Tuesday, September 30, 2014

Focus On Your Customers And Not Competitors


A lorry is a symbol of Indian logistics and the person who is posing against it is about to rethink infrastructure and logistics in India. Jeff Bezos is enjoying his trip to India charting Amazon’s growth plan where competitors like Flipkart have been aggressively growing and have satisfied customer base. This is not the first time Bezos has been to India and he seems to understand Indian market far better than many CEOs of American companies. His interview with a leading Indian publication didn’t get much attention in the US where he discusses Amazon’s growth strategy in India.

When asked whether he is in panic mode:
For 19 years we have succeeded by staying heads down, focused on our customers. For better or for worse, we spend very little time looking at our competitors. It is better to stay focused on customers as they are the ones paying for your services. Competitors are never going to give you any money.
I always believe in focusing on customers, especially on their latent unmet needs. Many confuse not focusing on competitors as not competing. That’s not true at all. Compete hard in the market but define your own rules and focus on your customers. Making noise about your competitors and fixating on their strategies won’t take you anywhere.
But there's also some opportunity to build infrastructure from scratch. When you think of facilitation commerce between small shops and the end-consumer there would be things you would build - I don't know what they are, we will have to invent some of these things - that you might not build in other geographies where infrastructure grew for different purposes.
All emerging economies are different and India is a very different market. Bezos does seem to comprehend that. Things that you take for granted and things that you would invest into in the western countries are vastly different in India. Amazon has a great opportunity to rethink logistics and infrastructure.
The three things that I know for sure the Indian customer will still want 10 years from now: vast selection, fair, competitive prices and faster, reliable delivery. All the effort we put into adding energy into our delivery systems, reducing defects and making the customer experience better, I know those things will be appreciated 10 years from now. We could build a business strategy around that.
Innovating doesn’t mean reinventing strategy, the "what." What holds true in the US is likely hold true in India as well. It’s the execution—the “how”—will be different.

Speaking of Amazon as a growth company:
I like a quote from Warren Buffet who famously said: You can hold a ballet and that's okay and you can hold a rock concert and that's okay. Just don't hold a ballet and advertise it as a rock concert. Are we holding a ballet or are we holding a rock concert? Then, investors get to select. They know we have a long-term viewpoint. They know that we take cash flow that gets generated from our successful businesses and invest in new opportunities. India is a great example of that happening.
Even though Amazon has been in business for a long time with soaring revenue in mature categories the street sees it as a high growth company and tolerates near zero margin and surprises that Jeff Bezos brings in every quarter. Bezos has managed to convince the street that Amazon is still in heavy growth mode and hasn't yet arrived. In short term you won’t see Amazon slowing down. They will continue to invest their profit in their future to build even bigger businesses instead of paying it out to investors.

When asked whether Google is Amazon’s biggest rival:
I resist getting in to that kind of conversation because it is not how I think about our business. There are companies who in their annual planning process literally start with: Who are our three biggest competitors? And they'll write them down. This is competitor number one, two and three. Then they'll develop strategies for each of them. That's not how our annual planning is done. We do have an annual planning process and actually we are right in the middle of it now. We start with,`What'll we deliver to our customers? What are the big ideas, themes?'
Amazon has innovated by focusing on what customers really care about and not what the competitors do. This approach has paid off and I can see why Bezos is keen to do the same in the Indian market.

I really liked what he said when asked about being gifted and being kind:
I believe that humans would achieve anything that we are determined to achieve, if we work hard. So, celebrate your gifts but you can only be proud of your choices. And, cleverness is gift. You cannot become Einstein no matter how much you work. You have to really decide on how you're going to make choices in your life. You get to decide to be a good husband and a good father.
I strongly believe in why making right choices is more important than being gifted. I share this with as many people as I can and I also tell them, “you control your effort and not the outcome.”

Photo courtesy: Times of India

Monday, September 22, 2014

Disruptive Enterprise Platform Sales: Why Buy Anything, Buy Mine, Buy Now - Part II


This is the second post in the three-post series on challenges associated with sales of disruptive platforms such as Big Data and how you can effectively work with your prospects and others to mitigate them. If you missed the first post in the series it was about “why buy anything.” This post is about “why buy mine."

