Social Media ROI for Beginners

This post is the first in a series dedicated to understanding how the ROI for Social Media can be calculated. The entire series will include many points for you to use this knowledge and understand the social media marriage with business objectives.

These posts will prepare you to demonstrate ROI before your management team knocks down your door and at the same time to make them understand the influence of social media.

Social media in itself is not magic and has no ready-made ROI models.  The way each business uses it will depend on the specific strategies and goals of the organization. However, I have tried to come up with common themes for how businesses can measure social media ROI. To start with, few basic points to remember:-

Social Media will Save Your $ $ $

Identify places where social media can replace/enhance a business process or system. For example it is several times cost effective to serve a customer on social media (like Twitter, Facebook) than in a call centre. Virtual social media training can save enterprises tons of money. You can even use social media in market research, consumer surveys, and business intelligence and as a part of day-to-day operations that saves you considerable amounts of time & money.

Social media campaigns are cost effective in nature that will help you to replace or target better in advertising and PR, and save dollars from those budgets as well.

Business Leads from Social Media

Social media represents another evolution of media that allows two-way communication between customers and brands. Use social media to find new connections for your business. Share tips, resources and information that add value to your audience. Promote you brand in an interactive way and look for opportunities to partner with them.

Monitor conversations with people looking to buy what you are selling and interact with potential customers & past customers.

ROI for Social Media

Strategic Objectives & Business KPIs

Social media reach & reputation of brand is a key success indicator. Insights from Social web not only give insights into day-to-day operations, but also directly influence business growth, innovation, revenue and profitability.

Insights from the social media can play a key role in reaching strategic business goals such as improving customer experience or understanding customer’s business problems. If you want to stay ahead of the curve and determine customer needs, you have to set specific business goals and track your progress.

It is important to understand the impact of reaching these business objectives, which in-turn will help you to quantify the ROI of social media easily.

Finally, report these business KPIs on a regular basis. Do good things and tell people what you’ve done.

Here is an interesting old post from Altimeter on business objectives and social media ROI – Social Marketing Analytics. Even Wikipedia provides a good overview on this topicROI of Social Media. This will set a good base to start with.

There is no single way to use social media, but if you don’t know where to start, focus on the “Big 3” – Facebook, LinkedIn and Twitter. Social media strategy isn’t a one size fits all solution, but with few basic things in mind brands can go beyond deploying social media from “awareness” & “engagement” to real “ROI” and save valuable marketing dollars while significantly expanding your reach on social media.

As this post has highlighted the basic things required for measuring “Social Media ROI”. In next posts, I will come up with attribution models and new examples of successful ROI calculations.

It will be very interesting to know your thoughts on this. What do you think? How do you evaluate social media ROI? Share your thoughts…..

And stay tuned for future blog posts…. 🙂

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Numbers Just Aren’t Enough

We live in a world where quantitative research makes up about 80 percent of the research industry. The findings are generally in the form of numbers and charts. However, organizations are now realizing that they want to know more about their consumers in their research efforts. They want to know the ‘why’ behind the ‘what’. The industry agrees that Quantitative information alone, does not give us the complete picture. In such a scenario it becomes imperative to complement it with Qualitative information. In the past, qualitative research was time-consuming and expensive.

The good news is that, it is now possible to combine quantitative research design with qualitative in a cost effective and quick manner. Please click here to download E-book by 20|20 Research, explaining through examples and case study, how they have combined quant-qual research and benefited their clients.

Social Media and Measurement

We know that Social Media is here to stay. It has been established that Social Media efforts needs to be measured to tell us (a) if we have been able to achieve our objectives (b) to feed into developing a social media strategy. There is a plethora of social media measurement tools available in the market. However, the focus is on quantitative metrics – e.g. number of Twitter retweets, Facebook fans, blogpost traffic, generated sales, etc. We have reached a point where these are fairly easy to track through the tools available in the market. The next and a very key step is to add qualitative to it.

