Tag Archives: disruptive technology

Post Disruption: Market research, what next?

Based on my personal experiences

Around 2011 the owner of the company that I then worked for came to Toronto and challenged the employees to go on social media. At that point, few had considered how important this was about to become for our jobs.

In the pre-historic days of market research, consumer data were collected by walking around the city, knocking on doors and interviewing people face to face. I caught the tail end of this when I started in market research in Russia (which is another story). Then, call centres became industry standard and most survey research was conducted on the phone. However, qualitative discussions were still held in person at focus group facilities.

In the 1990ies, online surveys took over as the main means of eliciting consumer data. Rather than finding consumers through random digit dialing, companies now started to build online panels of people who were willing to answer surveys. There was some concern about the representativeness of these panels at first, but it was soon put to rest when the considerable cost savings of this approach became apparent. Initially, research companies needed specific expertise, expensive software and sufficient server capacity to program, host and run these online panels and surveys. Quite a few even tried to develop their own software.

The 2000s saw a rise in self-serve online research solutions. The idea of software as a service (SaaS), and ‘freemium’ products such as SurveyMonkey made it possible for anyone to collect the data they wanted. It seemed no longer necessary to have any particular expertise or resources. At that time, I felt that many established market researchers were reluctant to open their eyes to the new reality. A few companies were blazing ahead, but the majority sat back and hoped it wouldn’t get too bad.

I realized myself around 2012 that things would never be the same again. When I had the opportunity to start my own business in 2014, I jumped with both feet into the world of SaaS, social media and startups, looking for ways to innovate and carve out a niche for myself in the insights community. I read Clayton Christensen and Eric Ries. I experimented with different tools and methodologies. I immersed myself into WeAreWearables Toronto, then a monthly event at the MaRS Discovery District, an innovation hub that offers services for startups and scaleups, and various other incubators and accelerators around the city.

Innovation was the buzzword, and also agile, real-time, lean and design thinking. The word disruption became popular in my neck of the woods a little later. Hard to believe that this was only six years ago. Now the word already seems a little tired. I feel that the dust of disruption has settled in market research, and firms have made adjustments.

Those who started off with the new technologies – for example Qualtrics, or the Canadian company Vision Critical – have probably done very well (not that I know their books). The traditional firms have incorporated various technologies into their offering. Many have social media analysis products. Some run large-scale customer feedback platforms for their clients. Some use virtual reality goggles for concept testing or shopper studies. Some offer online communities or hold virtual discussion groups. And to cut cost, many move business functions into the cloud, and outsourcing is widely used.

Whether this is sufficient to keep traditional market research organizations profitable, I don’t know. What I have learned in my own business is that I am still selling ATUs, segmentations, concept tests and one-on-one interviews. After the frenzy to be innovative and different, what clients seem to appreciate is my expertise. They trust me to know how market research is done properly, to execute the project for them, and to deliver them results that reflect a thorough understanding of their business questions, and in a form that adheres to their internal processes and requirements.

That’s why I stopped following the hype in the last couple of years. Disruption happened, but that was in the past. No amount of research automation can substitute understanding. And understand the customer we must.

But…I recently started renting a co-working desk at a business centre. I am with ease twenty years older than the majority of the other tenants there. So now I am back in a startup environment, and I see that while the hype has decreased, startups are here to stay for the foreseeable future. Businesses who are trying to disrupt various fields are numerous and, with the myriad applications of AI, far from done.

It will remain important to understand how new technologies fit into and change existing businesses. For example, I am intrigued by the anatomy of the Cloud, where servers are located, how data are moved around and what implications different factors have on data loading speeds, data security etc. When some people that I recruit for surveys complain about it taking longer than they thought, is that because their Internet is slow, or because the survey hosting company has switched from using their own servers to the cloud?

Data governance also interests me. With many market research companies using subcontractors, it is almost impossible to see all the way down the supply chain where data may be stored, processed or transferred to. The business risk related to data governance has increased exponentially for market research firms. Good times for lawyers and insurance companies.

But understanding AI, Cloud and computing in general is and will be immensely important for anyone interested in the affairs of this world. I think this is a topic that they should teach kids about at school, so that future citizens can make informed decisions about it. I want to learn more. So far, I have read three books on AI – it’s a start.

Barbara’s AI reading list:

  • Kartik Hosanagar: A Human’s Guide to Machine Intelligence
  • Ajay Agrawal, Joshua Gans, and Avi Goldfarb: Prediction Machines
  • Virginia Eubank: Automating Inequality

Update, also… Janelle Shane: You look like a thing and I love you

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Wearable Tech and Health – not quite there yet?

