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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.

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BootstrapLabs at Innovation Skåne – Advice for Swedish entrepreneurs

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Lund, Sweden | Wednesday 21 October 2015

In a room full of entrepreneurs, BootstrapLabs founder Nicolai Wadström shared his thoughts on Silicon Valley unicorns, and the speculation of tech bubbles and overvalued startups. He reminded his audience to focus on value, and the fundamentals of the companies being touted as Unicorns.

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5 skills founders better verify before deciding to add a core team member

Today’s guest post is from Tommaso Di Bartolo.

Startup is an amazing crazy ride. Unlike corporate business, every moment in startup makes you remember you live because of the thrilling paths and the amount of emotions you experience. The most compelling phase in a startup is the period of time before the product finds its product-market-fit. The time where you think you know what problem you are solving – but the market is not reacting the way you thought and the value you offer still hasn’t been proven out. This occurs usually in the third phase – out of four – of a startup life cycle. It’s the phase where the vision is being squeezed, where getting funded is hard if there isn’t enough traction, where releasing a sexy product is challenging if the right people aren’t on board, and the time where adding the “right core team members” is tough. But what does “the right” people mean in this case? What are the key attributes, skills or even qualifications the handful of key people you’ll call core team members will have?

Once upon a time, there were 3 friends that met at Stanford: a computer science engineer, a design guy and a business grad. During lunch time on a spring afternoon they, all together, came up with an idea, the prototype of which they released short after. They easily got traction and therefore funding from TOP Tier investors on Sand Hill Road. With the money they hired a stunning team, invested in developing a great working product which scaled globally and were acquired only 36 months later for a $1B …

… and then we all woke up … good morning!!!

The startup ride is a very turbulent one and “luckily” not for everybody – otherwise we would have even more competition ;-). Often we read about “overnight” successes – but it only was overnight for those who were not part of the journey… as stories like the one above don’t exist! Nor are most of the startups representing an “A-Team” that have already done it before. More often, early stage startup teams are a bunch of inexperienced hustlers, hackers and hipsters driven by the sentence to “change the world”, and more than 50% of them split up within the first 12 months… that’s where the shit hits the fan. Now, only teams who’ll write the most painful stories actually really make an impact. But what is it they do differently?

Startup Mindset goes over Education

While upcoming entrepreneurs have guidelines on how to build a lean startup or how to build a demand engine for products – there’s a lack of blueprints for how / what to consider to put the right people together, and therefore we underestimate the importance of how much business relies on relationships and their communication. And that behind every “tongue”… there is a mindset that is responsible for letting us do things the way we do… or simply don’t do. Mindset is often the make or break deal, especially in the early days. In other words, the people’s strength of mindset is what at the end makes a startup succeed or die. It’s what makes startup teams keep fighting and finding ways, or give up.

After 15 years of entrepreneurial experiences on three continents and four startups, I’ve learned the hard way that core team members’ soft skills were more important than their educational background. While I’m not saying that education doesn’t matter, make sure specific characteristics within core team players exist, especially during the initial delicate phase where things are not settled.

The biggest “bug” in an early stage startup is a “mismatched mindset” – Tommaso db

Watch out for genes – not qualifications

There’s a difference between the way you recruit a CFO for when you are scaling business and between “handpicking” a Senior Backend Engineer for an early stage startup. For the CFO you mainly consider qualifications, experiences and assure yourself with reference checks. For the Senior Backend Engineer the story is quite different. In an early stage startup it might be the case that you do have an MVP – but still not enough traction to prove assumptions and enchant with business success. Or – like in many early stage startups – you don’t have deep pockets and sweat equity is the compensation model to go to.

Even though on one hand your funds might be low and the pressure to move ahead with product development is high, you better make sure the person you decide to add as a core team member qualifies mindset-wise – before you match educational skills. This helps you avoid investing time and creating expectations with a candidate that sooner or later might leave if their mindset simply doesn’t fit. Don’t let circumstances hurry you on everything you are doing or pressure your decisions.

Don’t delude yourself because of an educational background or due to lack of options  – but seek for genes that are crazy enough to go through the real side of your daily business.

…continue reading “5 skills founders better verify before deciding to add a core team member” on Tommaso’s blog WhatItTakes


tommasoTommaso Di Bartolo | @todiba

Expert in Residence, BootstrapLabs and CEO at swaaag.

Tommaso is a serial-entrepreneur with 2 exits, an advisor & an angel investor. He lives with his family in Silicon Valley.