News & Views

 

5 Things I Learned from Studying Facebook Benchmark Data

Through my role at Madrona Venture Group advising startups on growth, I meet a lot of marketers using Facebook to acquire and engage with customers. It’s a powerful platform. Unfortunately, many companies are falling short of their goals and are left frustrated with what they see as unrealized potential.

Recently I began a Facebook benchmarking project with the goal of providing marketers in our portfolio with aggregate performance data on different stages of the funnel. This project used 2015 and 2016 data from companies who were targeting consumers. B2B companies were not included in this study, though many have successfully marketed on Facebook and many of the same trends likely apply.

Through this process, I came up with 5 recommendations that companies should consider as they try to get the most out of this large and increasingly competitive channel.

1. Know where you are on the calendardont ignore seasonality

Prior to my role at Madrona, I led a growth team investing millions on Facebook each month. Even with this budget, we were often surprised at how volatile the channel could be. A common explanation for this unpredictability was an increase in demand for ad impressions — supply stays the same, demand goes up, and your scale falls drastically. Our data show that there are three big bumps throughout the year when prices rise — April, September and December. It’s likely the tax advertisers in April, apparel companies in September (back to school and fall fashion shows), and general retail in December that are driving up prices. From our study, January and February featured the lowest prices of the year, so now could be a good time to scale up. Alternatively, April is the highest month in H1 so you may want to hold back spending there in favor of before and afterwards. Just be careful in November and December as prices during those periods that were 2x January.

2. Use in-month pricing cycles to your advantage

Different pricing within a month is another trend that appeared in the data. Prices were 20% higher in the last 10 days of the month compared with the first 10 and they increased throughout the month. Exceptions did apply in a few months with holidays like July and February, but this trend appeared in the majority of months. If you have a set amount you are spending per month, allocate a greater share of spend earlier in the month when prices are lower and your dollars go further. Doing so could lead to a meaningful increase in the efficiency of your ad spend.

3. Make sure visitors have great experience on mobile

This user visited a landing page not optimized for mobile.

Among our companies, 82% of newsfeed impressions occurred on mobile devices. That percentage will only grow. Mobile newsfeed impressions used to come at a large discount relative to their desktop counterparts and they remain cheaper, but the gap is shrinking. Because much of the discount has disappeared and because of how large mobile is, it is crucial for any prospective advertiser to make sure their mobile experience is solid before being aggressive on Facebook. This means limited text, clear calls to action, and extensive testing on different devices and screen sizes. Promoting app downloads and sending visitors to a mobile browser can both be effective depending on the situation, but in each case make sure your customer onboarding gives people a fast and clear understanding of your key value proposition(s) so you’re not wasting money.

4. Understand costs of reaching your audience

While it was once made up of exclusively college students, these days the Facebook audience is much more diverse. Nearly every customer segment is reachable on Facebook which is one reason why so many companies turn to it to reach prospective customers. The breadth of the Facebook audience does not mean, though, that all audiences are reachable for the same price. In our study, impressions served at females were 33% more expensive compared to males. Differences can also be found among different ages, geographies and other demographic qualities. Companies would be wise to understand their audience and what that means from a cost-to-reach standpoint before getting too far along in their Facebook marketing journey. Suggested Bids in Ads Manager has not been reliable when I’ve used it, so the best way I’ve found to learn prices of different audiences is through live testing. Also keep in mind that the more relevant your creative is for the audience you are trying to reach, the more cost effectively you can reach them.

5. Treat alternative placements differentlyInstagram does not equal Facebook

Over the last few years Facebook has increased its impressions in places beyond the newsfeed. Instagram and the Audience Network are the two that were used most often by our companies and performance data from those placements made for an interesting comparison to native Facebook ads. Instagram was used most often and, while prices were in the ballpark relative to mobile newsfeed, the clickthrough rate was much lower. This takes us back to social media 101. Think through what consumers are doing on each platform and have that in mind when building creative. It’s different by platform. Don’t waste money running ads that have been optimized for Facebook on Instagram. Alternate placements available through Facebook will increase in the future, so keep this in mind when extending your brand to other platforms.

Facebook can be a great place to reach customers, but it can also be unpredictable and frustrating for many. I hope some of the data that came out of this study can shed some light on what’s happening and leave you with ideas on how to exceed your goals.

