Posts by Jasper Snyder
The explosion of social media data is having a transformative effect on market intelligence and research.
An Economist article from late last year states the context well: “Big companies now obsessively monitor social media to find out what their customers really think about them…As communication grows ever easier, the important thing is detecting whispers of useful information in a howling hurricane of noise…the new world will be expensive. Companies will have to invest in ever more channels to capture the same number of ears. For listeners, it will be baffling. Everyone will need better filters—editors, analysts, middle managers and so on—to help them extract meaning from the blizzard of buzz.”
Being able to extract this meaning is a challenge – it’s not easy to do – but it represents a significant opportunity for market researchers to gain competitive advantage. In a series of posts, we’ll be addressing some of the questions that a researcher has to answer before they can drive that advantage for their employer. These questions include:
1) How do I make sure my research is based on relevant data?
2) Which social data are most useful to a researcher?
3) Is ‘automated’ analysis – for example, sentiment analysis software – usable by market research professionals?
Before we address the first question, let’s take a moment to consider the context. The fundamental challenge to anyone trying to make sense of how social media fits into a researcher’s toolkit has to understand is that social media ‘conversation’ essentially has two different types of use. First, it can be used for ‘monitoring’ purposes (e.g., crisis response or customer service); second, it can be used for ‘insights’ purposes (e.g., analyzing online conversations that might inform product development, or as a way to measure brand perception). These types of purposes have different requirements in terms of data, but the way in which social media monitoring tools are being used today often obscures this distinction. Buyers end up looking for a silver bullet to hit both targets. The difficulty with that approach is that when you’re using a monitoring tool for customer service, for example, you need to see every message that might be relevant; you have to err on the side of making sure you don’t miss any content, so you undoubtedly set your keywords or searches up with that in mind. On the other hand, someone trying to analyze social media conversation to understand whether their company’s key brand values are resonating online, for example, needs to make sure that they’re only analyzing relevant content; irrelevant content here only serves to muddy the analytical waters.
The competition between these types of purpose – an analog of the trade-off between recall and precision in text analytics, in fact – should be clearly understood by any researcher looking to use social media for market research purposes.
So how do we identify which data are relevant and represent the opinions that we want to analyze? Last year, my colleague Chris Boudreaux co-authored a research paper looking at the correlation between online sentiment and an offline brand-tracking. The research showed that there is a correlation between the two measures, but only after controlling for one or a range of factors. One of the controls identified as being key to any correlation was making sure that the person commenting online had experience with the brand in question.
This makes total sense: make sure you’re listening to the right people. Analyzing social media data without controlling for whose comments you’re looking at would be like sending out an online survey to everyone in your sample database; you just wouldn’t do it. Do you want to listen to what your customers thinks about your latest product? If so, don’t listen to your own employees, and don’t listen to your competitors. The opinions of both of these groups have their place, but not to answer that specific question. So how do you configure your social media research with that in mind?
First, at the author level, you can choose to only include messages in your analysis that are posted by the people whose opinions you’re interested in. The way you define groups of people here may in fact map to your existing customer segmentation taxonomy.
Second, you could choose to ‘listen’ only in those venues where the audience whose opinion you’re interested in is likely to be engaging.
Third, you can make sure that you’re only including in your analysis messages where your product is being talked about in a relevant context.
Using these three approaches will help you make sure you’re analyzing data from the relevant people, discussing the relevant issues – giving you a solid foundation from which to start your analysis.
For information on how Converseon can help you get to the right data, contact jsnyder@converseon.com.
Social media have emerged as a powerful force for social good. The power of social listening, specifically, to positively impact business, government and social policy is clear. This is creating a paradigm shift in business methods. To combat potential misuse of the data available from social media, we have to ensure that these new business methods are underpinned by ethical approaches meeting the new demands of the marketplace.
As a member of a strong subcommittee that included a diversity of thoughtful perspectives, we were actively involved in the drafting of CASRO’s guidelines for social media research. In addition, we submitted feedback to ESOMAR for their corresponding initiative. We’re pleased the guidelines enable the use of social media data for research purposes while also meeting what we think are two core requirements:
- The ability for businesses to collect data required to meet the new generation of services and consumer expectations for brand engagement in venues where public conversation is occurring. For example, a consumer may post about a product or service issue on Twitter, addressing a brand directly. There is a clear expectation that a brand’s customer service function is listening and ready to respond.
Converseon recently launched a collaborative research project with comScore, the Advertising Research Foundation (ARF), Communispace, and Firefly Millward Brown to explore the roles of social media in the purchase process. The project brings together a range of research techniques to understand how and when people turn to social media as they make purchase decisions.
Converseon will provide insights based on social intelligence for the project, helping the ARF to establish an expanded understanding of online conversations around purchase decisions for items in the CPG and other categories. Converseon analysis will uncover where in the purchase process social media conversation is taking place, who is taking part in that discussion and where it’s happening.
Our research will include psychographic and demographic analysis, in addition to a range of other metrics based on Converseon’ unique technology + human methodology, giving the ARF the opportunity to cross-tab the analysis and extract the most meaningful information to support its research goals.
We all look forward to the results of the joint project.
While some bloggers report that real-time search is dying, reality is a bit more nuanced. Specifically:
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While it may be true that consumer-focused start-ups are having trouble gaining market traction with free provision of real-time search, consumers do use it as a feature of larger services (e.g., Twitter), and large brands require it within their paid conversation monitoring solutions.
The key is that consumer uses may not generate revenue as a stand-alone business model, but business use of real-time search is an absolute requirement for brands today.