What is Analytics?
Modern Analytics, which began in the 1990s, is used to create real-time statistics using business intelligence in order to support various decisions in the work place. It can be used to answer various questions concerning consumer behavior such as: what time of day certain demographics shop, what demographics react to specific sales better than others, how long do visitors on your website stay on certain pages, what is the average number of re-tweets from ones Twitter page, or it can even recognize certain information collected that can help solve specific problems within an organization, to name a few. Specifically, marketing analytics is one of the many data-mining tools that help predict consumer behavior by retrieving data and then interpreting that data, thus making it valuable information in order to make key decisions. As businesses move forward into the 21st century, it will be imperative that they adapt to the changing technology concerning analytics if they want to survive heavy competition in their respective fields.
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Analytics in Decision Making
Analytics may help decision makers in five key categories like: Dependency Modeling, Clustering, Classification, Regression, and Summarization. By using analytics, businesses can obtain valuable intelligence such as determining if one product is usually purchased with another such as bread and cheese and be able to identify better opportunities for increased sales. As well, clustering can assist firms in determining how certain groups are somehow related like sumo wrestlers and teachers and can answer questions related to how this can affect decision making. Also, by using classification tasks one can determine for instance how much company sales increase year to year based on certain marketing techniques. In comparison, regression tasks are used to find causal relationships between different sets of data. For example, a business can measure whether using advertisement increased sales versus how the company performed while not using advertising. Finally, after all the data is gathered and translated into useful information such as: sums, averages, standard deviations, histograms, bar graphs, and so forth, one can consolidate all the information into a summarization, which will allow more accuracy for future decision making.
Two Main Types of Analytics
Predictive Analytics is a data mining tool that gathers data, so firms can use it to increase their business intelligence. By using predictive analytics it essentially allows firms to predict future results based on customers past behavior. Although, predictive analytics is widely known for it's use in a retail industry, it can also be used in other sectors like insurance, healthcare, sports, travel, transportation, financial services, and even public libraries. Massis (2012) states, The use of predictive analytics suggests that, while it is best applied to forecasting trends and seeking patterns in customer habits, it can also be used to solve operational issues. In one library at Columbus State Community College in Columbus, Ohio, they use predictive analytics to develop their budgets while asking themselves if certain wants justify their needs and is there going to be a return on investment on these decisions?
On the other hand, Text Analytics gets a little bit more complex because it uses statistical and linguistic models that change information from things like: emails, surveys, blogs, and even Facebook or Twitter updates to useful structured data that permits firms to detect things like positive and negative comments on customer surveys, so they can be addressed by quality control areas within ones business. Some statistical and linguistic models are: Lexical analytics, Named entity recognition, disambiguation, Co reference, and Sentiment analysis. Lexical analysis can be used by a search engine, like Bing, to identify certain keywords which could boost a firms page rank. But out of the five models, it appears that Sentiment analysis may be the most unique and innovative because it can actually detect and differentiate mood, opinion, and emotion within someones written communication.
The Life Cycle of Analytics
In order to create analytics as a useful tool for an organization there are certain steps that one needs to take for their efforts to be successful. First, one has to determine what key gauges would need to be in the organizations reports. For example, if it we're a retail tire store the firm may want to know what age range buys certain tires or when is the peak season that overall tires are sold? Also, the firm may want specific graphs and charts with certain colors assigned to different values in their reports which can make the information clearer to the eye. Secondly, one has to discover all the data to bring about all the key indicators that the firm needs and wants to determine if they're meeting certain goals according to what industry the business is in. The firm may even have to go outside their business records to answer questions. For instance, if the firm was in the business of selling life insurance, they would have to locate public records locally or online, in order to determine where certain age groups we're living and which ones can actually afford certain insurance packages and so forth, by obtaining things like income records or even determining the home values of respective neighborhoods for marketing purposes. Next, one has to initiate the process of extracting, transforming, and loading (ETL) the data in order for it to be of use. Finally, one could either hire a developer to create a tailored software to ones needs or if one is trying to analyze a website for instance, they can use an alternative like Google Analytics or Yahoo Analytics, which are considered in the cloud, therefore doesn't need to be uploaded onto ones computer. (Haag & Cummings, 2013)
Google Analytics is a free to use tool for basic service unless a customer wants to take advantage of it's premium service where they would get dedicated help from Google experts for a mere $150,000 annual fee. The main difference between the free service and the premium service is that the premium package involves a 24/7 dedicated support team, account management, implementation specialist, and an increase in data processing. Therefore, using the premium service is more suited for bigger businesses where they receive heavier traffic in which they would need assistance in optimizing their Return on Investment (ROI). Google Analytics offers insight using various tools to give a customer a wide variety of ways to improve analyzing and predicting customer behavior on ones website. As well, it permits users to view statistics in real-time, allows users to customize their dashboard on what they want to view and also what they choose to make invisible, and finally it empowers other individuals within their firm to cooperate by sharing files in hopes of developing new ideas and eliminating failed marketing