Autonomous Transportation- Rethinking Security, Infrastructure, and Experience

Announcing the BootstrapLabs Applied AI Insiders Series: Autonomous Transportation

Autonomous Transportation- Rethinking Security, Infrastructure, and Experience

BootstrapLabs is thrilled to announce the BootstrapLabs Applied AI Insiders Series: Autonomous Transportation

Event Summary:

  • Date and Time: Tue, October 3, 2017 | 6:00 pm to 9:00 pm
  • Location: Google Launchpad Space, 301 Howard Street, San Francisco, California 94105
  • Registration: this event is INVITE ONLY. If you did not receive an invitation you can request one below.

 

 

Event Description

Topic: Autonomous Transportation: Rethinking Security, Infrastructure, and Experience

Autonomous transportation goes well beyond cars and includes all modes of powered transport such as motorcycles, buses, trucks, trains, ships, helicopters, planes, and even spacecraft. There are also various degrees of autonomy levels which need to be considered in the context of fluid and frictionless mobility.
Secure and intelligent communication networks, smart sensors, reliable and fast identification, fail safe and redundancy planning must all come together for the dream to become reality and to ensure safety and security. Read more.

Speakers

Ben Levy, Co-Founder, BootstrapLabs

Ben spent the last 20 years in Silicon Valley building and exiting two FinTech startups to Mergent and NASDAQ, investing in disruptive software technologies, and advising CxOs of Fortune 500 Telecom, Media and Technology companies on corporate strategy, financing and M&A.
Ben is a member of the Association for the Advancement of Artificial Intelligence (AAAI), an Ambassador of La FrenchTech, as well as a frequent keynote speaker at industry conferences on innovation, technology investing, entrepreneurship, artificial intelligence, and globalization in the US, Europe, and Asia.

Dr. Allan Steinhardt, Chief Engineer, AEye

Steinhardt was Chief Scientist for DARPA, Chief Scientist at Booz Allen, co-author of a book on adaptive radar, and assistant professor in Electrical Engineering and Applied Mathematics at Cornell University among other experiences.

Tilly Chang, Executive Director of the San Francisco County Transportation Authority

Ms. Chang has also held posts with the World Bank, Metropolitan Transportation Commission, and a
technology startup. She serves on the boards of the California Transportation Foundation, SPUR and
the UC Transportation Centers.

Chad Partridge, CEO of Metamoto, Inc.

Chad is an accomplished executive making his recent mark as an entrepreneur in enterprise software contributing mission critical video, geospatial metadata, and computer vision within unmanned systems markets. His company Metamoto is a startup specializing in scalable simulation for autonomous systems.
Thank You To Our Host and Partner

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Applied Artificial Intelligence Conference 2018 #AAI18

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Applied AI Digest Review 2016

See the most impactful artificial intelligence news of 2016.

Applied AI Digest is a weekly email curated by BootstrapLabs to share the latest insights and innovations happening in the field of Artificial Intelligence.

Please help us to grow the community by forwarding this page to your friends who are interested in learning more about AI.

If you are already a subscriber and would like to help us improving the quality of this newsletter and contact us to collaborate with us, share feedback or propose new ideas.

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Job Automation Predictions from 2016 Silicon Valley Survey

This article was originally published on http://techemergence.com/ and it`s the result of a collaboration between BootstrapLabs and Techemergence.


Job automation predictions from an individual expert typically draw from years of academic research experience, or time “in the trenches” of industry. With growing interest and speculation on the job market of the next decade, we set out to garner a perspective as to what Silicon Valley thinks about the possibilities of automations in various business tasks.

We wanted to know – what work functions have the most potential for near-term automation?

In the infographics and article below, we explore the survey responses from nearly 80 Bay Area investors, founders, and tech folks – on which business functions have the greatest potential for automation today, and in the coming five years ahead.

Together with San Fransisco-based venture firm BootstrapLabs, we designed a simple survey that was handed out during their “Autonomous Corporation” event in November 2016. Below are the responses to this survey, and our interpretations and gleaned ideas:

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Automation Predictions – Current

It is interesting to note all three groups of respondents considered business intelligence to be the business function with the most current automation potential.

