Blog #2: Qualitative Research of Online Social Behavior

Let's say your brand or product has five thousand followers on Twitter, ten thousand fans on Facebook and both fans and foes are creating user-generated content in the form of blogs and video blogs. At most companies this would be considered great news, even a reason to celebrate, however most companies do not have a good idea about what the next step should be.

Growing the fan base and helping to engage customers should definitely be a priority and a core part of the marketing strategy, however most companies are still struggling with monetization.

First, companies need to understand what social media outlets their target customer is using to discover, consume and share content and how often.

Once this is understood a Groundswell approach of (1) understanding how they work (2) how many people use them (3) how they enable relationships (4) how they threaten institutional power (5) what you can do about them

Each company needs to understand what goals they hope to achieve and if it is different for different audiences such as:

· Customers
· Employees
· Partners
· Media

Once it is understood who in the company needs to be involved, meaning which department a clear strategic direction should be solidified.

Defining a social media strategy and implementing it is not an easy thing to do because if it was we would not see so many problems with social media proliferation. Today companies are paying interns or consultants to work in a full-time capacity to identify all social media sites and find out how to get control of these sites because some are being monitored while others are not, some have been re-branded while others have not, and valuable comments or data may have been missed because of the lack of process in place to check these sites.

Discuss the quantitative data that can be pulled.

When we consider how we could pull quantitative data today in the market place, the “Decoding Our Chatter” article provides great examples of using twitter data to do predictive analysis. If hundreds of social media, data-mining and financial services companies are paying a base rate of $360,000.00 per year for Twitter information, it is because they have computer scientists that can develop algorithms that help identify patterns which guide them on how to run their business better. Hedge funds are using it to beet the S&P 500, movie critics are using it to detect office hits, the military is using it for warfare and it is being used in medicine to track influenza outbreaks.

When we consider how we could pull quantitative data, Facebook is a much cheaper way to advertise than buying words from Google (search engine), however the click through rate on Google Ads can be up to 10% while Facebook is only .01% according to the “Facebook Sells Your Friends” article.

Statistics can be used to prove long-term benefits of engaging with customers on Facebook such as:

· New customer recruitment
· Higher conversion rates
· More frequent purchases

Forums and Reviews are helpful ways to gain quantitative data. For example I have a passion for technology so I love reading tech forums and I have preferences on where I go to get my technology advice such as CNET, PCWorld and TechCrunch. For the most part I feel as if the reviews are unbiased, however I think the reviews provide a great scoring mechanism for product features. I think companies should be sifting through review comments because hundreds and thousands of comments are written on new products in response to reviews and it is easy to find patterns to determine if your customer does or does not agree with the review and the comments tend to be detailed.

How might you get at interesting insights about social media use (or interesting insights about people that you extract from their social media use), using a more qualitative approach?

Interesting insights today are gathered by watching how users share videos, songs, product reviews, commercials and news articles with their friends. A lot of what goes on in the purchase decision is based on interactions between the buyer and the set of people in his/her group. These interactions are being tracked and the buyer and his/her group are being targeted with advertisements that should be appealing to him/her. Sprint used this mechanism to quadruple sales for the PalmPre after the product launch was not as successful as planned.

Facebook likes on non-Facebook websites are also tracked, several sports sites today have the option to like a website which will start generating a set of RSS feeds to you phone. I am subscribed to ESPN feeds and I prefer it so when I don’t have time to get onto ESPN I can read the short text messages about all my favorite teams.

33Across, Media6-Degrees and Lotame are some of the startups that are all using internet user data from social-networking sites and other sites that you interact with people (leaving comments, likes, review writing, etc.) to facilitate targeted advertising for large corporations.

What information would you try to elicit?

· Frequency of purchases
· Purchase influences (friends, colleagues, family)
· Purchasing Power
· Channels purchased through (online, in-store, couponing-site, etc.)
· Emotional connection or lack thereof to a particular brand
· Reason for Purchase / Reason not to purchase

How would you get at that information in a way that would be most accurate and telling?

I think the purchasing and searching for patterns from actual user data is better than asking people because when people are surveyed they don’t always to what they say they will do. I also think there are ways to manipulate survey data such as top box, but that doesn’t give a guarantee that it will mimic reality.

To get information in a way that would be the most accurate and telling I think you would need to develop a methodology that could connect the qualitative to the quantitative data. Although the consensus map method discussed in article “Metaphorically Speaking” seemed very thorough and comprehensive, I am not sure that it would be feasible to do this type of detailed analysis on every product or brand due to cost and time limitations.