AI First, the Overhype and the Last Mile Problem

AI is hot, I mean really hot. VCs love it, pouring in over $1.5B in just the first half of this year. Consumer companies like Google and Facebook also love AI, with notable apps like Newsfeed, Messenger, Google Photos, Gmail and Search leveraging machine learning to improve their relevance. And it’s now spreading into the enterprise, with moves like Salesforce unveiling Einstein, Microsoft’s Cortana / Azure ML, Oracle with Intelligent App Cloud, SAP’s Application Intelligence, and Google with Tensorflow (and their TPUs).

As a founder of an emerging AI company in the enterprise space, I’ve been following these recent moves by the big titans closely because they put us (as well as many other ventures) in an interesting spot. How do we position ourselves and compete in this environment?

In this post, I’ll share some of my thoughts and experiences around the whole concept of AI-First, the “last mile” problems of AI that many companies ignore, the overhype issue that’s facing our industry today (especially as larger players enter the game), and my predictions for when we’ll reach mass AI adoption.

Defining AI-First vs. AI-Later

A few years ago, I wrote about the key tenets of building Predictive-First applications, something that’s synonymous to the idea of AI-First, which Google is pushing. A great example of Predictive-First is Pandora (disclosure: Infer customer). Pandora didn’t try to redo the music player UI — there were many services that did that, and arguably better. Instead, they focused on making their service intelligent, by providing relevant recommendations. No need to build or manage playlists. This key differentiation led to their rise in popularity, and that differentiation depended on data intelligence that started on day one. Predictive wasn’t sprinkled on later (that’s AI-Later, not AI-First, and there’s a big difference … keep reading).

If you are building an AI-First application, you need to follow the data and you need a lot of data so you would likely gravitate towards integrating with big platforms (as in big companies with customers) that have APIs to pull data from.

For example, a system like CRM.

There’s so much valuable data in a CRM system, but five years ago, pretty much no one was applying machine learning to this data to improve sales. The data was, and still is for many companies, untapped. There’s got to be more to CRM than basic data entry and reporting, right? If we could apply machine learning, and if it worked, it could drive more revenue for companies. Who would say no to this?

So naturally, we (Infer) went after CRM (Salesforce, Dynamics, SAP C4C), along with the marketing automation platforms (Marketo, Eloqua, Pardot, HubSpot) and even custom sales and marketing databases (via REST APIs). We helped usher a new category around Predictive Sales and Marketing.

We can’t complain much we’ve amassed the largest customer base in our space, and have published dozens of case studies showcasing customers achieving results like 9x improvements in conversion rates and 12x ROI via vastly better qualification and nurturing programs.

But it was hard to build our solutions, and remains hard to do so at scale. It’s not because the data science is hard (although that’s an area we take pride in going deep on), it’s the end-to-end product and packaging that’s really tough to get right. We call this the last mile problem, and I believe this is an issue for any AI product whether in the enterprise or consumer space.

Now, with machine learning infrastructure in the open with flowing (and free) documentation, how-to guides, online courses, open source libraries, cloud services, etc. machine learning is being democratized.

Anyone can model data. Some do it better than others, especially those with more infrastructure (for deep learning and huge data sets) and a better understanding of the algorithms and the underlying data. You may occasionally get pretty close with off-the-shelf approaches, but it’s almost always better to optimize for a particular problem. By doing so, you’ll not only squeeze out better or slightly better performance, but the understanding you gain from going deep will help you generalize and handle new data inputs better which is key for knowing how to explain, fix, tweak and train the model over time to maintain or improve performance.

But still, this isn’t the hardest part. This is the sexy, fun part (well, for the most part … the data cleaning and matching may or may not be depending on who you talk to🙂.

The hardest part is creating stickiness.

The Last Mile of AI

How do you get regular business users to depend on your predictions, even though they won’t understand all of the science that went into calculating them? You want them to trust the predictions, to understand how to best leverage them to drive value, and to change their workflows to depend on them.

This is the last mile problem. It is a very hard problem and it’s a product problem, not a data scientist problem. Having an army of data scientists isn’t going to make this problem better. In fact, it may make it worse, as data scientists typically want to focus on modeling, which may lead to over-investing in that aspect versus thinking about the end-to-end user experience.