Similarly, investors, founders, and “other” respondents were also unanimous in ranking human resources as the business function with the least current automation potential. It seems plausible that job automation in the HR department is less likely than the BI department, and that the predictions of our respondents would reflect this.

A uniform response for “highs” and “lows” was not expected, and seems to signal that there is strong shared sentiment around those two particular business functions.

While there seemed to be relative consensus around business intelligence, human resources, and marketing, other business functions – such as security and finance – didn’t have the same uniform optimism or pessimism across respondent groups.

Automation Predictions – 5 Years Out

It seemed highly unlikely from the outset, but one major trend resounded from the “Current” survey section to the responses in the “5 Year Future” section: Business intelligence again ranked highest amongst all options by all three groups, and human resources again ranked dead last for all three groups as well.

Based on the survey size (78 respondents, about one third investors, one third founders, one third “other”), this is by no means conclusive. There are no “conclusive” predictions in the first place (good startup idea though, someone should work on that). Rather, this seems

Manufacturing stands out as a major outlier from “current” to “5 years out.” Ranked among the lowest areas of current automation, manufacturing jumped ahead to nearly overtake business intelligence for the area of most optimism in the coming five years. It is possible that this is due to the heavier hard-cost investment of industrial manufacturing processes as compared to domains like business intelligence, marketing, of finance – where “automation” never has to reach it’s robot arms into the real world.

It is interesting to note that the current industry trends in funding AI and machine learning doesn’t seem to provide much evidence for heavy investment in manufacturing, so we might presume that the optimism of the investors and founders in the crowd spawned from another source.

Read more at: http://techemergence.com/job-automation-predictions-silicon-valley-survey/

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The Autonomous Corporation presented by BootstrapLabs

Thank you to the Autonomous Corporation attendees!

 

We hope that you found the conference informative and enjoyed the networking during the event.

Seeing all of you hanging out until the end of the event made our team proud of their work and strongly encouraged us to keep working hard evangelizing AI around the world.

 

At BootstrapLabs, we believe that innovation and progress will happen at an accelerated pace in all corners of the globe, and that bringing people together around their shared passion for AI will drive positive impact in our world!

 

As many of you have been asking, we would like to officially announce that we have started working on next year’s Applied AI Conference 2017 (Check out the Applied AI Conference 2016 here) and will soon share more information about date, location and speakers line-up.

 

Below you can find some social media posts about the Autonomous Corporation event:

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AI is going to change everything

We are on the brink of a major disruption, which we think might be bigger than the industrial revolution. At BootstrapLabs we are focusing heavily on a major shift that is impacting almost every sector: Artificial Intelligence – AI.

AI has reached an inflection point, where it can now be applied to quickly drive efficient returns, and in our book, is ripe for building startups that will disrupt major markets and their incumbents.

Why now?

Let’s go back: The Industrial Revolution & Apple

James_Watt_Energy_02During the Industrial Revolution, the steam engine enabled a major technological shift as a large amount of manual labor was now able to be automated. Yet, few know that the first version of the steam engine was actually built the 1st century CE and was called Aeolipile. It was not until much later, in 1712, when Thomas Newcomen developed a commercially viable version of the steam engine that applied to “mechanical work” that serious increases in productivity started to take place. But, this major shift did not reach its full disruptive potential until 1781 when James Watt designed a model that provided a stronger and continuous rotary motion, resulting in an order of magnitude better output/cost ratio and allowed the technology to spread across applications and sectors,  spurring the Industrial Revolution.

As always with technology and its applications, it is a combination of factors that create an inflection point.

The Garages of Silicon Valley: A similar pattern and iterative process emerged in the garages of Silicon Valley, and eventually spawned the likes of Apple, Hewlett Packard, etc. This is a recurring pattern of how innovation emerges. The inflection point is always triggered by a number of factors, not just the main innovation of the steam engine or the silicon-based CPU chips (or COTS components to build a computers for that matter).

