What would you do if one day you find that the account or profile of your website in Google Analytics is no longer available or has been deleted. It happened with me ten days back and I was like blank when I realized that the statistics (data) for my website are not available in my Analytics account. It was during the course of adding another user (as an admin) that I mistakenly deleted one account / profile from the main user (administrator).

I Googled to find out the possible solution to recover the data but what I found is that once a account gets deleted it will not be recovered and if a profile gets deleted one can contact Google for recovering the data. Till then I was not sure what I had deleted – a profile or an account. My worst fears came true and a chill went through my spine when I found that I had deleted an account and not a profile. I posted my problem in several forums like hundred others to get a solution. And i did got a solution and recovered the statistics for my deleted account. This is how I recovered the account.

First, the basic difference between a profile and an account (as most of us must be aware of) is that a profile is created under an account. or an account can have one or many profiles. Once an account gets deleted all the profiles inside that accounts also gets deleted automatically. Secondly, after an account gets deleted all the administrators of that particular account will get a mail from Google analytics team informing about the account deletion, like the one below:

We want to alert you that the Google Analytics account abc.com has been deleted by asdf.aab.com. Please tell non-Administrative users of this account that the account has been deleted. Thank you for your cooperation.

Now the solution is:

If you are an AdWords customer, you can be rest assured that your account will get restored in no time. What you have to do is to log into your AdWords account and click on the Analytics email support options. (this is what I had done)

You can also write to: “AdWords Support”

You can also post your problem in the forum

http://www.google.com/support/forum/p/Google+Analytics/thread?tid=1b47f1608b93e735&hl=en

Data analysis is a process of searching for information that could be used to predict, understand, or support the courses of action taken by businesses.

Taking information as it is, studying it to draw a conclusion, using it as a basis for making decisions in the business world are all part of the science of data analytics. Data analytics is one technique under data analysis and is divided into several general parts, namely: confirmatory data analysis (CDA), qualitative data analysis (QDA), and the exploratory data analysis.

CDA also known as statistical hypothesis testing is used in arriving to decisions based on the outcomes of experiments. It tests a current statistic or theory and either makes it significant or insignificant.

EDA is in contrast with CDA. Its approach is descriptive – without preconceptions. Unlike the confirmatory data analysis, the exploratory data analysis derives a theory or hypotheses based on what is found in the research. The questions to be answered usually arise from the data gathered unlike CDA where there are already a set of questions needing specific answers.

QDA is the process of analyzing data from different angles, aspects, focuses or perspectives. For instance, two people may be looking at the same thing but have totally different thoughts about it. This concept applies to QDA. How the data will be interpreted would depend on the purpose. Qualitative data analysis focuses on information that cannot be contained in numbers but in what can be deduced from images, videos, people watching, behaviours, and so on.

That said, some might confuse data analytics with data mining, when both are quite different. As data analytics focuses on what is already known and seen, data mining digs deeper to find other patterns and connections not yet discovered.

Various businesses are investing in data analytics because it has already proven its necessity in the world of trade. It doesn’t matter if it’s about buying or selling goods or services, the business world is intertwined with data analytics.

To put it simply, data analytics is a systematic way to understand information at hand, and use it to further business ventures. It helps companies decide what the next step would be and if that step would take them forward, or not. It has come a long way in the years that have passed and due to technology; data analytics is faster and more efficient. With the use of computers, and other kinds of modern equipment, more can be accomplished in shorter amounts of time.

The explosion of data has laid to numerous tech evolutions. With the rise in mobile app development, the glut of data has inspired to the crave of better reports and dashboards. For a fact, the more accessible data becomes, the more your clients demand analytics to get the most out of their applications they use.

Today, as we wade knee-deep into the tech market, you will see the abundance of analytic tools available. Apparently, these tools are distinctly capable of serving specific tasks. So in the jungle of analytic tools, it becomes highly crucial for you to understand that which tool will work best for you. However, it is the structure and purpose of your product that will help you to decide an ideal analytic solution for your app.

So before diving deep into the motion, let’s see the most demanding analytics features today across industries.