techniques as well. The different categories that Google currently uses to help customers are: Content, Mobile, Conversion, Advertising, and Social Analytics. Content Analytics grants the user the ability to see how often people visit their site, how long they stay on their site, and how they interact with the pages on the users site. Mobile Analytics enables users to view exactly what mobile devices their visitors used to get to their website. Also, it helps firms determine which device worked best interacting with their website and it assists decision making on whether if one should dedicate more mobile capability for users or improve the more traditional desktop user experience or even spend more time on both. Conversion Analytics in essence help users decide why certain marketing efforts turned into sales and why others didn't while measuring their firms goals, flow visualization, and goal funnels. Ongoing, Advertising Analytics permits Google AdWords integration so one can determine which keywords and advertising banners are receiving the most hits. This helps to determine if the firm should change it's marketing efforts related to less competitive keywords, it's banner attractiveness and placement, and if increasing ones bid to get a higher placement on various other websites would be the best decision. Finally, using Social Analytics may be the most prevalent among a firms interaction with customers because it provides platforms where the firm can develop a good marketing relationship with them. For example, a business can promote customer service by answering questions and concerns directly on their Facebook or Twitter page. As well, many businesses use it to offer special deals to customers that are looking to save a few bucks. By using Google Social Analytics, a firm can measure what information customers are sharing and separate which Social Network engages the customer the best, which can provide insight on where the firm should market more. (www.google.com/analytics) Using Google Analytics can be a very useful tool for businesses. In fact, Wankhade, Ingle, & Meshram (2012) state, Identifying what visitors are looking for when they navigate through the website and cross matching that information with the business goals makes it easy to design successful marketing strategies.
In comparison, Customer Relationship Management (CRM) has been steadily growing in popularity and is described by many people as being a very cost effective way of developing a better understanding of customer behaviors and needs, which essentially would increase a firms Return on Investment (ROI). CRM is a process that brings various information together pertaining to sales, marketing, and trends in relation to customers, so a company can put the puzzle together which ultimately will build a better relationship with the firms customer base. After all, it is more cost effective to build on an already existing base, than to try to obtain new customers in the long run. Marshall Sponder, an SEO/SEM expert and author, feels that in the coming years, businesses need to adapt to the plethora of information and be able to interact with their customers better, while realizing how and what the history is of that interaction. (Bell, 2012) As stated earlier, analytics is not only used by a certain type of business sector like that of e-commerce websites, they're also used in business units like Human Resources. But, the problem with using analytics by Human Resources (HR) is not trying to become more efficient, but it's the effective use of data in order to obtain better business results. In order to achieve better business results, HR departments have been focusing more on the attraction, development, and retention of employees in order to add significant value to ones business. For example, during the development phase of employees, which is usually recurring, it was found that firms should focus not so much on the amount of instruction hours employees we're gaining, but rather the employees results following the preparation is what appears that should be more relevant. In the same way, talent management is critical for a business to be successful as well. On paper, there are many individuals who are talented and for a firm to be competitive it must obtain persons who are going to be also durable to the organization. For example, if a firm recruited an individual that was highly talented, but for personal reasons such as being an alcoholic that prevented that person from being consistently present at work, it would not benefit the firm. Further, for some companies that experience a high level of turnover amongst their workers, they should focus on offering solutions that will increase their employees overall satisfaction with the company such as: hiring better managers, offering more extrinsic and intrinsic awards, performance rewards, giving time-off, and having more employee engagement, to name a few. Moreover, many HR business units are learning that using specific forecasting techniques can enable a firm to view the future needs for their organization which will allow them to plan for the long-term, hence they would be more efficient in hiring future personnel. Finally, using an analytical HR application called Talent Supply Management; Valero Energy was able to transform their oil refinery business from a 2,000 employee operation with revenues of $118 million into a whopping 22,000 employee firm with revenues in the upward $75 billion range in only six years. Based on various factors the executives at Valero we're not only able to cut overhead costs by being more efficient in daily processes, they we're also able to project the necessary amount of talent they would need for future projects and also determine what employees would most likely do better than others. (Harris, Craig, & Light, 2011) Essentially, by using different methods and applications it has been proven that the use of analytics can create astronomical business results, pending if the analytics are used efficiently. According to Klatt, Schlaefke, & Moeller (2011), specifically combining IT-based Applications, Management Accounting Applications, and Analytical methods will provide the most efficient use of analytics for a business to experience optimal results.
Today's informational pool is larger than ever and having the ability for firms to access it is even greater. By using various forms of data-mining tools like analytics it can be very cost effective and very optimal in receiving extensive business results. The challenges will be in obtaining historical results and predicting future results to engage in making critical operational decisions because it seems that no matter how much analysis goes into business planning there will always be unexpected situations that arise. Finally, it will be critical that management trains subordinates properly for them to fully adapt and understand how to use analytics efficiently.
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Posted in SEO Post Date 09/17/2016