The Economist recently ran an article on “Big Data” in a special report on International Banking. Its assessment of banking elsewhere in the report is that the industry has been surprisingly resistant to embracing the Internet as an agent of change in banking practice. It reveals, counter-intuitively, that number of bank branches has actually risen by 10-20% in most developed economies during a period when most customers pass through their doors once a year rather than once a week.
The newspaper explains this paradox thus: banks with a denser branch network tend to do better, so adding more branches is rewarded by more business. But it’s business on the bank’s terms, not necessarily the customer’s. It does not increase efficiency – it increases cost. And, as The Economist points out, banks’ response in general to customers using mobile phones for banking has been lacklustre, even though customers love it and tend to use it to keep in daily contact with their accounts. It’s a level of engagement that most panel providers would envy.
All of which is to say that there are parallels here with our own industry. Here at Meaning, we have just released the findings of the latest annual MR Software Survey, sponsored by Confirmit. In a sneak peek, Confirmit blogger Ole Andresen focuses on an alarming finding about the lack of smartphone preparedness among most research companies.
But what interests me is the Big Data – both in The Economist’s report and our own. The former offered a fascinating glimpse into the way banks were using technology to read unstructured text and extract meaning, profiling some of the players involved and the relative strengths of different methods. This is technology which is improving rapidly and can already do a better job than humans.
In our annual survey this year, we have asked a series of questions on unstructured text. Research companies, in embracing social media, “socialising” their online panels and designing online surveys with more open, exploratory questions in them, are opening the floodgates to a deluge of words that need analysing: at least that was what we suspected.
Analysis methods cited by research companies for handling unstructured text, from the 2011 Confirmit MR Software Survey by meaning ltd
In our survey we asked a series of questions on unstructured text. Research companies – in embracing social media, “socialising” their online panels and designing online surveys with more open, exploratory question – are opening the floodgates to a deluge of words that need analysing: at least, that was what we suspected.
It turns out that half of the 230 companies surveyed see an increase in the amount of unstructured text they handle from online quant surveys, and slightly more (55%) from online qual and social media work. Yet the kinds of text analytic technologies that banks and other industry sectors now rely on are barely making an impact in MR.
Even a quick glance at the accompanying chart shows that most research companies are barely scratching the surface of this problem. It’s not the only area where market research looks as if technology has moved on, and opened a gap between what is possible and what is practised. There’s much more on this in our report, which will be publishing in full on the 30th May. Highlights will also be appearing in the June issue of Research magazine.
Social media research continues to be one of the hottest topics in research. I’ve just been reviewing the abstracts for this year’s CASRO Technology Conference in New York in June, which I will be co-chairing, and of all the topics, its the one with the longest string of submissions. Not only that, but there is some diversity of opinion into what it is, how to do it, and whether it adds anything at all to the existing researchers’ toolkit. Closer to home, it’s a topic that will be debated in next week’s Research conference in London too.
Analysis technology used on social media research projects, based on the 17% of firms who are active in social media research
Social media research is also one of the new topics we focused on in our 2010 annual software survey, sponsored by Globalpark, the results of which are published today. There are some curious findings – and some predictable ones too – that add perspective to the current debate.
Our survey of over 200 research companies of all size around the world, shows social media research is still at the early-adopter stage, accounting for revenue-generating activity in just 17% of the firms surveyed. Close to the same number – 19% – say they are unlikely to offer social media research, and of the remaining 63% who gave an answer, 31% say they are either experimenting with it and 32% are considering it for the future. Small firms and research companies in Europe are the least likely to be doing social media research and are also the most likely to have ruled it out, whereas large firms are the ones that are most active. The actual volumes of work are still low – we also asked how much revenue social media research accounted for. It is 5% or under for two-thirds of the agencies that do it and tails off beyond that – but there appear to be some specialists emerging, with a handful of firms deriving more than 20% of their income from it.
Many firms are bullish about the future, though, with 20% predicting strong growth, and a further 52% anticipating some growth, with North America, and again the larger firms, most optimistic about its future.
As a technologist, I was most interested to see what technology firms were applying to what is, after all, something born out of technology. Were the tech-savvy gaining the upper hand, or were researchers taking the conventional, low-tech approach beloved of qualitative researchers. Again, it’s a bit of both. Of all the software-based or statistical methods we suggested for data analysis, the one that came top, was “manual methods”, used by 57%. For analysis, this followed by 54% citing “text mining” (as correspondents could pick all that they used). Text mining, though it uses some computing power, is also very much a hands-on method – but it’s good to see more than half turning to this method. Other methods make much less of an appearance, and the method that I consider shows most promise for dealing with the deluge of data, machine learning-based text classification, was bottom of the list, cited by one in six practitioners.
For data collection, technology was much more apparent – although it is hard to avoid here. We were still intrigued by the massive 54% who say they are using manual methods to harvest their social media data from the web; 57% were using web technologies to collect the data, and the more exotic methods were also fairly abundant, including using bots (43%), crowdsourcing (41%) and avatars (24%).
I’ll pick up on some of the other intriguing findings from the study later. But as the report is out now, you can pick up your own copy by visiting this webpage – and there will be a full report in the May issue of Research magazine.