How Is Nike Creating a Brand Image Created By Analytics? - liveanalytics.org

A brand image is how a consumer visualizes a product. If I say "Nike" or "Jordan's", immediately, in our minds, a certain image is created. The good people at Nike would hope that the image that you get, especially, if you are between the ages of 16-40, would be "cool".

Branding has always been a critical factor in marketing, but it is even more so in our social media era because there is just so much more content. If you are to prosper, you're product must be the first choice in a Google search. In times past, brand image was created subjectively. A marketer would dream up a theme for a brand image and just hope this image would resonate with consumers. As an aside, I think one of the greatest brand image campaigns of all time was UPS---imagine creating a brand image around the color brown---but "Brown" is clearly defined in our minds when we hear the term, UPS.

Brand image can now be created objectively. In our social media world, a marketer can understand what resonates with their target market. When the image is created, they can quickly measure in real time whether or not this image resonates with their target market. Millions is spend on the creation of a brand image. It is one of the most important decisions that a marketer makes in the creation of a brand. If the image does not resonate with consumers, the brand will fail. Now, in our era, a marketer can quickly, in real time, change the image to a more acceptable theme.

Analytics has changed the face of modern marketing. In times past, branding was subjective----you just hoped your idea was the correct one. In modern marketing, social media has created the means that allows analytics to be developed that allows a marketer to see if their campaign is the correct. Today, marketers brand by fact and not by hope.

Nike is a prime example of how this works. In the late '80s and early '90s, Nike's image was based on "cool". They teamed with Michael Jordan to create a truly iconic brand image. The market place has changed. Many shoe companies have developed an "image" of cool. Nike knew that it had to evolve.

Nike evolved its image from "cool" to "friend". In the social media era, a brand must create a friendship with its customer. Nike was able to create a brand image based on a personal relationship with its customers. It was able to do this through measuring analytics and creating a brand image around "fact" and not "hope" (as in "Gee, I hope this works").

Nike+ cobranded with Apple to create a community of runners. Sensors were put into Nike sneakers that measure how fast and how far its customers run. The analytics of this data is uploaded to the runner's iPod and then to the Nike Web site.

Nike then analyzes the data to create its brand image within the new Nike running community. Nike's analytics has told Nike that Sunday is the most popular day for running. Nike has learned that the greater proportion of the community works out after 5 P.M. This allows Nike to target its advertising to communicate with its community during the work day. Early morning drive radio is a favorite of Nike.

Nike has also learned that its community sets new running goals for itself as part of a New Year's resolution. This is a critical metric, because now Nike is able to invest in a major advertising campaigns during the NFL playoff run. The key word is investment. Because of analytics, Nike knows that its target is primed for this campaign at this particular time of the year. Analytics allow Nike to maximize its advertising dollars.

How Companies Optimize the Functioning of Their Cloud Solutions - liveanalytics.org

Cloud solutions are being used in many region of world to change the way that companies are able to achieve their internal objectives. The intention behind the introduction is to enable these companies to perform their functions in a cost effective and efficient way. This technology now has companies wondering how they can optimize their systems to ensure that they get the best performance from the information technology infrastructure that they have setup. The question of optimization is central to creating momentum for companies that are using these solutions to invest more in the technology and, therefore, become a part of the technological revolution that it portends.

Cloud management systems as an enterprise tool

Cloud hosting is the use of virtualized systems to store data for companies and business entities. These platforms go much farther than just enabling companies to store data and create backups. These systems enable companies to manage their data and use it for enterprise data analytics for business growth. This means that what starts out as a hosting solution ends up becoming a complex data management architecture that the entire company is reliant upon.

This is why cloud technology developers have created the cloud management system. It is an enterprise software infrastructure that is installed on the company's computers. It enables the IT support staff the company has to provide maintenance and administrative services on the cloud solutions that the company is using. This is essential for companies that are operating cloud dispersed architecture for data and resource management. The cloud management system is not a replacement to the use of managed solutions but rather a complementary tool. For a company that is running a complex infrastructure with many network dispersed operational points, it is important to be able to requisition resources and assign them as needed.

Benefits of this system

Companies that use this software are able to assign and manage their cloud based resources much faster than those that are reliant on the service provider. It is for this reason the companies that operate in that way perform much better in their data management. If the company has skills in data system management, they are also able to customize their cloud hosting solution to provide the exact level of service that they need. This makes it easier for them to create custom applications for use in their company by employees, especially for use on mobile devices.

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How Companies Are Taking Advantage of Predictive Analysis Software - analyticsforprofit.com

Predictive analysis software tools can provide you with understanding of what is coming. It is no surprise that the demand for them is increasing. Numerous companies are accepting the competitive advantage the tool's predictive analytics provide. Companies that use these tools will have an advantage on predicting the future trends and probabilities.