To solve last mile problems, vendors need to successfully tackle three critical components:

1)  Getting “predictive everywhere” with integrations

It’s very important to understand where the user needs their predictions and this may not be in just one system, but many. We had to provide open APIs and build direct integrations for Marketo, Eloqua, Salesforce, Microsoft Dynamics, HubSpot, Pardot, Google Analytics and Microsoft Power BI.

Integrating into these systems is not fun. Each one has it own challenges: how to push predictions into records without locking out users who are editing at the same time; how to pull all the behavioral activity data out to determine when a prospect will be ready to buy (without exceeding the API limits); how to populate predictions across millions of records in minutes not hours; etc.

These are hard software and systems problems (99% perspiration). In fact, the integration work likely consumed more time than our modeling work.

This is what it means to be truly “predictive everywhere.” Some companies like Salesforce are touting this idea, but it’s closed to their stack. For specific solutions like predictive lead scoring, this falls apart quickly, because most mid-market and enterprise companies run lead scoring in marketing automation systems like Marketo, Eloqua and Hubspot.

Last mile here means you’re investing more in integrating predictions into other systems than in your own user experience or portal. You go to where the user already is that’s how you get sticky not by trying to create new behavior for them to do on your own site (even if you can make your site look way prettier and function better). What matters is stickiness. Period.

2)  Building trust

Trust is paramount to achieving success with predictive solutions. It doesn’t matter if your model works if the user doesn’t act on it or believe in it. A key area to establish trust around is the data, and specifically the external data (i.e. signals not in the CRM or marketing automation platforms a big trick we employ to improve our models and to de-noise dirty CRM data).

Sometimes, customers want external signals that aren’t just useful for improving model performance. Signals like whether a business offers a Free Trial on their website might also play an important operational role in helping a company take different actions for specific types of leads or contacts. For example, with profiling and predictive scoring solutions, they could filter and define a segment, predict the winners from that group and prioritize personalized sales and marketing programs to target those prospects.

In addition to exposing our tens of thousands of external signals, another way we build trust is by making it easy and flexible to customize our solution to the unique needs and expectations of each customer. Some companies may need multiple models, by region / market / product line (when there is enough training data) or “lenses” (essentially, normalizing another model that has more data) when there isn’t enough data. They then need a system that guides them on how to determine those solutions and tradeoffs. Some companies care about the timing of deals; they may have particular cycle times they want to optimize for or they may want their predictions to bias towards higher deal size, higher LTV, etc.

Some customers want the models to update as they close more deals. This is known as retraining the model, but over retraining could result in bad performance. For example, say you’re continuously and automatically retraining with every new example, but the customer was in the middle of a messy data migration process. It would have been better to wait until that migration completed to avoid incorrectly skewing the model for that period of time. What you need is model monitoring, which gauges live performance and notices dips or opportunities to improve performance when there’s new data. The platform then alerts the vendor and the customer, and finally results in a proper retraining.

Additionally, keep in mind that not all predictions will be accurate, and the customer will sometimes see these errors. It’s important to provide them with options to report such feedback via an active process that actually results in improvements in the models. Customers expect their vendor to be deep on details like these. Remember, for many people AI still feels like voodoo, science fiction and too blackbox-like (despite the industry’s best efforts to visualize and explain models). Customers want transparent controls that support a variety of configurations in order to believe, and thus, operationalize a machine-learned model.

3)  Making predictive disappear with proven use cases

Finally, let’s talk about use cases and making predictive disappear in a product. This is a crucial dimension and a clear sign of a mature AI-First company. There are a lot of early startups selling AI as their product to business users. However, most business users don’t want or should want AI they want a solution to a problem. AI is not a solution, but an optimization technique. At Infer, we support three primary applications (or use cases) to help sales and marketing teams: Qualification, Nurturing and Net New. We provide workflows that you can install in your automation systems to leverage our predictive tech and make each of these use cases more intelligent. However, we could position and sell these apps without even mentioning the word predictive because it’s all about the business value.

