Archive for the ‘machine-driven methods’ Category

PART FIVE: WATCHING THE CLOCK

April 28, 2021

By Peter Zaballos

TALES FROM THE EARLY-ISH DAYS OF SILICON VALLEY

The early 1980s were a sort of a “between” era in semiconductors – between the era of predominantly manual chip design and fabrication and the era of computer-driven design and highly automated production.

One awesome aspect of working at LSI Logic in this “between” era was that so many of the founders and executives there had been in the semiconductor industry from the very beginning of its existence, or pretty darn close to the beginning. And they had stories to tell as they helped propel the industry forward.

In my first year at LSI I was in a meeting with Wilf Corrigan (co-founder/CEO) and a small group of executives from one of our customers – they were there to check out our fab and processes. One of these visiting executives was someone Wilf had worked with ages ago. 

At a break in the meeting he and Wilf started swapping stories of the early days of the industry, back when he was a manufacturing engineer. He said something like “do you remember when we were at Transitron, and we’d hold the wafers with tweezers, dip them in acid to etch them, and then look at the second hand on the wall clock to time how long to keep them in the acid bath?”

Then they both laughed long and hard. Because that’s how they made semiconductors in the 1960s.

In the 1960s and 1970s semiconductor manufacturing was still largely manual. Wafers were literally carried from one manufacturing step to the next. Photolithography machines were manually set up, aimed, and operated.

In the 1980s all of that started to change. As a result of Moore’s law, the line widths of semiconductor traces were steadily shrinking, the density of devices per wafer were increasing, and the size of the wafers were themselves increasing – from 4 inch diameters in 1975, to 6 inch diameters in the early 1980s – to today’s 18 inch diameter wafers. And the reasoning is pure economics – an 18 inch wafer can produce 200x more chips than a 4 inch wafers.

An 18 inch wafer

This also meant that fabs had to get much cleaner. Contaminants smaller than specs of dust could get in the way of a photolithography trace or cause a short between two metal lines and as wafer sizes grew, process variations or human error were that much more prevalent.

So conditions you could tolerate in a fab in 1975 would cause serious manufacturing yield problems in the factories of the 1980s. This is when the concept of a “clean room” took hold – a manufacturing environment with a system that could control the amount and size of airborne particles.

That latter metric – size of particle – would become super important. In the early 1980s Class 10 fabs were just becoming necessary – so in a cubic foot of air in the fab, there could only be 10 particles whose size is 0.5 microns. Class 1 fabs were being talked about, but no one could afford them in the 1980s.

Today, clean rooms are orders of magnitude cleaner than Class 1. 

This is also when “bunny suits” became mandatory. It was when automated wafer transport systems were just coming on line, so wafers could be stacked in sealed “boats” and these could be transported between manufacturing stations and removed only when the wafer was being operated on. 

Back in the mid-1980s wafer defects were pretty evenly caused by airborne contamination (airborne particles), process errors, equipment errors, and human error. This has changed a lot, today automation has almost completely eliminated human error, and technology has almost completely eliminated airborne contamination.

Final yield ultimately determined how many chips met the specification the customer had ordered. Poor yield meant they were getting less than they expected – and would have to make some painful adjustments to their own manufacturing schedules and revenue plans. Good yield meant we could either sell the customer the excess or stockpile them for follow-on orders.

Back to the stories.

In my second year at LSI we hired a super talented VP of Manufacturing, Travis White. He was a talkative and friendly Texan and loved to wander into the cubicle area where the marketing folks were and tell us stories about “way back when.”

I remember him telling us a story about when he was running a fab for Texas Instruments, and their yield suddenly plummeted. It was a true crisis and he and his team spent days and weeks analyzing their manufacturing data and processes and couldn’t figure it out. They were running out of ideas and understandably, the CEO and TI’s customers were getting impatient. They’d gone through all their processes, checked all the machinery, again and again. But could not find what was killing their yield.

So Travis walked down to the fab and just spent time looking around, observing. 