Dash Boards

Dash Boards are the best ways to represent the efforts and the outcomes of your business. It is nothing but a visual representation of data pieces in panels that gives the user a comprehensive view of their data. The web based dashboard enables the users to configure their dashboards as per their needs and get required reports; they also allow users to drill down to more granular data to keenly understand the key performance indicators.

Data Visualization

Data Visualization techniques via gauges, heat maps, charts and geographic maps help the user to rapidly analyze and draw conclusions. Generally, it helps you to represent multiple operations in a single chat or individual charts for multiple operations. Data visualization features can be mapped with the dashboard to give the user a holistic look of your business. State-of-the-art dashboards help you to display high level summaries of important data, defined through colour, size and shape which resonate to the user context and needs.

Interactive and Static Reports

Interactive and Static reports help you see your data in tabular form with scheduling capacities with numerical figures segregated into categories. It makes the report quite interactive with filters, drop downs that empower users to understand specific pieces of operational data.

Self-Service Analytics

It caters to an intuitive mode of customizing the dashboards as per your business needs. This concept helps you to get reports on demand with the help of self-serviced analytics.

Benchmarking

It allows organizations to compare their state with the industry standards to locate the area of improvement. Today, a multiple cloud-based analytics solution can help you capture standard industry advancements and allow you to set benchmarks.

Mobile Reporting Functionalities

It helps you to check each and every report on mobiles. It caters to a great deal of mobile compatibility, primarily compatibility with smartphone features like touch inputs and interactive features etc.

These are the top 6 features having pretty high demand in the analytics market, so next time you plan to opt for an analytic solution; it’s recommended that you check the above features as per your need and take a decision.

A company wants to launch a new product in the market, what does it need other than a great product is the data to back its every decision, to make the product successful and profitable in the market.

According to techtarget.com

Business analytics is the practice of iterative, methodical exploration of an organization’s data, with an emphasis on statistical analysis. It is used by companies committed to data-driven decision-making.

The aim of analytics is to process large data set of the company and help in the decision-making process.

Business analytics is one of the most important domains of any business in the world. it has become an inseparable tool which is defining the growth strategy of a company.

Business analytics begins with a data set (a simple collection of data or a data file) or commonly with a database (a collection of data files that contain information on people, locations, and so on).

The data has become the most important asset of a company and they are utilizing their resources to find the meaningful information and key insights which benefit their company directly.

Types Of Business Analytics:

Descriptive Analytics:

This describes the current state of a company by tracking key metrics and determines trends from the current dataset.The aim of this type of analytics to determine what has happened.

it provides primary data processing method to proceed further.

It also analyses how the data looks like currently and identifies future behavior.

For ex. a location bar chart for a travel company which wants to target customers by location.

Predictive Analytics:

It is the most important and advanced analytics which creates models for the prediction of a particular event or performance of a particular product by using historical and current data sets.

it is generally an area of data scientist and data analysts who build predictive data models using the advanced algorithm, regression analysis, time series analysis, decision tree.

It has become more important with big data and financial companies have been the prominent user of it, to determine events before they occur.

Example: Multiple regression is used to show the relationship (or lack of relationship) between age, weight, and exercise on diet food sales.

Prescriptive Analytics:

It determines the best solution for a particular problem when the different set of solutions are presented.

It also provides decision options by processing new data to improve the accuracy of predictions and decision options.It is the mix of data science and management science which provides the best route possible for a particular path.

Example: A sports store has a limited marketing budget to target customers.

Application and Uses:

It provides insights into the key decisions of the company in various domains which help the company to gain an advantage over its competitor.

• It leverages the analytics to generate more profit for the company and improve its performance.

• Businesses all over have been using it to determine and improve their optimal resource allocation, supply chain optimization, inventory management, employee performance, project Completion rate, Skill Map, Improve their Product portfolio etc.

• Two key areas are Business Intelligence and Statistical Analysis.

• In statistical analysis, the statistical algorithm is applied on data to predict the performance of a service or a product.