Wearable tech is revolutionizing healthcare delivery – at least that’s what the pundits have been predicting for a number of years. The array of devices that are under development or already commercially available is stunning.

Looking only at devices that are used by patients on a day-to-day basis, there are three different areas of usage for wearable tech:

  1. Continuous monitoring of chronic illnesses

Many patients with chronic illnesses need to monitor certain biophysical parameters that indicate how well they are doing, if their medications are working and when an exacerbation of their condition would warrant visiting a healthcare professional.

Wearable devices that can sense and accurately measure heart rhythm, breathing rate, or blood glucose levels enable continuous tracking of critical markers and can help alert patients and their healthcare providers early to any arising problems.

  1. Improving the lives of people with disabilities

This is an area in which assistive devices have had a long history (think: hearing aids, wheel chairs, etc.). Digital sensor technology is now making devices more accurate, more personalized and more helpful.

Some examples of new technology that improves daily living include:

  • eSight Eyewear: A device consisting of a high-end camera, video processing software and processing unit and highest quality video OLED screens which project a real-time image that allow legally blind people to see.
  • Sensimat Systems: A series of pressure sensors that are placed under a wheel chair cushion. The sensors use a proprietary algorithm to monitor the seating pattern of the wheel chair user, and send a notification via smart phone when it is time to change position to minimize the risk of pressure sores.
  • TAPS Wearable: Velcro touch pads that can be worn on top of clothing or on the wheel chair. Each pad is a trigger for a smart phone app to play a pre-programmed phrase. This helps people who have difficult speaking (for example due to ALS or cerebral palsy) to communicate more easily.
  1. Recovery and rehabilitation devices

Also an area in which assistive devices have had their place for a long time, digital enhancements now tailor these types of wearables more to the patient’s needs. A number of companies are working on solutions to increase patients’ mobility – typically using some form of exoskeleton, together with sensors and algorithms to help with movement and recovery.

 

How do these new technologies fit into our healthcare system and how accessible will they be to patients who can benefit from their use?

Our healthcare system is already set up to evaluate new assistive devices, and potentially pay for them. Device makers would have to prove that their inventions are useful and enable patients to live more independently and / or return to work earlier and save or reduce disability payments or insurance costs.

Those who develop the wearables have to figure out which ones of the many institutions that share healthcare costs in our country they should approach to be considered for funding.

Funding is more difficult for wearable devices used in monitoring chronic illness. In most cases, there is no precedent for continuous patient monitoring.  Not only the patient’s engagement in the process is required, but a whole new infrastructure approach to healthcare is needed on the provider side. Currently, neither private practices nor hospitals are set up to receive, monitor and act upon myriad patient data coming in through wearable devices.

Many barriers impede adoption of new technologies for patient monitoring:

  • Concern about the reliability of incoming data – how accurate is the wrist-mounted heart monitor, are there differences between different devices and who is at fault if the device either gives a false positive and triggers an unnecessary medical intervention, or a false negative that puts the patient’s health at risk?
  • Integration with existing technology – how will data come into the clinic, will it be compatible with currently used IT solutions, how can staff easily access the data and how will confidentiality and privacy be safeguarded?
  • Integration into existing work flows – who will review the data, at what intervals, and which actions should follow particular cues? Will healthcare professionals need special training on how to read the data? Is extra staff required? How can incoming data be standardized to avoid confusion?
  • And last, but not least, who pays for the extra time that clinic staff spends on continuous patient monitoring?

Many of us are still in the phase of excitement over the wealth of possibilities that wearable tech affords us for delivering better healthcare. The successful players will be the ones who figure out how the possible can be turned into the doable, and profitable, within the constraints of our infrastructure and funding environment.

Confessions of a First Time Wearables User

Since I started my business in healthcare-focused market research, I have been paying attention to wearable devices. Wearables devices have great potential for monitoring health parameters and improving care for certain chronic conditions.

The business press has been making a big deal of wearable devices, predicting exponential market growth over the next few years.

Wearables market growth

I am interested in the user perspective – how useful are these devices, actually? Some statistics show that, similar to fitness club memberships, many people who purchase fitness tracking wrist bands abandon them after a few months of usage.

As an anthropologist, I believe the best way to learn about a certain area of life is to immerse yourself in it, to experience what it feels like and to understand how it works. So I started going to these meet-ups for people engaged in the world of wearable devices. They are awesome!

Run in Steve-Jobs-style corporate presentations by the inspiring wearables guru Tom Emrich, companies in the wearables space present their prototypes and the audience gets to try stuff out in the post-presentation mix-and-mingle. My favorite so far has been the mind-controlled beer tap.

