The ‘Easy to Use’ Myth

One of the most important requirements for any new product is that it should be ‘Usable’ or ‘Easy to Use.’ But in my role as Product Manager at Lattice Engines, I have always been puzzled about what this means to the product. There are really three different flavors for a product being easy to use; in this blog post, I discuss each of them.

Easy to get started

The most common meaning of easy to use, is easy to get started with. The most important criterion are:

1. The various controls you want to user to use more frequently, are easily available

2. The feature works as the user expects

For example, if you are creating a new feature that enables a user to search a e-commerce website. The expectations for this feature would be:

1. The search box is in the normal place - top and center, or top and right

2. The user can enter a text and just click enter, and expect the search to give you results

3. There is potential auto-fill and/or auto-correct functionality available

4. This feature works in a similar manner across all platforms and devices used to access your site.

In other words, the control and behavior mirror the mental model of how users expect your feature to behave.

Easy to learn/master

Let’s say you are designing a product with lots of advanced features. Your users need to often achieve a certain level of mastery to start using those features. These require a different set of consideration; you need to provide everything to the user to learn and master these features. This includes

1. Training/tutorials to walk through the advanced features

2. Clear separation of advanced features from the most common features

3. A non-threatening learning environment; by this, I mean that the consequences of using the features should be clearly visible to the user, and he/she must have enough information to decide whether to take an unfamiliar action, and ideally know that they can always reverse any change they make

High productivity after Mastery

A last important aspect of ease of use is what sort of productivity can the user achieve after he/she has learned the advanced features. A prime example is excel; there are tons of shortcuts which require lot of effort on part of the user to learn. But for the advanced users of excel (e.g., consultants, investment bankers) the shortcuts increase their productivity tremendously.

There is a trade-off

These three objectives are not mutually exclusive; given limited resources, and design considerations, you have to decide which is most important, and design for that case. For example, I was recently designing an advanced module at Lattice. My initial instinct was to design a very simple, intuitive interface, and I got pretty good feedback from most potential users. What I did not realize that a very small percentage of our users would use this module; those that would, use excel for such tasks. Designing an excel-like interface increases the effort required to learn the interface upfront, but helps them be much more productive when they have gained mastery.

2 Notes

Happiness and productivity: 7 principles (Part 1)

I recently did a talk on Happiness and productivity: 7 principles, at Product Camp Boston. While the slides are below, I thought that I would write a detailed blog post describing them.

This talk is based on three books:
1. Getting Things Done by David Allen
2. Happiness at Work by Srikumar Rao
3. Your Brain at Work by David Rock
Principle 1: The ideal life
Imagine your ideal life. Lets say you live in a 10 bedroom mansion. The bathtubs are made of gold. You have a pretty, understanding wife and two amazing kids - or perhaps four girlfriends. You are super successful at work. Everyday, people wonder at how you generate one amazing idea after another, and make the most insightful comments. The company you started is now at $5B in revenue. With $2B in profit. Tremendous growth lies ahead you. And that too working just 30 hours a week.
Imagine how happy you would be.
Now for the truth. Even if you got to that amazing life, you will be pretty much as happy you are today. There is not much you can achieve, or gain, to make you happy.
Being happy is a state of being; not a state of doing. Happiness is a journey, not a destination.
To clarify, by being happy, I mean truly happy and satisfied. Not just momentary happiness that disappears in an hour, but the kind of happiness that makes you grateful to be alive.
So how do you achieve that kind of happiness? Read on…
Principle 2: Reality: or is it?
We all blindly interpret what we perceive, as reality. But the truth is that what we perceive is largely governed by our mental models. Let me give an example.
Let’s say a man in a Red sports car whizzes you buy, narrowly passing you on the right at 90 miles per hour. Here you are, being responsible and driving at the limit in the middle lane. And here is this a#$%$# driving at crazy speeds. Perhaps he was a middle aged guy just trying to show off to his 20 year old girlfriend. Or perhaps it was a spoiled young kid, that has unlimited money from his dad.
Now how much of this situation was fact? The person whizzed you buy; the rest was your interpretation. It came from mental models that are deeply embedded in your way of thinking.
Now let’s try a different mental model. Let’s say instead of being an arrogant jerk, the guy was a responsible father, who had just heard that his kid was in an accident. And he was just trying to get to the hospital as fast as possible.
How much does your experience change depending on what mental model you choose to follow.
Note two points.
One, the mental model is a choice. Mental models are ingrained in us, but we can change them. It takes a lot of effort. But it does happen.
Second, what was the real truth in the above situation. Why was this person driving that fast? We will never know. But that is true for most situations in life. We will never know whether what happened was good or bad.
Let’s say your co-worker gets promoted. And you don’t. Is it good or bad? It probably sucks for you, but it might not be bad. You might finally leave a job that you hate, and go find a job that you really love. And you end up doing very well.
Whether something is good or bad, we don’t really know. The mental model we follow makes it good or bad. And makes us Happy.
Principles 3 - 7 will be written in subsequent blog posts. Do you like the first two? Let me know. Leave a comment, or share the post. I will be sure to follow up with the other principles.