The career opportunities in the field of data analytics or data science or other similar profiles are seeing a growth like never before. It is a great time to be alive and witness this extraordinary phenomenon where the industry is evolving so fast and the future isn’t clear but it is definitely promising. This background provides an excellent platform for young professionals to take advantage of and build careers in this exciting new field. Listed below are a few reasons why the data analyst/scientist are great prospects for young professionals graduating in a next few years.

1. Interesting Work – Data analytics is definitely not as challenging and fun as software development but it has its own set of challenges that make it very interesting work. For one, it aims to answer question related to market trends and behaviours. More advanced projects would involve learning mathematical techniques used for statistics. Apart from being interesting, one can be part of any industry since the skillset is transferrable in most cases.

2. Pays Well – Any professional in this field would testify that this profile pays well. And since, there is so much demand, this trend is not going to slow down any time soon. Most young graduates, with a technical education and some relevant industry experience (internships) can be hired as data analysts or business analysts and these jobs should pay around USD 60-70K. Of course, this number grows very generously as one gains more experience and grows in terms of designation. A middle management position would pay starting at USD 90-95K.

3. Growth Opportunities – As per recent studies, the world is only growing more curious, not less. This implies, that large companies are investing more and more to explore market trends more precisely. This has and will continue to result in development of new tools, experimental methodologies and business processes across industries. For young professionals, this implies ample opportunities to explore career options based on their industries of preference.

As lucrative and interesting the data analytics industry sounds, it can be very temperamental in its response to the market environment. Every tool in the industry is evolving constantly and coming with newer and improved solutions. This leads to very short and limited life on existing technologies. However, to be cautious, young professionals should look to build a skill set, which is conceptually complete so that skills are transferrable. There are broadly 3 areas to stress upon. These are data cleansing, data transformation & data visualisation. The industry standard tools for these are “Monarch”, ” Audit Command Language ” & “Spotfire” respectively. There are many options for these tools, however these make a good starting point.

Amino acids are building blocks of protein, they are necessary nutrients. Proteins are essential nutrients for the human body. They are the major structural components of all cells of the body. There are two different types of amino acids namely Essential and Nonessential. Nonessential amino acids can be created with chemical found in the body while Essential amino acids can not be can not be created from the body system, since they only way to acquire it is through food consumption.

There is high market demand for animal protein comparable to other vegetable protein, this is due to the fact that amino acid content in animal protein is more fundamental when compared to other vegetable protein. It has good effect in developing growth and energy in humans. However, the average consumption portion of Nigerian people for animal protein is very low at 8.3 gr / day from ideal standard 53 gr / day, this is highly due to insufficient supply in local markets.

How Data Analytics Can Increase Production Capacity

Taking advantage of data analytics can reduce operational process flops, save time and capital. It will also reduce waste in production process and thus increase production quantity and quality. With the complexity of production activities in animal protein production, farmers need data analytics approach to diagnose and correct process flaws.

Data analytics refer to the application of statistical tools to business data in order to assess and improve operational practices in production. In Animal production, supply chain expert can use data analytics to gain an insight into historical performance of past operations, forecast the future operational output and thus make a decision that will ensure optimization of the entire process. For example, application of data analytics in poultry production will increase quantity and quality of eggs and poultry birds production. Data analytics enables actionable insight resulting in informed decision making and better business outcomes.

Types of Data Analysis to Deploy

Predictive Analytics
Descriptive Analytics
Prescriptive Analytics

Predictive Analytics: uses data to foresee the future out of a pending event. It makes the business owners to know the likelihood of an intending business plan. It uses statistical techniques to integrate modeling and data mining to analyze historical and current situation and thence make predictions about the future events.

In animal protein production, a predictive model captures connections among many factors and enables evaluation of potential risk and opportunities. It will allow the operation managers to know the best production technique to apply in optimizing its production, this include raw materials procurement, operational system technique, cost, etc. This help in production of quality products at the right cost and right time.

Descriptive Analytics: uses data to analyze past events in order to have a better view of how to approach the future. Historical data are mined to give an insight to the level of past performances of events and view reasons for success or failure, and make necessary adjustment at when due.