 

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Top Silicon Valley VC firm Accel leads $15M round for Opal

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Seeq Cultivates Partnerships for Actionable Data

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ExtraHop’s newest product detects security breaches and other network anomalies with machine learning

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Jobaline raises $3.5 million to expand hourly jobs platform and grow its team by 25%

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Investing in Suplari – Madrona’s Latest Investment in Intelligent Applications

Suplari, which announced their $3.1 million funding round today, is our newest investment in an area we helped define – Intelligent Applications. Intelligent Apps combine data and machine learning to provide real-time, actionable insight into business and consumer applications. Going back 4-5 years, we were investing in more horizontal machine learning platforms like Dato/Turi, Algorithmia and MightyAI to help companies combine the talents of their business users, data scientists and programmers to design, text and build intelligent apps. More recently we have been focusing on building vertical or functionally focused intelligent applications including Amperity, Saykara, and Placed.com.

Suplari will be sharing more in the months ahead about their specific focus. For now, we are excited to be working closely again with three founders who are an incredible team. At Madrona we look for companies that fit our key investment themes, focus on real customer problems in large markets and have an outstanding founding team. The three founders of Suplari have all worked with Madrona over the last couple decades and have excellent track records at building innovative and successful companies.

For Jeff Gerber, CTO, this is the fourth Madrona company he has helped build. He was an early engineer at Performant (acquired by Mercury Interactive), then co-founded iConclude (purchased by OpsWare and then HP) with Sunny Gupta, and was most recently working at Apptio (NASDAQ: APTI) on their machine learning and intelligent apps that help CIOs manage the business of IT. Jeff is an incredible technologist who understands the needs of the business customer.

Brian White, CPO, was the first executive hire at iConclude back in 2005 (working with Jeff and Sunny there) as the head of product and then went on to be VP of Products at SkyTap where he drove their shift to focus on larger enterprise offerings across multiple clouds. He also was an early product executive at Amazon Web Services. Jeff and Brian are excited to be reunited at Suplari and have already started building a great engineering and product strategy team.

Nikesh (Niki) Parekh, CEO, was an EIR here at Madrona and has been a long-time friend of the firm. He has worked deeply in the real estate industry including at Market Leader, Trulia and House Values. We have regularly met and worked with Niki on a variety of initiatives in our innovation economy and are excited to be directly working with him at Suplari.

Suplari is staying stealth for now on their specific plans, but expect to share more later in the year! We were delighted to partner on this investment with our friends at Amplify Ventures as well as some highly strategic angels that we worked together with the Suplari founders to attract to the opportunity. The team is officing in Madrona’s innovation space so if you are visiting, please stop by!

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Xnor.ai – Bringing Deep Learning AI to the Devices at the Edge of the Network

Photo  – The Xnor.ai Team

Today we announced our funding of Xnor.ai. We are excited to be working with Ali Farhadi, Mohammad Rastegari and their team on this new company. We are also looking forward to working with Paul Allen’s team at the Allen Institute for AI and in particular our good friend and CEO of AI2, Dr. Oren Etzioni who is joining the board of Xnor.ai. Machine Learning and AI have been a key investment theme for us for the past several years and bringing deep learning capabilities such as image and speech recognition to small devices is a huge challenge.

Mohammad and Ali and their team have developed a platform that enables low resource devices to perform tasks that usually require large farms of GPUs in cloud environments. This, we believe, has the opportunity to change how we think about certain types of deep learning use cases as they get extended from the core to the edge. Image and voice recognition are great examples. These are broad areas of use cases out in the world – usually with a mobile device, but right now they require the device to be connected to the internet so those large farms of GPUs can process all the information your device is capturing/sending and having the core transmit back the answer. If you could do that on your phone (while preserving battery life) it opens up a new world of options.

It is just these kinds of inventions that put the greater Seattle area at the center of the revolution in machine learning and AI that is upon us. Xnor.ai came out of the outstanding work the team was doing at the Allen Institute for Artificial Intelligence (AI2.) and Ali is a professor at the University of Washington. Between Microsoft, Amazon, the University of Washington and research institutes such as AI2, our region is leading the way as new types of intelligent applications takes shape. Madrona is energized to play our role as company builder and support for these amazing inventors and founders.

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‘I want to touch and feel’: Online-only retailers open regular stores

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Madrona Adds Ex-King Digital CFO as Partner; Promotes ‘Soma’ Somasegar to Managing Director

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Investing in People – Developing and Adding to our Team at Madrona

At Madrona we are regularly encouraging our portfolio companies to hire and develop talented people. As organizations grow and change, including our own, it is important to continually be assessing your talent needs and promoting and adding team members. As we embark on a new year of innovation, invention and investment in our region and beyond, Madrona is pleased to announce several promotions and a new addition at the firm today.