The reports provided to the company by predictive analytics exceed the standard sales forecast and business reports. It can show you the risks and opportunities once it is done analyzing the patterns seen in transactional and historical data. Predictive analysis captures the connection between numerous factors so that it can analyze and determine the risks associated with a certain group of decisions.

It uses the data acquired from a group of people in order to determine their future actions. This prediction helps companies to transfer those future actions in a direction that is favorable to the company. This strategy helps companies decide on what creative way they can introduce new products to clients as well as find a way to maintain their existing clients.

Implementation of predictive analytics can improve the business processes of retailers. Retailers would be able to automate, direct and optimize decisions as well as improve decision-making, thus accomplishing goals in a short period of time. Using this analysis will enhance retailers' speed on their decision-making processes. The qualitative foundation of predictive analytics can quickly evaluate, identify and pursue fresh market opportunities. It improves retailer's operating performance, enable them to provide accurate forecast, improve sales productivity and resource management.

These tools that are used to identify predictive analysis in the financial markets are already very popular to sales staff, traders and quant. They use them to determine and spot arbitrage and trading opportunities. It is vital that you look for tools that provide accurate and quick data to allow you to perform effective analysis and even outperform your competitors. These tools have added features that can be very beneficial. There are tools that conveniently supply you with data and place them in applications such as Excel for computation.

These tools can help you analyze rewarding trading strategies such as volatility, yield curve and other strategies before your competitors can put data together for analysis. There are no boundaries for the application of predictive analysis. Some tools can provide effective prediction with their text analysis, speech analytics, social media monitoring and scoring models to analyze the customers' mood.

Collecting Data Using Packet Sniffing - analyticsforprofit.com

Even though packet sniffing is not quite as popular as using Web Logs, Web Beacons and JavaScript tags, it is one of the most sophisticated ways of collecting web data. A packet sniffer is a layer of software that is installed on the web servers and runs "on top" of the web server data layer. Otherwise, it can also be a physical piece of hardware that is affixed in your data center, and all the traffic is then routed to your web server via the packet sniffer solution.

One of the vendors who provide packet sniffing web analytics solutions are Clickstream Technologies. Some sites, such as SiteSpect use interesting ways of leveraging packet sniffers, by using the technology for multivariate testing, thus eliminating the reliance on tagging a website to do the testing.

1. One has to follow five steps to collect data through packet sniffing:

2. The customer types your URL in a browser.

3. The request passes through a software- or hardware-based packet sniffer that collects attributes of the request which can send back more data about the Visitor to the packet sniffer.

4. Thereafter the request is routed to the web server by the packet sniffer.

5. The request passes through the packet sniffer back to the customer. The packet sniffer captures information about the page going back and stores that data. Some vendor packet-sniffing solutions attach a JavaScript tag that can send back more data about the visitor to the packet sniffer.

6. The packet sniffer sends the page on to the visitor browser.

The benefits of using packet sniffers as your data collection mechanism are as follows:

1. Since all data passes through the packet sniffer, it first eliminates the need to use JavaScript tags for your website, or in theory, to touch your website at all.

2. The time to market is a bit longer than with JavaScript tagging because it relies on IT to approve and install additional software and hardware in the data center. Despite that the time required is less than that required for other methods.

3. A huge amount of data can be collected instantly - much more than with standard JavaScript tagging. For instance, server errors, bandwidth usage, all technical data as well as page-related business data will be available. Packet sniffing provides the most comprehensive data possible.

4. The nature of the solutions is such that you will have the ability to always use first party for cookies.

Packet sniffing also has certain limitations, which data collectors should be aware of.

1. Most companies find it difficult to make a case for and convince the IT department to add an additional layer of software on the web servers or to physically install hardware in their high profile data centres and route all web traffic via these solutions. Packet sniffers also create a layer between the customer and the web page, something that can raise concerns and create hurdles.

2. Packet sniffing involves collecting raw packets of Internet web server traffic, which can pose two challenges: (1) Nontrivial amounts of configuration work with packet-sniffing solutions to sift through just the needed data from all the raw data. (2) The second challenge is regarding privacy. Raw data captures all data, including PII data like passwords, names, addresses and credit card numbers. Privacy requires minute stress testing and legal review.

3. Packet sniffing solutions still require JavaScript tags to truly collect all the data needed for optimal analysis. For example, packet sniffers would not get any data for cached pages (since no request is forwarded to the web server). Data from Adobe Flash files or Ajax might fail to be collected too. In rich Internet applications one deeply interactive file goes over to the visitor's browser and then many interactions happen in the visitor browser that are invisible to traditional packet sniffers (again because rich media interactions don't send back request to the server). Same holds true for core structure and metadata about pages via pure packet sniffer implementation.