The Rear-Mirror Effect: These inflection points are often hard to spot. First there is disbelief, then some excitement. Then the general public tends to be disappointed by the slow(er) pace of adoption vs. their expectations, yet slowly but surely, additional improvements and iterations of the original breakthrough take place, quietly, unnoticed. Even additional breakthroughs building on the prior ones create an unstoppable wave of change that often catches people by surprise and most realize that the inflection point really happened a few year ago, when looking in the rear-mirror (and they are too late to the game).


The rise of Applied Artificial Intelligence (Applied AI)

Today we have reached a similar inflection point with Applied AI and we believe it will have a significant disruption potential on many established industries, and our society at large. Today smart software can process massive amounts of data to derive knowledge and conclusions that humans simply cannot.

AI methods and algorithms are not new, they have been around for quite some time, but there are a number of factors that are creating an inflection point for AI to be applied in a much wider sense, across applications and industry verticals that were not feasible before. Additionally, more recent approaches and methodologies like deep learning have yielded an order of magnitude better results than prior approaches.

Ability to compute, store and transfer data

  • We are buying twice the amount of CPU processing for each $1 every 18 months.
  • We are buying twice the amount of storage for each $1 every 12 months.
  • We are buying twice the amount of communications bandwidth for each $1 every 9 months.

More data recorded in last 18 months than the entire history of mankind

The vast majority of all information ever created by mankind was created, transferred, and stored in the past 18 months alone. There is a massive amount of data (and knowledge/information to be extracted from it), and it is growing exponentially.

DATA EXPLOSION

Multi-dimensional correlations

Also multi-dimensional correlations add a massive opportunity.  Take for example fitness trackers, such as JawBone and FitBit, that are tracking activities like movement and sleep. In just a few years they harvested more research data and conclusions (using machine learning) of how physical exercise and sleep are tied together than any research facility ever had in history (and at a fraction of the cost).

Oh – and they also track where (geographic position) exercise is happening, which adds another dimension to this information.

In another example, one of our portfolio founders is exploring how to connect your emotional state of being with what you read in your social communications channels, event calendar, etc., and have the machine learn and guide you on ways to improve your mental well being.

Cost to build keeps going down

The cost to build startups keeps going down, which is true for building AI at scale too, through hosted infrastructure, cost of hardware and open source software (backed by Google, Facebook, Yahoo!, OpenAI, etc.) AI is being applied right now in garages around the world where startups are being built. The hardware needed today is so commoditized that it is all more or less a software play (which increases the pace of innovation and efficiencies).

This all means that innovation by applying Artificial Intelligence is exploding and happening everywhere, not just in the large R&D labs and research facilities anymore.

It holds the keys to our future

All this data, that can now be stored and computed cost efficiently using smart software (AI), holds answers to everything from optimizing your retirement savings, global economic flux, health, education, and answers of how to mitigate climate change today and tomorrow.

The most exciting things are yet to come, but let me give you a few examples of what is already happening:

  • AngelList uses massive amounts of data and interaction patterns with machine learning to improve matching and interaction in their global network of Angel investors.
  • 23andMe uses machine learning and big data to find, in 20 minutes of compute time, the answers that took the CDC up to 7 years and a $100M study to accomplish.
  • George Hotz, the San Francisco “hacker” known for being the first to unlock the iPhone, has built a self-driving car in a month, using mostly off the shelf components, and using deep learning methodologies, resulting in potential self-installation kits costing less than $1,000 a pop.
  • JawBone has conducted the biggest sleep study in history (no company or research organization has had access to that much sleep data before).

As with building any startup today, the hardest thing is Scalable Product Market Fit, which is also why it is so hard for incumbents to tackle the innovation that comes out of startups (Innovators Dilemma).

The large and well funded R&D organizations are still good at cracking hard innovation with long time frames, often called “Moon Shots”, but much less so for finding new Scalable Product Market Fit. This is where startup innovation shines, and when the off the shelf components can be assembled quickly, innovation flourishes.

There is an unbundling of the big corporations, and we think that the key disruption driver for the winners is going to be their ability to apply Artificial Intelligence, and build better, more efficient products that scale.

unbundling-banks

So what are we doing about it at BootstrapLabs?