In our space, most VPs of Sales or Marketing don’t have Ph.Ds in computer science or statistics. They want more revenue, not a machine learning tutorial. Our pitch then goes something like this …

“Here are three apps for driving more revenue. Here’s how each app looks in our portal and here are the workflows in action in your automation systems … here are the ROI visualizations for each app … let’s run through a bunch of customer references and success studies for the apps that you care about. Oh, and our apps happen to leverage a variety predictive models that we’ll expose to you too if you want to go deep on those.”

Predictive is core to the value but not what we lead with. Where we are different is in the lengths we go to guide our customers with real-world playbooks, to formulate and vet models that best serve their individual use cases, and to help them establish sticky workflows that drive consistent success. We’ll initially sell customers one application, and hopefully, over time, the depth of our use cases will impress them so much that we’ll cross-sell them into all three apps. This approach has been huge for us. It’s also been a major differentiator we achieved our best-ever competitive win rate this year (despite 2016 being the most competitive) by talking less about predictive.

Vendors that are overdoing the predictive and AI talk are missing the point and don’t realize that data science is a behind-the-scenes optimization. Don’t get me wrong, it’s sexy tech, it’s a fun category to be in (certainly helps with engineering recruiting) and it makes for great marketing buzz, but that positioning is not terribly helpful in the later stages of a deal or for driving customer success.

The focus needs to be on the value. When I hear companies just talking about predictive, and not about value or use cases / applications, I think they’re playing a dangerous game for themselves as well as for the market. It hurts them as that’s not something you can differentiate on any more (remember, anyone can model). Sure, your model may be better, but the end buyer can’t tell the difference or may not be willing (or understand how) to run a rigorous evaluation to see those differences.

The Overhype Issue

Vendors in our space often over-promise and under-deliver, resulting in many churn cases, which, in turn, hurts the reputation of the predictive category overall. At first, this was just a problem with the startups in our space, but now we’re seeing it from the big companies as well. That’s even more dangerous, as they have bigger voice boxes and reach. It makes sense that the incumbents want to sprinkle AI-powered features into their existing products in order to quickly impact thousands of their customers. But with predictive, trust is paramount.

Historically, in the enterprise, the market has been accustomed to overhyped products that don’t ship for years from their initial marketing debuts. However, in this space, I’d argue that overhyping is the last thing you should do. You need to build trust and success first. You need to under-promise and over-deliver.

Can the Giants Really Go Deep on AI?

The key is to hyper focus on one end-to-end use case and go deep to start, do that well with a few customers, learn, repeat with more, and keep going. You can’t just usher out an AI solution to many business customers at once, although that temptation is there for a bigger company. Why only release something to 5% of your base when you can generate way more revenue if it’s rolled out to everyone? This forces a big company to build a more simplified, “checkbox” predictive solution for the sake of scale, but that won’t work for mid-market and enterprise companies, which need many more controls to address complex, but common, scenarios like multiple markets and objective targets.

Such a simplified approach caters better to smaller customers that desire turnkey products, but unlike non-predictive enterprise solutions, predictive solutions face a big problem with smaller companies a data limiting challenge. You need a lot of data for AI, and most small businesses don’t have enough transactions in their databases to machine learn patterns from (I also would contend that most small companies shouldn’t be focusing on optimizing their sales and marketing functions anyway, but rather on building a product and a team).

So, inherently, AI is biased towards mid-market / enterprise accounts, but their demands are so particular that they need a deeper solution that’s harder to productize for thousands. Figuring out how to build such a scalable product is much better done within a startup vs. in a big company, given the incredible focus and patience that’s needed.

AI really does work for many applications, but more vendors need to get good at solving the last mile the 80% that depends less on AI and more on building the vehicle that runs with AI. This is where emerging companies like Infer have an advantage. We have the patience, focus, and depth to solve these last mile problems end-to-end and to do it in a manner that’s open to every platform not just closed off to one company’s ecosystem. This matters (especially with respect to the sales and marketing space, in which almost every company runs a fragmented stack with many vendors).