Convincing  your prospects they need to buy a platform is just a first step in the sales process. You need to work with them to convince them to buy not just any platform but your platform.

Asking the right questions - empathy for business

This is the next logical step after you have managed to generate organic demand in your prospect’s organization a.k.a “why buy anything” as I mentioned in the Part I. Unlike applications, platforms don’t answer a specific set of questions (functional requirements). You can’t really position and demonstrate the power of your platform unless you truly understand what questions your prospect needs you to answer. Understanding your prospect’s questions would mean working closely with them to understand their business and their latent needs. Your prospect may or may not tell you what they might want to do with your platform. You will need to do it for them. You will have to orchestrate those strategic conversations that have investment legs and understand problems that are not solvable by standard off-the-shelf solutions your prospect may have access to.

Answering the right questions - seeing is believing

One of the key benefits of SaaS solutions is your prospect’s ability to test drive your software before they buy it. Platforms, on-premise or SaaS, need to follow the same approach. There are two ways to do this: you either give your prospect access to your platform and let them test drive it or you work with your prospect and be involved in guiding them through how a pilot can answer their questions and track their progress. While the latter approach is a hi-touch sale I would advise you to practice it if it fits your cost structure. More on why it is necessary to stay involved during the pilot in the next and the last post (Part III) in this series.

Proving unique differentiation

Once your prospect starts the evaluation process whether to buy your platform or not your platform will be compared with your competitive products as part of their due diligence efforts. This is where you want to avoid an apple-to-apple comparison and focus on unique differentiation.

Even though enterprise platform deals are rarely won on price alone don’t try to sell something that solves a problem your competitors can solve at the same or cheaper price. Don’t compete on price unless you are significantly cheaper than your competitor. The best way to position your platform is to demonstrate a few unique features of your platform that are absolutely important to solve the core problems of your prospect and are not just nice-to-have features.

Care deeply for what your prospects truly care about and prove you’re unique.

The next and the last post in this series will be about “why buy now.”

Photo courtesy: Flickr 

Sunday, August 31, 2014

Disruptive Enterprise Platform Sales: Why Buy Anything, Buy Mine, Buy Now - Part I


I think of enterprise software into two broad categories - products or solutions and platform. The simplest definition of platform is you use that to make a solution that you need. While largely I have been a product person I have had significant exposure to enterprise platform sales process. I have worked with many sales leaders, influencers, and buyers. Whether you're a product person or you're in a role where you facilitate sales I hope this post will give you some insights as well as food for thought on challenges associated with sales of disruptive platforms such as Big Data and how you can effectively work with customers and others to mitigate some of these challenges.

I like Mark Suster's sales advice to entrepreneurs through his framework of "why buy anything", why buy mine", and "why buy now." I am going to use the same framework. Platform sales is sales in the end and all the sales rules as well as tips and tricks you know that would still apply. The objective here is to focus on how disruptive enterprise software platform sales is different and what you could do about it.

The first part of this three-post series focuses on "why buy anything."

Companies look for solutions for problems they know exist. Not having a platform is typically not considered a good-enough problem to go and buy something. IT departments also tend to use what they have in terms of tools and technology to solve problems for which they decide to "build" as opposed to "buy." Making your prospects realize they need to buy something is a very important first step in sales process.

Generating organic demand:

Hopefully, you have good marketing people that are generating enough demand and interest in your platform and the category it belongs to. But, unfortunately, even if you have great marketing people it won't be sufficient to generate organic demand for a platform with your prospect. When it comes to platform sales your job is to create organic demand before you can fulfill it. This is hard and it doesn't come naturally to many good sales people that I have known. By and large sales people are good at three things: i) listen: understand what customers want ii) orchestrate: work with a variety of people to demonstrate that their product is the best feature and price fit iii) close: identify right influencers and work with a buyer to close an opportunity. While platform sales does require these three qualities like any other sales creating demand or appetite is the one that a very few sales people have. You have to go beyond what your prospects tell you; you have to assess their latent needs. Your prospects won't tell you they need a disruptive platform simply because they don't know that.

