If most digital marketing programs or campaigns have a weak area, it’s analytics. One recent study identified that the biggest talent and hiring gap in online marketing is in the analytics space. 37% of companies surveyed said that they desperately needed staff with serious data chops.
If you’re in the field of online marketing or content marketing and want to ensure that you’re bringing the best data to bear on your projects, here’s a quick look at some strategic approaches that can help you improve your performance in 2014. This applies whether it’s upgrading your own skills, adding strategically to your freelance stable, or improving your content planning skills.
The Case for Data
HBR declared that data scientist is the sexiest job of the century. Research company Gartner suggests that there will be 4.4 million big data jobs available in the next two years, and that only a third of them will be successfully filled. It’s no surprise. Everything is moving toward data: big data, mobile data, performance data, campaign data, product data, and even data about how we track our data.
This has two implications. The first is that any professional in the internet world from SEO to content marketer can give themselves an edge by strengthening their data capabilities. From ensuring the success of your campaigns to making yourself un-fireable, data skills will be a huge asset. The second implication is for brands and agencies: the ability to manage successful campaigns will require strong data analysis human and technological capabilities. As your competition becomes more sophisticated, your ability to keep up will be directly helped or hindered by your data capabilities.
For some quick insights into this topic, I recommend the following articles:
- 5 Insights Social Data Can Reveal for Your Business
- How Business Intelligence and Online Marketing (Should) Intertwine
- The Definitive Guide to Google Analytics for SEO Professionals
So where is a company or professional supposed to start if 2014 is the year to strengthen your capacity in this area?
A Breakdown of the Analytics World
Your company needs to evaluate your internet marketing performance on multiple dimensions to get the full picture. Creating and implementing an analytics program requires four steps:
- Defining your metrics and developing a plan
- Collecting the data
- Developing reporting features and capabilities
- Ongoing analysis and implementation
Understanding each of these core components enables a company to make the right investments at the right time to yield an ROI. Successfully building a data plan is more than just identifying specific tools or learning how to interpret charts. Instead, it’s about creating a culture that values data, ensuring that key business decisions are data-driven, and consistently finding ways to drive data deeper into the DNA of your business.
Democratize Your Data Analysis
Your company should strive for an environment of democratized data analysis. Successful data and analytics management is as much about creating the right culture as having the right technology or people. Corporate data culture is a spectrum that can often be classified as follows:
- No or limited data: This company is moving fast and hasn’t made time for data. Or perhaps the value of data isn’t understood, or resources are limited and the focus is elsewhere (usually on key performance indicators related to growth).
- Basic data: An organization at this stage may have a basic data program installed that offers feedback on some aspect of their digital marketing. For example, they may have Google Analytics on their website and track high-level trends, but it’s only used for directional indication rather than specific feedback loops.
- Deeper data that’s siloed or controlled: At this level, parts of the organization have access to deeper data collection and reporting tools. Access to the data may be limited to executives and reporting staff, or filtered through a reporting department. Data may be deemed relevant only at certain times of the fiscal year or sales cycle, or only for certain positions.
- Democratic data access: This company’s data efforts are led by a data expert setting the vision, and analysis and reporting is done regularly by qualified staff. By decision makers and stake holders throughout the organization are empowered as much as possible to access data that they need and want throughout the year.
Democratized data does several things. It eliminates data bottlenecks from limited staff that are overwhelmed with requests for reporting support. It helps ensure that data is being used regularly to make decisions affecting your business. This approach also helps sway skeptics to the value of data and helps create a culture where data-drive decisions are the norm.
Moving toward democratizing your data requires a thoughtful analysis of your culture. How transparent are you with data points, ranging from sales numbers to client information? Are you regularly fielding requests for information that team members want for their own success? Could more information empower your team to make independent decisions that move your business forward? If so, now may be the time to consider opening up your data capabilities.
Prioritize Your Data Analysis Tasks
When you’re faced with planning, collecting, reporting, and analyzing data, it’s important to anticipate the right ratios. One helpful suggestion comes via Google’s data experts, who suggest that about 15 percent data capture, 20 percent data reporting, and 65 percent data analysis are reasonable ratios. Your breakdowns may not be the same, but the idea is that your strategy and execution should take the following points into account:
- Data collection and reporting are fundamental and must be accomplished effectively. But these steps in the process should also be efficient. Find ways to minimize the time being spent on these efforts, or delegate to junior staff. Automate as much as possible through using great tools.
- The majority of your focus should be on analyzing the data, articulating the implications for business and coaching your colleagues through the implementation. What the data means and what your team is supposed to do with it are the priorities. The tighter your analysis and the more you focus on performance indicators that tie to the bottom line, the more successful you’re going to be.
- Data for data’s sake isn’t a good investment. Data must drive actions that generate conversions and revenue. Tie your data collection and analysis efforts specifically to your bottom line.
Tracking the Data That Matters
Neil Patel, founder of CrazyEgg and KISSMetrics, has a very straightforward philosophy on data collection and analysis that I like a lot: Measure what matters. With metrics, it’s easy to get caught up in vanity metrics. Vanity metrics are the things that make you feel good and may even give you some idea of what’s happening, but they’re not really indicative of what’s happening to your business:
- Visits to your site
- Page views
- Number of newsletter subscribers
- Followers on social media
- Bounce rate
- Time spent on your site
Seeing growth in these numbers over time can be a useful trend, but for the most part, you’re instead looking for the kinds of metrics that show one thing: action.
Useful data tracking comes down to evaluating:
- Who is coming to your site, and what are those people doing once they get there;
- What channels are driving buying customers;
- Who is converting;
- What conversions are deepening relationships;
- What conversions are driving revenue;
- Who is buying multiple times;
- What’s your lifetime customer value;
- What are your churn rates
Any solid analytics plan will take your business model into account and develop a set of metrics that maps to your unique needs and buying funnel.
If your business is one of the many that’s struggling with a gap in analytical capacity, it’s time to have a frank internal conversation about which data gaps can dramatically improve your business. Start by evaluating your current data state, what metrics you should be tracking, and what cultural impact adding data to your process is likely to have. Then you’ll be ready to take a deeper dive into understanding different analytical resources and technologies available to you.
What are your 2014 data objectives?
Originally published on Feb 10, 2014 6:56 PM, updated Feb 10, 2016