Complete Beginner’s Guide to Analytics

What Are Analytics?

The internet has changed drastically since it’s inception, and so has user behavior. Users have shifted from typing remembered URLs into the address bar to relying on search engines to find a site for them. A user will open and skim multiple tabs, rather than devoting full attention to one page. All of this complicates the metrics of a site or application: to measure success, an analyst cannot simply measure hits on the web server. They must measure human behavior.

When gathering information, researchers employ both qualitative and quantitative methods. Qualitative data is gathered through user research: observing people to understand why they do certain things. Quantitative data is garnered through analytics: identifying what actions users take when they come to a page, and how many users take those actions.

This quantitative data allows us to measure baselines, use those baselines to inform design decisions, and then measure the success or failure of the design. While there are myriad things that we could measure, we use data in only a few ways: to describe, to diagnose, to prescribe, and to predict.

Descriptive analytics are similar to the counters of old. Descriptive analytics show baselines, such as how many people visit a page, click on a button, or watch a video.

Diagnostic analytics might use the same metrics as descriptive analytics, but with a different purpose. Diagnostic analytics help us understand what happened, and why. For example, if an online retailer is losing money, they might measure the clickthru rates of the links and exit rates of pages along the customer journey, to see where they are losing people.

Prescriptive analytics refers to data that informs someone of what they should do next. For example, when Google Maps collects data about traffic at rush hour, they are able to prescribe a better route for drivers. For those of us who are measuring the effectiveness of design rather than traffic, prescriptive data still identifies patterns, and can thus inform our future design decisions.

Predictive analytics are the final category. Predictive analytics tell us what is likely to happen in a scenario. For example, if we A/B test a new site header against our current site, that test will tell us which header is more likely to convince people to stay on the site. If the new header is more popular, we can predict that our traffic will grow if we implement the new header.

All four types of analytics use metrics, often based around Key Performance Indicators (KPIs). A KPI is a measurable action or signal that is correlated to business success. For example, retweets on Twitter don’t directly increase how much users like or know an organization. However, a marketing team may correlate their retweets to brand recognition, in which case they may use the retweets as one KPI. Ideally, an organization should have multiple KPIs for one business objective, which increase the reliability of the data.

Common Methodologies

Although analytics may seem convoluted to many designers, the basic methodologies behind the field are simple and straightforward. Essentially, the field of analytics is based on research, measurement, and analysis.

Research

While web-based analytics is a fairly new area, research has been around for hundreds of years. Researchers exist across every field from science to marketing to anthropology, and the techniques they use directly influence the way analysts do work, and the things analysts decide to track. The work of researchers, particularly when combined with analytics, is closely analogous to the scientific method:

Researchers begin by prioritizing their goals or questions, in order to focus their attention. Once they know the goal of their project, they create a hypothesis, and test that hypothesis. Data analysts can then measure the results of the research and tests. Based on these tests, researchers and analysts may both begin to recognize outliers, or results that aren’t indicative of the greater whole, as well as patterns in the results. They then come to conclusions, and even predict future outcomes based on the patterns they identify.

Measurement

When it comes down to it, most metrics help us understand how an organization or brand is growing. Marketers, entrepreneurs, and business consultants all create their own methods of measuring success. They measure numbers of users, speed of sites, amount of time spent on a page, and offline details such as the amount of money made, the number of sign ups for a new product or mailing list, or the number of purchases.

The danger for organizations who aren’t familiar with analytics is that they only measure, without prior research or future analysis. For example, a team might measure the number of people who visit the site. However, without research into how many people visited in previous days, weeks, or months, and analysis into how the two measurements compare, the measurement is a meaningless number. This is why we often refer to data tracking rather than measurement. Data tracking is ongoing measurement supported with research, with an intent of analysis.

Analysis

Analysis is the process of breaking down information into smaller pieces, and examining what that information means. It’s used in mathematics, philosophy, chemistry, psychiatry, and even computer science. Without analysis, all of the information gathered in research might be measured, but it has no meaning. Analysis of information allows us to make connections. For example, you might research how people access a website, and measure the number of people who come from search engines. Analysis is then used to provide context and answer essential questions: how many people visited similar sites? How many visited your site today, compared to yesterday or last week or last year? How many came to your site from Google, vs. from Twitter?

An interesting note: “analysis” comes from the ancient Greek work ἀναλύω, which means “I unravel.” One of the earliest known uses of the word “analytics” is in the title of Aristotle’s writing, Prior Analytics, a work about deductive reasoning and the scientific method. Since we, as human beings, are naturally interested in breaking down information and understanding it logically, there is an obvious reason we find analytics so immensely valuable.

Daily Tasks and Deliverables

Data analysis is a part of many professions, from marketers to UX practitioners to data analysts. In this section, we will review some of the analytics-related tasks a UX practitioner may undertake, and the associated deliverables.

Setting Key Performance Indicators

Any time that a new initiative is launched, the analytics expert will need to identify and set up the relevant key performance indicators. These are closely related to the experience goals of a project, which is why it’s so valuable for UX practitioners to collaborate with the data analyst and understand analytics and metrics. The KPIs, as we explained above, are the measurable actions that correlate to organization or project objectives. For example, is the organization’s goal is to become a global company, one KPI might be more website views from around the world, or a certain number of sales coming from foreign countries. Ideally, each project objective should have a KPI associated with it, which will allow the team to measure the success of the project.

Optimizing Content

While we’ve been focusing mostly on the measurement side of analytics, we haven’t touched on how this impacts user experience. Analytics tell us what content or site areas need improvements—and that means that often our analytics experts are the ones who can best optimize our work. This can include understanding how Google’s search algorithms work, how to handle and improve metadata, what keywords are most likely to reach our target audience, and many more handy tricks of the trade. Before a page goes live or a campaign launches, the analytics team (or person) will want to review everything, and optimize the content so that it is most likely to succeed.

Setting up Analytics Tools

Once the KPIs have been determined, we need to add code to the relevant pages in order to track site engagement, conversions, and other metrics. Google Analytics is one of the most popular analytics tools, in large part because Google has made it very easy to add tracking code to almost any site. Sometime, development teams take on the task of analytics tracking, but more often the analytics expert will provide the dev team with the relevant code snippets required.

Monitor and Measure

Maintenance is a huge part of working with analytics. Depending on the project, an analytics expert may create reports on a daily, weekly, monthly, or bi-annual basis. For example, a social media campaign may require daily updates. However, a new product launch could take 6 months to bear fruit. Regardless of the time period, it’s in the monitoring, measuring, and reporting that an analytics expert digs into the analysis. It’s not enough to report on the KPIs; working with analytics means interpreting what the KPIs show, and creating recommendations for the UX team that reflect those interpretations.[Source]-https://www.uxbooth.com/articles/complete-beginners-guide-to-web-analytics-and-measurement/

A Web Design Training Institute teaches you to create own website with best java certification programs, core java, advanced java.