You're assuming a 1-1 marketing role to create this desire. Connect your prospects with (non-sales) thought leaders inside as well as outside of your organization and invite them to industry conferences to educate them on the category to which your platform belongs to. Platform conversations, in most cases, start from unusual places inside your prospect's organization. People who are seen as technology thought leaders or are responsible for "labs" inside their company or people who self-select as nerds or tinkerers are the ones you need to evangelize to and win over. These people typically don't sit in the traditional IT organization that you know of and even if they do they are not the ones who make decisions. These folks are simply passionate people who love working on disruptive technology and have a good handle on some of the challenges their companies are facing.

Dance with the business and the IT:

As counterintuitive as it may sound working with non-IT people to sell technology platform to IT is a good way to go. The "business" is always problem-centric and the IT is always solution-centric. Remember, you're chasing a problem and not a solution. Identify a few folks in a line of business who are willingly to work with you. This is not easy especially if you're a technology-only vendor. Identify their strategic challenges that have legs — money attached to it. Evangelize these challenges with IT to generate interest in disruptive platform that could be a good fit for these challenges.

IT doesn't like disruption regardless of what they tell you. If they are buying your disruptive platform they are not buying something else and they don't use some of the existing platforms or tools they have. There are people who have built their careers building solutions on top of existing tools and technology and they simply don't want to see that go away. You will have to walk this fine line and get these people excited on a new platform that doesn't threaten their jobs and perhaps show them how their personal careers could accelerate if they get on to this emerging technology that a very few people know in the company but something which is seen highly strategic in the market. Don't bypass IT; it won't work. Make them your friends and give them an opportunity to shine in front of business and give them credit for all the work.

Chasing the right IT spend:

Most enterprise software sales people generally know two things about their customers: i) overall IT spend ii) how much of that they spend with you. What they typically don't know is how much a customer spends on similar technology or platforms from that overall IT spend that doesn't come your way. There are two ways to execute a sales opportunity: either you find something to sell for the amount that your customer typically spends with you on annual basis or you go after the larger IT spend and expand your share of the overall pie. It's the latter that is relevant when you're selling platform to your existing customer (and not a prospect).

Platform, in most cases, is a budgeted investment that falls under "innovation" or "modernization" category. If you're just focused on current spending pattern of your customer you may not be able to generate demand for your platform. It is your job to convince your customer to look beyond how they see you as a vendor and be open to invest into a category that they might be reluctant for.

The next post in this series will be about "why buy mine."

Photo courtesy: Stef

Thursday, July 31, 2014

Inability Of Organizations To Manage "The Flow" Of Talent Management


The flow, a concept developed by one of my favorite psychologists, Mihaly Csikszentmihalyi, matches the popular performance versus potential matrix that many managers use to evaluate and calibrate their employees. For people to be in the flow they need to be somewhere in the middle moving diagonally up. Ideally, this is how employees should progress in their careers but that always doesn't happen. To keep employees in the flow you want to challenge them enough so that they are not bored but you don't want to put them in a situation where they can't perform and are set up for a failure.

Despite of this framework being used for a long period of time I see many organizations and managers continue to make these three mistakes:

Mistaking potential for performance

Performance, at the minimum, is about given skills and experience how effectively person accomplishes his or her goals. Whereas potential is about what person could do if the person could a) acquire skills b) gain access to more opportunities c) get mentoring. We all have seen under-performers who have more potential. In my experience, most of these people don't opt to underperform but they are put in a difficult situation they can't get out of. We routinely see managers not identifying this as a systemic organizational problem but instead shift blame to employees confusing potential for performance suggesting to them, "you could have done so much but you didn't; you're a slacker." A similar employee with equal performance but less potential would not receive the same remarks on his/her performance.

Treating potential as an innate fixed attribute

One of the biggest misconceptions I come across is managers looking at potential as innate fixed attribute. Potential is a not a fixed attribute; it is something that you help people develop.