And then he saw it. 

They’d changed suppliers of semiconductor boats and needed to secure them with rubber bands. And a well intentioned supervisor had gone out and purchased a container of rubber bands for each work station.

Rubber bands dusted with talc. To make the bands easier to pick up.

And with every ‘snap” those talc particles were floating through the air, finding their way onto the wafers. That was what was killing yield.

Travis was as horrified as he was amused that something that simple could wreak such havoc, and go unnoticed.

He could wander into the marketing area because we literally had a semiconductor fab attached to our building in Milpitas, CA. In fact, the fab was the majority of the building.

It was a key selling point for our customers. We could walk them to the back of the office portion of the building and they could look through observation windows to see a few steps of the manufacturing going on, with all the employees busy at work. Some of our competitors couldn’t afford their own fabs, or had other companies make their custom semiconductors. But we had our own fab and controlled that part of our destiny.

More generally back then there were lots of semiconductor fabs in the valley – AMD, Intel, HP, MMI, National Semiconductor and dozens of others. That’s where the “silicon” of silicon valley came from.

I believe only Intel has a fab in Silicon Valley today. Everyone else stared down the face of the multi-billion dollar investments required to stay on the leading edge of semiconductor technology and outsourced their manufacturing to a small number of companies in Asia who act as “foundries” – you give them your design and they make the finished chips. Companies like TSMC and Samsung.

Even Intel is staggered at the expense required to build state-of-the-art semiconductor processes and fabs, and has recently acknowledged it can’t compete with these foundries. They themselves may start using them, or be faced with some serious investments to keep up.

So I frequently think back to Travis White’s stories and Wilf’s stories. It was awesome to be working with them when this industry was truly growing up. A small window of time at a point of significant industry transformation. 

To this day I smile when I come across a rubber band that has talcum powder on it. Or see a wall clock and think of Wilf Corrigan, holding a wafer with tweezers checking the second hand on that clock.

Why marketing is a lot like software development. By Peter Zaballos

March 14, 2018

Four reasons why marketing is as important as code for tech companies.

We’re living in the golden age of marketing right now. The mechanics of marketing and its impact on the business have changed dramatically in the past three years. Put another way, when I hire marketing talent, anything anyone has done more than 3-5 years ago I literally don’t care about or evaluate.

In the past few years I’ve seen marketing shift its focus from providing air cover to sales teams to now being the group within the company that’s determining the messaging and tactics that salespeople can best put into action. The data tells everyone what’s effective, impactful. Fewer opinions, more facts.

SW DEV STRIP

There are four reasons for this:

First. Marketing is a quant business. Everything is instrumented – you know who is responding to which offers, who is engaging with what content, what paths they take. Over time you can correlate engagement to value, and use data to find where and how prospects find you, and what signals the right time to present them with an offer or a call to action. This is a quant-jock’s delight. And data analysts are the new “must-have” role on a marketing team.

In today’s marketing you also have the advantage of short feedback loops which lend themselves naturally to an Agile approach to campaign management. Deploy a campaign, use data to validate assumptions, refine the campaign, repeat. My last marketing team collaborated with our DevOps agile coach to embrace the sprint/retrospective approach, and the team held daily stand-ups to ensure they were cohesive and focused on the most valuable activities.

Second. Google, whether we like it or not, is enforcing quality. What this means is that Google’s ability to interpret page intent is staggering. You genuinely need to be developing content paths that answer the questions your audience has, and legitimately guide them to a solution. If your bounce rate, or worse, your conversion rate is too high or too low, you’ll get penalized. It’s fundamentally obsoleted the marketing tactics that came before this.

It’s as structural a change as containers have been to DevOps. It’s creating a situation where I don’t care what you did three years ago – the search marketing tactics that worked back then no longer work today. Yes, we’re all still focused on the customer journey, but Google’s ability to assess whether that’s a productive journey you’ve created is what changed. This is a good thing. The companies with clear and differentiated positioning and value propositions, who create high quality content paths will win.