We are, of course, focusing our entire Venture Building platform toward  the discovery of the best Applied AI innovation globally, and empowering the founders behind them to build globally disruptive companies from Silicon Valley.

We are vetting over a thousand startups per a year from Silicon Valley, Europe, and Asia, and are seeing some very exciting innovators (which you will read more about soon!) who are applying AI such as machine learning, image recognition, and deep learning.

Our upcoming seed fund will invest in Applied AI software startups with a focus on FinTech, IoT, and Future of Work verticals (with the ability to expand into areas like logistic, education, commerce, eHealth, etc.)

Why Invest in Artificial Intelligence now?

 

We are also bringing together the global AI community of hackers and builders at hackers.ai and hosting the biggest Applied AI focused conference in Silicon Valley on May 25th.

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Learn more about the Applied AI Conference 2017.

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BootstrapLabs: Venture Builders in Silicon Valley with a Global Community

When we founded BootstrapLabs eight years ago, we set out to create a company that would empower some of the world’s best technology entrepreneurs to build disruptive companies from Silicon Valley.

In our quest to build an efficient and scalable process to empower entrepreneurs, we transformed BootstrapLabs into a world class Venture Builder platform and brought together 3 critical elements for the success of any entrepreneurial endeavor:

  • Human Capital: Beyond access to our Core team, Global Venture Partners, and Experts in Residence, we have created BootstrapWorks, a proprietary platform that allows founders to find, vet, and compensate top advisors and experts in Silicon Valley and beyond, while removing the friction of contracts, vetting, etc.
  • Venture Capital: Because entrepreneurs need fuel to accelerate the growth of their ventures, we invest in our portfolio companies from our investment funds at the Seed and early Series A round stages. We have also built a great network of top-tier Silicon Valley co-investors and follow-on investors for our companies.
  • Global Community: Over the years, we have nurtured and grown a large and international community of like-minded entrepreneurs, investors, and executives that further reinforce our platform and deal flow.

BootstrapLabs recognizes that talented entrepreneurs have many choices when it comes to accessing capital, but money is only the tip of the iceberg. Experienced founders know that the human capital part of the equation is often the difference between breakout success and failure. For this reason, we strive to provide founders with a bespoke combination of capital, skills, experience, and network, a place were the world’s best founders WANT to come build and iterate their startups to scalable product market fit and success.

 

If this sounds exciting, get in touch, and come make a dent in the Universe with us.

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What most people don’t know about being a startup CEO/founder

Techblogs tend to paint a glamorous picture of how easy it is to raise a billion dollars in funding and build a startup. Reality is very different – it is hard work, a long journey and compared to a job, you are never really off the job.

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As a startup founder for 20+ years and counting, 10 years as an angel investor, and lately  a Venture Capital investor through BootstrapLabs, I have seen a number of interesting patterns in startup founder’s/CEO’s behavior.

One thing I think a fair bit about is the ‘obsessive’ behavior of successful founders that I advised and invested in for the past 10 years (and I see this trait in myself as well…).

Startup CEOs are working super hard, and not always at the office. They always seem to be preoccupied, which drives spouses and family members crazy sometimes.

It’s not that they are literally working 90+ hours a week in an office, doing work tasks such as coding, recruiting, selling, etc. Once they start to grow their team and begin getting traction, to be successful they need to shift into how to really drive the business. And they always need to be thinking about the next big thing, and how to get the company to the next level or stage of growth.

The reason for this is that startups are not really executing a business model, they are in search of a hyper scalable business model. And that search continues until they get escape velocity, die, or divest for other reasons.

So on the journey of a startup CEO you don’t have a quiet moment in your head most days, you are constantly thinking about your ‘baby’, and trying to figure out how to solve problems 24/7 around the clock 365 days a week (or sorry, I mean per year).

As I tell many founders that pitch us at BootstrapLabs, you need to surround yourself early on with a team that shares this ‘obsessive’ behavior to drive your startup forward. And your most important job is to find those people and make them excited about the being part of the journey.

