Microsoft spitting into Google’s soup

Has your computer been sending you ceaseless reminders to update to Windows 10? I have just updated mine, and now I understand why Microsoft is so adamant that everyone gets the new operating system. Because it is not just an operating system.  

Pumpkin soup

If you choose the ‘Express Setup’ features, you will give Microsoft access to all sorts of data that would not normally be shared with your operating system provider. It will allow Microsoft to make the sorts of rich data connections that so far only Google with its web of interconnected and super-user friendly services has been able to gather (and profit from handsomely).

My new operating system is also very persuasive in getting me to use its search engine Edge and so far I have found it difficult to stay with Google as the default search engine. What a huge coup for Microsoft and major threat for Google! I read an article or two about Edge, saying what a great new thing it is, but who knows who pays these blog writers… Also, when you search on Edge, ominously the old Bing logo appears – not very reassuring, since nobody really liked Bing, right?

But…so far I have not figured out how to keep Google as my default search tool and perhaps I’m starting to like Edge. So what’s Google going to do about that?

Have you upgraded your Windows yet?

Disclaimer: I am not in IT, perhaps I am not fully understanding all the technical details, but the business strategy seems pretty clear…

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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

 

Is big data transforming healthcare marketing research?

Yesterday (June 27, 2013), SAS and GSK announced a collaboration which puts clinical trial data ‘in the cloud’ in a secure way, respecting the privacy of trial subjects, and makes it accessible to other researchers. It is believed that other big pharma companies may follow suit and create an unprecedented shared data base that could potentially speed up the analysis process, make analysis more transparent and produce significant advances in medical discovery.

While this particular example of ‘big data’ pertains to clinical trials, many other big data sets exist (or are being created) in the healthcare space, awaiting data integration and analysis. One wonders to what extent this trend will impact the need for primary healthcare marketing research. Secondary data analysis is not new – it has been part of business intelligence for a long time. What’s new is the amount of data that is being collected, the multitude of platforms and interfaces through which it is collected and the ease with which the data can be accessed and analyzed.

Traditionally, primary marketing research has been faster than secondary data sources at delivering behavioural data such as prescribing of certain drugs. This is now changing. Mobile health apps, EMR, data warehouses for adverse event reporting, point-of sales data at the pharmacy level and many more points of data collection are becoming more readily accessible. Secondary data is going ‘real time’, well almost. On the other hand, primary data collection methods can also harness the power of ‘real time’, thinking of mobile surveys etc. Who will come out on top, or rather, which mix of primary and secondary sources will deliver the best insights?

Also, primary marketing research has been practically the only way to capture attitudes and beliefs and to explore how they relate to behaviour. Arguably, communicating with your target audience is still the best way to understand their motivations. However, social listening, drawing on tens of thousands of online conversations and powerful tools for text analysis, has made some inroads into this area as well. In addition, to what extent do stated opinions really drive behaviour, and how good is primary market research, even with creative methods and advanced analytics, at uncovering these drivers?

Big data is certainly transforming the primary marketing research industry, in healthcare as well as in other sectors. The question remains which solutions will bring the most value to clients and will become the new standard for companies who survive the transformation.