Data Based Storytelling And The Real Customer Journey – or: What To Learn From Data Journalism

Know your customers and build an ongoing relationship between them and your brands by looking beyond a on-time meet-up caused by a marketing campaign! Highly engaged users become loyal customers, have higher LTVs and contribute more to your bottom line than the casual customer. Yes to that. But how to to build a stronger relationship with your customers? Do you know enough about them to accompany them on their journeys?

explore geofencing

If you’re a reader of Nate Silver’s FiveThirtyEight, you’re aware of his disdain for opinion journalism (e.g. op-ed columns), which “[...] doesn’t seem to abide by the standards of either journalistic or scientific objectivity. Sometimes it doesn’t seem to abide by any standard at all.”

So, if a (well researched, if good) op-ed column does not fit to a journalistic or scientific standard – does a customer journey description, which (typically) ends when the customer actually starts his real life journey because she leaves her desktop, fit to any marketing research standard? Won’t the customer act and express herself in a different way when on the road, when actually being shopping, when passing by out-of-home advertisements in a hurry – compared with relaxing with her iPad in her living room?

Those real life customer journeys show that in order to fully understand your customers, you have to follow them everywhere. You have to plug into your customer to learn from her, to learn about the places she visits, about her habits and about her needs to eventually optimize her daily routine. If you manage to be that near to hear you can tell her the right story – in this case a story based on her data. Coming back to our analogy with journalism: The plural of anecdote is data,  as political scientist Ray Wolfinger said in 1969.

Every user of a smartphone or any other wearable devices produces an enormous amount of data ever day. If you manage to collect that data, to analyze it correctly and to translate it into useful recommendations and appropriate user stories, you will build strong relationships with you customers. With our explore app, you can accompany your customers, ask the the right questions at the right times and learn a lot about their whereabouts, habits and attitudes.

Datarella People – Florian Schumacher

Here’s the transcript of this Datarella (DR) interview with Florian Schumacher, Founder QS Meetups, Germany.

DR
Florian Schumacher, you started the first Quantified Self QS Meetup in Germany. Could you introduce yourself and tell us about your QS approach?

Florian
When I learned about Quantified Self in 2011, I immediately felt attracted by its very positive culture. Consequently, I started the QS meetups in Berlin and Munich in 2012 which is a lot of fun. Besides, I am a trendscout of Wearable Technologies AG. Here, I’m dealing with all devices you can wear and the sensors which collect behavioral data.

DR
What do you track yourself and how do you do that?

Florian
I regularly and passively track my weight, my body fat, my physical activities and my sleep – all those data are collected automatically, so that’s completely effortless. Then, I manually collect sample data on my blood pressure, blood sugar, my girth, and I try to keep an eye on my haemogram. This, I do on a weekly basis. Then I track other data as time, which is quite sophisticated, since it’s not automated at all and I track everything: every project, every lunch, every activity finds its place in my calendar. So I have a good overview of how I spend my time, and how my time is devoted to my hobbies.

DR
You told us that you have lost weight – voluntarily, that is – that means tracking has very specific consequences on your life!

Florian
Yes, indeed. I have been testing QS devices for three years now. While I kind of just tested theses devices before, I have started to use them for special purposes last year. I have been trying to lose weight, I’m on a special diet and let my ape count my calorie intake. Then I check what nutritients my food actually contains. Then I exercise regularly – and by tracking all that I can understand my body’s changes very well – and I like that very much!

DR
Then for you QS is more a motivational thing, rather than a supervisory body…

Florian
QS is a control body, but more in a positive sense: by comparing different measured values I learn and get a better understanding of my body and, consequently, I get motivated and become happy even if sometimes this way is somewhat hard.

DR
Do you share your tracking data with others?

Florian
Yes, but I only share some of my data: e.g. I share my steps within my Fitbit community – but only my steps – since for me these are indiscriminate data. All other data I would share with my doctor rather than with my friends.

DR
So you draw a line at sharing your data. Could you elaborate on that a little more?

Florian
I think that’s very personal everybody should define what data to share individually. Sharing your data can be very motivating if people have mutual goals. There are studies showing that everybody will be far more successful if all share their data which each other; e.g. people lose two to three times more weight if they share their goals and data. That means in case of very personal data (e.g. illnesses) I would share my data within a closed group of likeminded people, but I think everybody should decide that by herself – and there should be some control mechanisms preventing abuse of this data.

DR
At QS13, Gary Wolf [Co-founder of the Quantified Self] said that Tracking and data sharing would become a social responsibility to provide access to important learnings to everybody. Do you agree with Gary?