If you’re in marketing and you haven’t embraced this new world of content and data-driven optimization, you can still find a job, it just won’t be an interesting one. Just like in software, if you aren’t a full stack developer, if you aren’t learning new languages every year, you can still find a job, somewhere. It just won’t be an interesting one.

Third. Developing an effective marketing presence requires a system architecture. The category definition, positioning, awareness development, the demand generation – requires an architecture. Your category definition and positioning are that architecture, and inform how you will take your solution to your prospects and customers. Like with building software, you need this architecture to build the services that create the go-to-market “product” – the combination of campaigns and tactics you’ll put into motion.

One of my favorite marketing books is not about marketing at all, or rather, on the surface it’s not about marketing.

Building Microservices

The book is Building Microservices and while its purpose is to help the reader understand this new-ish phase of modern software development, it also describes how organizations can function efficiently. How “loosely coupled, trusted” relationships between organizations can produce resilient, agile performance.

Agility is important. There’s an abundance of data that modern marketing teams have access to today, and scrutinizing this data, and adapting campaigns and tactics are a critical success factor. Add to that just how much the mechanics of marketing have changed in the past three years (due to a large part on the above second point), and you have a landscape that looks a lot like…software development. Containers didn’t exist five years ago at scale. Serverless computing? Same thing.

 

Finally, the impact of marketing takes time to create. Just like any significant software development.  Assuming you have your category defined and your positioning solid, it will take 6-12 months to get scale from your demand gen. That means you’ll be iterating and iterating, refining, optimizing conversion rates, a lot.

It’s never been a better time to be in marketing. It’s never been a better time to be a CMO. You and your CTO will have a lot in common. And it’s likely your CTO will get jealous at some point, with more and more technology, and data, flowing into marketing, CMO budgets might just become bigger than CTO budgets.

 

 

Why User Activation Is Demand Gen. By Peter Zaballos

March 8, 2018

And why it’s super hard to measure

And it really is an essential component of demand gen. Essential.

Let me digress for just a bit.

Assume you’re zeroed-in on your category, your demand gen is solid and scaled, and now you’re creating a steady, growing stream of prospects to your sales team. And they’re closing them at a brisk pace.

As hard as all that was, now the real work begins. That solution to the problem you promised the prospect? It now needs to be delivered through the product experience. The very first time that new user signs on.

Saved DNA

I wrote about this in my blog post on conversion optimization of demand generation. That new customer found your product because of how it was marketed to them. The very first time a new customer experiences the product, it has to align with the value proposition your promised. So, how do you know if you’re delivering on that promise?

It’s super hard.

And it’s super important, because customer acquisition is pointless without retention. Jamie Quint explains this exceptionally well in a guest post on Andrew Chen’s blog, and goes further to highlight that retention is the core driver of virality. That means retention is a core driver of your…demand gen. Right. Full circle.

First of all, you need to look at each user in the context of a group of users called a “cohort” – usually this starts as the collection of users whose first experience with your product happened during the same timeframe (day or week typically). And you can see how the cohort segments into usage activity patterns – some will be super active, some moderately active, and some not active at all.

This can get you started, but doesn’t really tell you a whole lot. You really need to know two other “hard to define” metrics.

First – what constitutes a meaningful action for that user in their first session? That means you don’t just need to understand the core functionality of your product, but how that first time user is going to interact with that functionality to get something they consider valuable done. This is exactly where your product team and your marketing team should have a happy collision.

Didn’t your marketing start the acquisition process by trying to figure out what the exact words that prospect would use to describe their problem? At the very beginning of the customer journey? Well, now the product team needs to deliver the solution in the form of an experience, in terms that the converted prospect (now customer) will recognize as valuable. To them. Of course it’s not that simple (buyers may not be users, but buyers did buy solutions to problems your marketing team zeroed in on).