I have met many people in the wearables community, and they are certainly very different in style and outlook to my usual clientele (executives from pharma companies). However, I have hesitated to take the plunge into trying a wearable myself.

I am a pretty fit person, working out two to three times a week, to maintain my health and my sanity, eating pretty healthy, and most of the time walking to public transit rather than taking the car. Whether I run 5 minutes less today than I did last week is not really important to me, as long as I get some exercise every few days. Competing with others along fitness goals does not interest me at all. But I realized that not trying out a wearable myself would deprive me of certain insights that could be essential for conducting the user research that I am so interested in doing.

So I bought a Garmin Vivofit last week. Three things enticed me to purchase this device rather than some of the other ones that are very popular (Jawbone Up, Fitbit, Fuelband).

  1. It shows the time. I am of a generation that still wears a wrist watch, and wearing both a fitness wrist band and a watch separately seemed silly.
  2. Its battery life is supposed to be one year. Charging devices is a big pain, and in my household we are competing for outlets and charger cables to charge the various cell phones, iPods etc for the next morning.
  3. It has a red progress bar that shows up after you have been sitting around for too long. My occupation requires a lot of sitting in front of the computer. I tend to get into a state where I push myself to concentrate only half an hour longer, then another, then another, and then become all tense because I have not taken enough breaks. So a little nudge to get up and walk around seemed like a very useful feature to me.

Garmin Progress Bar

Here are my first experiences with the device:

  • Putting it on is quite uncomfortable. You have to press down on this clip to go into these holes, and doing that hurts the inside of my wrist. Watch wristband makers have certainly figured that one out better. Maybe if you are a tough man you don’t mind. But I’m a lady.
  • The red progress bar is very useful. It has actually helped me take more frequent breaks when I am doing computer work, and I feel better after getting up and walking around for a few minutes.
  • The red progress bar is dumb. This so-called smart device apparently registers only walking activity, i.e. when I swing my left arm back and forth. I was frustrated to see the red bar show up after I spent an hour in the kitchen preparing dinner, and after I was in the back yard, raking and bagging leaves. Apparently, either the sensor or the algorithm don’t realize that these are physical activities.
  • The red progress bar can be fooled. Just for fun, I tried out swinging my arm back and forth for a minute while I was sitting at the dinner table, and it actually tricked the device into registering this as physical activity, so the red bar disappeared.
  • The dashboard that shows my steps and my sleep is kind of interesting. I have only worn the device for a few days, so can’t say yet how useful this data is going to be long-term, if at all, but it’s neat to look at in a narcissistic way – the same way I look at my Twitter account from time to time and delight in the fact that I actually have some followers.

Anyway, it has been a very interesting experiment so far, and definitely proof of the value of ‘walking in the shoes of’ to really understand something.

The true potential of wearables is difficult to tell at the moment. There could be all sorts of useful applications that have not yet been developed or that have not yet gained broad acceptance. After a lot of enthusiasm in the media, there seems to be a bit of a backlash now.

Here’s a recent page from The Atlantic, with quotes of tech opinion leaders all questioning the enthusiasm for wearables:

Atlantic article

And here’s an article written by a health IT consultant about the more technical challenges of integrating mobile health monitoring devices into electronic medical records.

http://medicalconnectivity.com/2014/11/04/challenges-using-patient-generated-data-for-patient-care/

While I share some of the skepticism, the wearables space is certainly an area worth watching, and with great growth opportunities for companies who ‘get it right’. I am excited to be part of this journey.

Man vs Machine

So, Big Data. The market research industry continues to struggle with the concept. It was one of the buzzwords of 2013. Some have come up with a big data offering. Some are searching for a point of view on it. Some counter with small data. Many still have only a vague sense of what we are talking about.

I have asked many colleagues and clients what this concept means to them, in the hopes of developing a brilliant solution that would make me wildly successful. Well, this seems to be taking some time, but anyway, I’d like to share with you what I have learned so far. As I am working in the healthcare sector, this is my focus below.

1. Big Data (in Pharma) is IMS data

For some of my pharmaceutical clients, all they can think of when asked about large data sets is IMS data. IMS captures and sells information about the prescribing behaviour of physicians at the pharmacy level. Through this data, pharmaceutical companies track the sales of their products.

2. Big Data (in Hospitals) is Patient Records and Interaction Statistics

Healthcare providers, particularly hospitals and other large organizations, capture myriads of data on patients flowing through the system. The analysis of this data is largely off the radar screen of traditional market research, and falls under the discipline of health informatics.

