Did SAP Overpay For Concur?

Since SAP announced to acquire Concur and eventually closed the acquisition for $8.3B many people have reached out to me asking whether SAP overpaid for Concur. I avoid writing about SAP on this blog even though I work for SAP because this is my personal blog. In this case, I decided to write this post because this is the largest enterprise SaaS acquisition ever and this question unpacks the entire business model of SaaS enterprise software companies.

If you’re looking for a simple “yes” or “no” to this question you should stop reading this post now. If not, read on.

People reaching out to me asking whether SAP overpaid for Concur in itself is a misleading question because different people tend to compare Concur with different companies and have a specific point of view on whether the 20% premium that SAP paid to acquire Concur is justified or not.

Just to illustrate financial diversity amongst SaaS companies, here are some numbers:

This is based on a combination of actual and projected numbers and I have further rounded them off. The objective is not to compare the numbers with precision but to highlight the financial diversity of these companies based on their performance and perceived potential.

Market cap is what the market thinks the company is worth. The market doesn’t necessarily have access to a ton of private information that the potential acquirer would have access to when they decide what premium to pay. While the market cap does reflect the growth potential it is reflected in a standalone pre-acquisition situation and not post-acquisition.

The purchase price, including the premium, is a function of three things: revenue, margins, and growth (current, planned, and potential). However, not all three things carry the same weight.

Revenue

For SaaS companies, annual recurring revenue (ARR) is perhaps the most important metric. It is not necessarily same as recognized revenue what you see on a P&L statement and ARR alone doesn’t tell you the whole story either. You need to dig deeper into deferred revenue (on the balance sheet and not on P&L), customer acquisition cost (CAC), churn, and lifetime value of a customer (LTV) that companies are not obligated to publicly report but there are workarounds to estimate these numbers based on other numbers.

Margin

If you’re a fast growing SaaS company the street will tolerate negative margins since you’re aggressively investing in for more future growth. Margin is less interesting to evaluate a fast growing SaaS company, for acquisition purposes or otherwise, because almost all the revenue is typically invested into future growth and for such SaaS companies the market rewards revenue and growth more than the margins.

Margin by itself may not be an important number, but the cost of sales certainly is an important metric to ensure there is no overall margin dilution post acquisition. Mix of margins could be a concern if you are mixing product lines that have different margins e.g. value versus volume.

Growth

Current and planned growth: This is what the stock market has already rewarded pre-acquisition and the acquirer assumes responsibility to meet and exceed the planned or projected growth numbers. In some cases there is a risk of planned growth being negatively impacted due to talent leaving the company, product cannibalization, customers moving to competitors (churn) etc.

Growth potential: This is where it gets most interesting. How much a company could grow post-acquisition is a much more difficult and speculative question as opposed to how much it is currently growing and planned to grow pre-acquisition (about 29% in case of Concur) as this number completely changes when the company gets acquired and assumes different sales force, customer base, and geographic markets. This is by far the biggest subjective and speculative number that an acquirer puts in to evaluate a company. 

 

To unpack the “speculation” this is what would/should happen:

LTV 

This number should go up since there are opportunities to cross-sell into the overall joint customer base. LTV does reduce if customers churn, but typically preventing churn is the first priority of an acquiring company and having broader portfolio helps strengthen existing customer relationship. Also, churn is based on the core function that the software serves and also on the stickiness of the software. The most likely scenario for such acquisitions is a negative churn when you count up-selling and expansion revenue (not necessarily all ARR).

CAC

This should ideally go down as larger salesforce gets access to existing customer base to sell more products and solutions into. The marketing expenses are also shared across the joint portfolio driving CAC down. This is one of the biggest advantages of a mature company acquiring a fast growing company with a great product-market fit. 

Revenue growth

As LTV goes up and churn goes down overall ARR should significantly increase. Additional revenue generated in the short term through accelerated growth (more than the planned growth of the company pre-acquisition) typically breaks even in a few quarters justifying the premium. This is an investment that an acquiring company makes and is funded by debt. Financing an acquisition is a whole different topic and perhaps a blog post on that some other day.

