At a high level, customer analytics is an invaluable resource that (surprise) makes analyzing customer interactions and behaviors to discern trends and patterns easy for marketing.
At a microscopic level, though, the implementation of customer analytics is a process that is much more nuanced for marketing teams — and one that requires the right martech.
But many large-scale companies — including and especially leading enterprise organizations — have substantial amounts of big data stored in their marketing technology ecosystems.
Their best bet for combing through and comprehending customer analytics isn’t with an array of tools. Rather, it’s with a single source of truth that unifies all first-party data.
What’s more, this analysis is ideally carried out with the optimal mix of people and processes in place. After all, customer data analysis can’t be completed if no one within the business knows how to unearth actionable insights and intel related to their diverse audiences.
All this is to say you can’t predict future customer behavior, calculate customer lifetime value, or make important business decisions as it pertains to your marketing without a mature customer analytics setup — and a central database that syncs all data across sources.
The evolution of customer analytics (and its increasing importance for marketing)
Once upon a time, marketers didn’t have the ability to easily track or act on customer data related to online engagement, buying habits, or general behaviors in real time.
Thankfully, the times have changed. And marketing’s all the better for it.
The martech landscape has evolved to the point that day-to-day marketers can now do things like build and deploy predictive analytics models to forecast customers’ propensity to churn and conduct quick customer segmentation analyses to compare groups of contacts.
All without the help of additional BI professionals, data scientists, or analysts.
And this increased access to innovative analytics tools has paid off in spades:
- When it comes to B2C brands, use of customer analytics to inform marketing decision-making rose 21% from 2018 to 2019, per the CMO Council — a clear indication consumer-focused companies continue to see growing ROI from data-driven technologies and techniques.
- On the B2B side of things, researchers at the University of Eastern Finland Business School found its study of 400-plus B2B companies “empirically confirms that customer big data analytics improves customer relationship performance and sales growth in B2B firms” and helps in general with marketing-sales alignment.
Of course, not all martech offers the advanced capabilities organizations and their marketing teams now need to better understand their audiences: from their buying habits and lifestyle preferences to their distinct product interests and overall customer loyalty.
That means CMOs need to outline their data challenges and pain points, research the customer analytics software marketplace, find potential solutions that fit their needs.
(In other words, the same way they would go about refining their martech stacks.)
Fortunately, many marketing executives today grasp the profound impact consented, first-party customer data has on their cross-channel lifecycle orchestration efforts.
When considering which tech can enable deeper and richer audience analyses, though, these marketing leaders would be wise to consider platforms with predictive analytics.
The role of predictive analytics models in brands’ data-driven marketing programs
As Forrester Senior Analyst Tina Moffett stated, artificial intelligence is now a critical marketing resource — and one that can plays a big role in modern customer analysis:
“Customer analytics approaches, like customer segmentation and churn analytics, combined with marketing insights, like ROI performance, will help CMOs target the right customers, across the right channels.”
And, according to Moffett, “CMOs must embrace AI to achieve” success with analytics.
One element of AI that can streamline marketing teams’ customer analytics efforts? Machine learning technology that helps them develop and deploy predictive behavioral models.
For instance, some marketing technologies, like certain customer data platforms (CDPs), offer out-of-the-box machine learning models everyday marketers can deploy on their own.
(Translation: without a data scientist or IT professional standing over their shoulder.)
For instance, that means retail marketing teams can easily utilize ecommerce analytics data to deploy pre-set predictive models and gauge when an individual shopper (or segment of shoppers) is likely to churn in the coming weeks or months or make another online purchase.
This predictive information can then help these marketers know how to market to those buyers next: retargeting on social media, personalized offers to join loyalty programs, and other custom-tailored messaging based on their customer analytics data.
The pros of predictive analytics are apparent. According to a recent Gartner survey, though, just 29% of day-to-day marketers actively took part in data analytics modeling in 2019.
The main reason? The lack of strong customer data literacy among marketers.
However, the same poll found 80% of on-the-ground marketers planned to leverage customer analytics insights in their data-driven marketing by the end of 2020.
While improving their data literacy and analytics skill sets are critical tasks for marketers, upgrading their martech stacks to incorporate a more intuitive and advanced database setup is equally vital to enhancing their customer analytics maturity level.
More specifically, they must figure out how they can un-silo their first-party data and organize it in a manner that facilitates real-time data liberation across marketing channels.
The customer analytics software that can improve your organization’s data maturity
The downsides of data silos have long been evident to enterprise businesses.
Marketing, in particular, has long known the pain of dealing with disparate data sets across their organizations and having sub-360-degree views of their prospects and customers.
As Entrepreneur contributor and market expert April Rassa astutely noted:
“Data silos have become the scourge of the 21st century. Besides the costs you’ll have to pay — because eventually you’ll have to undo this problem — separating data into various databases and programs rather than fully integrating it significantly hinders efficiency and productivity.”
Thankfully, bridging data from different business systems and sources and unifying it into a centralized, readily accessible solution — one entirely owned and operated by the marketing department — is now easier than ever with a customer data platform (CDP).
Research from the University of Wollongong in Australia found two key technological barriers companies must address to ensure their marketing (and other teams) can easily access all data for analysis and, eventually, their marketing activation use cases:
- 1) “[E]nsure the connectivity among the different customer-centric data, which consequently helps to build more critical management of customer relationship”
- 2) “[C]ompatibility that helps to synchronize overlapping data and to fix missing information for real-time decision-making”
Without saying as much, this advice from the UoW researchers ultimately points organizational decision-makers (e.g., those charged with purchasing technology) to the CDP.
That’s because the CDP normalizes first-party data in a centralized location, creates persistent, dynamically updated customer profiles, and, ultimately, enables marketing to analyze and predict prospects’ and customers’ behaviors and actions.
In BlueConic, for instance, our customers use AI Workbench to perform thorough analyses of their customer base: from high-value subscribers to buyers with low lifetime value scores.
Moreover, customers who use our customer data platform frequently leverage the segment comparison feature to effortlessly examine the attributes, behaviors, and engagement levels for different buyer buckets in a simplified, dynamic chart.
From this regular analysis, marketing teams who rely on our CDP can both improve their customer analytics savvy in the long term and make the most from their daily data assessments by using their findings to inform near-term lifecycle orchestration activities.
No marketer becomes a customer analytics expert overnight. But with the right processes and technology in position to support your routine audience analysis efforts, you’ll be well on your way to better interpreting — and liberating — your customer data.
Check out our on-demand webinar to find out how you and your team can leverage AI in your marketing strategy, including predictive customer analytics.