With the latest advancements in data capturing and data processing technology, we have the ability to track the digital world and optimize our marketing efforts at a deeper level than ever before. Tools like Adobe Marketing Cloud offer complete suites of capabilities, from digital tracking to analytics, audience management to digital testing, and so much more.
While Predictive Analytics is becoming the next big buzzword in the industry, with books like Predictive Analytics For Dummies topping the Amazon charts, actually implementing an effective digital analytics strategy can be challenging. First, the knowledge and expertise required to set up and use digital analytics programs is complicated. Secondly, the investment for the tools and required expertise can be high. Finally, many clients see unclear returns from their analytics programs.
Mike Le is the co-founder and COO of CB/I Digital, a New York-based digital agency that offers performance marketing including search engine optimization (SEO), digital advertising, and analytics as well as digital product services (web/mobile) for clients. He shares five common mistakes you should avoid if you are trying to use digital analytics effectively.
Mistake 1: Starting digital analytics without a clear goal
“The first challenge of digital analytics is knowing what metrics to track, and what value you want to get out of them,” Le says. “We see too many web businesses that don’t have even a basic conversion tracking setup, or can’t link their business results with the factors that drive those results. This problem happens when companies don’t set a specific goal for their analytics. When you do not know what questions to ask, you cannot know what you’ll get.”
At its foundation, the purpose of analytics is to understand and to optimize your marketing efforts.Every analytics program should answer specific business questions and concerns. Le notes: “If your goal is to maximize online sales, you’ll want to track order volume, cost-per-order, conversion rate and average order value. If you want to optimize your digital product, you’ll want to track how users are interacting with your product, usage frequency and the ‘churn rate’ of people leaving the site. When you know your goals, the path becomes clear.”
Mistake 2: Ignoring core metrics to chase noise
“When you have advanced analytics tools and strong computational power, it’s tempting to capture every single possible data point to ‘get a better understanding’ or ‘make the most of the tool’,” Le says. However, following too many metrics dilutes your focus on the core metrics that uncover the pressing needs of your business. “I’ve seen digital campaigns that fail to convert new users, yet the managers still set up advanced tracking programs to understand user behaviors in order to serve them better. When you cannot acquire new users, your targeting could be wrong, your messaging could be wrong or possibly there’s no market for your product! These problems are much bigger to solve than trying to understand your user engagement. As in this example, it can be a waste of time and resources to chase fancy data and deeper insights while the fundamental metrics are overlooked,” asserts Le. Make sure you stay focused on the most significant business metrics before going broader.
Mistake 3: Choosing overkill analytics tools
“When selecting analytics tools, many clients believe that more advanced and expensive tools give deeper insights and solve their problems better. Advanced analytics tools may offer more sophisticated analytic capabilities over fundamental tracking tools. But whether your business needs all those capabilities is a different story,” Le notes. The decision to select an analytics tool should be based on your goals and business needs, not by how advanced the tools are. “There’s no need to invest a lot of money on big analytics tools and a team of experts for an analytics program when some advanced features of free tools like Google Analytics could give you the answers you actually need,” Le warns.
Mistake 4: Creating beautiful reports with little business value
“Many times, I see reports that simply present a bunch of numbers exported from various tools, that state ‘insights’ that have little relevance to the business goal,” Le says. “This problem is so common in the analytics world because a lot of people are paid to create reports and end up producing reports just for the sake of reporting. They don’t think about why those reports should exist, what questions they actually answer and how those reports can add value to the business.” A useful report must answer a business concern. Any metrics that do not help answer business questions should be left out. “Making sense of data is difficult enough. Asking the right questions early helps avoid the pitfall of excess window-dressing masquerading as essential data,” he advises.
Mistake 5: Failing to detect tracking errors
Tracking errors can be devastating to a business, because they produce unreliable data and misleading analyses. Yet too many companies do not have the skills to set up tracking properly, or to detect tracking issues when they occur. Le explains: “There are many things that can go wrong, such as a developer mistakenly removing the tracking pixels, transferring incorrect values, the tracking code firing unstably or multiple times, wrong tracking rules logic, etc. The difference can be so subtle that the reports look normal, or are only wrong in certain scenarios.” Tracking errors can often go undetected because it takes a mix of marketing and tech skills to ferret them out. “Marketing teams usually don’t understand how tracking works, and development teams often don’t know what ‘correct answers’ mean,” Le explains. “To tackle this problem, you should frequently check your data accuracy and look for unusual signs in reports. Analysts should take an extra step to learn the technical aspects of tracking, so they can better sense problems, and raise smart questions for the technical team when the data looks suspicious or incomplete.”
In today’s multi-channel world, effective digital analytics strategy is a “must-do” if you are going to fully understand your customers. However, a lot of brands and clients are still trying to figure out how to get business value from expensive analytics programs. Learning to avoid common analytics mistakes will help you save resources and be able to focus on core metrics that will drive your business forward.