What To Measure

The Quantitative measurements help us in locating the important places to look and dive deeper e.g. the sudden spikes or lows in follower growth/conversations/likes/dislikes. Thereafter, there needs to be a process, put together for understanding the language, topics and context of consumer/user conversations. From my viewpoint, following can be a few qualitative metrics to consider as a starting point:

  1. Tonality

This will not only tell us whether people are saying positive or negative things about our brand, rather it will also reveal what tone of voice the consumers are using i.e. whether it is sarcastic, playful, skeptic etc. This will enable us to draft our social media or even traditional media messaging in such a way that it matches with the way our consumers talk to each other.

  1. Topic/Themes

What is being talked about our brand? Is it prices, product attributes, mergers, financial results etc.? Is it good or bad for our brand? Identifying these topics will enable us to gauge sentiment around our brands and help us in taking corrective action when required. Moreover, it will also help us identify important messaging cues to enable us to engage with our audience.

Qualitative Insights to Generating Influence

Social Media interaction is similar to interacting and relationship-making in the real world. What we say, think and feel matters to our friends and family. Similarly, in the virtual world, we cannot control the people close to us – what they say, what they think, how they feel – but we can do several things to influence them. Perhaps this is the most important objective of social media efforts – the ability to influence your target audience – to influence their purchase decision making process, to build a perception about your brand. To top it, we need to engage in such a way that some consumers become our advocates and influence others in return. In social media identifying the most valuable consumer is not locating who spends the most money – but who influences the purchase decisions of others to spend money.

We will be in a better position to influence and engage with our audience, if we know what tone to use when interacting with them, what to discuss, what will generate interest in them, what topics are important to them. The answer to this will definitely come from a robust listening model. We must know what our audience is like and what makes them tick before we reach out to them.

The Way Forward

Qualitative measurement in social media is time-intensive and needs a lot of manual effort. We know that the value of the complete picture we can hope to achieve by combining quantitative with qualitative social media measurement is extremely high even if it needs time and effort at this stage. However, there is a strong ray of hope to make this approach scalable as organizations work on developing Natural Language Processing (NLP), text mining, content analysis etc. capabilities and we too have jumped onto the bandwagon.

Please click here for a related interesting read.

Do share your thoughts on this and let us know the approaches you are adopting to analyze social data qualitatively …

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Using the Twitter API for Analytics

Twitter is probably the most popular social media channel among businesses that are trying to harness the power of social media. It is therefore of paramount importance to the business community that there be an efficient way of retrieving metrics out of Twitter. The Twitter API serves this need just right. It is one of the most advanced and powerful APIs that are there in the social media space.

My focus in this post would be to introduce the capabilities of the Twitter API from a social media reporting and analytics point of view. I would also touch upon the restrictions that the API imposes on users.

Twitter has a REST based API which can be used by developers to access almost all the static data points and dynamic metrics that the channel offers. These data points and metrics are mapped to specific access ports in the API which are called API endpoints/resources. The developers need to direct their queries to these endpoints in order to access the datapoints/metrics. A complete list of the API endpoints that can be accessed is available here.

One can easily get overwhelmed with the variety and amount of metrics that can be retrieved using the Twitter API. It is therefore advisable that you identify and freeze on the Key Performance Indicators (KPIs) that you want to track before you start with the actual development process. This will give you better clarity on what is required and what can be ignored in your case. Below is a list of the most useful data points/metrics that can be retrieved for each Twitter handle/username. This list might vary based on the business requirement that one is trying to cater to.

  • Number of Followers
  • Number of Following
  • Number of Tweets
  • Number of Favorites
  • Number of Listed
  • Profile IDs of the followers
  • Profile IDs of the people the user is following

The API returns the above metrics/data points on a cumulative basis (till date basis) and not for a specified time frame. This might seem to be a problem for people who want to extract these metrics on a periodic basis. One way of getting around this restriction is by retrieving the metrics twice – once at the beginning of the time period and once at the end of the time period and then subtracting the values. For example – Let’s say that I want the above metrics for the month of July 2011. I would need to extract data once on July 1st and then once on July 31st. To arrive at the numbers for July, I simply subtract the July 1st values from the July 31st values.

