Google Analytics is a powerful and Free tool available for all website owners. Getting visitors to your website is a challenge all on it’s own, But an analytics enabled website will give you that extra edge that every webmaster needs. It will help you to identify weaknesses in your site and find your strengths. Learn to use the tools and reports to help your site grow and best of all get daily, weekly and monthly information about your valuable traffic such as where your visitors came from, What pages they visit, how long they stayed and what keywords they used.

While you may think your website layout, content and ads are 100% perfect, With detailed analysis you get to see and surf your site the way your users do. Figuring out what works and what doesn’t. You can optimize your site to make the most out of your visitors and the most out of the ads you are promoting. Because after all, we are all here to make money! If you are also running your own advertisements such as AdWords goggle analytics is an invaluable and must have resource. Learn what ads are bringing you traffic and which ones are converting, This alone will save you a ton of money.

You’ll only spend your valuable advertising dollars where and how works best for your website. Implementation is a snap as you only need a small snippet of Javascript code inserted before the body of your page and your ready to go. Best of all it is Free, Powerful and feature rich and no serious website owner can do without it. All you need is a Google email address and you are setup and ready to go. So do yourself a favor and check it out for yourself!

Social media analytics, the process of analyzing social media content as well as data to provide insight from the millions of conversations. It is primarily useful for brands that wish to know about what the market or customers are saying about them on social media. Undoubtedly, it has immensely changed the way of using marketing to promote several offers and businesses. With the help of it, we now have an unprecedented opportunity of disseminating targeted messages through a plethora of channels and tools, in a cost free manner.

Let’s discuss about a number of ways of keeping the track of your data in order to get the most out of your campaigns.

This is such an amazing concept with the help of which you collect all kinds of information related to your social media pages. Obviously, you cannot keep a record of each and everything, so you have to plan well regarding the data that is more important to you, ignoring the ones that are least important.

A few advantages of analytics are mentioned below that can extremely help you out with your marketing.

· Examine the results along with the reach of your posts

· Analyze followers

· Save time

· Identify influencers

· Compare platforms

· Improve your ROI

Gathering a huge amount of data from social networks won’t help you. It is essential to choose a tool that can help you get acquainted with the information available to you. In simple words, you require something that can show you the economic impact of your marketing efforts have on your business. You can begin by taking the advantage of tools that are located right within the social media networks themselves.

Twitter analytics will tell you at a glance about your number of tweets, profile visits, tweet impressions, mentions as well as followers. Facebook insights gives you page post metrics like the number of people posts, reactions and clicks. LinkedIn provides analytical data related to company pages for example measuring your posts, engagement together with supplying information about your follower’s demographics.

Refer below the lists of major social networks:

Google+

YouTube

Instagram

SlideShare

Pinterest

Vine

Vimeo

Tumblr

Blogs

Compare the effectiveness of all your social media accounts for your business. Statistics that are collected will help you out in tracking the latest trends, industry standards along with the interaction and engagement rates of it.

It is essential to track your data so as to produce better results through your campaigns. If you don’t track the data then you are definitely wasting your time on missing or hitting type campaigns. Social media analytics is mainly the difference between success and failure for obtaining the desired results.

As the corporate climate becomes ever more competitive in the face of a flagging economy, companies must search out new ways to surge ahead of the opposition. Streamlining data processing and using technology to improve corporate efficiency is one way that technologically savvy organizations are maximizing their ability to compete. One of the best ways a company can use today’s technology to get ahead is by employing a business data analytics program to increase their productivity and reduce errors in their day-to-day business functioning.

Here are just three of the many ways these programs can reinforce an optimal business model.

Reduce Fraud Risk

In today’s world of continuously evolving technological platforms and business models, devious fraudsters have developed even more complex ways to access and defraud business through electronic channels. By using a data analytics program, savvy businesses can access several data sources simultaneously to assess patterns and trends and create “hotlists” that can be shared throughout business lines. In addition, a strong platform will allow companies to identify all fraud types, from simple acts like usage, identity and payment fraud to the highly complex business of SIM card cloning and others. Good analytics platforms will also have functions to minimize false positives and will be able to automatically flag and generate cases and manage queries and reporting.