Descriptive analytics will help farmers to have a view on performances of past production activities. This will enable them to know the level of profit or losses that incur in their operations. Many farms run out of business due to lack of past production performance knowledge. This reduces the overall output of protein production in the country.

Prescriptive Analytics: integrates all sections in the supply chain system to suggest the best options for business operation that will optimize the entire resources utilized to achieve the set goal at the best minimum cost. This will enhance continuous business growth. With this analysis, farmers are guided on what technique they need to implement at every point in time to achieve their goal.

Prescriptive analysis will also allow farmers to know the time to make changes to their business operations. This is due to the fact that there are changes that affect business due to seasonality. On-time adjustment can be made to avoid flops in operations which can ever affect the bottom line.

In summary, implementation of a data analytics model in the operations of farmers is essential to increase the production of sufficient animal protein. Majority of farmers (Livestock, Crop, Fish, etc.) incur losses or run out of business due to non-implementation of a data analytics model.

To suffice and thrive, an organization should have the potential to acquire, retain, appease, and connect with their customers. Google Analytics is one such tool that helps in accessing how well your business is doing comparatively to your rivals. This tool gives you a great scope to comprehend your visitors (consumers), an important aspect for businesses of any type. No matter which domain your business dwells in or what marketing approach you follow, contemplating your customers will allow you to modify your practices so that maximum customer satisfaction and retention can be achieved.

With Google Analytics, it has become easier than ever to analyze your audience; get a better insight of who is interested in your brand, and know from where they have come from and what exactly they are seeking. With the right insights, you can modify your approach, anticipate your customers’ needs, and offer a superior user experience. Once you have configured the script, you will be able to gain information about diverse data about your users. It will subsequently help you to modify your efforts to better the conversion rate and web presence.

In this article, you will get to know about some of greatest tricks that you can apply to augment your SEO efforts with the help of Google Analytics:

Segment Your Target Customers: It is possible that your website attracts visitors with different wants and needs. That’s why it is important that you segment these different groups of visitors for better understanding. Google Analytics provides this feature to webmasters. The group can be segmented on the basis of age, gender, location, language, and behavior.

Gauge Your Speed: No visitor wants to come back to a site, if he/she encounters a bad experience on your site. So, as this issue should not happen in your case, you need to be really thoughtful about this factor. But, how would you know that your online marketing campaign fails to engage customers owing to sluggish loading speed? Before digging deep, let me acquaint you with the fact that today most of the search engines, including Google, consider sluggish speed a negative score for your site.

Your leads will directly give an idea that there’s something wrong, however, in this case, you can’t be sure whether the speed was the prime reason.

There could be a plethora of reasons owing to which you may not generate leads as expected. Herein, this analytics tool comes into the picture. It provides access to site’s speed report, where you can check the average page load time, page views, page load samples, bounce rate, page value, average redirection time, page download time, server response time, and the list goes on.

Website’s Real-Time Snapshot: The best thing you can do with this effective tool is to gain information about your website’s metrics, such as how many visitors landed on your website; from where they accessed your website; and which platform they used to access it.

Behavior Flows: The behavior flow helps in determining the path users have followed from one page to the next. It helps in understanding what content or which web page engage your customer the most. Apart from this, it also helps to discover the issues with your content. User’s behavior can be swiftly mapped, helping you in the segmentation process.

Email Campaigns: Nowadays, it is one of the best ways in practice to attract quality return visitors to your mission-important website. It allows you to check how many visitors have opened up your emails you sent them. This, in turn, enables you to make yourself aware of the visitors’ that are interested in your offerings. You can set this up simply in just a few simple steps. Login to Google Analytics, then click on the Admin, thereafter visit custom definitions, and lastly, for custom metrics, create a new metric referred as ’email opens’ with Integer formatting.

Analyze Site Searches: If you have already activated a search function for your website, you can click on Behavior and then select the Site Search feature to analyze what kind of data your users are seeking. It is quite possible that end-users, who lands on your website can easily turn into potential leads. The best thing is when you can analyze what your visitors are hunting for; you can modify the content as per their queries.

