All about Social Media Analytics


Some Interesting facts:

  • 1 out of every 8 people on earth are on facebook
  • 9 out of 10 US internet users are on a social network
  • 1 of 5 minutes spent online is on a social network and on average the time spent on social network has increased from ~8% in 2007 to ~20% in 2012
  • In one minute we produce: 694,980 status updates and 532,080 tweets
  • Average number of friends in real life: 150, whereas average number of friends on facebook: 245
  • People add friends because: 82% know in real life, 60% mutual friends, 29% due to appearance and 11% for business networks
  • 250 million photos are uploaded daily and 35% of users tag themselves

Now this is even more interesting: Half of all users compare themselves to others when they view photos or status updates:

  • As people spend more time on Facebook, they start believing that others have a better life than they do (Did u ever felt that ….)
  • People with high levels of narcissism or low level of self-esteem spend more than an hour a day on Facebook
  • When people get frustrated by seeing others high success, they start feeling depressed and many a times quit facebook or stop going to facebook

What is Social Media Analytics?

Social Media Analytics refers to the study of the structured and unstructured data generated by the users of the social networking sites like facebook, twitter, LinkedIn etc. to make informed business decisions. The most common use of social media analytics is gauging customer opinion to support marketing and customer service activities. It is about to know what consumers are talking about a brand or product or services, it is about analyzing the new trends in consumer preferences and taking feedbacks to improve on brand image and service offerings. Social media provides valuable data for your business, along with a medium for conversation. These tools enable you to spot trends, opportunities and get insights, enabling you to act on some of those insights by participating in conversations that are going on and increasing your ROI.

What needs to be done?

Step 1: The first step in social media analytics is determining your purposes for social media involvement in the first place. Typical objectives include increasing revenues, reducing customer service costs, crowd sourcing, getting feedback on products and services, and improving public opinion of your company or products or a combination of above.

Step 2: Define Measurable and actionable KPIs: The second step is to define and identify KPIs or Key Performance Indicators (business metrics used to analyze or answer specific objectives or achieve goals). You could evaluate customer engagement, for example, through numbers of followers of your corporate Twitter account and numbers of retweets and mentions of your company name, or number of likes or mentions.

  • Define specific KPIs for each social network
  • Define actionable KPIs
  • Choose Metrics that translates into business context

Few examples of actionable KPIs are: Number of people in a specific location who follow your company on twitter or like your page on FB, Number of product improvement suggestions, percentage increase in product reviews, percentage reduction is support costs etc.

Step 3: Configure your analytics

  • Create a filter or segment for social traffic – Identify quickly which actions work and from which social network
  • Add event tracking for social media – Customize your landing pages for different users based on different promotions they are looking for. Directing user to correct landing page is big factor for your online campaign success.
  • Add tracking to measure interactions and event responses – Which buttons do visitors interact with? How much time they spend on landing page vs. other pages on site? What graphics make users click often and more on? Is that button doing what you expected? If not Change It!
  • Add Campaign tracking to URLs – Figure out what wording leads to more click-through and conversions.

Step 4: Use Other Social Analytics Tools: There are a number of types of tools for various functions in the social media analytics process. These tools include applications to identify the best social media sites to serve your purposes, applications to harvest the data, a storage product or service, and data analytics software. Social media tools crawl blogs and social network sites for brand mentions and enable companies to build communities and engage with their customers.

Some Free and Popular Social Analytics tools are:

Social Mention, WhoisTalking, Howsociable, Backtype, Trendistic, Thinkup and

Some effective Paid tools are:

Radian6, Sysomos, Alterian SM2, Ubervu, Raven, Hootsuite pro, peer index and Lithium

Step 5: Understand each Social Metric

Quantitative data: New Likes, Total Likes, Page views, Referrals, Profile data including followers, following, tweets and Daily tweet average, number of clicks, number of retweets, what times, which tweet structure gets a better CTR?

Qualitative data: User Profiles, user location, language, Comments (sentiment), Interaction times, Brand mentions, mention content

Step 6: Action and Strategy

Based on the analysis of data generated above you need to evaluate if you are achieving your goals? Keep on measuring, act differently and keep on experimenting to get even better results. Identify worst performing metrics one by one and devise an action plan.

The ROI of Social Media

Return on Investment is a business metric which measures the effectiveness of the social media marketing efforts. Measuring ROI of social media marketing vs. traditional marketing is very tricky and also very different concept than measuring ROI through traditional marketing efforts. Different people, strategists and marketers have defined the concept of social media differently. This is precisely because Social Media ROI includes various intangible benefits other than the one that can be measured. Measuring social media ROI is to go well beyond the simple metrics of number of followers or likes. It rather depends on the combination or all of below attributes:

  1. Attitude towards the brand: There are various studies done which shows the linkage of returns based on the attitude of users towards a brand, like buying a brand, or recommending the product to friends.
  2. Engagement:The more engaged your user or fan means stronger brand and greater ROI. ROI increase happens because of
    1. Consumers who share about brand are more likely to buy the product (based on cognitive consistency theory)
    2. When consumers engage with your brand, your message travels to wider audiences
    3. When folks engage your brand, it acts as an endorsement to their friends and we know recommendations by friends are more powerful than any commercial marketing campaign.

