Based on my research, it seems as if social media monitoring will only become more widespread and critical for social media strategy success in the coming year and beyond. Monitoring in small and medium sized businesses (not just in large corps) will become more common and recommended, especially when it comes to engaging with targeted audiences and identifying new product niches.
Like the SAS case study, stressing qualitative rather than just quantitative data collection and analyzing will be crucial in the years to come. Given the breadth and depth of social media, focusing on sentiment and tone rather than just numbers is crucial to truly understanding the voice of customers.
Along this same vein, the abundance of data warrants even more layered and specific filters. Filters need to move beyond gender, channels used, and time spent, and into the world of consumer intent, influential people, most talked about trends, competitor strategy, and once again- sentiment.
Once more businesses and organizations are engaged in social media monitoring, it seems like predictive models will become more commonplace as well. These models gauge customer reactions in real-time and allow for planning, adjusting, and quick decision-making.
Another statistician-friendly trend in the future will be the merging of owned data with social intelligence. Or, more simply put, combining information from survey results, database records, and brand specifics with trend monitoring and reactions to overall concepts. Combining these two realms will paint a better picture of the effectiveness of the engagement strategy.
In general, it seems like social media monitoring tools will become more complex internally, while at the same time more user-friendly and commonplace for the outside world.
Chapter 14 case study questions:
1. What challenges did SAS face when it decided to develop a tool to measure social media mentions?
- Maintaining data accuracy – The name “SAS” itself created a challenge; SAS is a widely used acronym, which proved difficult for coding categories. In addition, due to the plethora of social media channels that are constantly on the rise, recognizing and defining metrics and terms created a challenge.
- Adequate timing of data gathering and analysis – Due to the fast-paced nature of the social media world, the SAS tool needed to collect, code, and interpret data extremely quickly in order for it to shape strategy.
- The stories behind the numbers, the context – Quantity does not trump quality when it comes to social media monitoring. The SAS tool needed to address the competitive market context and the sentiments embedded in the data.
2. What was the fundamental goal for SAS in developing their social media monitor tool?
…”to create a reliable, accurate and useful set of metrics that would help marketing, PR and communications professionals make better decisions.”
Pieces of this fundamental goal included the ability to use data to shape external communications, correlate that data with specific programs and launches, advise the social media team, and set benchmarks for future programs.
3. How did SAS overcome the challenges and obstacles in measuring social media sentiments?
To hone in the data coding categories, SAS social media analytics defined the top 100 sites/channels to pay most attention to, created exclusionary tables based on mention tone, positioning, and visibility, and classified competitor mentions into two categories (direct and indirect).
SAS also used a hybrid coding methodology inside their dashboard, and engaged in constant research and experimenting with coding.
4. What were the results of the company’s strategies for achieving its social media marketing goals?
The emphasis placed on quality over quantity proved to be useful, as “testing demonstrated that the sentiment of SAS mentions in comparison to its competitors was superior in terms of overall volume and positive mentions”.
The company learned about trends and spikes that happened surrounding the product launch, press releases, competition, etc., and how those trends and spikes have evolved in the past five years, two months, and day.