1 Notes

THIS IS WATER - By David Foster Wallace from The Glossary on Vimeo.

I recently gave a talk at Product Camp in Boston on Happiness, and Getting Things done. I plan to write a blog post explaining my perspective on the topic. This amazing video speaks directly to one of my principles, so I wanted to share.


What I learned at McKinsey

After nearly 2 years of total time at McKinsey, I joined Lattice Engines as a senior product manager. And I love my new job! But in the midst of this new job, I have often reflected on what I learned from my time at McKinsey. Was working there after business school really valuable?

Not even counting the network and brand, and the friendships I developed while at McKinsey, I completely believe it was. Just like Business School, I answer this question by the following statement - if the experience significantly changes (and improves) the way I think and act, I believe it is valuable. In particular, there are some elements of the McKinsey culture that have become part of the way I work. I will classify them under two categories: Work style and Communication and Interpersonal skills

Work Style

  1. Efficiency and Urgency: I have a sense of urgency to do things faster, more efficiently, that I did not before. At McKinsey, there was a constant emphasis on using the 80-20 rule to have maximum impact with less effort; this was the only way you could actually complete the work assigned to you. That has completely rubbed off on me, though I do sometimes push for getting things done in an unrealistic timeframe.
  2. Scheduled PS sessions: We had 2 problem solving sessions scheduled weekly, to proactively think through problems we might face. Having these on the calendar forced us to come up with answers with artifical deadlines, and kep the project moving forward. Also, these sessions gave us a way of stepping back and reviewing the progress as a group, look at the big picture, get input from multiple stakeholders and make better decisions about the future of the project
  3. First day answer: There was always a urgency of getting to an early answer. On a first day, it was just a hypothesis; you will spend several weeks proving or disproving the answer. But having to come up with an  early answer makes you focus on what are the things you need to do to come up with a refined answer, and use facts to back it up. In addition, this helps bring around a focus on Iterative problem solving and end-product focus: We began every study with a storyboard; an outline of what the final product (i.e., the final presentation) will look like. This helped us understand and prioritize our analysis, and also help drive prioritization of work
  4. Put something on paper: There was also an emphasis on coming in with a perspective, and in particular putting something down on paper which forces people to react. This is a powerful technique because most people are overwhelmed with too many things to handle, and if you ask them something, they might not put too much thought into their answers. Putting an opinion in front of them forces them to react, and either agree or disagree, and produces better results

Communication and Interpersonal skills

  1. Bucketing or ‘chunking’: Give someone a set of six points, and they will not likely remember anything. Give them three points, with two sub-points each, and they will likely remember what you said. This is probably the most useful habits I developed; rolling up points into themes, and communicating in sets of 2-4 themes at a time. This has made my presentations and communications so much more effective, that this alone is worth the time spent at McKinsey.
  2. Respect for different personality types: McKinsey lives on MBTI types; people use MBTI as a way of communicating how they work, and to understand how they can work better with others. For me, it helped in two major ways. First, even though I don’t use MBTI types anymore, when I start working with someone, I try to get a sense of how they like to work. I also respect people’s preferences more, and try to understand what they say based on their personality. Second, MBTI has helped me understand myself better. I know that since I am INTJ, I need time on my own to think through things before meetings, I love to organize things and build plan before I proceed, and that I love to think big picture, but need to watch out for the details when I work. 
  3. Team Learning: A unique thing we did close to the beginning of each project was to hold a team learning session, where everyone mentioned their MBTI type, their learning goals, and how they like to work. This session was very valuable, both as a team-building exercise, but as a way to surface information that can help the team tremendously in the future, to avoid misunderstandings, and to support each other in achieveing individual goals
  4. Dialogues and handling different points of opinion - At the end of my one year learning workshop, I learned two very valuable frameworks for conversations. One was treating dialogues as a balance between listening and asserting, and techniques to make sure that you do each of these more effectively. Second, I learned a way of handling different points of opinion by getting to an understanding of the facts and assumptions behind each persons arguments, and understanding what assumptions you need to test for everyone to get on the same page



I often attend startup events, with a panel or a speech, in which there is not one new idea presented.

Not one new idea.

I have heard all of them before. Lean Startup, Customer Development etc.

So why attend these events?


A common fallacy is to believe that once you have learned/read something, it stays with you. We go to a speech, learn something new, and we are delight. We hear something we have heard before, and we think that is a waste of our time.

But consider this: A person needs to hear something seven times before they remember it. And this is just a simple thing: this repetition becomes even more important when you are making a habit change, and have to remember the thing when it counts: when you are about to take an action.