All of these appointments speak to the growing opportunity that Madrona sees in our region to build lasting and large technology businesses. We had two successful IPOs last year – Impinj and Apptio – both of which are substantial forces not only here in the Seattle area but in the technology industry at large. In addition, the continued industry leadership of companies like Amazon and Microsoft and incredible talent in our region highlight the huge potential of our innovation ecosystem to create more great companies and products.

Today we are pleased to announce that S. “Soma” Somasegar has been promoted to Managing Director, Julie Sandler to Partner, Daniel Li to Senior Associate and that Hope Cochran is joining us as Venture Partner.

Soma joined us in 2015 as a Venture Partner. Since then he has added greatly to our existing portfolio companies with his deep, developer-centric understanding of both technological and management experience, and has also become a powerful voice internally and externally on key investment areas including DevOps, Machine Learning and Virtual Reality. Soma brings over 25 years of technology operating experience building world-class platforms at Microsoft that fuel cloud, mobile and client/server applications, and he is a passionate and experienced angel investor. Since joining Madrona, he championed investments including Pixvana, Heptio, Shyft and CloudCoreo. He will be a great addition to our Managing Director team.

Julie came to Madrona from Amazon, and has been a valued and invaluable member of the Madrona investment team for more than five years. Today we are excited to promote her to the role of Partner. At Madrona, Julie has led investments including Integris and Poppy, served on boards, helped companies navigate the challenges of finding initial product market fit and then scaling for growth. She has always been a strong and persistent voice supporting entrepreneurs and the community around us. She is a steward for our investors and a mentor to a growing number of people in the Seattle region. She recently completed the year-long Presidential Leadership Scholar program which brings together select national leaders from many disciplines to learn from the great political leaders of our country, and she is deeply committed to ensuring that the innovation ecosystem provides positive impacts for the broadest number of people. We look forward to a new year of company building guided by Julie’s leadership.

Daniel Li joined us in 2015 as an Associate, from the Seattle office of The Boston Consulting Group, and since then has had a tremendous and immediate impact, diving in to leverage communication platforms to help the firm communicate, penning many interesting pieces covering such diverse topics as eSports and autonomous vehicles, and helping our portfolio companies navigate business decisions. Dan is even known to code a few applications himself including a text polling app and a mobile game called Babel. Dan’s energy and attitude is appreciated by our companies and we are happy to recognize his accomplishments with the promotion to Senior Associate.

Finally, we are also announcing that Hope Cochran is joining Madrona as a Venture Partner. Hope most recently was the CFO of London-based, King Digital, which we all know as the creator of Candy Crush and other successful mobile games. At King, she helped lead the company through explosive revenue and employee growth, a successful IPO and a $5.9 billion acquisition by Activision. She has long been a friend and informal sounding board to us at Madrona and recently returned to the Seattle area from London.

Prior to King she was CFO at Clearwire where she worked closely with Craig McCaw, John Stanton and other innovators in our community. Prior to Clearwire, she started a company that grew to 200 employees, $10 million in revenue, and was acquired by enterprise software pioneer, PeopleSoft. Throughout these experiences she has navigated company growth, management and financing situations including raising more than $12 billion, driving two IPOs and a multibillion dollar acquisition. Hope was a popular speaker at our recent CFO conference and we are excited to have her on board to work directly with our portfolio companies. In addition, as a Venture Partner she will be investing time in developing our corporate development and later stage financing programs that connect portfolio companies with strategic partners and potential late-stage investors across a broad set of sectors.

Overall these promotions and additions are a great way to dive into 2017! Madrona is incredibly optimistic about the outlook for the technology industry and having these additions to our core team is crucial to our ability to help our companies grow as well as build the ecosystem in the region.

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This Startup Pays Real People to Answer Questions to Build Better AI

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‘Aha’ Virtual Reality Moment Leads to Founding of Seattle Startup Pixvana

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Apps Empower Workers, Ease Scheduling

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How Data Privacy Practices Could Make or Break the Sale of Your Company

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Why This Early Amazon Investor Bet On Jeff Bezos’ Vision, and How the Tech Giant Created Its ‘Flywheel’

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Technology Trends Changing the World As We Look Ahead

Drones, Cars, Intelligent Apps, Virtual Reality and More – What to expect in 2017
There’s an age old saying that humans tend to overestimate what can be accomplished in one day, but underestimate what can be accomplished in one year. As 2016 comes to a close, it is a good time to zoom out the lens, and get reflective on what has happened this year, and predictive about what we are excited about for the coming 3-5 years.

