As product managers, we rely heavily on gathering data in order to make product and business-related decisions. But even with the best intentions at heart, many times our decision-making is affected by inherent biases and preconceived notions - not by empirical data.

This is a universal pain across organizations. 

In this talk, we will reveal the most prominent cognitive biases and explore mitigation tips to make better data-driven decisions. From Sales to Marketing and from Support to Dev we'll dive into each stakeholder, cover the most painful bias, explore how it affects the product, and how to deal with them.

Product Managers are required to manage multiple stakeholders, across functions yet sometimes apply a "one size" fits all to influencing. We will go through a basic stakeholder mapping framework, generalize about personas in an org and walk through how to apply different type of influencing skills to different types of stakeholders.

The common PM would say the the PM is responsible for the "What" and the UXer is responsible for the How.

Reut think that this attitude is no longer correct. PM and US should collaborate on both: What and How.

She will present more advance model for the relationship.

In order to do so, she will explain the difference between a UX\UI expert and a classical UX expert.

By the end of the talk you will know how to use better the UX in order to achieve a better product and better product market fit.

The product manager’s journey to find the product/market fit is akin to the Templars quest of the Holy Grail. Everyone wishes to find it, but nobody knows what exactly is it, where is it and how to start. Often this process is very “soft”, based on gut feeling, emotions, anecdotes, and a few conversations with customers. How then, in a world where PMs don’t decide what to have for breakfast without A/B testing their cereal, the decisions on the most crucial element of product strategy are made as a form of art?

In this session, we will discuss how the product/market fit can be discovered, measured, and improved upon using quantitative frameworks. How to use it to engage and convince stakeholders of the product strategy and specifically how to collaborate with the marketing, sales and customer success organizations to establish the required synergy to deliver a great product based on the newly-found and proven product/market fit.

In the old waterfall days, product managers had to write very long and detailed requirements.

But even when we are working agile, many product managers spend too much time on managing the backlog and writing too detailed requirements, whether they call it PRDs, user stories, specifications or any other term.

Writing specifications and requirements should be a minimal task. Product managers should write as less as they can so they can spend more time on strategy, focusing the team, and making sure the product delivers the right value, while on the same time empowering development to come with new ideas.

Though the concepts of machine learning and artificial intelligence algorithms dates back to nearly 70 years ago it is mainly in the last 15 years that commercial applications of ML and AI started to appear with companies like Google and Facebook pioneering the use of ML for building products using speech recognition, image recognition and so on. Since then more and more products are being developed based on ML and AI algorithms and it is becoming more likely that product managers will face ML algorithms in their careers.

But what effect does this have, if at all, on our work as product managers? Is there anything different about being a PM for an ML product vs. any other product? How much should product managers know about these algorithms?

In this session I will take you through my own personal journey of being a PM launching an ML based product in taboola and will provide some personal takeaways on how to be more successful with your ML product.

A product which offers AI as a core business technology often triggers deep anxieties in our clients.

Suddenly, they are expected to trust a machine with tasks which were traditionally based on human experience and intuition.

In a world of rapidly evolving AI products, this "AI-anxiety" becomes a critical obstacle in the way to successful product adoption.

As product managers, we need to draw clearer boundaries between human and machine tasks, provide dedicated features for “anxiety relief”, and lead a conversation which will build

human-AI trust.

Most companies have a vision accompanied by a mission statement. "Bring the world closer together.” "Accelerate the world’s transition to sustainable energy.” "Change how the world works together." Did you ever incorporate these as practical tools in your product management methodology?

In times when we push our execution abilities to the limits, these mission statements transform from phrases to practical tools.  In this session, we’ll see how a vision, a mission statement, and company values can take an instrumental role in the inception, execution, and rollout of an ambitious product.

We are living in a hectic world, customers expect to get answers in seconds and the fact that we’ve got all the data in our “systems” can’t help us anymore, few of the shiniest stars in the software industry (Facebook, Youtube, Netflix) have one thing in common - that they know how to answer a question that you haven't asked yet, imagine that you are getting into a room and you have information and all the right answers about everyone at your fingertips – this is what I’ll present, an Innovative product that’s doing exactly this.

We keep trying to define our role as Product Managers, but shouldn’t we start with understanding the role of the Product in the company we work for, and then move on to what it means to Manage it? Let’s take 18 minutes to kick around a few suggestions and try to come up with an approach? I’ll bring along a case study from a recent chapter, and a hypothesis built around identity that goes with it.

As a Product Manager, it’s only reasonable that I believe this session will be engaging, valuable, and viral... but even if I’m wrong – what’s the risk in an 18-minute sprint, right?

This talk outlines the disruptions, both positive and negative, that are brought about by our immersion in the digital. The scope and depth of aspects of impacted individual and collective activities, range from our privacy to our education, and from our livelihood to our security. One can form dystopian or utopian views of the direction in which we are marching. This talk aims to inform the discussion.

The product manager’s journey to find the product/market fit is akin to the Templars quest of the Holy Grail. Everyone wishes to find it, but nobody knows what exactly is it, where is it and how to start. Often this process is very “soft”, based on gut feeling, emotions, anecdotes, and a few conversations with customers. How then, in a world where PMs don’t decide what to have for breakfast without A/B testing their cereal, the decisions on the most crucial element of product strategy are made as a form of art?

In this session, we will discuss how the product/market fit can be discovered, measured, and improved upon using quantitative frameworks. How to use it to engage and convince stakeholders of the product strategy and specifically how to collaborate with the marketing, sales and customer success organizations to establish the required synergy to deliver a great product based on the newly-found and proven product/market fit.

Collaboration with internal stakeholders is crucial to the success of any new product or feature, but how does one engage team members who aren’t under their purview. The challenge becomes much harder when the company grows quickly from 30 to 300 people. In this lecture we will try to analyze a few behaviors which can help in such a situation. 

Collaboration with internal stakeholders is crucial to the success of any new product or feature, but how does one engage team members who aren’t under their purview. The challenge becomes much harder when the company grows quickly from 30 to 300 people. In this lecture we will try to analyze a few behaviors which can help in such a situation. 

Many companies have designed their codes and developed their products over five years ago, when AI was still associated with Spielberg’s “Artificial Intelligence”. To adapt with today’s growing demand for cost-effective data-driven products, these companies need to take their “old” product and augment it with AI and ML capabilities. This journey is a hell of a ride, which won’t leave the company with technological enhancements alone. Adding these capabilities requires a fundamental change in the mindset of people, their skills, and the way they manage their product, while bearing immense impact on the overall business and its value proposition.

I've worked in both product and marketing, and I've seen friction and tension between product at marketing at every single company that I've worked at. But it doesn't have to be that way! Product and marketing can get along and even make magic together - the key is very specific communication and alignment. During my talk I'll provide several examples that show how miscommunications and lack of alignment led to big problems, and how they could have been easily solved.