This way, Google Analytics can help you do a lot of things with respect to your web performance. Analytics is a vast subject of study, which seems to have no end-point. The more you dig into it, the more you understand. A proper understanding of this tool will help you peddle your business into the right direction.

Amazon Redshift is changing the way companies are collecting and storing big data. Companies like Amazon can influence the control of cloud computing for data warehouse purposes. This Amazon cloud solution allows corporations to apply date warehousing more effectively than ever. Redshift is Amazon’s storage solution that allows business owners to move their date warehouse to the cloud for much less than outmoded options.

The main focus is on “storage”, and Redshift is prepared to meet your date warehouse needs. The accessible cost options are captivating. With no long-term commitments or up-front expenditure, Amazon provides “pay as you go” pricing giving you the freedom to choose as much storage as you need. It’s not always easy to get the requirements for the resources. You may distribute fewer resources than needed, or you could allocate unnecessary resources and not take full advantage of the return on your investment.

Amazon cloud solutions allows for flexibility so you can keep the right balance. If you decide to terminate your relationship with Amazon Redshift, you can cancel any time. it has opened doors for small businesses to make use of big data analysis and data warehousing without a large increase in price.

Amazon Redshift is built on SQL database technology. What does this mean for you? Compatibility. Nearly all SQL drivers, and compatible tools can be used. As soon as your data is downloaded onto Redshift, existing applications new web services can are easy to utilize.

Amazon has done it again with its amazing scalability compared to other data warehouses. For those of you needed to leverage big data, Redshift is easy to use. Now everyone can control big data in the cloud to open the way to a better tomorrow. Amazon Redshift cloud analytics… for the next generation.

Business use cases for BI and Analytics on Cloud

There are several operational and financial factors that work in favor of Cloud Business Intelligence (BI).

The key being:

• Speed of Implementation and Deployment: Immediate availability of environment without any dependence on the long periods associated with infrastructure procurement, application deployment, etc. drastically reduces the BI implementation time window.

• Elasticity: Leverage the massive computing power available on the Web, scale up and scale down based on changing requirements.

• Focus on Core Strength: Outsource running of BI apps and focus on their core capabilities.

• Lower Total Cost of Ownership: Convert some part of capital expenditure to operational expenditure, cost-effective pricing models, pay per use model, etc.

At the end of the day, what is the strongest determiner of whether a company will succeed in the long term? It is not pricing structures or sales outlets. It is not the company logo, the strength of the marketing department, or whether the company utilises social media as an SEO channel. The strongest, single most important determiner of business success is customer experience. And creating a positive customer experience is made easier through the use of predictive analytics.

When it comes to creating a positive customer experience, company executives obviously want to succeed at nearly every level. There’s no point in being in business if customers are not the focus of what a company does. After all, without customers, a business does not exist. But it’s not good enough to wait to see how customers respond to something a company does before deciding how to proceed. Executives have to be able to predict responses and reactions in order to provide the best possible experience right from the start.

Predictive analytics is the perfect tool because it allows those with decision-making authority to see past history and make predictions of future customer responses based on that history. Predictive analytics measures customer behaviour and feedback based on certain parameters that can easily be translated into future decisions. By taking internal behavioural data and combining it with customer feedback, it suddenly becomes possible to predict how those same customers will react to future decisions and strategies.

Positive Experiences Equal Positive Revenue

Companies use something known as the net promoter score (NPS) to determine current levels of satisfaction and loyalty among customers. The score is helpful for determining the current state of the company’s performance. Predictive analytics is different in that it goes beyond the here and now to address the future. In so doing, analytics can be a main driver that produces the kind of action necessary to maintain a positive customer experience year after year.

If you doubt the importance of the customer experience, analytics should change your mind. An analysis of all available data will clearly demonstrate that a positive customer experience translates into positive revenue streams over time. In the simplest terms possible, happy customers are customers that return to spend more money. It’s that simple. Positive experiences equal positive revenue streams.