These out-performers who are not labelled as "high potential" are mostly rewarded with economic incentives but they don't necessarily get access to opportunities and mentoring to rise above their work and a chance to demonstrate their potential and make a meaningful impact.

Fixating on hi-potential out-performers

Not only managers fixate on hi-potential out-performers but they are also afraid that these employees might leave the organization one day if they have no more room to grow and if they run out of challenges. As counterintuitive as it may sound this is not necessarily a bad thing.

We all live in such a complex ecosystem where retaining talent is not a guarantee. The best you can do is develop your employees, empower them, and give them access to opportunities so that they are in a flow. As a company, create a culture of loyalty and develop your unique brand where employees recognize why working for you is a good thing. If they decide to leave you wish them all the best and invest in them: fund their start-up or make them your partners. This way your ecosystem will have fresh talent, place for them to grow, and the people who leave you will have high level of appreciation for your organization. But, under no circumstances, ignore the vast majority of other employees who could out-perform at high potential if you invest into them.

Monday, June 30, 2014

Chasing Qualitative Signal In Quantitative Big Data Noise


Joey Votto is one of the best hitters in the MLB who plays for Cincinnati Reds. Lately he has received a lot of criticism for not swinging on strikes when there are runners on base. Five Thirty Eight decided to analyze this criticism with the help of data. They found this criticism to be true; his swings at strike zone pitches, especially fastballs, have significantly declined. But, they all agree that Votto is still a great player. This is how I see many Big Data stories go; you can explain "what" but you can't explain "why." In this story, no one actually went (that I know) and asked Votto, "hey, why are you not swinging at all those fastballs in the strike zone?"

This is not just about sports. I see that everyday in my work in enterprise software while working with customers to help them with their Big Data scenarios such as optimizing promotion forecast in retail, predicting customer churn in telco, or managing risk exposure in banks.

What I find is as you add more data it creates a lot more noise in these quantitative analysis as opposed to getting closer to a signal. On top of this noise people expect there shall be a perfect model to optimize and predict. Quantitative analysis alone doesn't help finding a needle in haystack but it does help identify which part of haystack the needle could be hiding in.
"In many walks of life, expressions of uncertainty are mistaken for admissions of weakness." - Nate Silver
I subscribe to and strongly advocate Nate Silver's philosophy to think of "predictions" as a series of scenarios with probability attached to it as opposed to a deterministic model. If you are looking for a precise binary prediction you're most likely not going to get one. Fixating on a model and perfecting it makes you focus on over-fitting your model on the past data. In other words, you are spending too much time on signal or knowledge that already exists as opposed to using it as a starting point (Bayesian) and be open to run as many experiments as you can to refine your models as you go. The context that turns your (quantitative) information into knowledge (signal) is your qualitative aptitude and attitude towards that analysis. If you are willing to ask a lot of "why"s once your model tells you "what" you are more likely to get closer to that signal you're chasing.

Not all quantitative analyses have to follow a qualitative exercise to look for a signal. Validating an existing hypothesis is one of the biggest Big Data weapons developers use since SaaS has made it relatively easy for developers to not only instrument their applications to gather and  analyze all kinds of usage data but trigger a change to influence users' behaviors. Facebook's recent psychology experiment to test whether emotions are contagious has attracted a lot of criticism. Keeping ethical and legal issues, accusing Facebook of manipulating 689,003 users' emotions for science, aside this quantitative analysis is a validation of an existing phenomenon in a different world. Priming is a well-understood and proven concept in psychology but we didn't know of a published test proving the same in a large online social network. The objective here was not to chase a specific signal but to validate a hypothesis— a "what"—for which the "why" has been well-understood in a different domain.

About the photo: Laplace Transforms is one of my favorite mathematical equations since these equations create a simple form of complex problems (exponential equations) that is relatively easy to solve. They help reframe problems in your endeavor to get to the signal.

Saturday, May 31, 2014

Optimizing Data Centers Through Machine Learning

Google has published a paper outlining their approach on using machine learning, a neural network to be specific, to reduce energy consumption in their data centers. Joe Kava, VP, Data Centers at Google also has a blog post explaining the backfround and their approach. Google has one of the best data center designs in the industry and takes their PUE (power usage effectiveness) numbers quite seriously. I blogged about Google's approach to optimize PUE almost five years back! Google has come a long way and I hope they continue to publish such valuable information in public domain.