1. Commercial Drone (UAV) Technology will Turn to Software

The 2015 hype around drones generated over $155M of VC funding in the second half of 2015, but 2016 has seen far chillier attitudes by VCs towards drone startups. However, we believe 2017 will be a year of renewal for investments and innovations in drone technology. For one, the FAA passed the first set of rules in June governing drone fly rules, allowing commercial drones to finally take to the skies without filing for lengthy and cumbersome case-by-case permission. Secondly, over the last year, the hardware war which has spooked many VCs from entering the space has been all but won. Forbes estimates that Chinese drone manufacturer DJI is valued at $8 billion and controls over 70% of the hardware market. Other contenders for this mantle such as 3D Robotics have retooled to focus on vertical software. For 2017, we see the main opportunity for drone technology to be in best-in-class tools and software deployed across platforms such as equipping drones with advanced sensing capabilities, or software for vertical industries such as real estate and farming.

2. Intelligent Applications

Customers nowadays demand their software delivers insights that are real-time, nimble, predictive and prescriptive. We have no doubt that in the future, every application will be an
intelligent application. However, the reality has not caught up to the hype. We believe data, not algorithms are the bottle-neck. Algorithms continue to become commoditized by the way of access to open-source libraries such as Algorithmia, Tensorflow, Hadoop and Cockroach DB. If products wish to do better than commodity performance, companies with machine learning at their core must figure out how to acquire proprietary, unique, clean and workable data sets to train the machine learning models.

Companies with a leg up are also likely to be vertically integrated in such a way that their data, learning models and product are all geared towards developing the best data network effects that will feed the learning loop.

We believe there is a big opportunity for companies focused on a specific industry such as healthcare, retail, legal, construction to build higher quality domain expertise at a faster rate, which facilitates the acquisition and labeling of relevant data critical to building accurate and effective machine problem solvers.

3. Virtual Intelligent Assistants with Focus on a Problem Space Will Succeed

A great example of vertical vs horizontal machine learning applications can be found in chat bots. There are some horizontal chat bot assistants that help you with any and all requests (viv.ai, Magic, and Awesome to name a few). It would seem obvious that building NLP and intelligent capabilities across all conceivable tasks and requests could be a long slow training slog of manual human validation. These companies are also at a heavy disadvantage to incumbent players tackling the horizontal assistant space. Voice enabled platforms like Alexa, Siri, Cortana, or the new Google Assistant still see limited usability despite enormous access to training data bolstered by the distribution platforms of three of the largest companies in the world. Realizing this, Amazon announced at Re:Invent that Lex, the software that powers Alexa, is now available for developers to build their own chat bots. Every developer who designs their conversation on the Lex Console is now feeding Lex’s data model. Microsoft followed suit with a similar announcement of the Cortana Skills Kit and Devices SDK.

Assistants that will be more successful in the short term are bots that are narrowly focused. There is Kasisto for finance, Digital Genius for customer service, or the many virtual assistant/meeting scheduler apps (Meekan, JulieDesk, X.ai’s Amy and later “brother” Andrew, and Clara). What excites us about these vertically oriented chat bot startups is that they are applying machine learning, artificial intelligence and natural language processing in a highly specialized and narrow way. It is far easier to train a bot to recognize and act appropriately on the finite set of lexicon and circumstances around scheduling a meeting, compared to the infinite set of scenarios that could occur otherwise. In machine learning, it is better to be a master of one, than a master of none.

4. Blockchain Will Expand as Enterprise Services Embrace it

2017-01-03-techcrunch-post-blockchain

The technological innovation of Bitcoin, blockchain, seeks to create a global distributed ledger for the transfer of assets (currency, cryptocurrency, music, real-estate deeds etc). This enables peer to peer transactions that bypass traditional intermediaries like banks, credit card companies, and governments whose centralized nature slows down processing speed, increases cost of transaction, and are vulnerable to security threats at the hub-level. Blockchain technology has been heralded by some as being as disruptive to the way people view, share, and interact with their assets as the internet was for information. However, adoption has significantly lagged this envisioned seismic shift.