Maximize Data Quality Management

Data quality management is at the heart of a thriving business. Many businesses, from accounting firms to retail operations, are challenged by duplicated data, redundancies and confusing entries. These types of errors can result in loss of leads, erroneous client contacts and increased downtime or an increased workforce to repair discrepancies. Using a data analytics program can clean and process data and organize this information much more time-efficiently than the usual manual database management techniques. This will free employees to pursue client leads or improve customer satisfaction and provide ease of communication and trustworthy data sharing among departments within an organization.

Manage Multiple Data Streams

Managing multiple data streams, or complex event processing, is at the forefront of the arsenal of tools for businesses that look to react quickly to changes in markets or internal issues like fraud or incorrect contact data, and increase communication between support, service and IT departments within an organization. Data can be collected on customer experience, market shifts, financial data or any combination of relevant statistics to increase operating efficiency, reduce error or miscommunication between departments or create a big-picture scenario for decision-makers in upper management to determine the course a business will take. Strong, reliable data from multiple sources results in smarter decision-making at all levels.

Whatever your business needs, an analytics program can provide your business with a level of efficiency that can’t be matched with outdated manual input systems.

The one thing that is consistent and never changes is the aspect of change itself. As the data analytics industry has evolved as the industry has grown. As a matter of fact, the growth of the industry has spawned inquiry into new services within the analytics domain. Risk analytics is one such area. As the name suggests, risk analytics can be applied across industries and across different data sets. After all, as the name suggests, the aim is to identify potential risks among various future or current options is to ultimately evaluate the best choices out of the many that may be available.

As the field of inquiry evolves, the tools may also evolve or existing tools need to be used slightly differently. Originally tools like MS Excel and SQL were decent placeholders to provide insights. Although these tools are just as relevant, they are not complete solutions in them. More powerful tools have become available like the ACL Audit Command Language. It provides some advantages over the traditional tools.

1. Easier User Interface – The Audit Command Language was designed to allow the end-user to be able to work with the data sets and see live changes. The tools and graphical user interface are laid out in a way to enable exactly this goal. For instance, the users can make changes to the table to create fields in existing tables and create new tables in the project itself and export these changes into report ready formats.

2. Logs – In the domain of risk analytics, it is not just important to be able to create new tables with data but also to keep track of them. It in these domains, it is expected that all data transformations are kept track of in case there is a need to go back to verify or rectify the approach.

3. Automation with accuracy – Tools which are designed like the Audit Command Language, allow for a focussed approach to work on data of a particular project. This allows for focus and data integrity. Additionally, a properly designed workflow in the tool allows for a degree of automation and accuracy which is not available in the traditional tools that were being used.

Investment in the traditional tools is something of a necessity as they provide certain familiarity as they have been around for a long time and have served well. They will be around as core technologies for a long time. However, tools like the Audit Command Language and SAS have some clear advantages which can no longer be ignored as the results of inquiries in the field of risk analytics become more and more relevant in today’s world and shape our choices of the future.

The software-as-a-service (SaaS) model is disrupting traditional approaches to business analytics. The long deployment cycles, high costs, complicated upgrade processes and IT infrastructure required of traditional on-premise business intelligence solutions are no longer acceptable in the era of on demand. Instead a new breed of analytic solutions has emerged that are simple to set-up and simple to use and deliver immediate business value.

The difficulty for salesforce.com customers is in knowing where to start. With well over 50 analytic applications to choose from on the Force.com AppExchange and native transactional reporting features constantly improving in the CRM application itself, it can sometime seem like the status quo of “Excel Hell” is the easiest and safest choice.

