Social media is, essentially, said to be the fourth dimension of existence today. Like any other mode of interaction, people are utilizing this platform to assert views, seek redressals and make recommendations. No wonder companies want this information to strategize. However, there is a lack of frameworks to measure the success of social media analytics projects. As with any emerging technology, many myths are yet to be discovered and eliminated. Dwell on these myths to succeed with your own social media analytics strategy.
Myth 1: Social media analytics analyzes data from micro blogging Websites or social networking platforms
Generally, as marketers look to exploit social media in increasingly creative ways, consumers demand more from apps. However, keep in mind that the scope of social media analytics offers is much wider and it isn't just about micro blogging Websites or social networking platforms alone. Most organizations in India have just scraped the surface. According to Rivi Varghese, chief executive officer-founder of Software Product firm CustomerXPs, "At present companies are focusing merely on figuring out what customers are talking about them. “The game changer will be when the technology is used to understand the micro needs of the customer."
Myth 2: Social media analytics can comprehensively replace the traditional survey
Social media analytics has been looked at as a replacement for tradition surveys. The popular notion about surveys is that they can be costly, time-consuming, and therefore unmanageable. Social media analyticscan influence an adept change in market strategy by near-time or real-time analysis. On the other hand, surveys are free from natural human bias owing to the anonymous nature of responses; this is not the case with social platform. Moreover, surveys can be a more scientific representation of people’s perception due to the statistical methods of sampling they follow. In case of social media, a small percentage of people who are vociferous can swing the result while the ones not voicing their opinions are not accounted for. Varghese from Customer XPS says, "If 90% of customers are satisfied with a product; but the disgruntled 10% exhibit their anger on social platforms, it can distort the results of the analysis." Social media analytics, therefore, must be used judiciously along with surveys.
Myth 3: Expertise lies with big vendors only
Although big vendors would want you to believe so, truth is, there are several small and niche players in this space. Initially, the large vendors may provide social media analytics as an add-on feature with their BI systems. But niche players will have a greater scope as they can deliver innovative and custom-built solutions based on the user needs. “Innovations come in small packages. The early adopters always go with specialist, niche vendors,” says Varghese.
Myth 4: Social media analytics in the cloud is full of hassles
In reality, the cloud-based social media is the easiest option at the moment, especially for the small and medium-sized businesses. “They might not be able to get customizations done but the game is easier for them as they will have a smaller base to analyze,” says Himanshu Manroa, Lead Manager - Research & Analytics at Datamatics Global Services.
Security-sensitive sectors like financial services and government have been slow to enable social media analytics and to put their data into the cloud. “Many resist public cloud networks in favor of private clouds. Nevertheless, organizations will want to experiment with social media analytics services to get started,” explains Pete Cittadini, CEO and President, Actuate Corporation. Actuate offers consultancy and implementation services on social media analytics.
Myth 5: Social media analytics is a complete solution in itself
Social media analytics does not analyze structured data. Choosing useful data is like hunting for a diamond in the rough. Getting a true sense of the opinions is an issue too. Text mining is a tool closely associated with it. Manroa of Datamatics explains this fundamental need: “Banks looking at social media analytics for sales would find a status that says ‘I am looking forward to my trip’ as an opportunity. The bank can then pitch travel insurance to such a person. This kind of micro-analytics is an advanced stage.” Nevertheless, it shows that text mining and analytics are indispensible for social media analytics to work.
Venkat Iyer, Business Information Management Practice Lead at Capgemini, adds, “Data quality is a major issue with unstructured data. Even with the addition of text mining a single, unequivocal version of the truth is going to be more elusive causing a dilution of overall data quality.”