Second – how frequently will that customer be expected to use your product? You need to know this to establish the baseline of your entire measurement approach. Is it hourly? Daily? Weekly? You may think you know when you’re developing the product, but product design is focused on personas and assumptions about usage. Now you’ll need to check those assumptions through cohort-based analysis of real world people. Amplitude has a great blog post about figuring out how often people use your product.

And everything I just described is virtually impossible to measure with Google Analytics. That free tool is awesome for measuring website activity, but is architecturally incapable of measuring cohorts (believe me, my teams have tried, hard and GA is miserable at cohort analysis). There are some really exciting companies filling that void who have designed cohort-based tools specifically for behavioral product analysis. Amplitude is one – who offers a free versions that you can use to instrument your product and get plenty of data, and then of course have much more sophisticated capabilities you pay for.

Finally, retention is made up of “activating” a customer – making sure they have not just a successful first experience, but that they have a second successful experience and then ensuring long term customer “adoption.”

This is where Product owes an obligation to Customer Success to ensure that customers activate, and then the success team can drive long term adoption. So the product team should own understanding what value needs to be delivered in the first two uses of the product. This will take intensive focus on data, cohort behavior, and many, many iterations with the product design and dev teams. Customer Success should be a part of this process because they will need to take those two experiences and ensure they become hundreds or thousands, or more.

What’s worked well is to have a weekly meeting with Product, Product Design, Development, and Customer Success, where the product manager leads the analysis of usage, and the resulting product and resource development to ensure successful activation. You can think of this as a smooth handoff of customer accountability:

Screen Shot 2018-03-08 at 11.27.29 AM

In my last role the company had gotten to scale without any focus on product usage or product usage measurement. I was fortunate to have a whip-smart product manager who spent an entire year of these weekly meetings getting grounded in the basics, and bringing that cross functional team to have a clear and compelling understanding of what drove the first two experiences.

And bringing this back to where I started. Product activation is a critical step in your demand gen strategy. It’s why a lot of CMOs have responsibility for both product and marketing, and if not, it’s why CMOs need to have super tight and trusted relationships with their product colleague.

Why conversion rate optimization is the most important role in marketing. By Peter Zaballos

February 26, 2018

And it’s as important as your product

Why? because conversion rate optimization is the function that reveals the truth of your brand, your product, your business. Holistically.

It’s where you have to think deeply about the problem your customer or prospect has, and the information path they will follow to find a solution. But it doesn’t stop there.

Many marketing orgs look at “conversion” as the final step. But it’s really the beginning of the customer journey. It’s when all that carefully crafted terminology has to be aligned to what the customer experiences with the product you just sold them. The customer journey is about delivering value. And having a happy customer come back. And bring their friends and colleagues.

analytics-ss-1920

I was having a conversation with a senior exec at a successful cloud application provider last month, and they mentioned that they were having a hard time converting free trial users to paid subscribers. They were asking my opinion about what communications strategies I’d used in the past to boost these.

My first thought was, “you may be too late to do a whole lot about it.” If the content path that caused someone to find your solution – all those carefully crafted conversion junctures – did not line up with the first experience of the product, then you’re stuck.

No amount of in-app or email or chat communications will fix that. You might make the bad situation a bit better, but you really need to see this as a continuum of your brand promise. It’s what creates the words that draw a prospect in, and the experience they have with your product.

Like with almost everything today you get one shot at establishing trust and a relationship. Whether you’re a marketer or a product manager. And as a marketer you’re ultimately marketing a product experience. So there’s got to be tremendous coherence and alignment between what you market and what happens the very first time that former prospect becomes the user of your product.

Activation is different from retention. Retention looks past that first experience and presumes activation. Activation is converting the promise of a solution into…an actual solution to a problem. Retention is ensuring that the solution is durable, compelling, and lasting.

So if I were to pick one discipline that a marketing org should master it’s conversion rate optimization. Above any other. It’s the moment of truth for your business. It’s measurable. It quantifies your ability to deliver value to your customers.

