Five Key Factors for Digital Analytics Success

Being successful with digital analytics requires more than effective tool selection. Other factors, such as forming a strategy, building a team and understanding the connection to other tools, come into play as well. Below are some excerpts from interviews with web analytics experts on launching a successful analytics program.

1. Executive-level support.

“It starts at the top. From the highest level of the organization, there should be a mindset that they want to use digital data to improve their business and their website. And they follow that mindset by putting their staff and their people behind it. Those companies tend to do better versus those where analytics is more of an afterthought.”
— Digital Analytics Consultant

2. Ask the right questions.

“Determining the right questions that you should be answering for your company is critical. Understanding those key questions leads to better choices in terms of which technology you'll use and better implementations.”
— Digital Analytics Consultant

3. Build the right analytics team.

“To staff your program, you often have a head of web analytics and a team underneath, split up by country or department or brand. It depends on how big the end-user base is, how big the company is, etc. Say a company has seven departments, and each department has a portion of the website that's really important to them, plus four or five mobile apps. Then you might need seven web analysts, plus one for mobile apps, one manager, and one person in charge of implementation, devoted to doing tagging, etc. That's 10. Then you’d build up from there.”
— Digital Analytics Consultant

4. Find the tool that fits your analytics maturity level.

Photo of Stephane Hamel - Director of Innovation, Cardinal Path“I've spent a lot of time looking into digital analytics maturity—a critical aspect in selecting the right tool. Too often we look only at the features of tool A versus tool B. We look at the technology side and say, ‘Here's the solution, the magic tool that will make things happen.’

If you look at why some organizations are successful at doing analytics, and others are struggling, there are clear drivers of success by the concept of the digital analytics maturity model. How will you deal with governance? What are your objectives? What's the scope? Do you have the right resources and skills? What's the process for analysis? And lastly, to what extent are you leveraging the tools and technologies?

Instead of looking solely at the feature set, think about your ability to use the tool. What's your team skill set? Are they more marketers, data scientists or IT-oriented? What's your process and methodology to handle analysis requests or communicate insight? Do you use Tableau to create advanced dashboards, or do you use the dashboard straight out of the tool?

If you are mature enough, something like Adobe often makes a lot of sense. At the same time, there are a lot of organizations that are just not there, and even if they purchase Adobe and put a lot of money in the tool, it won't work, because the culture is not there.”
Stephane Hamel — Director of Innovation, Cardinal Path

5. Other tools matter—web analytics is just one part of tracking the customer lifecycle.

Photo of Judah Phillips - CEO and Founder, SmartCurrent“It's important to understand the full customer lifecycle and take advantages of tools that can help collect and analyze digital data as customers move across phases in their lifecycle. Thus, digital analytics tools are just one piece of the puzzle. For example, other measures are also important to digital experiences, such as brand awareness, recall and favorability, and metrics related to the performance of acquisition marketing campaigns and ad reach and frequency. Testing and optimization tools are part of the puzzle as well, as is the overall analysis of cost, revenue and profit.

Analytics in its most powerful form means discovering insights that drive economic value. It helps to have a 360-degree view of who the customer is and what they do before, during and after a commerce or content experience. You want a dataset and tools that help analyze customers, their behavior, marketing channel performance and outcomes, and the impact of products and content. All this work needs to be understood and analyzed within the context of the customer lifecycle in order to drive conversion, retention, and loyalty. None of the digital analytics tools are doing all of this work, i.e. attempting to measure qualitative aspects in the pre-purchase lifecycle, or upstream into digital advertising or even lifetime value or ROI.

Tracking the full customer lifecycle still requires multiple tools. The marketer is left to cobble together tools and datasets from many different products. For example, you'll need qualitative feedback tools for gathering verbatims and market research data from users; then, a digital analytics tool for understanding behavior and conversion; BI and data warehousing for customer, merchandise, forecast and transaction data; technology for measuring retention, loyalty and lifetime value; A/B and multivariate testing tools; predictive analysis tools for things like personalization and targeting; and even tools for dashboarding and presenting stories and narratives about the data and analysis created from all your other tools and sources.”
Judah Phillips — CEO and Founder, SmartCurrent
Author of "Building a Digital Analytics Organization" and "Digital Analytics Primer"