So even when having dinner with friends, or taking care of the kids, or in the morning shower, the founder/CEO is thinking that to hit that $5 million annual run rate to raise the “A” round, you need to ramp up hiring of the sales force and marketing teams. And you need somebody that has experience in X and skills in Y. Or that a particular piece of the product is inhibiting the growth, or is not good enough to drive Product Market Fit, etc.

When launching a new startup: You usually start in a search & discovery mode – that seems to be all over the place for many of the people around you. But once you start to get data points and validation of what you do, you need to quickly shift into a very different approach that is laser focused. At the same time you need to stop every 2 weeks or so and question just about everything and make sure that your assumptions are still valid.

From early assumptions to Laser Focus: Once you have found what to zoom in on, your most scarce resource is actually neither money or time, but personal attention span – which is why most startup CEO’s go into a reductionist mode to create a clear focus on the most important things that need to happen to bring the company to the next level. If you focus on the right things at the wrong time, your company dies.

This is why there are a few key things you need to learn early on as a founder/CEO of a startup:

  • You need to recruit co-founders and team members that give you leverage (execute things independently better than you) – this will increase your attention to other things you do better.
  • Early on and for the core team, you need to find people that share your obsession & passion.
  • You need deep domain skills for the core things you are trying to do within the team, for everything else you need great advisors that can give you sharp insights a few times a month.

Because of the shift from discovery to reductionist mode, the early team is extra hard to build, as very few people are capable of shifting successfully between these two operating modes. In part, this is why it is so hard to find co-founders, since this prerequisite skill is so scarce.

After you have found a focus it becomes a tad easier, but you still need to build a core team with an almost obsessive drive to take things to the next level, and then work really really hard to become a Unicorn.

“No sleep for the wicked”

Venutre Capital Industry: at the dawn of a new era

Venture Capital Industry: At the Dawn of a New Era

2015 has been a banner year for BootstrapLabs. During the past 12 months, we led our first Series A round in an exciting FinTech company, all our portfolio companies raised follow-on funding at higher valuation and we continued to expand our Expert in Residence team to support our portfolio companies.

We believe that technology is a driving force for positive change in the global economy, in society in general, and our daily lives in particular. We continue to be impressed by the talent and passion our founders demonstrate every day and look forward to continuing investing in disruptive technology companies that can transform the way we live, the way we work, and the way we connect with the world around us.

Many believe private companies are overvalued, while others think the next tech bubble is coming. At the same time we see that seed stage investments, where BootstrapLabs focuses, are more vibrant and exciting than ever (e.g., a $25K seed stage investment in Uber would be worth ~ $125M at the $40B valuation mark; even if you assume that Uber is worth $1B, it would still be an investment worth ~ $3M, or 125x the invested amount).

BootstrapLabs is “deep in the stack” alongside its founders day after day, driving the venture market momentum forward. Our global innovation discovery network, combined with our Silicon Valley investment and execution model, provides us with a unique vantage point on what is happening in every corner of the world. Here is what we are observing:

The HOT tech industry is attracting new, mostly late stage, institutional investors that need to invest tens of millions per deal to move the needle.

In 2015, over 566 deals were financed by investment banks, mutual funds, hedge funds, asset managers, and others, while 78% of the deals over $1 Billion have been lead (read priced) by non-VC investors.

Rounds into Tech Companies

Rounds into Tech Companies

Late stage deals are becoming more competitive and less price sensitive due to a combination of i) pent-up demand driven by lower public market returns and the relative rarity of such high growth private technology companies and ii) more financial engineering and deal structuring that aims at lowering the risk for investors, independent of valuation paid (e.g. preferences, ratchets, dividends, etc.) Arguably, these higher valuations are behaving more like “out-of-the-money strike prices” of call options rather than rational valuations driven by operational and technological performance. The chart below outline the dramatic increase in valuation in the later stage as well as the larger amount of money invested by these non-VC investors.

late stage private company median valuation

late stage private company median valuation

Median round size for mid & late stage startup rounds by investor type

Median round size for mid & late stage startup rounds by investor type

There are also NEW sources of capital targeting the Tech Industry via Equity Crowdfunding and platforms that are driving retail investors into the venture market.

