SOCIAL NETWORKING & EXTRAPOLATING USEFUL INSIGHTS
By: Tara L Bradley, MBA
What is all the rave about social networking? As a marketing professional I have witnessed a total transformation of resource allocation as it relates to the marketing mix. I see it really as an extension of traditional marketing but the real question is how do you gain insights from your audience in the social network environment? How do you transform those insights into profitable relationships? I think this is the biggest challenge that companies face with integrating these tools in the mix.
As marketers we are not only challenged with identifying a need for a service or product but also to communicate powerful reflections of the brand culture that will attract and influence purchase decisions. First, I will address the question of how to gain insights. There are several ways you have to think about researching social networks; it's similar in traditional research that you set systems and processes up to help you understand the overall importance of the conversation. The conversation should really be the core of what you are trying to understand about the Twitter followers, the FaceBook (FB) fans, the MySpace friends, etc. The substantial amount of information provided thru these conversations can ultimately be built into modeling the individuals. Once modeled, you can use the information for useful predictions, decision-making and innovation.
EXAMPLES of how I approach the systemic aspect of organizing the conversations so they don't just become sound-off boards:
1. ENGAGE: on Twitter for example, you should follow everyone who is following you. Communication is a two-way street. If they are following you and you are not following them, you've missed your opportunity to find out who they are, what is happening in their lives, issues of importance and most importantly any response they may or may not have to your tweets. For FB, take a look at your fans and befriend them or search for your brand name or product description in the FB search engine. Everyone who populates, attempt to befriend them as well. Obviously at one point in time they made an expression relating to your business interests. You can do the same in LinkedIn and MySpace.
2. ORGANIZE: group your individual connections. In Twitter, you can set-up group columns that consist of individuals who you group together based on your research needs or any preference you decide upon. FB and MySpace allows you to group your connections based on your preferences as well. These group preferences can be based on whether they are close friends, family, fans, women, men, celebrities, business contacts, specific common interests, etc. LinkedIn is a little different in a sense that they pre-determine the group type (i.e. -colleague, done business together, manager, etc.) at the time the connection is made and the application limits your ability to engage based on how the connection was established.
Well you may still be wondering in what other ways can you be organizing your connections beyond the given such as friends, family, fans, celebrities, etc.
SEGMENTING & GROUPING:
• One way to think about it is the level of connectivity of each individual connection. This will require a bit of easy research that entails gathering numerical data (yummy :). Mostly all of the popular and mostly used social networks provide details on the number of followers, friends and connections each individual in your network has. I have 149 friends on FB, 53 followers on Twitter, and 35 connections on LinkedIn. Look at the numbers, you can determine the level of connectivity by reviewing these numbers on individuals in your connection. You will easily be able to identify who in your network has the highest connectivity. Obviously, the higher the numbers the bigger the network is. A person with 500 friends on FB, 1200 followers on Twitter and 125 connections on LinkedIn has a broader network than I do. These numbers can make a huge difference when it comes to campaigning, advertising, and ROI.
• Other numbers you should be reviewing are the number of tweets or updates each individual has made in the past 6 months. These numbers can really be of importance when you think about viral marketing and who you should be focusing your advertising spend on. Focusing on individuals who engage more than others is very important. These are the individuals who most likely have more of an influence on their acquaintances. You can also gain an idea on the level of influence by reviewing how many people respond to their updates, tweets, and blogs. With high connectivity and influence, these connections can be very effective in helping you not only spread the word but be effective in persuading decisions as it relates to purchasing and advocacy.
• When you are reviewing conversations with your connections on a daily basis, make sure you capture and segment or group those individuals who respond directly to you, whether the response is negative or positive. By grouping individuals by their responses to your brand, product, cause, management, etc., you can began to build network models that could later be used to form focus groups. These online focus groups can assist in product launches and be creatively used to gain further insights using traditional marketing research methods that rely on quantitative measurements .
If you found these tidbits useful, next week I will continue to address this rising challenge in the new age of marketing. I love what I do and enjoy sharing my thought processes and opinions on competing in a business environment which is becoming flatter everyday. I blog often on marketing, travel, design, and various viewpoints on current hot topics. Stay posted on upcoming blogs and events. Website coming soon!
Where to find me:
FaceBook: http://www.facebook.com/TaraLBradley
Twitter: @TaraLBradley
LinkedIn: http://www.LinkedIn.com/in/TaraLBradley
MySpace: http://www.myspace.com/TogetherWeWillRise
Another resource on social networking and data mining:
M. Richardson and P. Domingos: Mining Knowledge-Sharing Sites for Viral Marketing. In Proceeding of the Eigth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 61-70, Edmonton, Canada, 2002. ACM Press
http://www.cs.washington.edu/homes/pedrod/papers/iis04.pdf