Unless you’re happy managing and maintaining those unwieldy spreadsheets, pivot tables, disconnected Access databases, and numbers that often don’t even add up, here is primer to help you move from sales force automation to salesforce.com acceleration with on-demand business analytics:

Understand Your Sales Analytics Requirements

If you’re a Salesforce administrator, you already know how important it is to become proficient with the built-in reporting and dashboard capabilities of the application. If you’re not already up to speed, be sure to sign up for a training course, watch a Dreamforce presentation on the success.salesforce.com community website, and try downloading a few of the free dashboard applications on the AppExchange. (Adoption Dashboards, for example, are a great introduction and jumpstart to salesforce.com dashboards and they’ll also get you familiar with the process of installing applications on the AppExchange.)

But this is only the beginning. Inevitably with Salesforce, as is typical of transactional reporting, you’re always 4 or 5 reports away from answering the question you really want to answer.

To understand your sales analytics requirements, you need to consider the following:

o What information do sales managers, the CFO and the CEO need today to be successful? (Having a clear understanding of their objectives and success metrics is critical. How many of these questions can you answer today?)

o What business questions are the most difficult to answer today? Who is asking these questions? When and why?

o Would people prefer to answer their own business questions or are they content relying upon the sales operations, business analysts, and/or IT function for information?

o How do managers prefer to access and analyze business information–dynamic dashboards, spreadsheets, pdf, PowerPoint, email, mobile device, etc.?

o What other sources of information do people need to access and analyze in order to achieve sales success? (Note that critical sales data is often locked in financial systems – orders, bookings, billing information; or lives outside of the CRM system in spreadsheets–commissions, quotas, forecasts .)

Know Your AppExchange Analytics Options

In their paper, Sales Management 2.0: Metrics, Not Hunches, Barry Trailer and Jim Dickie from CSO Insights describe the key sales analytics criteria this way:

“You can decide whether you need to pull and analyze data from multiple data sources (e.g., accounting, inventory, sales, etc.) or just one (CRM). Solutions are available either way; what you want is an application that will allow you to defi ne business rules, historic trends and exception reporting with a minimum of administrative/set up effort.”

Here’s an overview of some of the AppExchange choices available to salesforce.com customers and their pros and cons:

Production Reporting

These are tools designed for advanced report developers to create virtually any report on transactional data. Also known as “enterprise reporting”, these tools typically provide built-in scheduling of pre-authored, highly formatted, “pixel perfect” reports that may include prebuilt prompts or filters to make them seem interactive. For salesforce.com customers these tools, can create virtually any join, but the data size must be small. Attempting to replicate all of your transactional data in a desktop reporting tool in order to get the queries you need will not work. If people want to be able to ask spontaneous, iterative, or trend-based questions of their business data, these tools are not a good fit. If you have someone in house who understands SQL (and SOQL), and you just need a couple of static reports delivered, start here. Just be sure to find out about customization, support, and advanced report-writing costs up front and keep an eye on the enhancements coming in the native salesforce.com transactional reporting features. Also keep in mind what Neil Raden noted in his salesforce.com paper called, Accelerating Analytics Success with On Demand:

“Porting a desktop application by removing its user interface and replacing it with a Web front-end masks the fact that its internal operations have not been migrated to an on-demand, multi-tenant architecture. The result is likely to perform poorly, to require time-consuming labor for upgrades and patches, and quite possibly to be discontinued when the vendor releases its “real” on-demand product at some point in the future, likely with no satisfactory conversion path.”

Native Dashboard Applications

There are many interactive, real-time charting and Adobe Flex-based analytic dashboard components on the AppExchange today. Some are easier to set up and use than others. Most are eye catching. The native dashboard applications often impress executives and non-analyst roles in the company, but because they are built on the underlying transactional Force.com platform, they typically do little for the people struggling with disparate Excel spreadsheets and historical reporting and analysis requirements. Keep in mind that a nice-looking, mashed-up dashboard widget may have “demo sizzle” and may even make sense for your business process, but all dashboard-focused applications on the AppExchange are not alike. Be sure to find out about the vendor’s vision to go beyond operational or embedded business analytics for one transactional system in order to determine if they’ll be able to meet both your short-term tactical and long-term strategic on-demand information access and analysis requirements.

As stated earlier, definitely download the relevant free native AppExchange dashboard applications to jumpstart your sales analytics initiatives and to get comfortable with the AppExchange experience.