It’s also much easier to solve these end-to-end problems without the legacy issues of an industry giant. At Infer, we started out with AI from the very beginning (AI-First), not AI-Later like most of these bigger companies. Many of them will encounter challenges when it comes to processing data in a way that’s amenable for modeling, monitoring, etc. We’re already seeing these large vendors having to forge big cloud partnerships to rehaul their backends in order to address their scaling issues. I actually think some of the marketing automation companies still won’t be able to improve their scale, given how dependent they are on legacy backend design that wasn’t meant to handle expensive data mining workloads.

Many of these companies will also need to curtail security requirements stemming from the days of moving companies over to the cloud. Some of their legacy security provisions may prevent them from even looking at or analyzing a customer’s data (which is obviously important for modeling).

When you solve one problem really well, the predictive piece almost disappears to the end user (like with our three applications). That’s the litmus test of a good AI-powered business application. But, that’s not what we’re seeing from the big companies and most startups. It’s quite the opposite in fact, we’re seeing more over generalization.

They’re making machine learning feel like AWS infrastructure. Just build a model in their cloud and connect it somehow to your business database like CRM. After five years of experience in this game, I’ll bet our bank that approach won’t result in sticky adoption. Machine learning is not like AWS, which you can just spin up and magically connect to some system. “It’s not commoditizable like EC2” (Prof. Manning at Stanford). It’s much more nuanced and personalized based on each use case. And this approach doesn’t address the last mile problems which are harder and typically more expensive than the modeling part!

From AI Hype to Mass Adoption

There aren’t yet thousands of companies running their growth with AI. It will take time, just like it took Eloqua and Marketo time to build up the marketing automation category. We’re grateful that the bigger companies like Microsoft, Oracle, Salesforce, Adobe, IBM and SAP are helping market this industry better than we could ever do.

I strongly believe every company will be using predictive to drive growth within the next 10 years. It just doesn’t make sense not to, when we can get a company up and running in a week, show them the ROI value via simulations, and only then ask them to pay for it. Additionally, there are a variety of lightweight ways to leverage predictive for growth (such as powering key forecasting metrics and dashboards) that don’t require process changes if you’re in the middle of org changes or data migrations.

In an AI-First world, every business must ask the question: What if our competitor is using predictive and achieving 3x better conversion rates as a result? The solution is simple adopt AI as well and prop up the arms race.

I encourage all emerging AI companies to remain heads down and focus on customer success and last mile product problems. Go deep, iterate with a few companies and grow the base wisely. Under-promise and over-deliver. Let the bigger companies pay for your marketing with their big voice boxes which they’re really flexing now. Doing so, you’ll likely succeed beyond measure and who knows, we may even replace the incumbents in the process.


Filed under AI, Blog Stuff, Entrepreneurship, Google, Machine Learning, Microsoft, Non-Technical-Read, Trends, VC

4 Products Microsoft and LinkedIn Need to Ship

An op-ed piece I wrote for VentureBeat:

Last week, Microsoft stunned the tech world with the largest ever software acquisition – the purchase of LinkedIn for $26.2 billion. While early news coverage has addressed plans to keep LinkedIn independent, there’s been little discussion about what exactly the two companies will do together. As someone who’s entrenched in the LinkedIn and Microsoft ecosystems, I thought I’d share four exciting products this acquisition makes possible:

1. Redefined business email

The quickest and broadest impact Microsoft can make with LinkedIn is to redesign its Outlook interface. The companies could easily bring LinkedIn insights, profile photos, etc. into the email experience (similar to what Rapportive offers today but with a seamless, actionable approach). Outlook could even show recent updates and thought leadership pieces from a particular profile as talking point suggestions to automatically populate in an email when selected.

Microsoft could also add automated email filtering and prioritization features with folder recommendations that improve email productivity. Imagine if you could get emails that meet certain criteria — say they come from a particular job title and are second-degree connections with at least 500 connections themselves — to stick in the top of your inbox until they receive your attention.

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Amazon, Google and Apple vs the Big 5 Unicorns on Hiring and Churn

I’ve received multiple requests to analyze employee churn and new hiring rates for big companies and unicorns with the approach I took earlier for studying engineering and sales retention rates. I figured I’d give it a shot – and combine all of the key metrics in one chart …


How to read this:

Blue bar represents the number of expected new hires that particular company will make in a 30 day (one month) period. Black bars (negative values) indicate how many employees will churn in a one month period. The orange line (the top most numerical labels correspond to the orange line plot) represent the net change in hires per month (new hires less churn). The companies are ranked by churn from left to right in descending order (so highest churn on the left).