Famous sportsmen and sportswomen practice their common plays over and over again; then why do we shy away from just listening to the same ideas again and again?

And going to these events have the added advantage of listening to stories that bring these concepts to life, help generate ideas etc.

So next time there is a lean startup event - Sign me Up!




1 Notes

Startups behaving badly - and other observations from my job search

I recently finished a job search, and transitioned from McKinsey to a product manager role at Lattice Engines. There are several interesting things that I observed/learned from the job search. Some of these are pretty specific to transitioning from Consulting to a startup, while others are true for any job search. So here we go:

1. A startup job search is unpredictable - in several ways. Sometimes you get 4 job interviews in 3 days, and other times, you sit for 2 weeks without any interview. This is because things change so fast in most startups - so be prepared for a long job hunt (depending on how particular you are). Just as an example - one of the companies I was recruiting with was BlueFin Labs - and it got acquired by Twitter while I was talking to them!

2. What have you done for me lately: The recency bias is very prevelant for companies recruiting. For example, I was always perceived as a Consultant first, and everything else second. This is despite that I have seven years of experience working in tech, and have spent only 1.5 years in consulting

3. Some startups behave badly - while others outshine: There are two places which I really liked, but did not get offers from. One of them told me that they loved me, and wanted me to come back. Then they never returned my emails. The other one interviewed me, did not hire me, but gave me really good feedback. Just a little bit of extra effort on part of the second startup, and now I highly recommend that one.

4. Focus helps - Once you start looking for a job, there are so many opportunities available. It is good to be focused in some way or another. Narrowing down by geography, role, stage of company and ‘sector’ helps a lot. By sector I do not mean something as broad as ‘tech’ or B2B - sector is something like e-Commerce, or big data applications, or mobile advertising networks.

5. It’s all about the network - I got introduced to my current boss through a former co-worker. Your first degree connections matter a lot, but it matters even more who they know. But don’t let that limit you - reach out to people who don’t know you at all. Use anything you have in common, and be specific - tell them what you are looking for, and why they are in a unique position to help you. I got a lot of great responses through cold emails and LinkedIn messages.

6. Don’t forget the VCs and recruiters - A few recruiters were pretty helpful for me in my job search - either by finding opportunities directly, or connecting me to people. I will highly recommend (for Boston) Sean McLoughlin from Hireminds, Keith Cline, and Paul Blumenfeld.

   The one thing I did not do enough in this search was to reach out to Venture Capitalists enough. I was surprised that some of the most prominent VCs in Boston agreed to meet me, and see if there was any opportunities in their portfolio. I had not done this soon enough, so wasn’t able to take advantage of it.

7. Interview preparation needs to be done at various levels- 

  • Industry knowledge - what are the major trends going on
  • Company and product - what’s unique about the company? Their product? What could they do better? What would you change in the product?
  • Job level - what would you go the first day? What would be important to get done in the first 30 days? In the first 90 days? What change in your role would you make?
  • Why you - what are your unique skill sets that make you well suited for the role? Also, why are you passionate about the company and the role? Making an emotional connection was super important

8. Consultants! beware - I did find a fair number of hurdles I had to overcome transitioning from Consulting to Product Management. A key thing was proving that I would be willing to do the detailed work part of any startup job. That I will not just want to operate at a 30,000 foot level and leave the details to others. Think through carefully - transitioning from Consulting requires both leveraging the great things we learn in consulting, as well as proving that one can work in an operational role.

In the end, all worked out wonderfully, and I am super excited about my new role!


The State of Product Management Event

I recently attended an Event called ‘The State of Product Management' sponsored by VentureFizz and General Assembly. There was a pretty cool lineup of speakers, including

  • Ravi Mehta, Senior Director of Consumer Product at TripAdvisor
  • Sam Clemensfounder and head of product at InsightSquared
  • Shereen ShermakCo-Founder & VP of Product Management at BuysideFX
  • Mike Putnam,Vice President and GM of Mobile at Rue La La
  • Rob Go, Co-Founder and Partner at NextView Ventures (Moderator)

There was a lot of interesting discussions and several insights that I gleaned from the event. 

How has the product management role changed over the years?

