The challenges of marketing analytics no longer revolve around a lack of data -- quite the opposite is true today. With so many different sets of data to sift through, how do you identify what's most important to your business and which tools will make that data ultra-usable? Because in the end...it's all about utility and actionable insights.
In a recent report by VentureBeat (VB) Insight entitled "The State of Marketing Analytics: Insights in the age of the customer," Analyst Jon Cifuentes meticulously outlines what today's leading organizations are doing to ensure data is both understood and applied aptly. From my experience, as someone who is interested in both the marketing stack (as it relates to PRTech) and how marketing decision makers are thinking about technology, I've culled through the report and highlighted the top 20 insights for your reading pleasure.
If you keep these in mind as you build out your marketing organization, I'm pretty sure you will gain a competitive advantage over those who are still getting "stuck" in information overload. The key is to embrace data, while at the same time knowing the difference between what's important and what isn't...
1. The obstacle of fragmented data.
One of the biggest challenges with marketing analytics today is that of centralizing and integrating fragmented data. We need to be better at weaving the right pieces together, aka, building the "connective tissue" between disparate data points. There's also a lack of ability to prove which data is most important, and until recently, many of the available tools required advanced skills to understand and use.
2. Hard data science skills are increasingly needed for marketing roles.
Capturing customer data is more complex than ever given the increase in marketing channels and devices. There are tons of tools out there to help, but a lack of data literacy is making it difficult for marketers, from CMOs to media buyers, to navigate this highly competitive and complex marketplace. If you don't get data, it's difficult to understand what you're buying.
3. Advanced measurement is currently the exception, not the rule.
Duke University Professor Dan Ariely comments: "Big data is like teenage sex: everybody talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it..." Some companies are indeed using advanced measurement methodologies but this really is the exception, not the norm.
4. Take a self-assessment of your analytical capabilities.
A whopping 55% of the marketers surveyed (in the report) undergo some kind of formal evaluation for their analytics quality. Yet that doesn't speak to how effective their analyses end up being. Take a mental inventory of your strengths and note where you need to grow. Do you feel equipped to leverage cross-channel intelligence or has your approach been segmented thus far? How many data sources are you using? Ask yourself some of these tough questions to set the stage for analytics-focused betterment.
5. Give yourself a metrics refresher.
Refamiliarize with the following three types of metrics before beginning to identify which are most important to you.
Performance metrics: Channel and campaign volume, visitors, sources, response/conversion rates, etc.
Customer Experience metrics: Individualized, behavioral customer metrics, path analysis, lifetime value, churn, retention, etc.
Advanced metrics: Optimization and attribution.
Ask yourself: Do I have a firm grasp on any of these? Which ones are most important to my function?
6. Assess your current organizational state.
Do you have a data analyst on your team or are most folks who contribute to your marketing analytics considered marketing generalists? Take an organizational inventory so you can set some clear expectations around what you can achieve. If you don't have the right people in place, which roles would be ideal to have?
7. Identify or establish a process for testing out new marketing analytics tools.
A well-defined process for testing new marketing technology will help you explore more vendor options quickly without getting derailed. Who typically manages this? Would it help to designate a lead? That lead will need to have a clear idea of what success looks like, an invaluable skill when vetting potential vendors
8. Establish technology requirements.
Once you have a solid understanding of what's out there, and have identified who should weigh in, you can begin to build upon the tools you have at your disposal. Explore what's working with tools like Segment that help to collect your analytics data all in one place.
9. Today's customers expect relevancy.
Customers have come to accept that they'll be marketed to, but in return they expect to be dished content and ads that are highly relevant to them. It needs to happen at the right time, be personalized, and ultimately speak to their interests.
10. Learn what true customer intelligence looks like.
Those who have a solid understanding of customer intelligence are:
Customer-oriented: As your customers flow through a myriad of interactions with your brand, you have the analytical firepower to understand those interactions individually, in aggregate, with visibility across all channels, platforms, and devices.
Channel and data-agnostic: You have a data ecosystem to collect, store, and distribute any range of data types that may impact marketing.
Have analytics down to a science: You can support rapid segmentation without the use of 3rd-party tools, can use data science to create timely insights. You can build models that don't rely on human interaction to generate insight.
Know these ideal scenarios so you can inch closer to them.
11. Avoid volume metrics (and other vanity metrics) at all costs.
Don't be fooled--these are not indicators of success. Sure, they can point you in the right direction, but they can often be more deceiving than they are helpful. Remember that volume and vanity metrics often have no bearing on the bottom line. Instead focus on customer relationship-based metrics.
12. Collect more data than you think you need.
More than one data scientist on your team? Have a hefty data management platform? Great. Take advantage of it. Collect more than you think you'll need as to not be limited by the questions you say you'll query later.
13. Invest in data visualization tools.
Get yourself in the headspace to consistently share business-level metrics with fellow colleagues by investing in a data visualizations tool such as Tableau. When data is highly digestible, it's used more.
14. There's no one-size-fits-all solutions.
There isn't one key channel to focus on nor is there one "right" kind of metric that translates to success. Be realistic about the need to customize your analytics, start with a historical analysis of your customer's cross-channel journey, and begin creating a custom digital marketing analytics roadmap to meet your goals.
15. Cultivate data science leadership.
Create a feedback loop with everyone on your team who touches data. From social media marketers to your UX team, all parties should be testing, learning, and sharing. Cultivate a 'test and learn' mindset.
16. Strongly consider outsourcing analytics to a niched provider.
It's often more effective to solve a specific analytics problem for a wide range of companies than to solve a wide range of problems for a single company. Companies that specialize in analytics as a service can hone in on a single analytics problem and solve it in a robust way. As these service providers acquire a wide range of experience working with many different types of companies, they can gain greater visibility into what is likely to work and not work for a company-something that can take years of experimenting to figure out if you are working in a silo.
17. Channel explosion = vendor saturation.
New channels mean new data opportunities, (and the growing need for real-time data). Consequently, there are more vendors than ever. The report alone mentions 800 analytics and tool vendors! Surprise, surprise: Analytics spending has increased greatly as well.
18. The mobile app ecosystem is the fastest-paced and growing marketplace in history.
To keep up with the rapid pace of the mobile app marketplace, you'll need a top-notch analytics platform for understanding what's happening across your apps and how that influences behavior via other channels.
19. Another common analytics challenge: marketing attribution.
Identifying a set of actions that contribute to a desired outcome, and assigning specific values to these events, is known as marketing attribution. It helps marketers and CMOs understand the steps leading to conversion. Not only is it difficult to do in a repeatable way, but the practice of "last click attribution," the last trackable engagement your customer makes before converting, is an all-too-easy alternative to doing it right. Don't get caught cutting this marketing corner.
20. True "marketing analytics" accounts for rationalizing users, platforms, and devices.
Easier said than done, we know. Although these three cornerstones present data collection challenges, there's an army of tools and teams out there ready to help you achieve your goals.
Now that you're keen on the state of marketing analytics landscape, take a close look at your own methods for data collection and analysis and don't be afraid to admit areas for growth. As Confucius once said, "Humility is the solid foundation of all virtues," (and the key to optimizing each stage of the customer life cycle), right?
VB Insight's report is comprised of a survey of more than 1,000 marketing analytics professionals across 10 key marketing analytics data use cases, plus a review of hundreds of vendor-driven best practices and interviews. Get the full report here.