As you can see in the chart, the big three companies included in this analysis are Amazon, Apple and Google. The unicorns are Uber, Lyft, Airbnb, Pinterest and Snapchat. “Big 5” combines these unicorns together as if they were one whole company. Also note, this is looking at employees worldwide with any job title.

Key Insights:

  1. Apple is not hiring enough new heads when compared with Amazon and Google. In fact, the Big 5 unicorns combined will hire more net heads than Apple with almost 50% less employee churn.
  2. Amazon’s churn is the highest – losing a little over 10 people a day. However, this is not bad relatively speaking – Google loses 8-9 people a day, and Apple is a tad over 9 a day (and Amazon has 36% more employees than Google). Given the recent press bashing Amazon’s culture and the periodic press envying Google’s great benefits, their retention rates tell a different a story – that it’s closer to a wash. Big tech companies with great talent churn people at pretty similar high rates regardless it seems (have some more thoughts on this but will save those for another post).
  3. At these current rates, all of the companies here (collectively) will increase their employee size by 20K (19, 414 to be precise) heads by year end (this is new hires less churn). That’s a measly 5% increase in their current collective employee size – and this is across the Big 3 Tech Companies and Big 5 Unicorns.
  4. Let’s compare Amazon to the Big 5 Unicorns. The Big 5 will hire 79% as many incremental heads as Amazon in a month, even though their collective employee size is 24% that of Amazon’s. Amazon has been in business for much longer (2-3x days since incorporation), and the Big 5’s churn is 43% of Amazon’s figure – both factors contributing to the closeness in the incremental head rate between the two.

Want more details?

How did I calculate these figures? Take a look at my previous post on engineering retention for more details. Same caveats listed there apply here, and then some (such as how this depends on the participation rates of LinkedIn which may differ considerably internationally compared to the US market which my previous posts exclusively focused on). Feel free to connect or email me if you have any questions or feedback.


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Filed under Data Mining, Economics, Entrepreneurship, Google, LinkedIn, Management, Non-Technical-Read, Research, Startups, Statistics, Trends

Ranking Companies on Sales Culture & Retention

A company’s sales retention rate is a very important indicator of business health. If you have a good gauge on this, you could better answer questions such as: should I join that company’s sales department, will I be able to progress up the ladder, are reps hitting their numbers, are they providing effective training, should I invest money in this business, etc. But how does one measure this rate especially from an outside vantage point? This is where LinkedIn comes to the rescue. I essentially cross applied the approach I took to measuring engineering retention to sales.


This chart reveals several key technology companies ranked in reverse order of sales churn – so higher on the chart (or longer the bar) the higher the churn (so from worst at the top to best at the bottom).

So how are we defining sales churn here? I calculated the measurement as follows: I took the number of people who have ever churned in a sales role from the company and divide that by the number of days since incorporation for that respective company (call this Churn Per Day), and then I compute the ratio of how many sales people will churn in one year (the run rate i.e. Churn Per Day * 365) over the number of current sales people employed.

For ex. if you look at the top row, which is Zenefits, the value is 0.40 – which means that 40% of the current sales team size will churn in a one year period. In order to maintain that sales team size and corresponding revenue, the company will need to hire 40% of their team – and sooner than in a year as that churn likely spreads throughout the year as well as given new sales hire ramping periods (if you’re churning a ramped rep and say it takes one quarter to ramp a new sales rep, then you need to hire a new head at least one quarter beforehand to avoid a revenue dip).

A few more notes:

The color saturation indicates Churn Per Day – the darker the color, the higher the Churn Per Day.

Caveats listed in the previous post on engineering retention apply to this analysis too.

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Filed under Data Mining, Economics, Enterprise, Entrepreneurship, Job Stuff, LinkedIn, Non-Technical-Read, Startups, Statistics, Trends, Venture Capital

Top Tech Companies Ranked By Engineering Retention

(TL;DR) Here’s the ranking going from top to bottom (so higher / longer the better):


How did you measure this?