Global Equity crowdfunding amount

Global Equity crowdfunding amount

As these platform emerge and private companies can do “public offering of private equity”, secondary market for private equity trading/exchange will gain momentum and importance. One big signal of such trend was the recent acquisition of SecondMarket, the leader in the space by Nasdaq.

Why are non-VCs investing in the tech space?

Startups are staying private longer prior to IPOs today, which means that private investors are making the most of the value from their investment during the pre-IPO period. Traditional public investors, like hedge funds and mutual funds, are starting to realize that in order to capture more value they have to move earlier in the game and start investing in pre-IPO rounds (Private IPOs). See this prior post from Ben Levy, Co-Founder of BootstrapLabs on How to Milk a Unicorn…

Also, traditional VCs have realized that they have to invest earlier in the cycle in order to maximize their investments and not become irrelevant themselves in a world that is changing fast.

Value is Captured Earlier

Because it takes a lot less capital and people to build a proven and scalable product/model, early stage investment has become the most important and possibly the most lucrative part of the value creation chain in our opinion.

late stage valuation

late stage valuation

Later, Access is King

Late stage investors will only succeed if i) they can identify outliers early and ii) they can win a seat at the table during the next fundraising round (hint: money is not enough)

These structural changes, combined with deregulation, have created a once in a lifetime opportunity to form and scale new ventures, as well as new VC firms to finance them. As shown by this recent research report published by Cambridge Associate, more than ever before in the history of the Venture Capital industry, newly formed VC firms have been able to invest and capture some of the top performing startups.

Yet, the opportunities for individual investors remains limited as the industry is shifting to a new model/structure. Similar to the situation with established VCs and hot startups, an individual investor better gain access to future hot new VC funds/managers now, because the best performing funds will have limited access for existing LPs and possibly no access for non-existing LPs in their future funds.

Quality vs Quantity

The number of startups created each year has exploded and will continue to grow quickly as the cost of building technology companies has decreased by at least 10x in the last 20 years, and success stories continue to be blasted across the media as a source of inspiration and validation. The problem will be to identify the good startups as the noise level continues to rise.

Early stage growth no longer signals long term success and the ability to iterate, build and improve your product has become one of the most valuable success skills in the tech space. At BootstrapLabs we excel at finding top talent, bringing them to the best ecosystem (Silicon Valley) and supporting them in their full-cycle “build-measure-learn” iterations.

Innovation is a constant requirement for corporations to remain relevant and it is a pillar of subsistence for our society. Tech innovation will continue to grow and generate outlier returns for the best VCs (and their investors) in the industry. As someone recently mentioned to us “VC is at a dawn of a new era”. Just look at these numbers:

  • 3.6 Billion unique mobile subscribers in 2014
  • 2+ Billion people connected on major social media networks (1.4B FB, 250M TW, 300M LNKD, 300M Instagram)
  • 120x faster online speed (6.7 Mbps US average today)
  • 243 million machine-to-machine connections
  • 50 Billion connected devices by 2020
  • $1.7 Trillion e-commerce spend

The total of all the Unicorn valuations today is worth about half the value of Apple. Some of them will go public, some will be acquired. Apple could actually acquire most of the Unicorns and still have billions in its bank account to spare.

The slow growth in the number of IPOs is a consequence of a historical switch and the growing importance of innovation. Companies need to invest most of their cash flow in innovation, while public market investors expect short term revenue first. Many startups are building for long term success, and if they go public too early they will be unable to maximize their innovation or opportunity. As Marc Andreessen said during a recent interview: “It’s not a tech bubble, it`s a tech bust”… many of the innovation and technology companies are still undervalued and we are strongly optimistic about the great future in front of us”. So is BootstrapLabs!

Behavioral Analytics- Just Keep It Simple

Behavioral Analytics: Just Keep It Simple

Today’s guest post is from Marianna Yanike Graf.

