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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.

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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.

Read more …

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Top Tech Companies Ranked By Engineering Retention

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

eng_ret

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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Filed under Blog Stuff, Computer Science, Data Mining, Entrepreneurship, Job Stuff, LinkedIn, Management, Non-Technical-Read, Research, Statistics, Trends, VC

How LinkedIn Could Take On Salesforce

An op-ed I wrote for TechCrunch:

Today’s B2B sales and marketing folks struggle with the overwhelming number of channels for finding and reaching new leads. The customer “funnel” continues to expand as buyers do more of their own research before raising their hand to connect with a sales rep. But imagine if you could make the funnel taller by identifying leads when they’re just browsing your site and haven’t yet filled out your “contact me” form, or leads who haven’t yet visited but are likely to be a good fit for your product? That’s hard to do with the primitive tools that are available for sales and marketers today, unless you bring together some very rare assets – which just so happen to all exist at LinkedIn.

LinkedIn is the only company with fairly clean, accurate details on pretty much every contact that matters in the business world (unfortunately, most other data providers’ contact info contains 80% garbage, and they can’t really improve it without violating CAN-SPAM laws). LinkedIn also reflects the direction sales is heading with strong channels for thought leadership. Via LinkedIn, you can educate and advocate for your customers vs. just selling to them.

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Salesforce’s Wave Hits the Analytics Market

An op-ed I wrote for VentureBeat on why Salesforce launched Wave and the impact that will have on the analytics industry at large:

At Salesforce.com’s Dreamforce conference in San Francisco on Monday, Marc Benioff unveiled his company’s much anticipated Wave Analytics Cloud product. Marketed as “analytics for everyone” with a focus on mobility and slick visualizations inspired by video games, Wave aims to bring more analytics to decision makers more quickly.

Wave is great news for Salesforce’s massive customer base. Current customers will gain the ability to easily attain valuable insight via modern dashboards on any device, and to even execute advanced analytical operations on all types of business data. This business-intelligence (BI) approach, which appears to treat customer analytics (sales, customer support, and marketing) as a first-class citizen, is quite a departure from current horizontal BI tools like GoodData, Birst, and Tableau that focus more on performing analytics across a myriad of functions. This Salesforce Analytics Cloud is sure to deliver real business value by offering a platform that’s more specialized for customer needs, which makes sense since most use cases for BI are related to sales and customer analytics.

Why analytics is a great move for Salesforce

Anyone close to Salesforce knows that Analytics Cloud is a huge step beyond the company’s standard reporting capabilities, which have historically been rather limited. Until now, the system really just scratched the surface of a business’ sales data (try to report on something like your sales cycle lengths by lead source over time, and you’ll see what I mean). That said, it was smart of Salesforce to leave analytics up to its AppExchange partners in the beginning, because the company was busy building the SaaS world we all play in today, starting with its customer-relationship management platform. Tackling analytics at that time would have been like running two entirely different companies.

However, over the past couple years, the data needs of modern sales and marketing leaders have grown dramatically with the rise of big data. Customers are hungry for insight, and have been asking why Salesforce doesn’t offer seamless, built-in, advanced analytics. Most companies just don’t want to move data between multiple services, especially if they have rigorous security policies or huge data sets. In this data-driven environment, Salesforce’s customer satisfaction has become heavily dependent on partners it can’t control, making it increasingly important for the company to shift toward a hands-on approach to analytics and meet customers’ needs directly.

(Read More …)

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Why Most Product Managers Suck

A piece I wrote for The Next Web:

The first product manager (PM) is a crucial unicorn hire that no startup should compromise on. The reason is simple – your PM is responsible for managing your team’s most precious resource: time.

Unfortunately, nearly everyone seems to think they’d make a great PM (engineers, consultants, you name it), but the reality is that most folks just can’t hack it. I’ve worked with countless PMs at huge companies like Yahoo and Google, and over the past two months have interviewed over twenty PM candidates.

Out of all these folks, I’ve only encountered two PMs who actually do the job well … (read more).

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Taking Seed Money from VCs Is A Risk Worth Taking

Here’s the link to a a guest article I wrote for VentureBeat arguing the benefits of including VCs early on as well as how the VC “signaling effect” (negative or positive) is sometimes a good thing for entrepreneurs to experience.

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