Social Media Analytics - Facebook

Maintaining brand presence in social media has become one of the top priorities for marketing departments. Brands are doing everything possible to create buzz and present themselves in the digital window of every potential consumer. While we move towards an era where consumers can see, experience and buy almost everything online, it is crucial that companies retain their brand recall in the social media world.

This study analyzes the effort spent by two major car manufacturers on facebook and their user response. Products chosen are two mass market cars - Maruti Suzuki Alto 800 and Tata Nano GenX. Both products fall under category A cars and are meant for volume sales. The original intention was to include Renault Kwid and Hyundai Eon too, but sadly, renault has'nt bothered much to post anything on their official facebook page – hardly 4 posts over a year, while Hundai did'nt even bother to create a page for Eon.

Facebook posts made by the manufacturer and the number of likes obtained are collected and analyzed. Posts are collected since launch date of each car to analyze the trend.

Following chart shows count of facebook posts made by each manufacturer on a monthly basis,since their market launch.

The blue line shows Maruti Alto 800 's facebook post counts per month, since 2013 till date and red line denotes TATA Nano GenX , since May 2015.

Inferences:

1. Alto800 has spent considerable effort compared to Nano GenX in terms of maintaining buzz on facebook, right from product launch till date. The graph showing post counts shows significant difference until January 2016. There after, both maintain almost same number of posts.
2. As expected, the initial euphoria on social media comes down as the product ages. In 2016, both products are spending almost same amount of effort on facebook. However, Alto800 came down to this point after two years from launch, whereas Nano GenX came down in an year's time.

Further to this, monthly sales figures of Alto 800 and Nano GenX are compared against number of likes they collected on facebook.

Note:

1. Nano Genx was launched in May2015. Hence sales data is captured only from May 2015.
2. Monthly Sales Data is obtained manually from www.team-bhp.com
3. These manufacturer-reported sales numbers are factory dispatches to dealerships. They are NOT retail sales figures to end customers.

There are some interesting patterns emerging out of these two graphs.

Inferences

1. For both cars, the number of likes collected on facebook (green line) follows same pattern as of the sales line (blue) . Although sales figures are not moving with the same intensity as that of facebook likes, the peaks and dips are showing a distinct correlation with the pattern of facebook like volumes.

   This means that buyers are expressing their positive sentiment in social media around the time of purchase. The smoother sales line compared to likes pattern indicates that everyone who likes the product are not buying it.The difference between sales and likes figures could be the layer of potential customers. This trend also indicates that facebook is a very good medium to connect with customers.

2. Likes are substantially high during product launch and the interest flattens out as the product ages. There are events happening from the manufacturer's side whenever there is a dip in facebook activity of users. This is evident from the increased number of posts from Maruti in the months of April/May2015, and again in Nov/Dec2015. During these months, although sales were flat, they managed to improve facebook activity from users by increased number of posts.

   In Aug2015, maruti launched two contests that reached approximately 130 000 facebook users whereas the month before, only around 5000 – 10000 people liked Alto. However, in the case of Nano, there were no events that broke the regular pattern of like counts (less than 5000 per month).

Technical Details

Technology platform used for this study is R (www.r-project.org). The “Rfacebook” library facilitates API level connectivity to facebook data if you have an application registered on facebook.

Besides connectivity to social media like facebook and Twitter, R provides a good ecosystem for statistical analysis and visualization.

Further Analysis Proposed

It will be beneficial for manufacturers to understand what people are talking about in social media with respect to their products, so that they get better insight into the minds of their potential customers. Lexical analysis of comments made by facebook users will produce a word cloud surrounding a product. Classifying and summarizing this data and then relating it to product related features or shortcomings can be very helpful for the industry.