Wouldn’t it be great if brands could just sit in while a woman did her makeup, listen to her complain about what’s not going right and see her smile when she gets the look she wants? Thanks to social media, they can do just that.
[image by Flickr user Mustafa Sayed]
In his blog “3 new use cases for social data in research” our COO, Job Muscroft, talked about how smart social media research, done right, can help develop new product innovations. People are sharing their experiences with products and services daily, allowing us to observe the thoughts of people shopping for groceries, cooking, commuting to work and hundreds of other activities. As soon as a new product hits the shelves, consumers are providing feedback on it. And through using social media listening tools, this feedback can be fed right back to the product teams to help with improvements.
In this blog, I’ll lay out the advantages of using social media for innovation, and explain how brands can go beyond listening to individual mentions to finding the new ideas and insights that can help them build their products.
Going Beyond Monitoring
Using social media for innovation is different from the customer service monitoring that many or most brands are doing now. Now it is not just about helping customers when they are unhappy. It’s about figuring out the root cause of their unhappiness and fixing it in the next edition. This a deeper form of research that endeavors to reveal wants, needs, pain points and motivations by looking at behavior around current products.
This is already being done through focus groups and customer service surveys, but social media is immediate and intimate. As soon as a product or service is released, people can be responding and talking on social media. Moreover, they are sharing this information as they use the product in their regular lives. These aren’t prompted trials. These are spontaneous and organic responses. Researchers really can, in a way, watch a dad cook dinner for his kids. This allows researchers to examine unspoken needs and frustrations as they listen not only to what people say about a product, but also how they use it.
From Many Mentions to One Innovation
At its heart, using social media for innovation is qualitative research. Numbers and quantitative metrics do come into play – the data is just too huge to not rely on some form of numerical metrics. But we try to get at the numbers in a way that recognizes the unstructured nature of human language.
The first step is for the researcher to read a bunch of social media messages, just like a qualitative researcher would read the verbatims from a community or focus group. From here, we can develop a code frame of different scenarios and use-cases that we can group the messages into.
[image by Flickr user Seth Woodworth]
Next, we build a lexicon to help us sort the mentions into our code-frame. A lexicon is the keyword filter we use to examine our data and find relevant mentions. For instance, a lexicon might include “blades” and “knives” for a search about a food processor to narrow in on discussions about chopping power. Then we can compare the volumes between the different scenarios to find the most pertinent areas of discussion.
Finally, we do more reading and analysis in order to identify the common themes and patterns that are driving these mentions. Here, tools like the Bundle visualization in our social media research platform Pulsar come in real handy. They help us see the patterns in the language, which we then test by going back to the actual search mentions, applying a level of qualitative rigor to our analysis.
This research method can help develop new products as well as innovate around existing products. Social media searches can gather data on product categories, consumer needs, issues, and even competitors just as easily as finding mentions of a particular brand or product. The only thing that changes in the research process is the original search we run to find the dataset of mentions and behaviors to analyze. For innovation around a specific brand or product, we would craft a rather specific search string.
But when doing a white-space innovation project, it is better to cast the net widely and look for all mentions of the category or interest area. This allows us to adapt to the way people actually speak, finding the relevant mentions we might otherwise have missed. Other options are to look for verbs and activities to see how people are currently achieving desired results.
This is what we did for an FMCG client recently, who was looking to come up with new platforms for product development across a range of haircare brands. How had women’s hair needs moved on since the era of GHDs and poker-straight locks? What were the emerging styling trends, information sources, and the language used to discuss these looks?
Honing In On Your Target Audience
Whether the goal is to innovate around an already existing product or develop ideas for new products, the first step is to figure out what words to mine the internet for. This is of crucial importance as this initial search will provide all the data for the following analysis. The search string must reflect consumer language or you simply won’t get back anything useful.
However, sometimes this kind of keyword tracking search is too general, bringing back results from people that you aren’t interested in. If your product is targeted at college kids living away from home for the first time, then you want to hear about that market’s frustrations with doing laundry, not what their mothers are saying.
[image by Flickr user Brian Ingmanson]
This is when we would layer on an audience search, looking at just your target audience. An audience search allows us to sample the online population by behavioral or demographic traits. That way we can be sure that the data we are analyzing comes from the relevant market. The downside of this is that it can be limiting – you only see mentions from the people you are sampling. On the plus side, all those mentions are relevant to the market you are looking to innovate in.
Beyond allowing companies to better tailor their products and services to consumer needs, this form of social media analysis offers a competitive advantage as well – speeding up the innovation cycle. As soon as a product is released, brands can begin looking for improvements. As soon as a new trend appears in the magazines, brands can start looking for ways they can jump in. Companies using this kind of social intelligence will be set up to win, moving faster and more strategically than their competitors.
Social media research offers us new ways to observe and understand consumers. In a way, social media is a window into their homes, revealing how they live with the products companies produce. By looking at this, understanding trends and the consumer experience, we can help companies produce better, more targeted products. Everyone wins – consumers get products that actually do what they want them to do, and companies get products with a waiting market.
For more more new use cases for social media research read “3 New Use Cases for Social Data in Research.“