Florian
I don’t think that self-tracking should become a, obligation for everybody. But what I strongly support is the anonymous sharing of data to realize potential benefits in scientific research and pharmaceutical product development. I’m sure that this will promote our culture to the next level.

DR
A last question: which tracking app or gadget do you like most?

Florian
I really like this Basis band, since it automatically tracks my sleep, my movement and it accurately calculates my calorie intake. At the moment the Basis is the most accurate tracking device on the market and I like it very much!

DR
Thank you!

Florian
Welcome!

How Big Data Changes Marketing

About 0.5% of big data collected is actually being analyzed and monetized. The “rest” ends up unused. 90% of all data ever created has been generated in the years 2012-2013, and 30% of it contains valuable information. During the last years, the amount of data has been increasing exponentially. Enterprises around the world are beginning to put Big Data on the top list of their operations.

Big Data comes with a paradigm shift not only in research: here, the industry is shifting from finding and asking the right questions to letting the data speak for themselves, enabled by smart algorithms doing their analysis automatically. In marketing, the same paradigm shift is taking place: CMOs are provided with factbased, individualized answers in realtime, rather than with intuitive, retroactive perspectives of traditional market research.

While it’s not the data in Big Data which is important. Rather, it’s the insights derived from it, the quality of decisions you make and the actions you take that transform big data into smart data, or “affinity data”, if you drill the data funnel down to the individual: the more information marketers have on their user’s likes, preferences and interests, the better they can target the individual with content personalized to her interests. This is helping companies generate insights into their customer bases, and thus create new and better targeted products and services.

There are twice as many “things” connected to each other on the internet than people. By 2020, this number will rise to 50 billion connected devices. Devices connected with themselves and people – this gigantic communication network will provide unprecedented insights into human behavior. From customer segments to the individual customer, from retroactive long-term research to realtime knowledge – the new status quo of data amounts to enormous opportunities on the one hand and crystal clear obligations on the other: enterprises that do not understand the implications of big data will lose traction against their smarter competitors who have built up vitally needed expertise in big data analytics.

Over the next weeks we will describe how big data can make a substantial impact in customer engagement, customer loyalty and the overall marketing performance. We will start with the airport industry which, beside its aviation business, heavily relies on the non-aviation business, such as shopping malls.

Quantified Self or Quantified Us – A Social Responsibility

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Life logging, tracking, the Quantified Self, the Quantified Life – and now the Quantified Us? Do we need more or better expressions for this global trend which motivates people to change their behavior?

Matthew Jordan and Nikki Pfarr from Artefact make their case for changing the Quantified Self into Quantified Us. The first degree of meaning, that is to know your personal data, is the first step for all life loggers: by collecting data about their behavior they can compare their subjective perception of movements, food intake etc. with the reality. And get meaning from that, such as: “Ah, I see – I don’t run 10 kilometers every second day but I run 7.5 kilometers twice a week – on average.”

After having learned about oneself, the user takes action – the second degree of meaning: she buys new running shoes to please herself and then she extends her weekly parcours to 10 kilometers, completed every second day. Lesson learned, quality of life of the individual improved.

The third degree of meaning would be added, when people get advice to make better use of their – and other people’s – data in the moments when decisions are actually made. A basic requirement for the third degree is that people (anonymously) share their personal data.

Matthew and Nikki prefer a Quantified Us approach to the Quantified Self. They call for groups of like-minded people quantifying themselves and sharing their data with each other. Apps which support those groups should help the users to make it easier to collect the data and to get a personal meaning from the data.

We could not phrase that better – and this is exactly the our approach at Datarella with our app explore: By asking our users questions we make it very easy for them to track those parts of their individual behavior which cannot be tracked by sensors but have to be added manually. She does not see a blank page which he has to fill by being highly creative , but by answering questionnaires, the user is guided and is able to add lots of contents in a very short time.

Second, explore user get feedback on their own behavior as well as the behavior of other users. They can use the answers of others as benchmark – and they see their individual position within the explore community.

These two aspects let every user provide their personal individual data as useful community data: by adding her own data, everybody is acting as an important piece of the puzzle. And, as known from P2P networks, such as Skype, the result for each individual will improve with every new participant in the network or community.

Coming back to Quantified Self vs Quantified Us: yes, we totally agree that the social – or community – factor is necessary for the movement to become socially relevant. But we think that the individual – the self – is the key factor in the game: the individual must decide to participate in one of the most important movements ever, or to stand still and rely on traditional eveolutionary mechanisms.

We’d love to read your thoughts on that!