There are a couple of key takeaways.

In his presentation at Data Centers Europe 2014 Joe said:  
As for hardware, the machine learning doesn’t require unusual computing horsepower, according to Kava, who says it runs on a single server and could even work on a high-end desktop.
This is a great example of a small data Big Data problem. This neural network is a supervised learning approach where you create a model with certain attributes to assess and fine tune the collective impact of these attributes to achieve a desired outcome. Unlike an expert system which emphasizes an upfront logic-driven approach neural networks continuously learn from underlying data and are tested for their predicted outcome. The outcome has no dependency on how large your data set is as long as it is large enough to include relevant data points with a good history. The "Big" part of Big Data misleads people in believing they need a fairly large data set to get started. This optimization debunks that myth.

The other fascinating part about Google's approach is not only they are using machine learning to optimize PUE of current data centers but they are also planning to use it to effectively design future data centers.

Like many other physical systems there are certain attributes that you have operational control over and can be changed fairly easily such as cooling systems, server load etc. but there are quite a few attributes that you only have control over during design phase such as physical layout of the data center, climate zone etc. If you decide to build a data center in Oregon you can't simply move it to Colorado. These neural networks can significantly help make those upfront irreversible decisions that are not tunable later on.

One of the challenges with neural networks or for that matter many other supervised learning methods is that it takes too much time and precision to perfect (train) the model. Joe describing it as a "nothing more than series of differential calculus equations " is downplaying the model. Neural networks are useful when you know what you are looking for - in this case to lower the PUE. In many cases you don't even know what you are looking for.

Google mentions identifying 19 attributes that have some impact on PUE. I wonder how they short listed these attributes. In my experience unsupervised machine learning is a good place to short list attributes and then move on to supervised machine learning to fine tune them. Unsupervised machine learning combined with supervised machine learning can yield even better results, if used correctly.

Wednesday, April 30, 2014

Product Vision: Make A Trailer And Not A Movie


I have worked with many product managers on a product vision exercise. In my observation the place where the product managers get hung up the most is when they confuse product vision for product definition. To use an analogy, product vision is a trailer and product definition is a movie. When you're watching a movie trailer it excites you even though you fully don't know how good or bad the movie will be.

Abstract and unfinished

A trailer is a sequence of shots that are abstract enough not to reveal too much details about the movie but clear enough to give you the dots that your imagination could start connecting. Some of the best visions are also abstract and unfinished that leave plenty of opportunities for imagination. Product visions should focus on "why" and "what" and not on "how" and most importantly should have a narrative to excite people to buy into it and refine it later on. Vision should inspire the definition of a product and not define it.

I am a big believer of raw or low fidelity prototypes because they allow me to get the best possible feedback from an end user. People don't respond well to a finished or a shiny  prototype. I don't want people to tell me, "can you change the color of that button?" I would rather prefer they say, "your scenario seems out of whack but let me tell you this is what would make sense."

Non-linear narratives

Movie trailers are also the best examples of non-linear thinking. They don't follow the same sequence as a movie - they don't have to. Most people, product managers or otherwise, find non-linear thinking a little difficult to practice and comprehend. Good visions are non-linear because they focus on complete narrative organized as non-linear scenarios or journeys to evoke emotion and not to convey how the product will actually work. Clever commercials, such as iPad commercials by Apple, follow the same design principles. They don't describe what an iPad can do feature by feature but instead will show a narrative that help people imagine what it would feel like to use an iPad.

Means to an end

The least understood benefit of a product vision is the ability of using the vision as a tool to drive, define, and refine product requirements. Vision is a living artifact that you can pull out anytime during your product lifecycle and use it to ask questions, gather feedback, and more importantly help people imagine. I encourage product managers not to chase the perfection when it comes to vision and focus on the abstract and non-linear journey because a vision is a means to an end and not an end itself.

Photo courtesy: Flickr