Showing posts with label BI. Show all posts
Showing posts with label BI. Show all posts

Sunday, February 2, 2014

How to Initiate Social Media Analytics in your Organization? Here is the Simplified Implementation Framework!

As a prerequisite for social media marketing, you need to have a clear understanding of your overall vision, what your goals will be, and how you will track and measure the success of your social media initiatives. In other words, you must create a social media analytics plan.

The first step in a social media analytics initiative is to determine which business goals the data that is gathered and analyzed will benefit. Typical objectives include increasing revenues, reducing customer service costs, getting feedback on products and services and improving public opinion of a particular product or business division. This can be achieved by monitoring how the users are looking at your brand/products and how they are looking at your competitors. When we are talking about ROI, there is no single ROI we can point out. The business impacts of social media various at different levels in the organization. For example, the C-level executives care about metrics such as brand reputation, revenue, customer satisfaction etc. At the same time, the business unit heads and line managers are interested in more granular metrics specific to their goals.

Define Strategy

A successful social media strategy requires alignment with the strategic business goals of the organization, organizational alignment and required support/commitment to enable the execution. We have to define what we are trying to achieve and how we are going to approach it. Defining the success criteria will help to drive the initiatives and evaluates the effectiveness. Strong organizational commitment and support is needed to implement the strategies.

Define Measurements

Always advisable to begin with few key metrics that you believe are practical to deliver and actionable. They should give the most impact on your business. Formalize those metrics with process and dashboard and then expand further by adding other metrics in an incremental approach. Here are a couple of guidelines you should follow when defining KPIs:

  1. Select key performance indicator(KPI) metrics that translate into business context like sales, revenue, business leads, lead conversion, customer interaction, conversions, etc.
  2. Create specific social media analytics metrics for each social network site and specific elements of your website
  3. Define actionable social media analytics

The most important guideline above is to define actionable metrics and here are few examples
  • Reduction in support costs
  • Number of people in a specific location who follow your company on Twitter
  • Reduction in sales cycles
  • Increase in product reviews

Resources & Tools

You have to asses your readiness to measure social media in terms of present state of the organization, probable barriers and strategies to overcome it, resources, required analytical and tools expertise and communication strategies. A successful social media analytics implementation and operation depends on the integration and effective utilization of all means in the organization.


The organization identified financial performance improvement as a key objective. Identified call center operation cost reduction as one of the ways to improve the financial performance. In order to provide the same quality of service to customer, organization decided to respond proactively to users through social media like twitter and facebook. Here the call center cost performance becomes the business KPI and number issues solved via social media become the social media metrics.

Check list

Since social media analytics is in the early stage of adoption, there aren't any well-established frame works are published anywhere. However organizations have to follow the discussed minimum critical steps to build a business focused social media strategy.

Sunday, October 13, 2013

Social Media Analytics Overview and Value Metrics

Social media is a emerging medium to understand real-time consumer preferences, sentiments and intentions. Social media analytics is the tool to uncover the customer sentiments from social media dispersed across online sources.
According to Gartner’s Definition, Social analytics is monitoring, analyzing, measuring and interpreting digital interactions and relationships of people, topics, ideas and content. Interactions occur in workplace and external-facing communities. Social analytics include sentiment analysis, natural-language processing and social networking analysis (influencer identification, profiling and scoring), and advanced techniques such as text analysis, predictive modeling and recommendations, and automated identification and classification of subject/topic, people or content.

What your Management is Looking for?

The management wants to hear how the organization strategies, initiatives and business goals performing over a specific period of time. Social Media Analytic helps your organization answer many questions like:
  • What are consumers saying and hearing about my brand/Company? Is my reputation affecting?
  • Which initiative/effort has the greatest impact? 
  • What are the most talked about product attributes in my product category? 
  • Is the product/service feedback good or bad?
  • Competitor insights
  • Customer growth metrics
  • Customer Quotes
  • Customer Action Taken

Social Media Analytics Value Metrics

Social media analytics solutions in general can help your business by

  1. Capture consumer data from social media to understand opinions, attitudes, trends and manage online reputation
  2. Predict customer behavior and improve customer satisfaction by recommending next best actions
  3. Create customized campaigns and promotions that resonate with social media participants
  4. Identify the primary influencers within specific social network channels

Here is the Detailed Social Media Analytic value metrics

Brand Health and Awareness

This is nothing but the measure if how people feel about your brand, talk about and act towards your brand. By measuring the you can mitigate or block crisis and understand the scale of reputational threats and possible opportunities. The brand health can also be a measure of net promoter Score(NPS). NPS is the difference between percentage of promoters and detractors. You will have a positive brand health if the NPS is above Zero. 
  • Sentiment analysis
  • Competitive performance
  • NPS measurements