Crowdsourced environmental monitoring

Many parameters a smartphone accidentially measures are useful in monitoring the environment. We have recently discussed, how air pollution with particulate dust can be monitored with an easy ad on to the phone’s camera. But there are even more subtle ways by which users can help to research and monitor environmental conditions.

Another example is given by A. Overeem et.al. who track urban temperature over time in various metropole regions arround the globe. The approach is as simple as powerful: a regression over the battery temperature (that is measured by every smartphone anyway).

The microphone, too, can give valuable data on local environmental conditions for an unlimited mass of individual users that participate. Sound level show noise emmission that can be located in space and time. Noise is regarded as a prime source of stress, but rather little is known about the changes that occur in different microgeographic regions.

Apps like Weather Signal use thus a combination of the phone’s sensors to contribute to a richer model for weather conditions.

Appart from just passivly deploying the phones as sensor boards themselves, it is of course also possible to collect data from other local sources and just transmit the results via smartphone. This can be done by letting the users take a picture of some reading of a scale which can then be processed via image recognition. Or you just ask people to put in the readings or their observations into some kind of questionnaire.

The fascinating thing is: since so many people in almost every country carry a smartphone, monitoring environmental conditions and changes is now possible on far larger scales than ever before.

Big Data helping people to understand real-time pollution risks

From rapid urbanization in China to dung-fired stoves in New Delhi, air pollution claimed 7 million lives around the world in 2012, according to the World Health Organization. Globally, one out of every eight deaths is tied to dirty air – which makes air pollution the world’s single biggest environmental health risk. And, in areas with very bad air pollution, people live an average of 5 fewer years than those in other areas. 

airquality

Not only in Chinese megacities or indian agricultural areas, people are trying hard to keep air pollution at bay. In Portland, Oregon, a local initiative called Neighbors for Clean Air is using Big Data to make bad air visible. The group is part of an experiment in initiated by Intel Labs, that uses 17 common, low-cost sensors, each weighing less than a pound to gather air quality data. This data feeds to websites that analyze and present comprehensible visualizations of the data. The sensors itself are built using an Arduino controller. They measure carbon and nitrogen dioxide emissions, temperature and humidity.

By making the air pollution problem visible, the experiment not only made people recognize the importance of technology in understanding air quality, but Neighbors for Clean Air could forego an agreement with a local metal foundry to cut emissions.

If you want to have a look at your own air pollution, go to Air Quality Egg - perhaps one of the several hundred eggs worldwide has been installed in your neighborhood.

 

Tracking Lung Function with the Phone’s Microphone

Asthma is one of the most common chronical conditions. For many who are affected, it would be necessary to monitor their lung functions much more frequently than by visiting their doctor once or twice a year. Spirometers which measure the volume of air taken in and out whith a breath are expensive and even if you’d buy one, you’d still have to carry another device with you. Smartphones are ubiquitous, everybody carries one – this is what makes mHealth so powerful after all.

SpiroSmart is an app that makes use just of the most basic function of any phone: the microphone. By exhaling all your lung’s content into the phone’s mike at the distance of your full arm’s lenght, SpiroSmart calculates the breath capacity. The app analyzes the dynamics of the sound, the exhaling makes to fulfill the task of the classic spirometers that do the same with a small fan that gets propelled by the exhaled breath inside a mouthpiece. The error rate lies close to the parameters set up by the American Thoracic Society ATS.
SpiroSmart is developed by an interdisciplinary team at the University of Washington in Seatle.

Links:
“Tracking Lung Function on any Phone”. Poster by E. Larson et.al.

SpiroSmart on Youtube:

Socially Relevant Technology

explore results

If a technology wants to be respected it should demonstrate its social relevance. Then it will be approved by the people and its implications will be accepted. Otherwise it will be dead on arrival.

Generally, there are two different sorts of people: tech lovers and tech skeptics. The first are open to any innovation and happily embrace new technologies, products and services. The others look for risks and potentially negative implications of new tech. Ok – that may sound a little black and white – but for this post it helps. I think, if a new technology wants to be regarded as valuable, it should demonstrate social relevance. What do I mean with that? Let me explain using the example of or app explore.

A quick reminder for those who don’t now explore: the app helps you to learn more about yourself, your behavior. It does two things:

  • it tracks you by collecting data from your smartphone’s sensors – like geolocation data, and
  • it offers you questionnaires regarding your behavior to be answered by you.

The more questions you answer, the more does explore know about you and the better is the feedback you get from explore: your behavior, presented in nice-to-read graphs, with comparisons of your own behavior with that of other explore users. explore is a quantified self app, fully functional without any additional gadget.