True Analytic Applications

Also known as online analytical processing (OLAP), it’s important to look for analytic applications that are built on a separate data platform designed from the ground up with user interactivity and information analysis in mind. They will allow you to monitor and track historical trends and get answers to ad hoc questions, not just static reports. These applications must be simple to set up and simple to use. They should also be built on an underlying on demand business intelligence platform that takes care of the “heavy lifting” by integrating, cleansing, and aggregating data from multiple sources into a single reporting and analysis interface.

But beware of tools approaches. Instead look for true applications that deliver prebuilt best-practices and are designed for specific industries and roles.

When Henry Morris coined the term “analytic application” over 10 years ago, he defined three key criteria as being essential:

1) Process support

2) Separation of function, and

3) Time-oriented, integrated data from multiple sources

Also be sure to find out about the trial process and how easy it is to get up and running with an on-demand analytic application on your company’s data.

Whatever You Do, Don’t Wait for Sales Analytics!

Putting off sales analytics is like putting off winning. But for many organizations, getting started can be equated to getting fit and joining a gym. You know you need to do it, but there always seems to be a good excuse not to. In order to get more out of your CRM investment and drive sales performance with data, not opinions, here are a few suggestions to help make sales analytics a top business priority for your company:

o Make analytics a business initiative. Determine the metrics that matter and build a plan. Executive sponsorship is critical to analytics success.

o Think big, but start small. Starting in one department or even one region will allow you to get some quick wins and you’ll be amazed at how fast word of your success travels. Have a vision to go beyond one area of the business, but don’t let a broader vision slow you down. The most important thing is to get started.

o Make it about business process. Sales analytics and the lead-to-cash cycle is a great place to start. And given that this is an analytics initiative, be sure to set clear goals and measure your success against those goals at every step along the way.

In difficult economic times, more and more companies are relying on sales analytics to give them the competitive edge and win. Make sure you’re one of them. No more excuses. No more surprises.

In today's world, there are a lot of companies that face sudden increase in costs, production delays and experiencing operational risks due to lack of Predictive Analytics in Testing. Predictive Analytics is a data driven technology which can be leveraged to predict failure points in testing and determine the future. It has the power to optimize project data and enables the business leaders to make really quick strategic decisions.

Broadly, there are two types of companies. In one type, there are software testing companies which perform testing using its in-house testing environment and in the other type, there are software development companies which simply outsource the entire testing activities to preferred vendors.

• A software testing company focuses on timely launch of the product by using its in-house testing team.
• A software development company outsources testing and expects on time deliverables.

Typically, testing companies follow a lengthy process for any testing project in an effort to reduce operational issues and costs. In this process, these companies encounter many issues with every new project.

Let's look at some of the challenges involved with in-house testing.

In-house Testing Challenges

• Finding right testers and aligning them to the project
• Fix time and budget for the project
• Need of Multiple testing tools and infrastructure
• Meeting productivity goals
• Current testing issues leading to unknown future challenges
• Different Stake-holders expecting different reports

Testing companies need to perform Predictive Analytics at operational level to avoid productivity delays and issues while dealing with root causes at the early stages. The development companies that outsource all the testing activities would prefer looking forward to focus more on core business while avoiding increasing costs associated with testing but these companies face a lot of delays for deliverables and the costs keep outstanding.

Let's look at some of the expectations from outsourcing testing.

Expectations of the Client from Testing Vendors

• Complete requirement understanding of the projects
• Flexible to quickly adapt to changes in the requirements
• Adherence of delivering on time
• Communication and Coordination
• Testing effectiveness, consistency and satisfaction
• Test coverage
• On time product delivery

The development companies need to perform Predictive Analytics at the business level to avoid slow deliverables by identifying right vendor and a right team for right project.

Predictive analytics is rapidly becoming one of the most-discussed topics in software testing projects because it can mitigate operational risk and help in planning, quality, and delivery. Predictive analytics is widely used today in many industries such as healthcare, life sciences, insurance and finance but it is not limited to only these industries. It can be leveraged in Software Testing to significantly improve business.

