Demystifying Analytics: Understanding the 3 Sides of Enterprise Analytics
Web analytics. Big Data. Data Science.
I am sure at some point over the last year you have heard, read, or even used one of those buzzwords. But I’ve found there’s a common problem enterprise organizations run into when it comes to buzzwords: people love to use the terms without trying to understand what they mean or how they are being used within their organization.
How are your departments using these terms—and are they all using them the same way?
Analytics means many different things to different companies; and that picture becomes even more muddied when you’re talking about enterprise analytics, where analytics can even mean different things to different departments. However, a better understanding of how analytics is commonly used by different teams within your organization can help cut through some of the confusion and narrow down the specific key performance indicators that are important to each part of your business.
I take enterprise analytics and break it into 3 different areas:
1. Web Analytics
2. Marketing Analytics
3. Business Analytics
Less is more: The 3 sides of enterprise analytics and where to find the data
Before going deeper into each area of enterprise analytics, I want to make it clear that these are not three different sources of data. All the data needed for each of these areas of analytics can easily be drawn from a single analytics platform like Google Analytics or Adobe Analytics.
So if you are using a CRM, a marketing automation tool, a website change ticket system, or are gathering information from other tools just to get a complete picture of your enterprise’s analytics, then you may be pulling data from too many different data sources and it probably makes sense to consider simplifying your systems.
1. Web analytics
Web Analytics has more to do with the health of your site than anything else. This is primarily the analytics your IT department or the person who handles the changes on your company website will need. The top KPIs would be around page load times, browser issues, changes between devices, indexing of 404 or conversions pages, etc.
Once broken out properly, web analytics can do an amazing job of helping to answer questions like:
- What browsers are breaking site architecture on mobile/tablets?
- Which pages need to be optimized due to really slow load times?
- How do we prioritize all of the requests coming in?
2. Marketing analytics
Marketing Analytics, as the name allows, is focused on marketing performance. The focus of these analytics are therefore specific to the marketing initiatives your organization runs; for example this includes email marketing performance, link tracking, AdWords performance, conversion optimization, landing page metrics, and content effectiveness. Marketing departments are constantly trying to use analytics to convey how what they do, which is often viewed as a cost center, is actually contributing to generating revenue.
Breaking out specific reports and dashboards around marketing analytics help with this, while answering questions like:
- Which campaign converted the highest?
- Are we spending too much or too little on advertising?
- Which channel should we be investing more money into?
3. Business analytics
Business analytics might include a few of the same components as the other two, but everything that goes into this group is ultimately mapped back to revenue or net income. This is true for all enterprise organizations, not just ecommerce sites; business analytics picks up where marketing leaves off—it can actually showcase the dollar value of conversions. This set of analytics is great for tying marketing analytics to dollar amounts and showing the business team the true effect marketing had on sales.
Business units are the biggest culprits when its comes to attempting to track too many metrics. Providing them with a report or dashboard specific to key business questions will help keep them focused.
Breaking out the right metrics here will help answer questions like:
- Which campaign generated the most revenue?
- What is our acquisition cost from Advertising?
- Did we have a loss in revenue due to specific browser issues?
- Where are our performance issues across devices
The goal is to split up your analytics into the metrics and actions that are most relevant and important to each audience. No one ever cares about bounce rate, time on site, or page views, unless they understand how it impacts other areas of their job. Analytics should provide a source of actionable insight for its immediate audience; if the audience doesn’t understand something or why it’s being presented to them, they could be the wrong audience for that specific metric.
How to talk about enterprise analytics (without getting tuned out)
Making sure the right departments get the right metrics and data will take some work. It often makes sense to first think about the main questions each department will be asking, then to use those questions to determine what metrics can help provide the answers.
Specifically, there are three main rules when addressing each department about the specific analytics information they’ll need:
- Make sure the analytics meet a strategic business need for that department.
- Explain the analytics in a way that each department can understand (technical and non-technical).
- Demonstrate ROI (both around their main initiatives & the ROI analytics have provided). This provides stronger results than tribal knowledge and it’s worth the investment in dollars to build analysis.
Figuring all of this out often requires sit-down meetings with the relevant parties from each department. But how can you make sure you’re all talking the same language? Tables are often a great way to showcase raw data, but in these meetings they’re often not perceived as time well spent.
Instead, here are some things to remember when communicating your Analysis:
Walk a mile in their shoes. Try to understand where they are coming from and what they are being asked to do by their superiors; this can often help you anticipate what questions they will have and can help you explain why the data you’re presenting will help make them look good—which will help get and keep them engaged.
Keep it super simple. Run your presentation past a person not in that specific department to make sure all of your explanations are simple enough that everyone will be able to understand them. Pretend your audience is 10 years old and use analogies to ensure your key points will make sense to your audience. You should also aim to keep your explanation of your methodology to two minutes or less. If they have questions, they’ll ask—and you should be prepared with answers. But most of the time, more than two minutes is just too much information for a non-technical audience.
Create “proof points” for your techniques. Be prepared with examples of other executives, departments, or companies that are using the same approach that you’re suggesting; this helps build trust. You can also get buy-in from other team members within your organization who are well respected.
Watch the assumptions… Try to keep your assumptions to a minimum. Choose only 2-3 that might be highly suspect, and only use them if you must.
Need help with data and analytics? Let’s talk.
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