Behavioral Analyticsba

Summary: Behavioral analytics is the process of systematically sorting through data to uncover patterns of user behavior in order to gain an insight into user decision making. The better you understand your user, the better your company is at catering to your user’s needs and at anticipating them.  So, how do you make sense of human behavior? Just keep it simple.

 

Know Your User to Optimize the Present and Shape the Future

Behavioral analytics deals with understanding patterns of user behavior. Nowadays we have access to a wealth of human data.  Computing resources allow us to sort through it in a systematic way in order to gain understanding of human behavior.  Any company, large or small, wants to use this knowledge to deliver the most optimal interactions with its users and make meaningful predictions about the future.

Of course, what is considered optimal is unique to each company.  Optimal is often equated to individualized.  If you know your user, you can do many things to tailor your products or services to this user.  For example, you can optimize the design of your website to deliver the most relevant and timely information to your user (e.g. Amazon shopping card or Google Ads).  Ultimately, though, you want to be proactive at communicating with your user.  You want to predict, for example, when a next marketing campaign will be the most effective.  You want to stop guessing and start making informed decisions.

The accuracy of such decisions will depend greatly on how well you lay out the process starting from collecting data, analyzing them and eventually drawing conclusions from them. For rapidly growing businesses operating in an ever-changing environment, this can be a challenging task. How do you make predictions? How far into the future? Also, human behavior tends to be irrational in general.  Often our behavior is not driven by logic alone. So how do you analyze complex beings in an ever-changing environment?  By keeping it simple.  By asking specific questions, by finding simple answers and by doing it often.

Define Your Problem and Ask Specific Questions

When it comes to looking for answers, the most important part is formulating your question right.  You can always find a data scientist to apply the most rigorous model to your data.  However, if the question is ill posed, the answers will not be useful.  Make sure you know what your users want, as opposed to what you think they want.  For example, you want to figure out the best time to send out marketing updates to your users. Either you ask your users directly, or you test a few options by selecting a representative user subset and seeing how they react to the same updates sent out at different times. In other words, if you want to know X (best time for updates), change Y (time: beginning/end of the week; weekly/biweekly) and evaluate outcomes. Essentially, it’s about keeping it simple.  First define a problem, then formulate a question and think of possible answers.  Keeping it simple doesn’t mean that you have to ask only one question.  Quite the contrary, try to formulate many specific questions and find simple solutions for each one of them.

Find a Simple Solution

Once you have formulated your problem, you need to decide the best way to address it.  While it may seem like a logical approach, analyzing all data with a complicated model will not necessarily give you the most accurate answers.  Why? Well, it’s like looking for a needle in a haystack.  You are better off breaking down the haystack into many small ones and looking through each one individually.  You are even better off if you can find the ones that are most likely to contain the needle.  Big data are complex, capturing many dimensions and patterns, and yet often biased and incomplete.  So overreliance on data analysis alone can produce complicated answers devoid of reality.  Your logic and intuition are very useful for guiding you towards the simplest solution. For example, you want to create a new marketing campaign and tailor it to different users.  Typically we think of demographics (e.g. age, gender, social platform preferences).  But, you can also think about the timeline of how you have acquired those users (e.g. cohort analysis).  

Do it Often

Probably the most important reason to use behavioral analytics in the first place is to make informed decisions about the future.  Predicting the future, though, is challenging because all we can do is look into the past.  Nowadays businesses operate in a fast paced environment and the dynamics of user interaction and/or needs can evolve or shift rapidly.  When it comes to making predictions under such conditions, doing it often (frequently) is a reasonable approach. Why? Many models tend to train themselves on data to extract trends and make future projections.  To accommodate for the fast dynamics of your business, you constantly go through a cycle of formulating a problem, collecting data, and making an informed decision to resolve it.  Every time a change is implemented, user behavioral patterns can change too and the data become biased by the change.  That is why time is an important factor.  The dynamics of your business operations will determine how often you should forecast.  


Marianna Yanike Graf, Ph.D.

Marianna Yanike Graf is a research scientist with strong interest in behavioral analytics.

In addition, Marianna is also a brain decoder, data-artist, art-constructor and an Expert In Residence @ BootstrapLabs.