Advantages of Predictive Analytics in Software Testing

• Predict testing issues at the earliest which can lead to unknown future challenges
• Predict deliveries
• Mitigate Communication and Coordination issues
• Predict right environment / right Vendor
• Improve Planning, Quality and Delivery
• Meet business needs

Conclusion: Predictive Analytics helps development and testing companies in identifying the root causes of all the problems and in making proactive decisions at the earliest.

Over a single question: “How much effort did you personally have to put forth to handle your request?” the conventional survey method seeks to measure Customer Effort Score (CES). In a bid to answer this question, respondents are asked to rate their effort on a scale of 1 to 5. The lower customers rate their effort, the better it is considered to be. This is based on a conviction that by reducing customer effort, organizations increase customer loyalty.

Instead of asking customers about their effort involved on a 1 to 5 scale, Interaction Analytics embroils measuring CES by analyzing customer interaction data. Each customer interaction over phone, email and chat is utilized to score customer effort. The interaction data is further evaluated to identify and address issues leading to high effort across numerous touch-points.

On the other hand, a CES survey is meant to collect only customer effort rating on a scale of 1 to 5. Survey scores tell whether the effort is high or low. But, these scores by themselves cannot reveal the root causes of high or low effort. Furthermore, a CES survey alone cannot help detect processes that are forcing customers to call the contact center or switch from one channel to another. Also, conducting a separate survey for gauging CES can be a time-consuming and costly affair.

Since interaction analytics eliminates the need to run a distinct CES survey, it occurs to be a better option in terms of cost-efficiency speed, and results. 100% of customer interactions are evaluated to identify customer sentiments and phrases to gauge customer effort across various touch-points and discover areas where customers are exerting undue effort. Thus, factors like inclusiveness, problem identification, root cause analysis, and accuracy make interaction analytics a better way than the survey method to measure and reduce CES.

The entire process of scoring customer effort with interaction analytics will broadly involve the following steps:

Identify negative customer emotions: Interaction analytics analyzes customer interactions to pin down negative customer emotions. Emotions expressing customers’ anger, frustration or dissatisfaction with the customer service or self-service channels are detected during this phase.

Correlate negative emotions with customer effort across touch-points: These identified emotions are then linked to effort types that customers are unnecessarily exerting across different touch-points to address their needs or issues.

Quantify the degree of customer effort based on the severity of expression. The degree of effort types is then quantified based on the severity and frequency of emotions expressed by customers during interactions.

Get an organization-wide customer effort score on a scale of 1 to 5: According to the degree of effort, each effort type is ranked in an ascending order and given a score. Overall organization-wide CES is then calculated on a scale of 1 to 5 by summing up the value of all effort types and dividing the aggregate value by the total number of effort types.

Interaction analytics is a more comprehensive and efficient way to calculate and reduce CES. Thus in an attempt to improve customer experience, satisfaction and loyalty, organizations must leverage this groundbreaking technology. By making it easier for customers to interact or transact, organizations can take their business performance to newer heights.

Every business has a website and every website has a lot of third party tags on the websites. These tags are used to track the business which would help make the decisions on the business optimization.

You can’t get the actual metrics or statistics of any website without adding any tracking pixels or java script to get those metrics. With the advancement in the technology and the advancements in the product from the vendor or these 3rd party tags, it is crucial to upgrade these tags to get the accurate and deeper insights into the website performance.

There are separate group of professionals involved in managing these tags on the website for a large organizations.

It is very important to know what version of the 3rd party tags present on the website. There are some tag audit tools available in the market and are not cheap. There are also free tag audit tools available. I have listed below the some of the audit tools available in the market.

Following are some of the best tag auditing tools available in the market:

1. WASP
2. HubScan
3. Google Tag Audit
4. Observe Point
5. Accenture Digital Diagnostics

WASP: Web Analytics Solution Profiler

WASP is a tag auditing tool developed for analytics and online marketing professionals for auditing the tags on the website to ensure the quality assurance and to know how tags are implemented.