Margin improvement

This is a key metric that many people overlook. Concur has -5.3% operating margin and SAP has promised 35% margin (on-prem + cloud) to the street by 2017. To achieve this number, the overall margins have to improve and an acquiring company will typically look at reducing the cost of sales by leveraging the broader salesforce and customer base.

This is a pure financial view. Of course there are strategic reasons to buy a company at premium such as to get an entry into a specific market segment, keep competitors out, and get access to talent pool, technology, and ecosystem.

Based on this, I’ll let you decide whether SAP overpaid for Concur or not.

Disclaimer: I work for SAP, but I was neither involved in any pre-acquisition activities of Concur nor have access to any insider Concur financial data and growth plans. In fact, I don’t even know anyone at Concur. This post is solely based on conventional wisdom and publicly available information that I have referenced it here. This post is essentially about “did x overpay for y?,” but adding SAP and Concur context makes it easy to understand the dynamics of SaaS enterprise software. 

Photo courtesy: Iman Mosaad

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

This is the third and the last 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 previous posts the first post was about “why buy anything” and the second post was about “why buy mine." This post is about “why buy now."

Platform sales is often times perceived as a solution looking for a problem a.k.a hammer looking for a nail. In most cases your prospects don’t have a real urgency to buy your platform making it difficult for you to make them commit on an opportunity. There are a few things that you could do to deal with this situation:

Specific business case

It’s typical for vendors to create a business case positioning their solutions to their prospects. These business cases include details such as solution summary, pricing, ROI etc. If you’re a platform vendor not only you have to create this basic business case but you will also have to go beyond that. It’s relatively hard to quantify ROI of a platform since it doesn’t solve a specific problem but it could solve many problems. It is extremely difficult to quantify the impact of lost opportunities. If your prospect doesn’t buy anything do they lose money on lost opportunities? Traditional NPV kind of analysis goes for a toss for such situations.

As a vendor not only you will have to articulate the problems (scenarios/use cases) that you identified leading up to this step but you might also have to include more scenarios that were not specifically discussed during the evaluation phase. Getting a validation from the business on expected return on their investment while fulfilling their vision is crucial since your numbers will most likely get challenged when your prospect creates its own business case to secure necessary investment to buy your platform.

Leveraging the excitement

What seemed like a problem when you worked with a variety of people inside your prospect’s organization may not seem like a problem in a few weeks or months. It’s very important in platform sales cycle not to lose momentum. Find a champion of your pilot keep socializing the potential of your platform inside your prospect’s organization as much as you can while you work on commercials of your opportunity. People should be talking about your disruptive platform and wanting to work with you. Cease that moment to close it.

Knowing who will sign the check

Platform sales are convoluted. People who helped you so far may not necessarily help you with the last step—not that they don’t want to but they may not be the buyers who will sign the check. It’s not uncommon in enterprise software sales to have influencers who are not the final buyers but the buyers do have somewhat defined procurement process for standard solutions. When it comes to buying a platform many buyers don’t quite understand why they should be spending money on disruptive platform that may or may not necessarily solve a specific problem.

To complicate this further, for disruptive technology, it typically tends to get cheaper as it becomes more mature. This gives your prospect one more reason to wait and not buy your platform now. As I mentioned in the previous post your focus should never be on pricing (unless of course you are the best and cheapest vendor by a wide margin) but on immediate returns, no lost opportunities, and helping your prospect gain competitive differentiation in their industry.

Despite of working with your prospect for a while helping them define problems and piloting your platform to prove out the value proposition, you might get asked again to do things all over again. There are two ways to mitigate this situation: a) involve buyers early on in the sales process and not at the very end so that they are part of the journey b) work aggressively with your influencers to establish appropriate communication channels with the buyers so that it’s the influencer’s voice they hear and not yours.

Happy selling!