And this is why it’s awesome to be a CMO and to be responsible for Product and Marketing. Because you are accountable to the business for ensuring the brand promise gets delivered. Everywhere. Every time.

CRO

 

Data is our copy editor | Peter Zaballos

February 12, 2018

There has never been a better time to be in marketing, and to be a CMO.

That’s because a CMO has never had more data to drive decisions. And marketing today is all about orchestrating digital experiences – if you aren’t leading with a digital strategy, well, then you simply aren’t leading. And the best part – digital experiences are fundamentally measurable. Or can be. And should be.

I remember the day this was made blindingly obvious. I remember the day like it was yesterday, but it was really close to three years ago.

data-science-illustration-_Feature_1290x688_MS

The woman that ran search marketing on the demand gen team came into my office – which she only did when she had something really important to share – not because she wasn’t welcome, but she had no time for fluff. She loved what she did and what she did was figure out how to optimize what we did in marketing. She started telling me a story.

Before sharing her story, let me tell you a bit more about her. I’ll call her Mollie to protect her identify (I use Mollie because I think that’s simply an awesome name).

Mollie is the kind of person you dream about being on your team. Profoundly curious. A voracious learner. No ego. Lets data and learning drive her decisions and behavior. I have lost count of the number of times she’d pulled me aside to disclose (a) she’d identified a significant source of opportunity or risk, (b) she’d spent a fair amount of time researching how to unlock this opportunity or address the risk, and (c) she’d run enough experiments to confirm the plan she’s proposing will work. All I had to do was ask a few questions (which she had answers to) and say “yes, let’s go.”

So on this day, Mollie mentioned that she had observed that some of our best trafficked awareness and engagement pages had been benefiting from heavy SEO-based revisions. That seems kind of obvious. But here’s where she demonstrated true insight. She’d asked herself “what if every page we developed began first with the SEO strategy – not with a talented writer using Word offline to create what we publish – and then we let performance testing tune (edit) the copy?”

She’d taken the initiative to find out. She’d picked one of our pages written solely by a talented copy-writer (and was destined for future SEO optimization) and created a substitute page, which she herself had written from scratch on the same topic, but started with the terms we wanted to optimize the page for. Then she let the data tell us what to do next.

What she learned was that the SEO-originated copy outperformed the traditional “write first, optimize later” page by a factor of 10x.

What she proposed we then do was to convert all of our copy writing to “SEO-first.” Which meant cycling through our contractor pool to determine who could do this, and replace the ones who couldn’t. It meant changing the process for all the in-house copywriters.

It meant, as Mollie put it, that “data is our copy editor now.”

It was one of the easiest decisions I had to make as a CMO. The curiosity, the experimentation, the data made it obvious.

It fundamentally changed everything we did. Not only did this improve the search performance of the new pages we created, it changed how we curated all of existing content. We no longer had “static” copy on our website, of any kind. White papers are now routinely revised for SEO performance.

Every page is a living document, revised for search performance as algorithms and search term popularity evolves. Every page has data as its copy editor.

The Unfamiliar State of Funding a Startup

March 8, 2012

I work with a lot of startup companies, and am currently involved with three that share the same characteristics: pre-product, pre-revenue, and at the very beginning of fundraising. And I’m having the same conversation with all three. It goes like this:

  1. The cost of getting a company to scale and even to profitability has dropped dramatically in the past ten years.
  2. The nature of venture capital has shifted from an early stage focus to late stage or even growth equity investing.
  3. Angels and experienced high net worth folks have stepped in to fill the role VCs served for early stage investing.
  4. A viable fundraising strategy can default to a path that doesn’t assume VCs participate at all, or perhaps only towards the end.

Let me expand on each of these points.

COST OF GETTING TO SCALE – THE RISE OF THE MACHINES

There are a lot of factors at work here, to the benefit of entrepreneurs. The rise in cloud computing means that fixed infrastructure expense has largely been eliminated from the business plan, and this will only get better (Amazon just announced it’s 19th price decrease in six years). Virtual teams + Google Docs drive OPEX down even further unburdening you from lease costs.