Sales & Marketing optimization

Marketing and sales divisions can measure how their marketing and sales promotion activities are performing in the market. Based on this organization can understand how the initiatives are influencing purchase behavior, brand perception etc and to plan future initiatives. Suitable correction can be applied for the ongoing initiatives as well for better benefits. Fe examples of marketing measurements are 
  • Campaign measurements
  • Influencer impact
  • Content Performance

Improved Revenue Focus

Social media cannot directly generate revenue. The revenue has to be considered as a combination of tangible components like real revenue and intangible components like generating loyal customers. Social media plays an important role in purchase process and understanding this, you can change the customer intents. But focused and continuous customer engagement and interaction, you can convert the detractors to promoters and probable customers to customers. All these efforts will be resulted in the overall revenue of the organization. Few example measurements are
  • Impact of social media search results
  • Loyalty measurements
  • Revenue

Operational Optimization

Social media interactions with customers improve not only customer experience, but also provide scale as solving one person’s problem is visible to others as well. This will help to reduce effort for repeatedly addressing the similar issue to other customers. In the same way, by effective social media customer interactions/engagement, organizations can reduce the call center call volumes and intern reduce the operation cost by answering through twitter page or similar social media forums. Few examples are
  • Cost containment opportunities
  • Potential cost savings from Contained calls
  • Super fan/Advocacy identification 

Better Customer engagement

Better customer experience will provide brand health, cost saving and revenue optimization. Social media can e considered as an early warning system. Whenever organizations found an issue in the media, they proactively fixed before the issues getting aggravated and customers keep informed. Examples of customer experience measurements are
  • Attitude
  • Intensity (Momentum of otpic)
  • Issues & crisis

Product/solution/ Service innovation

Companies can get innovative ideas from social web by monitoring feedback, comments, issues from own products and from competitors. So far companies used to get these ideas from their own websites (Eg. and now they no longer need to depend on their own sites which can even shutdown for cost optimization. Few Measurement samples are
  • Opportunities and threats
  • Idea impact and response

Major companies adopted SMA

Dell Social Media Analytic Command Center 

Social Media Analytic is widely adopted organisations especially those who are in to consumer products, entertainment business and Service. Few examples are Dell, Proctor & Gamble, Warner Bros, Star Bucks, American Express, DIRECTV, JetBlue Best Buy, Royal Bank of Canada, Whirlpool, etc.

Popular Social Media Analytic Tools

Salesforce Radian6, Alterian SM2, Adobe, IBM, Oracle, SaaS Analytics, Social mention, scout lab, Trenderr, Blogplus, Apark, Collective Intelluct etc

Wednesday, October 17, 2012

Business Analytic - Top Five Business Intelligence Trends

Gartner’s 2012 predictions for business intelligence focus on the challenges around Cloud, alignment with business metrics and a balanced organizational model between centralized and scattered. Forrester has looked toward 2012 with everything from the rise of individualized BI tools to Cloud to mobility to Big Data. At the recent Gartner BIIM Summit, industry panelists discussed issues such as BI spending under IT and finding the right people with BI skills. 

There’s no doubt that coming years will be an exciting time for business intelligence and information management. While there are many factors that will continue to influence and shape the industry data quality, rising storage and network requirements, IT capabilities and business requirements, we have identified what we think are the top five BI trends for the year ahead.


Analytics is the next progression of modern BI. It uses algorithms to search for patterns and explanations from historical data to predict future activity for better business and decision making. Organizations using analytics are more likely to substantially outperform their competitive peers.Analytics will help companies differentiate themselves, it will allow them to run more efficiently, make the most of their customers and increase profitability. Analytics provides organizations with actionable intelligence. Analytics is going to continue to grow at a dramatic pace. The three biggest likely trends around analytics to be are:

Optimization: the combination of business rules for optimized decision management
Consumable analytics: the visual presentation of increasingly complex data;
New data analytics: the analysis of new types of data, such as social media, location information, etc.

Just like any other systems like business intelligence system, the companies getting the rewards and gaining true value from analytics are the ones who have made a suitable investment, especially in establishing an enterprise grade solution.

BI in the Cloud

Cloud BI is nothing but a combination of software as a service(SAAS) and Platform as a service(PAAS). Most SaaS-based BI will be delivered in the form of analytic applications; that is, BI built into a SaaS application that solves some specific business problem for an end user. I've noticed that most analysts and journalists today do not distinguish (in their research and writings) between SaaS BI, BI for SaaS, and BI for PaaS. Typically, it's all called "Cloud BI" and it almost always means SaaS BI.

SaaS BI :  hosting a BI platform or application in the cloud and delivering business user functionality on demand

BI for SaaS: building BI functionality into a SaaS-based application for the purpose of delivering specific, data-driven functionality within that application

BI for PaaS: delivering a reporting and analytic service that can be easily built into PaaS-based applications and managed by the PaaS

PaaS will do for software developers what SaaS has done for business users.