We believe blockchain’s path to mainstream adoption will be more likely to arise from the enterprise and infrastructure side (creation of APIs and protocols that enable ease of adoption) as opposed to consumer adoption of cryptocurrencies (i.e. Bitcoin). An example is R3 which has gathered a consortium of 42 banks to create the technological base layer for various systems including Bitcoin, Ethereum and Ripple to talk to each other and facilitate global payment transfers.

5. Autonomous Vehicles Have More Validation Work

Aside from machine learning, autonomous vehicles were one of the most hyped technologies in 2016. This year, we saw major product announcements and technology demos from Uber, Lyft, Ford, GM, BMW, Tesla, Cruise, Comma.ai, and many other startups and corporations. Google went so far as to create an entirely new company, Waymo, devoted to their driverless car technology.

Nearly all of the major car manufacturers have announced they will be releasing autonomous vehicles in the next five years, and Lyft has stated that they are planning for the majority of rides to be autonomous within the next five years. Even President Obama said “The technology is essentially here” in a November WIRED interview.

However, despite the hype, there is a tremendous amount of heavy lifting that needs to happen in technology, infrastructure and policy to say the least. Companies still need to solve basic problems related to sensors (e.g., see Tesla Autopilot crash where cameras could not distinguish white truck against bright sky), and billions of edge cases due to construction, pedestrians, and weather, and a murky regulatory environment.

We are huge believers in the long-term benefits of autonomous vehicles, but 2017 may be a year when autonomous vehicle companies and startups are heads-down solving tough problems rather than continuing to push out flashy tech demos.

6. Augmented Reality and Virtual Reality

We believe there is still a three-year runway before VR and AR sees wide adoption by mainstream audiences. Consumer adoption will be mobile-first and/or low-end tech – think the successful recent launch of Snap Spectacles, and the cheaper price points of Google Daydream, and the Samsung Gear. VR uptake today is still burdened by hardware adoption and ease of use. Prices are still too high for anyone but the hardcore technologist or gamer.

On the enterprise side, we see 2017 as a continuing year of innovation and activity particularly in core applicable industries like engineering, science, medicine, real estate education and manufacturing. However, until the dominant form factor (whether it is glasses, head-mounted-display, or some other yet to be seen hardware) emerges, time spent in VR will still be miniscule compared to time spent in this reality.

Ultimately, if gazing into the future of technology was really so straightforward, there would be no need for speculation and VCs would be out of a job. We’ll be back next year to see assess how many of these predictions hit the nail.

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Working Geek: How these 13 tech execs run their meetings

By Monica Nickelsburg, Geekwire

Meetings get a bad rap. They’re time-consuming, labor-intensive, and they can easily get off-topic.

But it’s difficult to run a business without getting the team together, at least once in a while. If you want to improve your meetings in 2017, take some advice from Seattle-area tech execs.

We asked 13 leaders in the Pacific Northwest how they run their meetings, as part of our regular Working Geek feature. Continue reading for their tips and strategies for more efficient and effective collaboration. To see each exec’s full Working Geek profile, click on his or her name.

For full article go to Geekwire.

 

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Madrona Labs Adds CTO, long time entrepreneur and big data, machine learning, computer vision technologist, Jay Bartot

New Role Brings Deeper Level of Technology Expertise to Madrona Venture Labs Team
We are thrilled to announce that Jay Bartot has joined our Madrona Venture Labs team as Chief Technology Officer. Jay has been the cofounder of four successful startups, each of which leveraged big data and machine learning to provide targeted services to consumers and businesses. These startups include Farecast, where Jay and I partnered to change how consumers purchase airline tickets by using algorithms to predict airfare price fluctuations.

Joining Madrona Venture Labs is like a homecoming for Jay, as Farecast was incubated at Madrona Venture Group and he worked closely with Madrona’s Managing Director Matt McIlwain and Venture Partner Oren Etzioni in the earliest days of the company’s formation. At Farecast, Jay and I formed a highly productive engineering and product partnership and we aim to bring that same collaborative spirit to our Labs culture.

Jay’s other startups include AdRelevance, acquired by Media Metrix, Medify acquired by Alliance Health and most recently Vhoto, acquired by Hulu. Jay brings a wealth of deep technical and engineering leadership experience to our team and in support of our spinout founding teams. With Jay onboard, we will look to explore new, innovative technical startup ideas that leverage his experience in machine-learning and data-mining.

Jay is one of the most creative and inventive engineering leaders I know and we could not be more excited about our future with his influence and leadership.