3. Customer Service: How a business handles complaints of negative remarks is going to be major differentiating factor in increasing ROI. Complaints travel faster than praises. So there needs to be a mechanism in place to address negative comments by companies.

Overall, social media marketing doesn’t translate directly into social media ROI, but it has an impact on overall ROI of business. Social media marketing is a communication plan, it can act as change agent, increased visibility and wider reach and it translates into revenues through various intangible means. The increase in ROI through social media needs to be managed by engaging with clients, tracking what clients and users are discussing on various platforms and forums and addressing customer complaints.

According to Forrester, identifying the value of social media marketing efforts comes down to looking at four factors:

1. Financial: Have costs decreased or sales increased?

2. Brand: Have perceptions of the brand improved?

3. Risk Management: Are you better prepared to respond to issues that affect brand reputation?

4. Digital: Has the brand enhanced its digital assets?

Disclaimer: Some or all of above facts and figures are compiled from different open source websites licensed under creative commons license and hence reproducible. This post contains various opinions and judgemental statements which are based on the research and studies done by me. However various facts and figures are sourced and compiled  from the research done by several institutions including but not limited to Forbes, Dreamgrow, Psychology degree, FB newsroom,, comscore, pewinternet. Please use this information as per your discretion and I would not be responsible for any loss attributed due to actions taken by you based on information presented herewith.



What is Big Data Analytics?

There is a lot of buzz around big data analytics. Infact, big data is termed as major growth engine and revenue driver for the outsourcing companies in coming years and more so every IT company in its latest quarterly performance release has spoken about their focus on Big Data and big data analytics..

So what is Big data?

Big data refers to the enormous amount of data generated on daily basis by individuals and firms through phones, internet, websites, intranet sites, social media and business transactions. According to IBM, 2.5 quintillion bytes of data are being created every day and industry expects 4300% increase in data generation by the year 2020. As per IBM report 2.7 Zetabytes of data exist in the digital universe today and 90% of which is generated in just last 2 years.

Opportunities in Big Data

Social, Mobile, Cloud and Big Data all play a role in this emerging era of engagement. In its simplest form, Big Data’s role is to better inform operations, drive more intelligence into our automated processes, inform interactions through context awareness, and optimize our outcomes. It will enable the inclusion of a broader base of data (both structured and unstructured), delivering more insight to decision processes and more intelligence to drive automation.

Big Data opportunities exists in both database software and hardware installation at the clients site. Since BIg Data analytics is applicable in all major Industries like Manufacturing, healthcare, Retail & Logistics, Social Media to name a few., the opportunity for IT services companies are mammoth and would definitely be the next revenue / growth drivers for these companies.

Companies’ worldwide are adopting technology and know-how to synthesize these data which help them to make better business decisions. The ever-increasing demand by the companies to unravel the gold mine gave rise to new branch of analytics called Big Data Analytics. Analytics or processing raw data into information help companies to explore hidden patterns and trends that can add value to their business. Many companies are seeking the assistance of service providers to analyze the huge data generated every day to make into useful information. Market research firm IDC expects that Big Data Analytics service market will reach $16.9 billion by 2015. As per another study by Wikibon big data will be a $50 billion business by 2017.

Challenges in Big Data Analytics:

Big data analytics is still in nascent stage and lit of firms like IBM, Oracle etc. are in foray providing host of software and hardware solutions to firms to capture their unstructured data and synthesise them to reveal patterns and as such meaningful insights which can deliver enhanced business meaning to it.  There are other challenges on front of skilled manpower to convert the raw data into information. In fact, they are short in supply. According to a recent report by McKinsey global Institute, US alone will face shortage of almost 200,000 data analysts by 2018 to synthesis the data into information relevant for decision-making.

The amount of data being generated is exceeding the supply of talent required to analyze the same. This gap between demand and supply will only widen in next few years.  The reasons are:

 Every fraction of second, new data is being generated and the demand for creating value from the data is increasing. It is estimated that every two years the size of existing data will be doubled. It is said that even though supply (talent) increases, demand (data) will increase at a faster pace.

 There will be demand for value creation from data from all the industries again adding on to the situation of demand increasing supply.

Case study on how Big data analytics helped a major firm in saving millions of dollar:

OPower said its software and big data tools will be able to help save one terawatt hour worth of energy — which is the equivalent to the energy consumed by 100,000 American homes per year — collectively from U.S. homes by the end of 2012. That’s worth a whopping $100 million in consumer’s utility savings. Opower’s algorithms collect and crunch utility energy consumption data, combine it with other large data sets, analyze it for behavior-changing tidbits, and package the results into a detailed utility bill that can help consumers save around 2 percent on their energy bills. Per person that might be small, but as a whole, the company, is making a real difference.

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