Product leaders are inundated with data. SaaS product and website analytics can slice and dice every aspect of the customer journey. I can see that 22% of my customers between the ages of 30 and 40 spent over 3.2 seconds looking at the new graphic on my website. I know that 16% of freemium users converted to paid in the last month since we added 3 new features. 

Quantitative measures like this can point to areas of interest that require investigation and experimentation, but they won't tell you why these are of interest. Qualitative data, ie. talking to people, gives you the why. Qualitative data tells you what was motivating the user when they spent 3.2 seconds looking at your graphic, what problem they were trying to solve. You need both, quantitative and qualitative.

B2B companies have woken up to the idea of marketing their business by writing content: articles, blog posts, fact sheets, and case studies. But with so much competition, the writing has to be the best in its category in order to stand out above others.  How do you ensure what you are about to write is clearer, more interesting, more valuable, and ultimately gets more views than the competition?

Studying for the PMI-ACP agile PMP exam? For this test you not only have to understand and have lived agile processes, you have to remember a lot of terms: names of techniques, collaboration games, lists of principles,  etc. Here is a cheat sheet to review before the exam, to help answer all the trick questions. 

I've also included a comprehensive inventory of free PMI-ACP online tests, over 1,400 questions in total. With a cost of ~$400 for this test, it is definitely worth going through all the free mock exam material to make sure you are thoroughly ready. Good luck!

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.

There are lots of reasons why a good lead may not convert into revenue, or even a qualified sales opportunity. This can be very frustrating, but don't throw the baby out with the bathwater! There is so much you can learn from leads that don't convert. This intel is pure gold for adapting your value proposition, your messaging, your operations, to get that much closer to product-market fit.

When you think data protection and data privacy, you might think of hackers trying to get past your company's firewall and into your computer to steal your data. But by far the main reason why data breaches are so rampant today has little to do with external hackers. The main cause of data breaches is insider threats. Insider threats are trusted employees, contractors, suppliers and partners, who leak private data into the wrong hands. Sometimes insider threats leak intentionally, but the vast majority of the time, it's just people innocently leaking your data without even knowing it.

Because insiders - your employees, contractors, suppliers, vendors - have access to data to do their jobs, it is really hard to prevent them from leaking it! Few good solutions exist today, but the race is on to solve the insider threat problem. The key is to first deeply understand the roots of the insider threat problem.

From research and prototyping to commercial development, AI/ML requires making decisions and trade-off's. This is where the PM needs to provide loads of context to the R&D team: what are the customer problem(s) are that we are trying to solve? What parameters and trade-off's would be acceptable to a customer? Have we considered the cost trade-offs of operating in practice? ​

There is a strong and immediate demand for Data Science in all industries, but a limited supply of qualified data scientists. There is also a fuzzy understanding of how data science actually works. Data science is "black magic" to most managers. These factors result in companies paying a high premium for data scientists today.

In such a competitive job market, how do you build your own AI / ML / data science team?

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. 
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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. 
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But how do leaders run all of these very different teams that depend on each other in parallel, and still deliver to market fast?