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Pixvana Unveils VR Editing and Production Software One Year After Raising $6M Seed Round

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AWS re:Invent – the Big Announcements and Implications

The momentum continues to build and scale in leaps and bounds. That’s the overwhelming observation and feeling at the end of the 5th annual conference that Amazon hosted in Las Vegas last week for AWS (Amazon Web Services).

Here are some of the key take-aways that we think will have the highest industy impact.

Event-driven Functions and Serverless Computing

Serverless has definitely arrived. As expected, there were a number of new capabilities announced around Lambda, including C# language support, AWS Lambda@Edge to create a “CDN for Compute” and AWS Step Functions to coordinate the components of distributed applications using visual workflows through state machines. Beyond this, it was clear that Lambda, and the serverless approach overall, is being broadly woven into the fabric of AWS services.

In the world of event-driven functions, thinking about a standard way for people to publish events that make it easy to consume those events is going to be critical. Whichever platform gets there first will likely see a tremendous amount of traction.

Innovation in Machine and Deep Learning

AWS has had a machine learning service for a while now, and it was interesting to see a whole slew of new machine learning, deep learning and AI suite of services including Amazon Image Rekognition, Amazon Polly (Text to Speech deep learning service) and Amazon Lex (Natural Language Understanding engine inside Alexa that is now available as a service).

While the concrete use cases are still relatively spare, we – like Amazon – believe this functionality will be integrated into the functionality of virtually all applications in the future.

It is also clear that the proprietary data used to train models are what create differentiated and unique intelligent apps. The distinction between commodity and proprietary data is going to be critical as algorithms become more of a commodity.

Enterprise Credibility

In past years, whether it was intended or unintended, the perception was that taking a bet on AWS meant taking a bet on the public cloud. In other words, there was an unintended consequence of AWS as “all in on public cloud or nothing”. With the VMWare partnership, which was announced a couple months ago, but solidified on stage with VMWare’s CEO, Amazon clearly is supporting the hybrid infrastructure that many enterprises will be dealing with for years to come.

Equally noteworthy was the appearance of Aneel Bhusri, CEO of Workday, on stage to announce that Workday was moving to AWS as their primary cloud for production workloads. Clearly no longer just the realm of primarily dev and test, this is perhaps the strongest statement yet that the public cloud – and AWS in particular – is enterprise production capable.

Moving Up a Layer From a Set of Discrete Services to Solution-based Services

One big theme that showed through this year at AWS was the movement from a set of discrete services to complete solutions both for developers and for operators of applications and services. The beauty of this all is that AWS continues to move forward on this path in a way that is highly empowering for developers and operators.

This approach really shone through during Werner Vogels keynote on Day 2. He laid out AWS’ approach for the “modern data architecture” and then announced how the new service AWS Glue (fully managed data catalog and ETL service) covers all the missing pieces in terms of their end-to-end solution for a modern data architecture on AWS.

Eat the Ecosystem

One of the implications of AWS’ continued growth towards complete solutions is that they continue to eat into the domain of their partner ecosystem. This has been an implied theme in years past, but the pace is accelerating.

Some of the examples that drew the biggest notice:

• AWS X-Ray (analyze and debug distributed applications in production) which aims directly at current monitoring companies like New Relic, AppDynamics and Datadog
• AWS Lightsail (virtual private servers made easy) that, at $5/month, will put significant pressure on companies like Digital Ocean and Linode
• Rekognition (image recognition, part of AI suite described above) that provides a service very similar to Clarifai, who had actually been on a slide just a few prior to the service announcement!

No one should be surprised that AWS’ accelerating expansion will step on the toes of partners. An implication, as @benkepes tweeted, is that the best way to partner and extend AWS is to go very deep for a given use case because AWS will eventually provide the most common horizontal scenarios.

Partner Success = AWS Success

Although some of the new services conflicted with partner offerings, the other side of the coin was that AWS continues to embrace partners and is vested in partners’ success. They clearly understand that having their partners be successful ultimately contributes to more success for AWS. Having customers like Salesforce, WorkDay and Twilio take a complete bet on AWS , making the product of a partner like Chef be available as a fully-managed service on AWS, having a partner like Netflix excited to switch off their last datacenter as they are completely on AWS, and having a company like VMWare embrace AWS as their public cloud partner are some of the great examples of how Amazon is systematically working to ensure that their partners remain successful on AWS, all of which accrues more value and consumption of AWS.

Summary

The cloud opportunity is gigantic and there is room for multiple large players to have a meaningful position strength. However, as of today, Amazon is not just the clear leader but continues to stride forward in an amazing way.

First Published by Geekwire.

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