By running advanced Linkedin searches and counting up the hits. Specifically, for each company, at their headquarters location only, I searched for profiles that were or are software engineers, and had at least 1+ years of experience. Then I filtered these results in two ways:

1) Counting how many of those profiles used to work at the company in question (and not currently). Call this result Past Not Current Count.

2) Separately (not applying the above filter), filtering to those who are currently working at the company for at least 1+ years. Call this Current Count.

I also computed the number of days since incorporation for each respective company to be able to compute Churn Per Day – which is simply dividing Past Not Current Count by the number of days since incorporation.

Then I took this rate, and computed how long in years it would take for each company to churn through all of their Current Count or current heads who were or are software engineers and who’ve been with the company for at least 1 year (those who possess the most tribal wisdom and arguably deserve more retention benefits). Call this the Wipeout Period (in years) figure. This is what’s plotted in the chart above and is represented by the size of the bars – so longer the better for a company.

What does the color hue indicate?

The Churn Per Day (described in the previous answer). The darker the color the higher the churn rate.

Who’s safe and who’s at risk?

I would think under a 10 year wipeout period (esp. if you’re a larger and mature company) would be very scary.

In general (disclaimer – subjective – would like to run this over more comps) greater than 20 years feels safe, but if you’re dark green (and hence experience more churn per day) then in order to keep your wipeout period long you need to be hiring many new engineering heads constantly (but you may not always be hot in tech to be able to maintain such a hiring pace!).

What are the caveats with this analysis?

There are several, but to mention a few:

Past Not Current Count biases against older companies – for ex. Microsoft has had more churn than # of present heads because they’ve been in business for a long time.

I needed more precise filtering options than what was available from Linkedin to be able to properly remove software internships (although could argue that’s still valid churn – means that the company wasn’t able to pipeline them into another internship or full-time position) as well as ensure that the Past Not Current Count factored only software engineers at the time that they were working at that company. So, given the lack of these filters, a better description for the above chart would be Ranking Retention of Folks with Software Experience.

Also, this analysis assumes the Churn Per Day figure is the same for all folks currently 1+ years at their respective company, even though it’s likely that the churn rate is different depending the # of years you’re at the company (I’m essentially assuming it’s a wash – that the distributions of the historical Past Not Current vs Current are similar).

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Want to compete with Salesforce? Buy Marketo

An op-ed I wrote for TechCrunch:

There are several enterprise players that want a share of Salesforce’s business, but just aren’t making headway by knuckling up against the company’s dominant, entrenched SaaS CRM offerings. Rather than competing head on, a smarter approach for these businesses is to “front door” Salesforce, instead.

By acquiring Marketo, a competitor could get into Salesforce’s accounts, then, over time, work themselves down the funnel and leverage better integrations with Marketo in order to eventually displace Salesforce. Marketo’s strategic foothold in the enterprise and its current market value relative to potential acquirers like IBM, Microsoft, Oracle, SAP and even Salesforce make this a great time to buy the leading marketing automation vendor.

Many industry watchers overlook the mission-critical role Marketo plays in its customers’ go-to-market operations. The majority of Marketo’s 4,000 customers also use Salesforce, but the marketing automation system has access to more data about the funnel than its CRM counterpart. Marketo can sync bi-directionally with Salesforce, capturing all the data stored there, while also holding top-of-the-funnel lead behavior data that doesn’t get stored in CRM. Hence, it has access to an invaluable superset of data about a company’s potential and existing customers.

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Filed under Enterprise, Entrepreneurship, Management, Non-Technical-Read, Startups, VC

How sales is disrupting marketing

An op-ed I wrote for VentureBeat:

The line between marketing and sales is getting blurrier by the minute. Sales reps are leveraging new sales acceleration tools like Tout, Yesware, Sidekick, and Outreach, and it feels like a new one comes out every quarter.

These specialized apps have become so sophisticated that they’re enabling sales to run their own campaigns and sidestep marketing automation. They help teams increase response rates through more personalization and control, a 1:1 touch, simple plain text messages, and more follow up vs. blanket general marketing blasts.

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