Photo Courtesy: Wierts Sebastien  

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

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 

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

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.

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.

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.

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 

Amazon's Cloud Price Reduction, A Desire To Compete Hard And Move Up The Value Chain

Recently Google slashed price for their cloud offering. Amazon, as expected, also announced their 42nd price reduction on their cloud offerings since its inception. Today, Microsoft also announced price reduction for their Azure offerings.

Unlike many other people I don't necessarily see the price reduction by Amazon as waging a price war against the competition.

Infrastructure as true commodity: IaaS is a very well understood category and Amazon, as a vendor, has strong desires to move up in the value chain. This can only happen if storage and computing become true commodity and customers value vendors based on what they can do on top of this commodity storage and computing. They become means to an end and not an end itself.

Amazon is introducing many PaaS like services on top of EC2. For example, RedShift is the fastest growing service on EC2. These services create stickiness for customers to come back and try out and perhaps buy other services. These services also create a bigger demand for the underlying cloud platform. Retaining existing customers and acquiring new customers with as little barrier as possible are key components of this strategy.

Reducing hardware cost: The hardware cost associated with computing and storage have gradually gone down. Speaking purely from financial perspective existing assets depreciate before they are taken out from service. Also, new hardware is going be cheaper than the old hardware (at the original cost). If you do pass on the cost advantage to your customers it should help you reduce price and compete at the same or a little less margin. However, hardware cost is a fraction of overall operations cost. In the short term, Amazon being a growth company will actually spend a lot more on CapEx and not just OpEx to invest and secure the future.

Economies of scale: The cost to serve two computing units is not the sum of cost to serve two one computing units. There are many economies of scales in play such as increasing data-center utilization, investment in automation, and better instance management software. Confidence in predicting minimum base volume and reducing fluctuations also gives Amazon better predictability to manage elasticity. As the overall volume goes up the elasticity or the fluctuations as percentage of overall volume go down. On top of that offerings such as Reserved Instances also are a good predictor of future demand. Amazon views Reserved Instances as how banks view CDs but many customers are looking for a "re-finance" feature for these Reserved Instances when price drops. These economic and pricing implications are great to watch.

To offer competitive pricing to win against  incumbents and make it almost impossible for new entrants to compete on the same terms is absolutely important but it would be foolish to assume it is the sole intent behind the price reduction.

Photo courtesy: Flickr

Why And How Should You Hire A Chief Customer Success Officer?

For an ISV (Independent Software Vendor) it is everyone's job to ensure customer success but it is no one person's job. This is changing. I see more and more companies realizing this challenge and want to do something about it.

Sales is interested in maintaining relationship with customers for revenue purposes and support works with customers in case of product issues and escalations. Product teams behave more like silos when they approach their customers because of their restricted scope and vision. Most chief technology officers are fairly technical and internal facing. Most of them also lack the business context—empathy for true business challenges—of their customers. They are quite passionate about what they do but they invariably end up spending a lot of time in making key product and technical decisions for the company losing sight of much bigger issues that customers might be facing. Most chief strategy officers focus on company's vision as well as strategy across lines of businesses but while they have strong business acumen they are not customer-centric and lack technical as well as product leadership to understand deep underlying systemic issues.

Traditional ways to measure customer success is through product adoption, customer churn, and customer acquisition but the role of a Chief Customer Success Officer (CCO) extends way beyond that. One of the best ways to watch early signs of market shift is to very closely watch your progressive customers. Working with these customers and watching them will also help you find ways to improve existing product portfolio and add new products, organically or through acquisitions. Participating in sales cycles will help you better understand the competition, pricing points, and most importantly readiness of your field to execute on your sales strategy.