HubScan:

HugScan is used to audit or scan the Google analytics tags on their implementation on the pages, events and make sure the right pages are tagged correctly.

Google Audit Tool:

Designed to audit the Google analytics tags implementation on the website and to ensure correct variables are used to capture the data. But the only drawback is that this tool can only be used for Google Analytics.

Observe Point:

Observe Point audit tool is designed to audit any web analytics tool to know the version of the tags, type of variables used, information on SEO including the redirected URL’s,etc.

Accenture Digital Diagnostics:

Another tags auditing tool that provides the details on the site not only the tags but also details on SEO and usability. The accenture digital diagnostics tool also provide the details duplicate pages, java script errors, page load time,etc. The details on the number of variables used help you ensure that the correct variables are used to capture the analtycis data. Technical details this tool provides help you optimize the website.

Do you know how to interpret data from Google Analytics, such as visits and page views?

Clicks, visits, website visitors, page views, and unique viewed pages in many cases are misinterpreted website visitor statistics recorded from Google Analytics. Online businesses read about the variations while they examine any Google Analytics tutorial. This lets to distinguish related lingo used for numerous analysis presented. Through an appropriate expertise in customer information, site owners will be able to tailor the approaching online marketing endeavors to address faults and also capitalize on strong points.

Clicks are actually a part of Google AdWords Strategies research, whereas visits are actually presented as part of Website visitors and Search Engine reports. The quantity in the clicks of the mouse column presents how frequently ads were clicked from traffic. Visits denotes the number of unique visits initiated for visitors. The two stats are critical on a marketing and advertising perspective.

These particular figures will not consistently match, for several good reasons. The same visitor to your site may well select an advertisement over and over again as they are comparison shopping, causing AdWords to log a number of clicks of the mouse. Analytics, however, is able to distinguish each and every pageview for a unique visit. A user may also click an advert only one time and create a bookmark to go back right to the site for their next visit. In this case, the referral details from the preliminary page visit end up being saved, causing a sole click to lead to numerous visitors.

Web-site visitors sometimes mouse click on an advert although stop the website page at absolutely loading by means of pushing the stop key for their web browser or maybe moving to another webpage mid-load. Ppc will bypass the track the click though the monitoring code inside Analytics will not fully carry through as a result tracking details are not submitted to Google? Fs web servers. On the other hand, Google AdWords filters an incorrect ad click to help keep accuracy intended for billing reasons, while Analytics captures all these clicks of the mouse, documenting them in the form of web-site hits so as to present full data in relation to website visitor page views.

Google Analytics measures figures for visits and site visitors. Visits represents the quantity of individual sessions for all traffic to the website. Any action following an inactive duration of at a minimum A half-hour is recognized as a brand new visit. When a person leaves this website and returns inside of A half hour, the activity will be considered to be a single visit. An initial session by the individual during any date range is viewed as both one more visit and extra website visitor. The next session with that website visitor through the time frame is considered as an additional visit, but the visitor is not one additional visitor.

A page view is really a look at a Internet page that is tracked by an Analytics traffic monitoring code. When the visitor refreshes that web page after landing in it, the reloaded webpage is recorded as a different page view. If the user navigates to the next webpage on the webpage after which goes back to your original internet page, it will be counted as a 2nd page view. A unique page view symbolizes the amount of visits when a particular internet page had been seen at least once.

This is a small sample of the terminology cleared up in a Google Analytics guide. Before getting rolling when it comes to Google analytics, site owners should really invest time to appreciate such words and phrases. Learning the right explanations helps website owners acknowledge exactly what the details discloses so they can use this data to increase prospective internet website marketing initiatives.

If you would like to learn more about Google Analytics and how to make sense of all the data presented in their reports, visit our Marketing Analytics page and sign up for a free assessment of your website's data TODAY!

What parts of your training program are the most or least effective? When are your employees really engaged and when are they daydreaming? What training units / simulations / assessments / employee actions are most associated with learning? How does training influence the success of your employees and your organization? Would you like to be able to answer these questions? According to the ASTD 2012 State of the Industry Report, in 2011 U.S. organizations spent more than $156 billion on training, averaging just under $1200 per employee. For that kind of dough, companies want to see some results.