The Twitter API imposes certain limits on applications that access it. The limits are primarily in terms of the number of requests that an application can make to the API. The limits vary depending on the authorization type being used by the application.

  • For unauthenticated calls, the rate limit is 150 calls per hour. These calls are measured by keeping a track of the public facing IP making the calls.
  • For authenticated calls, the rate limit stands at 350 calls per hour. The OAuth token is used to keep a track of the number of requests.

Each data point queried by the application comprises one API call.

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Social Media Platform APIs – What, Why and How

Social Media Platform APIs have generated a lot of attention and excitement among professionals in social media and web analytics. However, the picture is still hazy for most of us. Majority of us have heard about a “Facebook API application” or a “Twitter API application” but we really don’t know what exactly they are and how they can be leveraged in our day to day work around these social media platforms. The voluminous API documentations and their technical parlance are not very easy to understand for Analytics professionals.

My objective of writing this post is twofold:

  •  To try and explain in layman’s language what Social Media APIs are, why we need them and how applications interacting with the APIs can be designed & customized to your requirement from an Analytics perspective.
  •  To gather valuable feedback, opinions and experiences from my readers who have or are planning to experiment with Social Media APIs.

I will start with the “what” aspect by defining APIs in general and Social Media APIs in particular and then I will try to answer the question of “why”, we as Analytics professionals should bother to know about them. I will delve into the details of platform specific APIs in my subsequent posts.

Application Programming Interface (API):

An Application Programming Interface is a window that applications provide to developers for accessing them in a programmatic manner. It is very similar to the way a Graphical User Interface(GUI) facilitates interaction between humans and computers; the basic difference being that the interaction happens through an API Script (Code). The API therefore is a level of abstraction between the user and the application and it acts as a translator and enables communication. You will find a more technical definition here.

Social Media APIs refer to an ever increasing list of APIs that are coming up for the different social media platforms or services such as Twitter, Facebook, LinkedIn etc. Using these APIs, one can programmatically access almost all of the features that the platform offers to a user. Along with that API programming enables one to customize the platform offerings to their own requirements.

Why APIs:

One of the most obvious problems that professionals in Social Media Reporting and Analytics have to face in their day to day job is the lack of a convenient mechanism of pulling out data and metrics for their social media assets like Twitter profiles and YouTube videos. While retrieving metrics through the GUI of individual platforms is possible, it is very manual in nature which makes it very time consuming and sometimes inaccurate as well. This is where APIs can help. Platform specific API applications can retrieve data and metrics related to these social media assets (profiles, videos etc.)  in an automated manner. What this essentially means to an end user is that one can retrieve data and metrics for multiple assets on a periodic basis by simply clicking a button.

Some other ways in which Social Media APIs can help are:

  • Integrating Social Media assets like video feeds and twitter feeds in your own web apps.
  • Administration and governance of assets (eg: uploading, editing, deleting etc.) in an automated manner.

As mentioned earlier, the focus here would be on Social Media Analytics and therefore I will restrict myself primarily on how data and metrics can be extracted from Social Media platforms using API applications.

Stay tuned for my next post if this excites you!

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Audience Analytics of YouTube videos

In my last post I have expressed my desire to use common statistical methods and multivariate analysis to analyse the Social Media data.Here I am going to explore if Cluster Analysis can be used to segment the videos according to the audience attributes in my current blog and future blogs .This is divided into two parts.The current post will explain how the data can be obtained from YouTube platform.The second will go deeper into understanding how the data can be cleaned and how Cluster Analysis can be used.

How many of us log into our YouTube account everyday and watch one video regularly? The number is staggering. This made me choose a platform like YouTube.As the viewership of YouTube videos rise, we the people of analytics have wondered if it is possible to get meaningful results by analyzing this data. The first question is where is this data available? It is quite interesting to know that the YouTube insights feature has a ready solution. The insights feature in YouTube gives you all possible data which can be used for detailed analysis of your audience. If you are the owner of an account in YouTube it is quite easy to have a graphical dashboard view of your audience characteristics. Just go to the link where your account name is written. Click on the drop-down. A list will emerge which has the following links.