I often get reached out by folks asking what kind of people they should be looking for when they plan to hire a CCO. I tell them to look for the following:

T-shaped: Customer don't neatly fall into your one line of business and so is your CCO. You are looking for someone who has broad exposure and experince across different functions through his or her previous roles and deep expertise in one domain. The CCO would work across LoBs to ensure customers are getting what they want and help you build a sustainable business. Most T-shaped people I have worked with are fast-learners. They very quickly understand continuously changing business, frame their point of view, and execute by collaborating with people across the organization (the horizontal part of T) due to their past experience and exposure in having worked with/for other functions.

Most likely, someone who has had a spectacular but unusual career path and makes you think, "what role does this person really fit in?" would be the the right person. Another clue: many "general managers" are on this career track.

Business-centric: Customers don't want technology. They don't even want products. They want solutions for the business problems they have. This is where a CCO would start with sheer focus on customers' problems—the true business needs—and use technology as an enabler as opposed to a product. Technology is a means to an end typically referred to as "the business."

Your CCO should have a business-first mindset with deep expertise in technology to balance what's viable with what's feasible. You can start anywhere but I would recommend to focus your search on people who have product as well as strategy background. I believe unless you have managed a product—development, management, or strategy—you can't really have empathy for what it makes to build something and have customers to use it and complain about it when it doesn't work for them.

Global: Turns out the world is not flat. Each geographic region is quite different with regards to aptitude and ability of customers to take risk and adopt innovation. Region-specific localization—product, go-to-market, and sales—strategy that factors in local competition and economic climate is crucial for global success of an ISV. The CCO absolutely has to understand intricacies associated with these regions: how they move at different speed, cultural aspects of embracing and adopting innovation, and local competition. The person needs to have exposure and experience across regions and across industries. You do have regional experts and local management but looking across regions to identify trends, opportunities, and pace of innovation by working with customers and help inform overall product, go-to-market, and sales strategy is quite an important role that a CCO will play.

Outsider: Last but not least, I would suggest you to look outside instead of finding someone internally. Hiring someone with a fresh outside-in perspective is crucuial for this role. Thrive for hiring someone who understands the broader market - players, competition, and ecosystem. This is a trait typically found in some leading industry analysts but you are looking for a product person with that level of thought leadership and background without an analyst title.

About the photo: This is a picture of an Everest base camp in Tibet, taken by Joseph Younis. I think of success as a progressive realization of a worthwhile goal.

Recruiting End Users For Enterprise Software Applications

As I work with a few enterprise software start-ups I often get asked about how to find early customers to validate and refine early design prototypes. The answer is surprisingly not that complicated. The following is my response to a recent question on Quora, "How do we get a target audience for enterprise applications, when you dont have an enterprise customer yet for rapid prototyping?"

Finding a customer and finding end users are quite different. In enterprise software end users are not the buyers and the buyer (customer) may or may not use your software at all. To recruit end users, there are three options:

Friends and families: Use your personal connections through email and social media channels and ask for their time (no more than 30 minutes) to conduct contextual inquiries and get validation on your prototypes. Most people won't say no. Do thank them by giving them a small gift or a gift card.

Find paid end users: This does seem odd but it works. I know of a few start-ups that have used this method effectively. Use Craigslist and other means to recruit people that match your end user role and pay them to participate in feedback sessions. It is worth it.

Guerrilla style: Go to a convention or a conference where you could find enough end users that fit your profile. Camp out at the convention with swag and run guerrilla style recruiting to validate and prototype. Iterate quickly, preferably in front of them, and validate again.

A Design Lesson: Customers Don't Remember Everything They Experience

My brother is an ophthalmologist in a small town in India. In his private practice, patients have two options to see him: either take an appointment or walk in. Most patients don't take an appointment due to a variety of cultural and logistics reasons and prefer to walk in. These patients invariably have to wait anywhere from 15 minutes to an hour and half on a busy day. I always found these patients to be anxious and unhappy that they had to wait, even if they voluntarily chose to do so. When I asked my brother about a possible negative impact due to unhappiness of his patients (customers) he told me what matters is not whether they are unhappy while they wait but whether they are happy or not when they leave. Once these patients get their turns to see my brother for a consultation, which lasts for a very short period of time compared to how much they waited, my brother will have his full attention to them and he will make sure they are happy when they leave. This erases the unpleasant experience from their minds that they just had it a few minutes back.