  • Over the years, the role has become much more technical. In fact, Sam Clemens mentioned how he and his fellow product managers deploy code to production on a daily basis (might just be minor screen changes). Part of this has to do with the fact that it has become much easier to become a ‘good enough’ programmer
  • There is a lot more focus on the importance of design and UX - both on consumer and enterprise side
  • The Lean Startup movement has brought in a lot of changes, including focus on talking to the customers to define what product to build, doing more customer testing (leveraging tools such as
  • There is a growing difference in how risk is perceived, and organizations are starting to focus on Mean time to fix, rather than mean time to failure. So you could potentially create a bug with a lot of changes, but the impact can be minimized by rolling out the change to a small percentage of users, test, and rolling back in case of issues. Of course, the engineering environment needs to be able to support such code deployments

Tools and methods used for product management

  • for user feedback
  • Go sit on their lap’ - or contextual design. Insight from observation was cited as very critical. There is a diminshing result from too many interviews and observations though - especially beyind 10 interviews
  • There was significant debate on how to write specifications; some members felt strongly about not using specs, and instead relying on conversations between developers and product managers for building products. Specifications could be an easy way of losing touch with the mental model of the customer. However this model is harder to work with in larger companies and in cases where some of the development team is offshore
  • Pivotal Tracker for agile product management, PRDs, stories etc.

What do panelists look for in hiring product managers?

  • Passion for product - are you able to cite examples of products you love, and analyze what makes the products good?
  • Balance of strategic and tactical thinking
  • Understand limits of technology, but do not be limited by it
  • Be creative and visionary, but at the same time, analytical to track metrics and use them ti influence the product
  • See things from different points of view, including different customer personas


Difference between Enterprise and Consumer Product management

  • It is much easier to test changes on the consumer end, because of a large number of users. Even testing 1% to 2% of audience can get you a statistically significant test result
  • Consumer Product Management is more ‘glamorous’ - hardly anyone will know what your Enterprise product is about
  • It is also much harder to figure out what 80% of the market needs, based on a few customer interviews you can do in Enterprise

Does an MBA help?

  • The standard MBA answer is, ‘it depends’ :). All the panelists had an MBA, so clearly it did not hurt. It helps in strategy and business model analysis, and also tends to help earlier in career when you are doing a lot of work analyzing data

Other pearls of wisdom

  • When you start as a PM, avoid making big commitments early. Take time to understand the company, strategy, products, customers, engineering organization etc.
  • Pay attention to technical architecture as your company grows. A product rewrite has potential to stop a company’s progress completely
  • Getting in tune with the mental mode of the customer is the most important success factor determinant for a PM
  • Pay attention to design
  • Vet offshore firms carefully. Ideally give them all the same task and see which one comes out ahead
  • One way of prioritizing is to allocate quotas to various aspects e.g., 15% of engineering resources are dedicated to customer service requests, 15% to sales, 10% to architecture changes etc. Then let the respective groups/departments pick their priorities to fill the queues up
  • Another way of prioritizing is by business goals - see what goals are important, and prioritize change requests based on how they contribute to it


Big Data - what’s next?

Big Data is the buzzword everyone loves. The world is all about big data. Big big big - bigger than you ever thought possible. More complex. Needing more processing power than you ever thought possible. 

Let’s break it down.

In this post, I am using a Framework I came across in Roger and Mike’s Hypernet blog, thanks to a blog post by Rob Go of NextView Ventures. It talks about Technology Waves.


Technology waves begin with the infrastructure, on top of which enabling technology and platforms are built. End-user applications are then built on top of enabling technology and platforms, and take the whole wave to the mass market.

Just to bring this framework to life, consider the social networking wave. Infrastructure = broadband internet, Enabling tech/platforms = Facebook, Twitter and LinkedIn, and end-user applications including social games, apps etc. One of the key points is that the winning enabling tech and platform companies almost always are super hits! This makes sense - app developers would want to build on one well-known platform, rather than having to support multiple platforms.

Let’s see how it breaks down for big data. 


  • The continually lower cost of storage
  • Cloud computing with cheap hardware, that enables distributed processing of large amounts of data

Enabling technologies and platforms

  • Emergence of new types of Databases, especially the NoSQLvariety that support real-time analysis of a growing data source (e.g., Twitter streams, website logs)
  • Frameworks that support data-intensive distributed applications such as Apache Hadoop


I think this is where the primary action is at this point in time - in applications that aid developers and data scientists in big data analysis. Key examples include:

  • Cloudera, built to ease the adoption of Hadoop in the enterprise. I debate whether Hadoop belongs in the enabling tech category rather than application category, as it seems to be serving as a single technology that everyone is adopting
  • Emerging statistical programming languages such as R and SAP HANA

So where are we?

I think we are in the Application phase of wave 1 of big data - the wave whosw end users are developers and data scientists.There is a whole of set of enabling technologies that still need to be ironed out; especially on the database side where a lot of new DBs such as MongoDB, CouchDB etc. are emerging.

All this infrastructure and enabling technology is also enabling applications focused on non-technical end-users; applications that take this data and generate insights through analytics. Whether it be Lattice Engines, which uses Big Data analytics in sales, or Quant5, that provides big data analytics for Marketing, the focus is going to start turning towards what this data is supposed to provide in the end - business focused insight, decision-making and automation tools.

I believe that these two applications sets will continue to emerge, but there will be a set of standardization on the enabling technologies, and on the DB side. Surely an exciting time for all!