Module Web - Web Analytics

Module 3 is all about web analytics. I was very excited for this module because of a special cross-functional team which had me working with the marketing team. During that time they rolled out a new website that would let them track all sorts of metrics to track our brand awareness. Brand awareness was a new concept to me and they weren't technical at all so I always wondered how it could work and if it did work. Enter my introduction to web analytics.

The definition from lecture 9, from the Web Analytics Association, defines web analytics as "The measurement, collection, analysis, and reporting of internet data for the purposes of understanding and optimizing web usage." That's a pretty broad charge - what you want to understand or optimize far is going to depend on if you're a University recruiting students and staff, an opensource project sharing your results, a small business selling your wares, or a large business selling your wares. That is something that jumped out to me as I worked on homework 3. There's so much data and without a purpose or questions you need answered it is kind of meaningless. On the same note, not asking those questions and having a clear, definitive purpose for your website is a waste because there is a treasure trove of data available to improve the results you're getting. All of that is backed by the Web Analytics Cycle which starts with setting goals, measuring metrics related to the goal, reporting on the metrics, analyzing the metrics and finally optimizing based on those metrics. How do you set the goal? You need to start with the Five W's of Web Analytics - what, who, when, where and why. As you delve into the user base for your website you can start to understand their demographics by where they're coming from, type of device used, age, gender and so forth. You'll be able to see where traffic comes from to track success of an ad campaign or to see what areas to focus on. There were more metrics out of the box than I could have thought of mattering but as I tried to understand my website for the homework I wished I had more metrics! Only the bare bones had been set up because the business wasn't really using the metrics to their full advantage.

Half of the module focused on Google Analytics. We learned how it integrates with websites and gives a simple, free tool to wade through all of the metrics and questions that we have. Much like with dashboards from module 2, Google Analytics makes the metrics extremely consumable with visual reports that can be exported or converted into a grid if you want just poor numbers. Exploring the data was easy and it was easy to customize the views to focus on just one specific areas - like comparing new vs returning sessions while at the same time comparing that to new vs returning sessions of people who made purchases.

Catherine Brock in "What Web Analytics Says About Your Brand" brought up some really interesting points about using metrics to tell the story of your brand. She talks about rationalizing metrics and the need to really confirm it to make sure you understand the story your metrics are telling. She talks about what a website with the majority of session times less than 10 seconds means and how most website owners will defend this. The defense will come with what they believe is the cause - such as they were just looking for a phone number and got it - but this is a guess with no data behind it. There are tools can be used, in this case a tracking phone number, to tell the story more clearly. Her thoughts spoke to me because we see online all the time how statistics are misconstrued or understood.

Web analytics are powerful tools but must be combined with clear business goals and a commitment to reviewing and acting upon the data.

Citations

Brock, Catherine. 'What Web Analytics Says About Your Brand'. Business 2 Community  15 Feb. 2015. Web. 18 Feb. 2015.

Module 2 Reivew

Module two covered a lot of ground - all focused on tracking performance, understanding and making decisions within an organization. The data used was all internal to an organization. Governing these action is the Date Warehouse Design Cycle which is used to translate the Online Transaction Processing (OLTP) systems to the Online Analytical Processing (OLAP) system(s). This process involves the extraction, transformation and loading of the data from disparate source systems - like an ERP or CRM - into a single data warehouse. This allows for the organization to agree upon definitions of terms and for the data quality to be checked.  Before the data warehouse can be populated it has be to be designed which we learned about the star schema to design. A star schema is made up of a fact table with dimension tables. The facts represent business measures and the dimensions represent the grain.

Creating a star schema should reflect the business events the company wants to track and make decisions about. This is done using a four-step dimensional modeling process. The steps are (1) selecting the business process to model, (2) declare the grain of the business process, (3) choose dimensions that apply to each fact row, and (4) identify the number facts.

I've spent my professional career with OLTP type databases. The data is normalized as much as possible with minimal duplication of data. As we began reading about and designing star schemas it felt wrong - there were more efficient ways of doing it! In the class readings, Ralph Kimball and Margy Ross mention several times that it's a natural instinct to normalize but there's not enough savings to merit the extra effort it would cause users. As I read more and focused on the purpose of this database - not real time for the purpose of a transaction in the moment but for analysis for trends, direction, and performance it started to change my perspective on the need for data warehouses. Data warehouses don't mean the data is significantly lagging behind - it can be updated in any cadence but it's not the production system - like a point of sales system. Data warehouses aren't meant for the programmers but for the end user. The end user knows how to run reports but the underlying structure is optimized for those reports.

Let's be honest though - I'm still more comfortable with a normalized, OLTP database but this module really helped me see the importance of the data warehouse.

The beginning of the module focused on the balanced scorecard which really took me until the end of the module to understand why. Interestingly, the day before I listened to the lecture on the balanced scorecard my company introduced one for the first time. It was replacing a program that had been in use for 3 years to help us make commitments and ensure our execution was aligned with the strategy. No one ever felt that program was effective. As a Systems Integration Engineer I can tell you how I directly impact revenue and profitability. Unfortunately, my impact is often delayed - I will have an impact in a year or two - my work rarely impacts the current year. Instead of focusing on projects that had strategic value in the medium to long term we were asked to do what felt like busy work, in addition to our day jobs, to show impact that year. We were promised something new this year that would better track our progress. So the scorecard was unveiled. None of us had any idea what it meant. As this article from the Harvard Business School "Why Strategy Execution Unravels - and What to Do About it" indicates though - alignment doesn't always mean success. The balanced scorecard has the organization focus on all the areas indicating success. Now, just having a scorecard won't bring you success but it's a good way to track measurement. If you don't seem improvement or sufficient improvement it can be an early warning system some part of the execution plan is off. Which leads back to big data and data warehouses and how they help answer strategic questions.

An organization amasses so much data on a daily basis and it’s a gold mine for analysis but data is only as useful as its quality. You must be able to access the data, terms must be consistent, and the data quality has to be good. If all of this is in place, which the ETL process is supposed to make happen, now the executives have a treasure trove of data to draw from to keep track of company progress and to make course corrections along the way. Without good data the scorecard stops being an accurate picture of company success. Without the scorecard, or other strategic requirements for data, the data warehouse has little merit. All of these efforts are to support and help organizations receive a competitive edge.

Citations

Sull, Donald, Rebecca Homkes, and Charles Sull. 'Why Strategy Execution Unravels - And What To Do About It'. Harvard Business Review 2015: Web. 14 Feb. 2015.