I was always amused at this fact until I got introduced to the concept of experience side versus memory side by my favorite psychologist Daniel Kahneman, explained in his book Thinking, Fast and Slow and in his TED talk (do watch the TED talk, you won't regret it). While the patients waited the unpleasant experience was the experience side which they didn't remember and the quality time they spent in the doctor's office was the memory side that they did remember.

Airlines, hotels, and other companies in service sectors routinely have to deal with frustrated customers. When customers get upset they won't remember series of past good experiences they had but they would only remember how badly it ended - a cancelled flight, smelly hotel room or production outage resulting in an escalation. Windows users always remember the blue screen of death but when asked they may not necessarily remember anything that went well on a Windows machine prior to a sudden crash resulting into the blue screen of death. The end matters the most and an abrupt and unrecoverable crash is not a good end. If the actual experience matters people will perhaps never go back to a car dealership. However people do remember getting a great deal in the end and forget the misery that the sales rep put them through by all the haggling.

Proactive responses are far better in crisis management than reactive ones but reactive responses do not necessarily have to result in a bad experience. If companies do treat customers well after a bad experience by being truly apologetic, responsive, and offering them rewards such as free upgrades, miles, partial refund, discounts etc. people do tend to forget bad experiences. This is such a simple yet profound concept but companies tend not to invest into providing superior customer support. Unfortunately most companies see customer support as cost instead of an investment.

This is an important lesson in software design for designers and product managers. Design your software for graceful failures and help people when they get stuck. They won't tell you how great your tool is but they will remember how it failed and stopped them from completing a task. Keep the actual user experience minimal, almost invisible. People don't remember or necessary care about the actual experiences as long as they have aggregate positive experience without hiccups to get their work done. As I say, the best interface is no interface at all. Design a series of continuous feedback loops at the end of such minimal experiences—such as the green counter in TurboTax to indicate tax refund amount—to reaffirm positive aspects of user interactions; they are on the memory side and people will remember them.

In enterprise software, some of the best customers could be the ones who had the worst escalations but the vendors ended their experience on a positive note. These customers do forgive vendors. As a vendor, a failed project receives a lot worse publicity than a worst escalation that could have actually cost a customer a lot more than a failed project but it eventually got fixed on a positive note. This is not a get-out-of-jail-free-card to ignore your customers but do pause and think about what customers experience now and what they will remember in future.

Photo courtesy: Derek 

Focus On Abstraction And Not Complexity

I am a big fan of software design patterns. A design pattern is a general reusable solution to a commonly occurring problem within a given context. Software design patterns are all about observing technical abstractions in complex problems by identifying patterns and applying well known solutions to them.

My management style is largely based on abstractions. When things get muddy I step away from complexity for a few minutes and explore abstractions. This helps me keep in touch with the bigger picture while I look for solutions to a given problem. When you're too close to a topic you do tend to fixate on complexity leaving sight of the bigger picture. I make a conscious attempt to go between complexity and abstraction when I need to. And, that's perhaps the only way to manage it effectively in pursuit of working smart and not just working hard. Complexity invariably makes people get into an analysis paralysis mode resulting into a decision gridlock that affects the bigger picture. In many cases, not being able to make a decision has far worse consequences than not solving a problem which may or may not be important in long run. Abstracting complexity helps me make a decision with focus on consequences as opposed to a short term solution. Abstraction also allows me to spot behavioral and systemic problems as opposed to tactical and temporal problems.

Ask yourself what you remember the most about a couple of complex problems that you solved last year and the answer most likely won't be how great your solution was but it very well would be what the problem actually taught you. It's not the complexity that you will cherish but the simplicity, the abstracted experience, is what will stay with you for the rest of your life to help you find solutions to similar problems in future.

Photo courtesy: miuenski