Product 2019

DEC 31 TLV

Everyone loves data. Everyone loves talking like they understand data. Everyone loves using ML and AI buzzwords - but still, they are usually just throwing them out there into the open. Since the data world does not usually excel in explaining itself with nice user-facing UI, how can we show stakeholders what we are working on while presenting to a group that only sees the tip of the iceberg? In this talk, Sarit will share from her experience how to build presentations for stakeholders that have the correct balance between HL concepts and explanations of specific ongoing data features.

Thought process and a few anecdotes on product innovation in a non-existent market

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.

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.

With more than 500 application in a year ,  IBM Alpha Zone accelerator is exposed to the most innovative technologies and products of the Israeli startup ecosystem

As the business development executive which owns the scouting and work closely with the startups on their product and business development - I can share my insights and point of view on the following:

The top 3 trends we see in the startups' products in Israel

1.  AI - (Machine learning)

2.  Data (especially in digital health - ( gathering relevant dataand data analysis.

3. Blockchain  

   How startup are adjusting their products to be work with big enterprises (enterprise grade solution - scale  + high availability  , fast integration and implementation, immediate ROI GDPR and regulations  ,  

Betterhave a high quality notch product that answer a very specific need thana vast platform

What should an innovative startup's product include and how it is aligned with the latest technologies and IBM technologies in general .

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.

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. 

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.

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.

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.

As a product manager, working effectively with the Sales and Marketing teams is essential. Sales and Marketing represent your channels to promote and sell your products in the market, but some Product teams can find this collaboration to be tricky. The language is different, incentives are different, the way the teams work is different. So how can you bridge those gaps? In this session we will go over real use cases and examples and provide pragmatic tips on how to overcome the natural challenges in this relationship over the course of  the product lifecyle.

Thought QA is the boring part of the product life cycle ? think again!

Come see why the word quality can be misleading and mean different things to different people in different products and how to truly define the quality level of your product in an agile manner.

Learn why the Product-QA relationship is a two way street, how a good QA will influence your roadmap and how a good product manager can become an excellent product manager by embracing a methodology of a “Product review” before each release.

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. 

The relationship between product and sales teams is often a highly tenuous one.  This is due to structural differences in vantage points and conflicting objectives. 

When it comes to machine learning based products, the challenge is even greater.

In this talk I will share, using examples, how by implementing the right processes, and maintaining a product strategy and implementation which is in sync with relevant stakeholders (sales, operations, marketing) we were able to overcome these challenges and turn them into productivity drivers.