MOOCs (massive open online courses) are currently redesigning the educational and training landscape. In January 2013, the Harvard Business Review blog called “the advent of massively open online classes… the single most important technological development of the millennium so far.” Did you get that? The single most important technological development of the millennium so far.

Why are they making such a huge impact? The reasons are many and growing. Not only do they offer unprecedented scalability and access and challenge the long-held notion that content is king, but they can provide large amounts of user data. We’re not talking just how long people engage in a particular task or who got what question right; we’re talking the ability to track and analyze every aspect of the learner experience.

The current model in training analytics is “small data” – data based on reports, assessments, and so on from small numbers of learners. But MOOCs can provide data from millions of people and the data are collected at many different levels: the keystroke level, the question level, the learner level, the instructor level, the program level, and even the organizational level. This “big data” can be used to model learner and organizational characteristics and outcomes and, most importantly, to predict future trends and patterns. It can help organizations identify which programs are working and which are not, where additional training is required, and the best way to deliver that training.

In a 2012 report on educational data mining and learning analytics, the U.S. Department of Education’s Office of Educational Technology identified several questions that big data can help educators answer. Here are a few of them:

• What sequence of topics is most effective for a specific learner? When are learners ready to move to the next topic?
• What learner actions are associated with more learning? What actions indicate satisfaction, engagement, learning progress, etc.?
• What features of an online learning environment lead to better learning? What will predict learner success?
• When is intervention required?

When the entire learning process takes place online, the entire learning process can be tracked and analyzed, and the data generated goes far beyond what is available in a classroom. Students in MOOCs don’t just watch videos and answer questions – they interact with each other and with the instructor through discussion forums, social networks, blogs, and many other streams, leaving long and rich trails of digital data. These data can reveal trends and patterns that can’t be detected in traditional formats, and they allow us to move beyond what people are learning to how they are learning. As Coursera co-founder Daphne Koller said: “The availability of these really large amounts of data provides us with insights into how people learn, what they understand, what they don’t understand, what are the factors that cause some students to get it and others not that is unprecedented, I think, in the realm of education.”

This knowledge can be used to improve both instructor-led training (ILT) and online learning. Here are a few major areas where big data from MOOCs can inform training practice:

• Improving results. This is the obvious one. Of course the goal of all training is to increase employees’ skills and effectiveness. MOOC data can be analyzed on both micro and macro levels to improve individual and organizational results.
• Clustering and relationship mining. These two concepts have to do with discovering relationships between variables. The data can be used in many ways, such as for organizing employees with complementary skills into teams and work groups.
• Customizing programs on a large scale. MOOCs started out as a one-size-fits-all solution, but they are rapidly evolving into adaptive learning environments tailored to individual learners. In the near future, the learning experience will be optimized individually and in real time.
• Predicting future trends. What will the return on investment (ROI) be for your training program? Big data will help organizations predict the impact of training programs on individual, business-unit, and organizational success.

Businesses already use big data to make decisions about sales, financial services, advertising, risk management, pricing, supply chain management – you name it. But until MOOCs came on the scene, most organizations could not amass enough data to inform decisions about their training programs. Now data is being collected from millions of learners in virtual educational and corporate classrooms all over the Internet.

The field is very new and educators are just starting to realize the power of having this data available. In a first attempt to quantify this learning experience, Duke recently released a report on its first MOOC. The results provide insights not only into student achievements, but into their activities and outcomes, motivations and attitudes, and the factors that both promote and provide obstacles to learning. As more organizations collect, analyze, and (in true MOOC spirit) share their data, we will begin to develop new models to increase instructional efficiency and effectiveness. Smart companies will use that data to make sure they are getting the best possible return on investment in their training programs so they will have something to show for that $156 billion.

So, now you are convinced that the learning-framework is the way to go and that big data will transform your approach to training, but you don’t know where to start with the implementation? No worries – there’s a MOOC for that!