Social Media Analytics: Three Perspectives

The analytics discipline within this channel can comprise a number of different perspectives. These three perspectives are:

• network analysis
• marketing attribution
• text mining  

In the area of network analysis, the analytics approach is somewhat similar to what is done within the telecommunication sector. There are often many deliverables that are related to this kind of analysis within this sector. But one such deliverable is the ability to identify influencers vs. followers. With this kind of knowledge, the telco can perform other analytics such as customer profiling  in order to determine what are the key telco customer characteristics that differentiate influencers vs. followers.

 This approach  can be mimicked in the social media world as each individual has a  network of contacts just like each telco customer has a network of calls that that have been made within a given period of  time. The key in determining whether an individual is an influencer vs. a follower is through examination of both the  breadth and depth of that individual’s network. Breadth refers to the number of contacts within the person’s network while depth refers to the level of engagement that each person has with all their contacts. Various metrics and indices can be calculated that allow the analyst to create an ‘influencer score’. A component of this type of analytics exercise would be to determine the threshold or benchmark from these metrics/indices that classifies someone as an influencer vs. a follower. This, of course, will vary from exercise to exercise.

The second area of marketing attribution attempts to determine how much ROI from a given marketing campaign can be attributed to social media. There may be no right answer or panacea to this challenge. Unless there is some direct method whereby a given person logs onto a certain web page and registers for a given product or service within the social media campaign, it is very difficult to determine direct dollars that can be attributed to social media. Yet, even this functionality exists, one could argue that the  non registrants participating in some fanbook contest might be contributing to ROI by purchasing at a store, through TV or direct mail. These  same kind of challenges exist for the mass advertising industry except the key advantage with social media is that  we do have the denominator in determining the number of people that clicked onto some fan page or contest. Given these limitations, all analysts can do is to provide general direction in whether the campaign created additional engagement and whether or not this additional engagement translated into additional dollars. On the engagement side, metrics such as # of people logging onto a fan page or contest plus how long they are on the site  can represent some kind of proxy and comparisons of these metrics  can be made to other social media campaigns to determine the level of success. By the same token, anecdotal analysis of social media campaigns overtime can determine the overall impact on incremental sales. Classification of campaign periods into high, medium, and low social media marketing allow executives to see the general impact of social media marketing on sales. However, one cannot directly attribute the ROI  back to a specific social media campaign. All we can say is that the campaign generated an improvement in engagement relative to other campaigns. The leap of faith for executives is that this improvement in engagement translates to incremental dollars but we don’t know the precise number.

The third area of text mining presents the most exciting opportunities for marketers in being able to actually analyze what is being said within this medium. The ability to identify key themes or topics as well as sentiment allows marketers to craft communication stategies that better address what is being discussed within this space. At an even higher level, brand strategies can be created that better reflect the needs and wants of its core customers. As an analytics practitioner, most of our deliverables end up producing marketing solutions that provide better targeting of customers. The traditional deliverables of analytics have always yielded solutions that are of a more tactical nature. Text mining in the social media space offers the analyst opportunities to build solutions that are of a more strategic nature which ultimately will have higher profile within any organization. The strategic nature of these type of solutions just serves to reinforce the ever-growing importance of analytics as a key business discipline and more importantly a key competitive advantage within  a very dynamic business environment.