 Click on any link and you have a toolbar on the top. Following this click on the link called insight on the toolbar and your dashboard is here.

The dashboard is divided into six groups as follows:

In the summary dashboard you can customize the time frame to see the number of views over a period of time. The demographics of the audience in terms of their age and gender and in which regions they are popular. Even we can see which videos got what kind of attention and the views as percentage of total views in the channel.

The views link opens another dashboard and gives a few more options. We can watch the daily totals, 7-day totals and 30-day totals and the regional popularity.

The discovery dashboard gives the link followed to this video or what we call the referral statistics. It is available by absolute numbers and also as a percentage of total views. It also gives the location of the player when it is viewed as percentage of total views.

The 3rd link contains the demographics view which is a part of the summary views. It divides the age into 7 groups starting from 13 to above 65 and reveals the percentage of male and female in each age group. This further provides us with the pie chart of overall male and female percentage of video views.

In  the community link we can get four kinds of views. one is all community engagements which gives the summed up view of all engagement activity done by the audience like the Sharing, Rating, Comments and Audience and individually  views on the basis of sharing Rating etc are also available.  Also a list of top countries and top videos are available on the basis of engagement activity by all countries.

The last link is the subscribers link for the channel which gives a list of top countries on the basis of maximum change in subscribers. For individual videos this is replaced by a tab called hotspots which gives a graphical representation how the length of the video has been watched over time and it shows at what point it has been watched more and at what points most audience has left the video. Each of this links can be adjusted by date according to the needs.

For forming these dashboards there must be some data at the back end.  The good news is YouTube provides us this data for free. This is really exciting as it allows us to process the data according to our needs and find meaningful results from it. We just need to click on the Download reports for this video option. There is a “csv” and “csv for Excel” option which we can choose from.

When we click on the “download as csv file” link YouTube provides us a zip file with the video ID name as the name of the zip file. The zip file contains four excel files and they cover four aspects of the data namely the views, location referrers and demographics. Each of this file contains the video ID as the unique key in the data which allows us to process the data into one single file using the unique key.

There is one pitfall to it. The data that YouTube gives currently is for only 28 days at one click. To download the data for an entire period of one year requires adjusting the data time period about 13 times and clicking to get the data. This may sound very easy for one video but if you have a channel which contains more than 600 videos this is a daunting task. One option to that we can explore is API programming. This is the link for the Documentation related to API calls

All the programmers out there can try a hand at this.

Now after you have downloaded the data and we need to process the data for carrying out any multivariate analysis. Now what kind of multivariate analysis technique can be used? Well, it depends on the needs of the business or the person concerned and what we need to answer. Our motto of carrying out the analysis is to group the individuals on the basis of similar characteristics and profile the personas on the basis of these characteristics. So we considered using a common statistical method called clustering By clustering the data on the basis of video IDs we will be able to group the audience characteristics. I will explain in my next blog how we can go about this process of clustering.

Please feel free to leave a comment and let me know if this excites you and also provide me with your blog or website if you have done something like this. I will very enthusiastic to learn from it.

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Social Media and Statistical Methods:Are they reconcilable?

Social Media has been the Buzzword in recent times and I feel analytics companies are bored with structured data with different metrics and are exploring new realms of data which are not-so-structured. In this process, companies are using different methods to find patterns within the chaos. If you have read the earlier Blog post then you are already introduced to the analytics lifecycle. I will talk about the  techniques in social media analytics. Among different techniques that are employed, notable are the various social media tools that have come up from different vendors for different platforms, API’s of the platforms themselves, machine learning and other methods used for text analysis. But have we ever thought about using statistical methods which are well established in the field of analytics to evaluate and find insights from the data that we get from these platforms? This is what I am going to talk about now.

