An analytic tool should enable a company to create powerful data driven culture, so as to measure customer’s interaction with the web and understand how different people and groups behave as well as to analyze campaign return on investment and to optimize the website increasing profitability. The company should be able to clearly define the business goal as this is the first step in any website optimization. Goals have to b understood in order to improve its website, by doing this they will be able to build set of keys performance indicators that will facilitate in tracking goal achievements (Turner, 2010).
The key performance indicators are usually few but truly good for any company and different persons in the company are usually interested in different key performance indicators. The top management can be interested by the overall achievement of the website’s goals; the middle level management can be interested with the campaign and site optimizations results while other analyst can be attached to every single metric of the entire website (Clifton, 2012). These indicators should contain attributes of relevance, un-complexity, instantly useful and timely. Accurate data should be collected and analyzed to give insightful thoughts. The data should be true and accurate.
Data analyst should be able to measure multiple goals as this will facilitate improving primary conversation which might be decreasing newsletters and primary signups by the customers, analyst should be in a position to try the tools with small experiments as this helps to cover any loopholes that may have not been noticed while implementing the goals. Testing for different segments is very important, geographic locations and operating systems can have different behaviors thus these tests will help understand different behaviors. Implementation is the last stage that will give an analyst an overview of whether the entire experiment was worth. A web analyst who overcomes all the challenges and properly implements the test is likely to have a success in the project. When implementing an analyst should always ensure s/he is friendly, is able to start small by setting realistic expectations, ensure people understand the tool and what might be required from them as well as get the require support such as resources that will be required for the entire project (Cutroni, 2010).
Different reports can be achieved when using the analytics reports. Time of the day report is among the many reports generated and this shows the tracking posts by publish time with a fully formed custom report thus saves analyst loads of time it can be used to see which days of the week are the most popular and use of data to experiment with ones publishing schedule. Browser report shows the different browsers who are working from ones site and the number of visits, purchases and revenue (Plaza, 2011). This report facilitates in picking up of potential problems. Referring site reports are the ones that show referring site alongside goal completion and conversion rates. Also linking analysis reports help one to see the inbound links that are sending goal completion, traffics and visits from the SEObook.
When setting up a Google analytic account one should first ensure that the codes are in place in all the site’s pages and ensure that they are tracked. Any missing tracking codes can throw off metrics throughout an analytical account. No sessions can be tracked if the codes are not in place. No data will be seen if there is a missing code on any page in that particular site. Analytical tracking codes can be verified by use of manual methods whereby codes are looked at in every page or one can use tools such as GA Checker to crawl and counter check whether the codes are on page on the site. Pages that have codes, can be double checked to see whether it contains the tracking ID for the correct analytic property (Turner, 2010).
Once verified the analyst should check goals tracking setup is tracked in all pages correctly, one should also familiarize with what their client is tracking. Values and goals of a website are shown by use of a goal tracking but when there is an improper setting, faulty data can easily come up. All active goals on a website can be seen we select admin from the top navigation in the Google analytics (Clifton, 2012). Then desired account is selected, property and view from the down drop goals is selected. A goal page, all the goals a client has setup will be shown. Goals that are active as well as the switched off goals can be seen from the far right column.
Understanding past events occurs when we have accurately checked tracking code and goal set ups of data from the previous periods. Redesigning, email marketing and advertising have had an impact on traffic and conversions thus a review of past metrics is very important. A new client will have to add Google analytics so as to mark the key events. This annotations help in explaining major spike in traffic by noting an advert buy that involved taking over display of advertising space on a couple of local sites. This reviewing of data helps in understanding reasons for the increases at glance without digging into traffic source to find out where extra sources came from. In a nutshell we can say getting ready to manage reports on digital marketing for ne w clients with assessing the setup of the Google analytical to ensure received proper data on those who visit the website focus should be on accurate goal tracking so as to ensure there is proper received and correct data that relates to proper data to those who visit the website (Cutroni, 2010).
A Google analytic comes with different features that are helpful to any computer user. People use computers to get real time statistics, people use Google analytic because it has an inbuilt real time statistics. It helps in site search where it provides a list of every keyword people search for on your site (Plaza, 2011). This will help in knowing what is exactly that they think is missing and what they have trouble finding, it also acts as a data visualization tool that includes a scoreboard, dashboard and motion chart which facilitates in display changes in data over time. It facilitates in email based sharing and communication whereby it measures the return on investment of all the marketing campaigns which communicates if it’s worth building and spending on it, has segmentation for analysis of subsets such as conversations, custom reports has enabled Google analytic track pages.
The system is more powerful in that the system is flexible enough to let one build a model of a site’s content scheme within the Google analytic, harnessing this power has enabled sticking custom variable directly into page codes, as well as has integration with other Google products such as AD Word which has a wide wealth of data that one pays for thus there is a need to make use of it, website optimizer and public Data Explorer. Safety net profile is another important feature of the Google analytic tool as it creates a backup plan that provides protection against corrupt data, this can be caused by a typo in one filter or setting up goals incorrectly. SEO Reports via Google webmaster tools are important as they help one see their performance within the Google search ranking (Turner, 2010).
Multi channel report is among the elements used in the goggle analytic. They are the reports generated from conversations paths, sequences of interaction that led up to each conversation and transaction. Conversation path data includes digital channels which are email newsletters, referral sites, custom campaigns and social networks. The multi channels funnels are accredited for the role in conversation, they show the number of sales and conversation each channel initiates, assists and completes (Clifton, 2012). The top conversation path shows the path the customer took on their way to purchase.
The kiss-metrics tools are focused on people while the Google analytic added people tracking as a feature. Most people are known to use kiss-metric tool to track individual while most people who use Google analytic will never touch that feature. When tracking people user identification is used, analytic tool must be able to identify users when they tell you who they are. The user has to sign in and identify themselves on each of their devices. When a user visits the website both the kiss-metric and Google analyst assign the anonymous ID to that person (Cutroni, 2010). In Google analytic visit and registration takes place in the same session, if one leaves and logs in after a couple of days and registered, the only last session is tied up to the user ID while with kiss-metric all data from a previous session is assigned to an alias.
In tracking the kiss-metric assumes that activity on one device comes from the same person, if one of the users visit the site using tablet or any other gadget kiss-metric will recognize them once they sign in and tie them back to their customer ID. Google analytics assumes that each and every visit is from a new person and the only way is to identify people in each session in order to view everything a person does (Plaza, 2011).
When there is multiple people using the same device kiss-metric data of person A will be assigned to Person B once person B registers, any previous visit to a particular site using the device will be tied to person B and it has no technical ay around this. Google analytic on the other hand report correct data. Since person A visited the site and didn’t register the session will be lost. Person B on the same session registered the data will correctly be tied to person B. When sessions of activities from several devices are used the kiss-metric all the data gets assigned to alias once the person registered. When you visit using your I pad kiss-metric assigns a new anonymous ID. In the Google analytic all the sessions going forward will be correctly be assigned to you. Once you log in on a device, the data from the same session gets tied back to the user ID that was assigned when you register on the desktop (Turner, 2010).
References
Clifton, B. (2012). Advanced web metrics with Google Analytics. John Wiley & Sons.
Cutroni, J. (2010). Google analytics. " O'Reilly Media, Inc.".
Plaza, B. (2011). Google Analytics for measuring website performance. Tourism Management, 32(3), 477-481.
Turner, S. J. (2010). Website statistics 2.0: Using Google Analytics to measure library website effectiveness. Technical Services Quarterly, 27(3), 261-278.
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