2014 marks my one-year-milestone living and working in Washington DC and three-year-marathon doing advertising analytics for clients ranging from Fortune 500 enterprises to startup companies. What’s fascinating about analytics is two-fold: the data itself and the problems my clients look for me to solve. In short, having machine learning or regression analysis skills isn’t going to be enough (let alone all the new techniques using R, Python, and Hadoop clusters for data-mining). In my opinion, analysts should have business acumen and keep looking out for analytics applications.
This year I worked across several industries including government, retail, health/lifestyle, and finance. Below are some personal analytics best practices that you can take away:
- Be mindful of the fundamentals during tag implementation:
Most of my work involves defining key metrics (also known as KPIs) and crafting technical implementation guidelines for tracking purposes. Some clients want their site to serve as an acquisition mechanism, and some want their eCommerce site to properly record the entire journey visitors take. Different goals yield different tracking focuses. For example, one recommendation I gave my eCommerce clients was to use friendly page name (not the URL) in order to track every step visitors take on the site. For others, understanding the integration of back-end content management systems and different ways to track online behaviors are the keys to proper implementation. Most important of all are the basics – always define the objective of your digital ecosystem. A few months ago, I shared some best practices in analytics implementation. You can find the slides here
- Dive into the age of mobile analytics:
Advertising spending has gradually shifted to the mobile space for the past few years, and most advertisers now understand the importance of monitoring mobile users using different methodologies. However, there is still room for growth in terms of the reporting accuracy in mobile analytics, because users on different devices behave differently. The other change I noticed is that more companies are now developing “embedded mobile web”. Instead of building native mobile apps for different operating systems, developers now build a frame and pull content from mobile websites. (Mobile web: you open up a browser and find the info you’re looking for via search, previous bookmark, or type in the URL; Mobile app: you download the specific app from the iTune store or Google Play.) Analysts now have to find ways separating app traffic from mobile web traffic, which in many ways, are limited due to different toolkit and an universal methodology identifying a user between an app store (native app) and cookies (mobile web).
- Connect the dots between offline and online behavior:
Have you ever heard someone complaining about analytics lacking actionable insights or any “aha moments”? The reason is because many companies still have silos between their online (website, banner ads, mobile, etc.) and offline (call center or retail) business. With the majority of online shoppers converting to offline, companies fail to establish a way to connect the dots will end up wasting their analytics resources. For example, if your retail stores have displays showcasing promotional products online, you need to track customers’ interactions with actual in-store revenue (to understand how their interactions influence their purchases). Both Facebook and Google Analytics are moving towards this goal, how can your agencies or clients afford not to?
What are some of your biggest takeaways in 2014?