Statistical methods have been significantly prevalent in the field of analytics which is common knowledge and I feel there is no reason why they cannot be used in social media analytics space. Over the years multivariate regression models, logistic regression, survival analysis, inter-correlation matrix, factor analysis and chi-square automatic interaction detector and cluster analysis have hogged the analytics space in any segment that you can think of like credit card analytics, risk analytics, retail management, corporate intelligence or supply chain. It is time we use them for social media analytics. By saying this I would like to point out that using statistical methods for social media is not a new concept and some notable research has been done already. Some research papers worth mentioning in this context are

  • Predicting Future with Social Media” by Bernado Huberman and Sitaram Asur, where  they use regression models to predict the success of Hollywood Movies
  • Golder et. al. showed how the number of messages sent versus the number of users sending this messages on Facebook follow a Power-law and thin-tailed exponential distributions using the log-likelihood test. They also give a lot of insights on the time of use of Facebook and their social variations
  • Another notable research in the context of twitter is by Meeyoung Cha, where they use Spearman’s rank correlation coefficient to identify top influencers and their relative influence ranks and they identify that million followers does not necessarily imply huge influence as their  paper is aptly named “Measuring User influence in Twitter: The million follower fallacy”

There is a rich literature in academics which uses the well-known statistical methods on social media data which all analytics companies deploying social media strategies will be able to leverage. In my next post I will talk about the publications of Bernado Huberman in detail and in the upcoming posts I will talk about how we can use them in Social Media analytics.

Few thoughts that pose my mind are Can we do a tree modeling to find out the probability of a blog being viral? Can we exactly find out the influence of a person when he/she is operating on multiple platforms by common statistical methods? Is it possible to understand the trend of conversations by looking at the time series data? The point I am trying to make here is I believe we “can” develop social media advanced analytics techniques like other established analytics streams and the time is Now!!. Let me know what you think about it.

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Defining a “Social Media Analytics Life Cycle” Framework

The framework defined in this post would set the context and scope for the blog as such. It is easy to get lost in the love for Twitter & Facebook, but as social media analytics practitioners, our responsibility is to always align the social media marketing efforts to business objectives & benefits (This presentation by TheBrandBuilder brings out the need for business connect in a fun way). Fundamentally, social media analytics is one of the pieces in business analytics puzzle. In this context, it is worth considering a logical business analytics life cycle framework.

Business Analytics Life Cycle

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Presented below is a simplified adaptation to social media analytics life cycle.

Social Media Analytics Life Cycle

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Enablers and benefits remain the same as in the business analytics framework. Strictly speaking, all social media marketing efforts should be directed towards achieving either a cost impact or value impact.

First step in social media analytics process is EXTRACTing business relevant data. Broadly, data extraction could have 2 different scopes. For requirements such as brand or campaign monitoring, the scope is all posts from entire social media universe that match to a defined set of keywords or search terms. On the other hand, for requirements such as performance measurement or competitive intelligence, the scope is all posts from a defined set of social media profiles.

After extraction comes the ANALYZE step where we try to clean and make sense of the gathered data. This may involve aspects such as volume trend analysis, ranking posts, ranking profiles, etc. EXTRACT and ANALYZE steps performed on a regular basis constitutes a social media listening program. The findings and insights from a listening exercise could feed into various business functions such as product development, customer support, sales, etc. as depicted in the social analytics lifecyle defined by Ken Burbary and Chuck Hemann (more than a year and a half ago).

Findings from listening exercise should also feed inputs into a brand’s social media PARTICIPATion strategy and plans (e.g. as simple as identifying popular topics relevant to a business/brand in order to become part of those conversations). Once there is active participation, it naturally entails performance assessment for continuous improvement. Focus should be on identifying best practices from the participation experience. This completes the ASSESS part of the cycle.

Now that we have introduced the analytics framework, the next post would focus on a social media tools framework based on the life cycle stages. While we are at it, we would like to know your comments on our framework and if there are related perspectives, please link to them in the comments.

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