The shift to “inbound marketing” – social media, blogs, SEO, viral – can drive large volumes of traffic at significantly lower costs (60% less or more) than traditional “outbound methods – and at higher conversion and retention rates. It takes a lot less of your marketing budget to reach and acquire users. With the shift to freemium and subscription business models you can also let your most active users decide for themselves to pay for your services through in-app messaging and offers – significantly reducing the cost of sales.

I call this the “Rise of the Machines” because metrics and machine-driven resources/methods do much of the heavy lifting at a fraction of the cost of human-intensive alternatives. Josh Kopleman surveyed his portfolio and found “…that companies today are 3 times more likely to get to $250K in revenue during an eighteen month period than they were six years ago. ”

VENTURE CAPITAL IS DEAD – LONG LIVE VENTURE CAPITAL

The money that VCs invest comes from “institutional investors” – pension funds, endowments, insurance companies – and these institutions allocate their investments across a wide range of “asset classes” to manage and diversify risk. They tend to make these allocations based on ten year return performance averages, and beginning in 2009 (as my partners and I found out with unfortunate timing) the ten year return for the VC asset class went negative.

That’s for tough the VC industry overall, but if you look at the top 20-25 firms, the ten year return is quite good. So what institutions did was stop putting money in general into the VC asset class, and only put money into the big, established firms. This caused fund sizes to swell (Accel’s most recent fund was $1.35B+ comprised of $475M “early stage” + $875M “growth equity” funds), which incents those firms to put larger and larger investments to work in each deal (to justify their partners’ time).

So at a macro level, investment into VC funds dried up for all but the top firms (reducing the total number of VC funds) and poured into the top firms, shifting their focus to larger investments in later stage firms.

ANGELS BECOME ANGELS ALMOST LITERALLY

At the same time early stage VCs moved out of the market, a wave of experienced tech executives who had made fortunes building internet companies became very active investors. They brought more than deep pockets, they brought valuable insight and experience and even better – intensive, engaged roles with the companies they funded.

And along the way, incubators emerged as mini-factories where angels could become involved with lots of companies and let the law of large numbers help them there. Overall, angels are investing 40% more than they were even a year ago – now over $700K per round, and there are concerns there’s a bubble happening with incubators. But the headlines are, angels have stepped into early stage investing at a scale and role traditionally reserved for VCs.

STARTUP FUNDRAISING HAS NEVER BEEN BETTER, AND WORSE

What this means for startups is you can get your business to scale with ten times less money that you needed 10-15 years ago. $3M – $5M. If you plan well and are well connected you can do this with individual investors who add a ton of value and will roll up their sleeves to help out. The real benefit is you can also find individuals who share the same expectations you have for the outcome of the business. A 5X return on $3M may be the right outcome for the business and for investors who define success as a financial return coupled with a durable business that solves a problem they care about.

It also means you can liberate yourself from having to map your business and outcome to the trajectory that many of the larger VC firms need their investments to align with – they need billion dollar exits to generate the billion dollar returns they committed to their institutional investors.

Don’t get me wrong here. VCs are an important and valuable catalyst to the technology sector and the economy – and many are out there doing what they’ve always done to identify the next great disruptive business. And for your business, a VC can be the exact right fit either at the beginning or once you’ve gotten to scale.

It’s just that now VCs are playing a different role than they have in the past, and for startups this means it’s a brand new, unfamiliar, day out there.

Back online

February 29, 2012

Well, that was a long hiatus. But for a lot of good reasons I needed the time away from this and feel ready and enthusiastic about resuming the exploration of technology and startups and how failure critically enables their success.

Next post to follow, and will be on the theme of how user acquisition costs and leverage have dramatically reduced the financing required to get a company to break-even (and to a seven figure user base), and how that’s reshaping not just early stage businesses, but mature enterprises.

Stay tuned, and thanks for your patience these past months.

Pete