As Cloud computing continues to dominate the IT landscape, however market researchers like Gartner is skeptical of Cloud BI take up, predicting that cloud offerings will make up just 3 per cent of BI revenue by 2013. Reason is user adoption will lag far behind the expectations of vendors. Cloud BI will continue to chip away at on-premises BI, but it’s still a long road ahead.

Decision makers are still questioning the Cloud. The greatest challenge for organizations is the logistical issue of moving data into the Cloud initially. They need to look at the security network and bandwidth, the quality of the data they are transferring and planning to analyze and think about a usable interface.

Once data has been transferred to the Cloud, there are numerous cost-effective BI and big data tools available for organizations to take advantage.

Mobile BI

Mobile business intelligence offers huge advantages for organizations, particularly those with increasingly mobile and remote workforces. It means that staff and management are never disconnected from the tools that help them make business decisions.

We will see in 2012/13, based on the need to make decisions when and where they need to be made, mobile BI go mainstream. A large number of companies are rapidly undertaking mobile BI owing to a large number of market pressures such as the need for higher efficiency in business processes, improvement in employee productivity (e.g., time spent looking for information), better and faster decision making, better customer service, and delivery of real-time bi-directional data access to make decisions anytime and anywhere. Common Mobile BI types are

Mobile Browser Rendered App: Almost any mobile device enables Web-based, thin client, HTML-only BI applications. These apps are static and provide little data interactivity. Data is viewed just as it would be over a browser from a personal computer.

Customized App: A step up from this approach is to render reports and dashboards in device-specific format. In other words, provide information specific to the screen size, optimize usage of screen space, and enable device-specific navigation controls.

Mobile Client App: The client app provides full interactivity with the BI content viewed on the device. In addition, this approach provides periodic caching of data which can be viewed and analyzed even offline

Mobile BI is a cost-effective and sensible addition for organizations that rely on business intelligence to make important decisions and define future directions. Due to the ease of consumption, more C-level executives will see the value of better business decision making, more often, when and where they need it.

In-memory Analytics

In-memory analytics tools query data in RAM, unlike their conventional BI counterparts that query data on disk. Because accessing data from memory is up to a million times faster than accessing it from disk, in-memory analytics greatly speeds up response times. As memory becomes cheaper, we’ll continue to see the increasing popularity of in-memory analytics. Qlikview, Spofire and Tableau are few sample In-memory analytics tools. The benefits of in-memory analytics include:
Dramatic performance improvements: Users are querying and interacting with data in-memory which is significantly faster than accessing data from disk.

Cost effective alternative to data warehouses: This is especially beneficial for midsize companies that may lack the expertise and resources to build a data warehouse. The in memory approach provides the ability to analyze very large data sets, but is much simpler to set up and administer. Consequently, IT is not burdened with time consuming performance tuning tasks typically required by data warehouses.

Discover new insights: Business users now have self-service access to the right information coupled with rapid query execution to deliver new levels of insight required to optimize business performance, without the IT bottleneck.

Users are able to download large amount (up to 1 terabyte) of data onto their own computer and explore that information for proving theories and making business decisions throughout an organization.

Considering the speed, ease and affordability with these tools, we are already seeing the adoption of in-memory analytics and its popularity will continue to grow at a similar pace.

As its popularity and adoption grows, however, it’s important to remember that ‘quick-fix’ and ‘short cut’ tools are no substitute for quality data. To ensure an organization’s BI and analytics are accurate, in-memory analytics tools should be used in conjunction with a structured, quality data warehouse solution.

Big Data

In information technology, big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools. The challenges include capture, curation, storage, search, sharing, analysis, and visualization. The big data usually represented as 3Vs(Volume, Velocity and Verity) or three-dimensional, i.e. increasing volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources).

Big Data analytics analyzes the micro-details of business operations including unstructured data coming from sensors, devices, third parties, Web applications, and social media - much of it sourced in real time on a large scale. Using advanced analytics techniques such as predictive analytics, data mining, statistics, and natural language processing, businesses can study big data to understand the current state of the business and track evolving aspects such as customer behavior. There are two types of big data analytics say

Deductive analytics: This consists of a top down understanding of the rules of the business. These rules are drawn from assumptions that business leaders take for granted. This approach tend to miss changes, the new and disruptive customer behaviors or trends, or even changes in trends, that may shift the business rules.

Inductive analytics: This is driven by observation of real time (or near real time) data, events and behaviors. New shifts, changes from the norm are quickly detected and scrutinized.

Big Data will help organizations better manage risk and improve the customer experience, fundamentally changing the way information is managed and used. The Big Data is set to change the information landscape and, for those who use it, will get strong competitive advantage and insight.

We expect to see banks, telecommunications, government agencies and retail take up Big Data technology in the coming months.

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