3. Big Data is Social Media data

This is a view that many market researchers adopted when social media first appeared on our professional horizon as another form of human expression. Last year’s MRIA NET Gain conference, dedicated to big data, featured a number of presentations in this area.

For those who do not want to develop their own proprietary solutions, subscription-based social media analysis tools are available and used by both end clients and market research vendors.

4. Big Data analysis is a different way of saying Data Mining

Some sectors have worked with large data sets for some time. I am thinking of scanner data in retail, and loyalty programs (Air Miles, Petro Points etc.). Fifteen or so years ago, the statistical techniques used to sift through such data sets were called ‘data mining’.

This practice is still ongoing, and the size of data sets ever increasing with more and more customer touch points being added. Some think of this type of analysis, when hearing the words big data.

5. Big Data is Data that is created by Machines

This type of big data is rarely mentioned and obviously not in the forefront of a market researcher’s mind. However, it has grown exponentially and is increasingly viewed and used as a source of customer information.

For market researchers, the question (and the fear) is to what extent human analysts are still needed, and to what extent ‘the machine’ can do it on its own. And how we can make sure we are still needed.

We say: “You need an expert to interpret what your data means.” We say: “A consultant is needed to guide the analysis process.” We say: “Meaningful data analysis is the development and testing of hypotheses, and only people can come up with those.”

And we are right.

How many times have I looked at the results of a statistical analysis and said, “This does not make any sense.” And then we discarded the analysis and started fresh, because results need to make sense. To another human. To your client. They need to lead to actionable insights and recommendations.

So we are still needed. But…

But many, many processes are now automated, from data analysis over producing charts even to highlighting key insights in charts. Far fewer people are needed to work with the data then before. Take a look at www.beyondcore.com – will get you thinking.

Some companies function with very little market research, in the sense of interactions between real live researchers with real live respondents. Machine-generated user data that streams back from devices guides the refinement of these products. New Apps are developed by split testing and seeing how early users interact with certain features, following the logic of clicks. Service companies integrate their customer interface and CRM software with their enterprise management system. Automated cues let managers at different levels know how they are performing, and notify them if there is a problem.

Technical skills are essential for survival. How can you tell a successful agency these days? If you look at their ‘careers’ page, most of the open positions are for developers (i.e. IT people). Those who win in providing business intelligence are either companies who are focused on the digitalization and automation of data collection and analysis, or companies who make intelligent use of available software products and platforms within the research process.

Do you know what Hadoop is? A wireframe? CSS? If not, perhaps it is time to google it right now…

Man vs Machine

 

Innovation, the new normal?

Companies in many sectors are facing rapid change. Following Clayton Christensen’s terminology, established businesses are being disrupted by new technology, and new business models are developed around these technologies.

Whether it is 3D printing of medical implants, crowd sourcing of clinical trial data analysis, software that supports pre-clinical studies and identifies the most promising drug candidates, ‘big data’ capturing patients’ genomic profile or personalized health records that patients can carry from physician to physician, fundamental transformations are afoot in the healthcare industry.

Consultants to the healthcare sector struggle to stay on top of all the different angles that are emerging. How much reading can you do in a day? Should you rather update your skills in data mining (i.e. working with ‘big data’) or become an expert in social media platforms and the many ways they are being used by patients and physicians or study government initiatives to incorporate new technologies in reorganizing the way healthcare is delivered to the patient?

The state of confusion is pretty typical for market changes. Initially, there is a whirlwind of new ideas and approaches. Are electric cars going to be the way of the future or ceramic fuel cells? Or will biking emerge as ‘disruptive technology’ in a reorganized urban neighbourhood? Will patients carry their own health records around on a USB stick or will they become universally accessible through a (password protected) cloud? Will pharmaceutical companies find ways to make drug development cheaper or will fewer drugs be approved or will best supportive care with the bells and whistles of comfortable retirement living ‘disrupt’ the oncology pipeline? Will iOS, android or Windows 8 emerge as the dominant ecosystem for computer / tablet / phone or do we need to learn all three to know what works how in which environment? Etc, etc.

Should we wait until the dust settles before we decide how to focus our efforts?

I am not sure that the dust will ever settle. The pace of change is accelerating, with no sign of stopping or settling down. Then where should we pitch our tent? What should we hold on to? I believe that companies and individuals will succeed who develop mechanisms, routines, practices that allow them to deal with change. Not just once, but on an ongoing basis. Those who effectively survey what is going on in the whirlwind, who systematically capture their own ideas on how to ride the storm and who devise an easy process that allows them to test, develop and implement these ideas will have a chance.