The real challenge in predictive analytics is to collect the right data and then find ways to use it in a manner that translates into the best possible customer experience company team members can provide. If you cannot apply what you collect, the data is essentially useless.

Predictive analytics is the tool of choice for this endeavour because it measures past behaviour based on known parameters. Those same parameters can be applied to future decisions to predict how customers will react. Where negative predictors exist, changes can be made to the decision-making process with the intention of turning a negative into a positive. In so doing, the company provides valid reasons for customers to continue being loyal.

Start with Goals and Objectives

Just like beginning an NPS campaign requires establishing goals and objectives, predictive analysis begins the same way. Team members must decide on goals and objectives in order to understand what kind of data they need to collect. Furthermore, it’s important to include the input of every stakeholder.

In terms of improving the customer experience, analytics is just one part of the equation. The other part is getting every team member involved in a collaborative effort that maximises everyone’s efforts and all available resources. Such collaboration also reveals inherent strengths or weaknesses in the underlying system. If current resources are insufficient to reach company objectives, team members will recognise it and recommend solutions.

Analytics and Customer Segmentation

With a predictive analytics plan off the ground, companies need to turn their attentions to segmentation. Segmentation uses data from past experiences to divide customers into key demographic groups that can be further targeted in relation to their responses and behaviours. The data can be used to create general segmentation groups or finely tuned groups identified according to certain niche behaviours.

Segmentation leads to additional benefits of predictive analytics, including:

• The ability to identify why customers are lost, and develop strategies to prevent future losses
• Opportunities to create and implement issue resolution strategies aimed at specific touch points
• Opportunities to increase cross-selling among multiple customer segments
• The ability to maximise existing ‘voice of the customer’ strategies.

In essence, segmentation provides the starting point for using predictive analytics to anticipate future behaviour. From that starting point flow all of the other opportunities listed above.

Your Company Needs Predictive Analytics

Companies of all sizes have been using NPS for more than a decade. Now they are beginning to understand that predictive analytics is just as essential to long-term business success. Predictive analytics goes beyond simply measuring past behaviour to also predict future behaviour based on defined parameters. The predictive nature of this strategy enables companies to utilise data resources to create a more qualitative customer experience that naturally leads to long-term brand loyalty and revenue generation.

Microsoft had the lead in commercial data analytics with its MS office software and its evolution through the 90’s. Now all these fancy tools have become available. All the latest tools tend to do one aspect of Microsoft Excel well. For instance, Audit Command Language works well with regards to data transformation and Tableau focusses on the creation of graphs and charts.

Having said all that, it is extremely important to remember that Excel is still relevant to some companies for a variety of reasons. Let’s explore the reasons to understand how it is still useful to companies around the around for data analytics.

1. Familiar UI – There is a generation of the workforce to whom, using Microsoft’s productivity software is like second nature. Microsoft Office comes installed in every computer sold at a fraction of cost of any other tool for data analytics. That implies that every person can easily be trained for analytics projects on Microsoft Office Excel.

Check out a few common uses of the familiar user interface for shortcuts and inserting and deleting rows.

2. Scripting – Most people don’t realise that they can script and automate a lot of MS Office tasks, just like other scripting languages/tools. VBA for Microsoft office, is a sought after skill because it allows for user to be creative with the kind of applications they want to create for automation of such tasks. Using this aspect of MS excel, end users can become very creative by working with data sets by connecting to databases like MS SQL Server. No other allows for this level of automation capabilities.

3. Cost – One major factor is deciding which tools are to be used for any project in any industry is amount of the initial investment. And of course, this is a valid question. Even on this count, MS Excel along with VBA costs significantly lesser than any other tool available in the market. It is certainly worth having this tool around as the last line of defense with the respect to the cost involved.

MS Excel obviously has certain limitations which leave gaps to be filled by other tools (just the other tools do so MS Excel). However, it is worth pointing out that tools like ACL Audit Command Language, Tableau, Spotfire etc. do not provide end-to-end solutions i.e. from data transformation to dashboards (analysis) and application development capabilities. It is a must have to perform basic and quick analysis. MS Excel is a solid investment as a strong foundation to any operation.