The goal of explore is to help you improving the quality of your life. And that’s why you provide explore with your personal data: you will learn a lot about yourself – how you behave in certain situations and how this correlates with other factors, such as weather conditions, your individual communication behavior, your stress level, etc. If, for instance, you don’t feel well for the last few days, explore might find out a strong correlation with a higher than normal coffee intake. And since we all forget quickly – we even don’t remember what we did last Monday – explore supports you by showing your behavior in a time line. That might be the first time you don’t have to speculate about the reason for your not-so-well-being, but you actually see the reason!

“The first thing you have to know is yourself. A man who knows himself can step outside himself and watch his own reactions like an observer.”
Adam Smith

We are individuals, all of us are different. There is no standard recipe for illnesses or bad feelings. There are as many recipes as there are people. And this is where explore comes in: since you provide explore with your individual data, you will get individual feedback and recommendations about what to change, if necessary. And here we are: I think that our app explore – and its behavioral analytics in the background – is socially relevant.

3 Aspects of explore’s Social Relevance

  • Everybody can use explore. The app is free and there is no need of using an additional gadget like a fitness band, or else. It’s in your smartphone – with you all the time.
  • It’s absolutely easy to participate: explore asks the right questions at the right time – nobody must be overly creative and fill in an empty diary – just answer short questionnaires in under a minute.
  • Users get individual personalized recommendations to change their behavior, if necessary. No standards, but individual advice.

For me, it’s absolutely great to work with a product (and a team, of course!) that helps people to change their lives for the better. Depending on the usage and the individual user, these might be minor changes – but with every small improvement is a good one. And since human beings can only change themselves for the better by changing their behavior (and not by waiting, taking pills or expecting any other external help), explore is a well applicable tool. And then it becomes socially relevant.

Please send me your perspective on socially relevant technology – would love to discuss!

Mapping particulate dust with phones

iSpex device on a smartphone. Image by Sebastiaan ter Burg , published under licence CC BY 2.0

iSpex device on a smartphone. Image by Sebastiaan ter Burg , published under licence CC BY 2.0

iSpex is a plastic contraption that can be clipped on top of a smartphone’s camera. In this simple slit spectrograph light is defracted and polarized by shining through birefringent plastic sheets and a polarisation film. iSpex measures how aerosoles – microscopic or nanoscopic particles hovering in the athmosphere – change the polarization of the highly polarized light that shines from an unclowded, blue sky. This change in polarization renders a distinct pattern in the spectrum, that is cast by the iSpex-device into the phone’s camera. By this approach, iSpex can measure how the air is polluted with particulate dust, which is regarded especially unhealthy and has become topic of fierce political discussions, when the EU ordered city governments to regulate and even lock out automotive traffic.

Behind iSpex stands a consortium of the Netherlands Research School for Astronomy at University of Leiden, Netherlands Institute for Space Research (SRON), National Institute for Public Health and the Environment (RIVM), and the Royal Netherlands Meteorological Institute (KNMI).

Over the course of summer and autumn 2013, thousands of people in the Netherlands participated in “national iSpex days”, jointly measuring particulate dust. The first results of this awesome social effort are published, and we can hope this project will find epigones in other countries.

iSpex website:
Measuring aerosols with spectropolarimetry

iSpex on Youtube:

Smartphone Geiger Counter

Smartphones carry versatile sensors. With appropriate apps, expensive instruments can be very well replaces - even sometimes the Geiger counter.

Smartphones carry versatile sensors. With appropriate apps, expensive instruments can be very well replaces – even sometimes the Geiger counter.

When photons, the particles of light, hit the chip of a smartphone’s camera, they excite electrons on the chip’s surface and change the conductivity or even generate voltage within the small area arround the impact.

Gamma rays which are often products of radioactive decay, are also electromagnetic waves, just like light, however much more energetic. That means: as radioactive radiation can expose a chemical photographic film, it can as well effect the camera chip in the smartphone.

A team of researchers at the Idaho National Laboratory in Idaho Falls have used this property to change common smartphones into detectors for radioactive radiation. The radiation is recorded via the camera an an app, which calculates the radiation intensity from the data collected.

With this approach we learn again, how versatile mobile devices can be deployed. Up to thirty sensors in each smartphone measrure all kinds of variables like temperature, magnetism, brightness, sound and many more. With a little creativity we can combine these measurements and get valuable data about the environment around the smartphone and its user, that not rarely can replace expensive, specialized methods.

Here the link to the original publication:
Joshua J. Cogliati, Kurt W. Derr, Jayson Wharton: Using CMOS Sensors in a Cellphone for Gamma Detection and Classification