Why Analytics?

Disclaimer: this was written with Google's 360 Suite of tools in mind, but a large portion of the concepts can be applied to nearly any analytics implementation.

At its core, Analytics – tracked and reported via Google Analytics, Google Tag Manager, SalesForce MC, Tealium, Ensighten, Adobe Analytics, whichever tool you decide to use – should be an ongoing effort at the core of any digital marketing organization. Involve as many parts of the business as possible: input from each group, including IT, marketing, and data science is important in defining and optimizing processes which make analytics a success, otherwise an organization-wide data-driven mindset cannot possibly thrive.

Being “data-driven” is more than knowing how data is captured, building tagging, monitoring, normalizing, and reporting – it’s about thinking critically about all the angles. Even the least technically-minded person can and should be taking part in asking questions, learning about tools, and becoming at least incrementally involved in the data-gathering process.

Analytics as a Mindset

As a mindset, Analytics means asking the difficult questions and challenging what some might refer to as “what we think we know”. If the data you see differs from what others expect or have seen, perhaps a landscape is changing and it’s an opportunity for you to tell that story. Most of the time, user behavior follows a trend (whether seasonal or cyclical), so keep that in mind when investigating and reporting anomalies. Spikes in data can be anything from bot traffic to legitimate business; therefore, part of an analytical mindset is not only questioning what you see, but validating what you say. Be skeptical, but do your due diligence and know your sources. Don't let your analysis slow you down. Unless you have a specific reason to believe data isn’t correct, there’s a very good chance it is.

Analytics needs to be thought upon throughout all parts of the marketing funnel, whether that means adding UTM parameters to an email campaign, tagging a new feature, or dynamically remarketing to users who have visited your website. (Did someone say "abandoned carts"?) Without analytics, it’s impossible to quantify the success of your efforts. When your stakeholders ask, “What’s the rate of return on new features we’re providing for our customers?” – Analytics helps answer those questions directly! Similar questions will be easy to answer, like, “Are customers using this new feature we spent months to build and deploy?” or “Did this outreach campaign drive relevant traffic and action to the correct parts of the website?” once we use insights to precede decisions.

In the end, it’s up to each part of a company to think about analytics and data collaboratively, since data drives results, and should in theory move the business forward. A great deal of this involves self-motivated learning through trainings, typically held by an internal analytics resource, where employees can ask questions, walk through ad-hoc reporting, and learn about new tracking additions. 

I'm sure some old adage goes, “Give a marketer a GA report, and they'll look at numbers for a day. Teach a marketer to use GA, and they'll discover deep insights for a lifetime.” See? This is why I'm an analytics geek and not a philosopher. I digress.

Analytics as a Process

As a process, Analytics means integrating data design and standardization into how features are built and how media campaigns are measured, while providing a consistent way of defining success across an entire company.

Upon building a new website feature or function, the first question asked should be, “How will we know if this is successful?” before adding it to your website. Has your Analytics team been consulted with to determine how to track new content via Google Tag Manager, or will the feature be captured by existing on-element tags? No feature should go live without basic tracking – make that your deal-breaker, and don't budge on it. 

Another important question to ask in tandem to the above is, “What is motivating us to add this component?” Is it a primal urge to produce features to meet a status quo, or a verified and quantifiable set of data points which identify your users' problem or need? If you aren’t asking and fully answering these questions, you aren’t doing yourself or your coworkers any favors. Don't waste your time or anyone else's – life is too short to not think about data!

As much as I enjoy giving those folks a hard time, these concepts do not apply only to code slingers. When an email is sent or a media campaign is sending traffic from off-site, are UTM parameters being added to links in a consistent manner that allows any Google Analytics user to compare and quantify over periods of time and different campaign types? If not, you’re missing the mark there, too. 

Standards are extremely important to consider when building analytics into existing processes. Without a defined method of measurement and consistent set of naming conventions, every user can (and will) report their own results uniquely, and roll-up reporting will be nearly impossible. (Trust me, speaking from experience: it's a nightmare.) A defined measurement method allows for apples-to-apples results across all pages, traffic channels, and users, while a consistent set of naming conventions makes concrete reporting (at any level of detail or management) a manageable task. For example, setting a specific standard for UTM medium parameters which can be used avoids issues with variations in capitalization, spacing, or naming where mediums like “cpc”, “ppc”, “paid search”, and “CPC” are the same in a marketer’s eyes, but look different to Google Analytics.

Another important item to consider is user access: who's viewing, modifying, and reporting your data? If you control this part of the system, it's okay to be a control freak. I understand and appreciate you for it. Control is necessary, and while it's fine to hand out unfettered analytics credentials like they're the latest iPhone and you're Steve Jobs circa October 2011, know your limits. Manage and delegate appropriately, because it'll come right back to you if it goes wrong. Train your users twice before giving them access to an analytics tool, and assure the tool is both: A. relatively intuitive to use in the first place, and B. set up to serve those users upon first login. I wouldn't give anyone except my devs access to Tealium, just like I wouldn't give marketing users access to an unfiltered Google Analytics account. Make sure shared assets are readily available, especially since some tools place limits on shared reports, segments, and goals. Sometimes, the best answer to a request for access is, "Why?" Would it take less time to build that requestor an evergreen Custom Report than it would to teach them the intricacies of Google Analytics and Data Studio? Alternatively, would a one-time ad-hoc report satisfy the request for at least another quarter? Again, know your limits, and your audience.

There's so much more to dig into around analytics process and best practices, which I'll cover in a later post, but for now, Google is your friend. The ramblings – er, musings – of LunaMetrics and Simo Ahava are great places to start. As some may recognize, I'm also trying to channel the legendary Avinash Kaushik in this post.

Final Thoughts

From the beginning of the ideation and development cycles to the end of a campaign where results are finally reported, analytics should be reflected on, always. The more you know about analytics and its context in your organization, the better questions you ask – and in turn, the more analytics-minded you become. In the long run, this can only help you become more proactive and effective in all you do – and your organization will thank you as it grows and prospers.