap on sources. But for those that really care about a product that sells and users love, you need to balance this with more direct and predictive sources of intelligence.
Product and service offerings follow a growth lifecycle:
Once you hit Flatline and Decline, it is very hard to bounce back. Stories of flatlining businesses that suddenly take off again are rare indeed. While there are ways to resuscitate a flatlining business, the ideal is to NEVER GET THERE IN THE FIRST PLACE and instead take the right actions to ensure continuous growth.
Talking to customers to uncover unmet needs is one of the most exciting parts of product management. It's being a detective! Hunting down members of your target audience for meetings, and, once there, asking them the right questions to get to the root of what they really care about.
But most PMs find it difficult to book customer interviews on a regular basis. And even when you do have a customer interview - how do you go about it? What questions do you ask?
This article is a step-by-step guide to landing, planning and executing customer interviews in a way that directly connects with product-market fit and growth, and hopefully takes the stress out of it and makes it fun!
SaaS businesses are all the rage! Even companies that have success with traditional products are re-thinking their business as a SaaS offering, to reap the many benefits:
These benefits are so attractive that every and any product and business model is being re-imagined as SaaS, leading to some great ideas (e.g. Spotify, Coursera, bacon-of-the-month dropped off at your doorstep!). But not every business lends itself intuitively to a SaaS model. Especially if you have years of legacy technology and processes established with an existing customer base, the transition won't happen overnight. There are key questions to answer in each facet of the business.
A Program Manager is responsible for managing multiple interrelated streams of work and ensuring that - taken together - they produce specific business outcomes and benefits for an organization.
However the title "Program Manager" is not very common. Organizations have Project Managers, Product Managers, General Managers. But apart from some large organizations, "Program Manager" is rare. This is because often executives and middle managers play the role of Program Manager even if they don't have the title.
Who are the people that can't live without your product? Why is that product a must-have for them? And what is the difference between these must-have users compared to other users for whom it's just a nice-to-have? These questions are at the root of scaling growth. Find your must-have users.
Yet, when planning target markets, it’s human nature to want to go broad. There is a feeling of safety and comfort. “Why, my product has hundreds of uses, for everyone! Let me list the ways…” But one of the great paradoxes of growth is that, in general, the more broadly you define target markets, the less business you actually take in. It literally pays to get more targeted.
Now that you've read the The Complete Guide to Customer Research Interviews, you know that gathering intelligence from customer interviews, market analysis, online research, win/loss analysis, is critical to developing a strategy that drives product-market fit and growth. But once you have accumulated all your customer and market insights, what do you actually do with it? Here are 5 immediate steps to infuse your strategy with intelligence in practice.
"How would you feel if you couldn't use the product anymore?" According to growth hacking pioneer Sean Ellis, this is the question to ask to determine your level of product-market fit. In response, you are looking for at least 40% of your customer base who say they would be "very disappointed". This represents user passion.
As a product leader, you need customer intelligence to plan your strategy. But the customer data you collect from sales is biased. The data you get from market analysts is indirect. Even the data you collect yourself from customer interviews can be artificial, as customers are all too willing to be positive and tell you what you want to hear.
But there is one undeniable source of raw unfiltered customer intelligence that is too often overlooked - the Customer Success team. The Customer Success team gets customers when they are at their most passionate, emotional, even angry. Where there's emotion, there's usually a real pain point. It's rare to find that sort of honesty elsewhere.
As a CIO, if your enterprise relies on solutions that look like something out of the 1990s, it's often because that's exactly what they are. These applications - ERPs and home-grown core operations systems met an immediate business need at the time, then layer upon layer was built on top and entrenched into the foundational processes of the business. Now you are at a catch-22: pressure to modernize to meet the growing digital needs of the enterprise, while at the same time not risking the legacy software that is vital to day-to-day operations.
How do you prioritize legacy transformation as part of a digital transformation roadmap?
One of the biggest challenges of modern software creation is having multiple team members — product managers, UX designers, data scientists, software developers, QA — working in parallel rather than sequentially. The world would be a much simpler place if the Product Manager completed a detailed requirements definition, then handed it off to data scientists who prototyped and refined their algorithms, then handed off to UX designers to create the full design, who then handed it off to development to build, who then handed off to QA for testing.
Unfortunately this is not the world we live in. Every software initiative is a race to getting value into the hands of users, and building sequentially is not an option.
Moreover, teams are continually learning and need the ability to iterate. Product managers keep getting fresh market intel that needs to be injected into the product, data scientists keep making breakthroughs in the predictive algorithm’s accuracy, designers keep refining based on user feedback. The name of the game is agile, iterative, and mass parallelization of teams.
But how do leaders run all of these very different teams that depend on each other in parallel, and still deliver to market fast?