Data Enablement and the Dirty Secret Behind Automation

Data is having a bit of a moment. Nearly ten years ago, Harvard Business Review predicted that “data scientist” would be the sexiest job of the 21st century. While data science and analytics are rooted in tech, the expertise that helps organizations mine data to uncover marketing gold has emerged as mission-critical across various industries—from hospitality to retail, entertainment to finance, and healthcare to manufacturing. CMOs tout their data-driven approach to marketing and customer engagement.

But what does it mean to be data-driven today? How can organizations leverage existing CRM systems to drive more personalized engagement? How much data enablement is automated?

The past five years have seen an explosion of data growth as businesses invest in centralized data warehouses, grow their customer base, and collect and aggregate data from across the company. Whether you’re following a customer’s web or mobile activity, monitoring offline experiences in stores and at events, tracking delivery and customer support, or managing inventory, today’s technology enables a wealth of data collection to be analyzed and translated into actionable insights.

Helping to drive this data revolution are multibillion-dollar businesses like Snowflake, Databricks, and Confluent. One can also observe the rise of data via the explosion of the data-related job market: the Bureau of Labor Statistics considers data science one of the 20 fastest-growing fields with 31 percent job growth over the next 10 years. Midmarket and enterprise brands can now bring the data, infrastructure, and specialists together for a dynamic view of business performance.

Data technology has traditional marketing systems, and savvy marketers understand that data is the basis for future-focused marketing strategies. Though everyone claims to be “data-driven” these days, there’s a difference between being merely “informed by data” versus being truly data-driven.

“Data-Driven” Is More Than Just a Buzzword

In the context of CRM, we’re focused on an end state that facilitates a deep understanding of the customer and highly personalized messaging across the entire life cycle.

Some examples of core capabilities include:

1. Segmentation: Identifying where your customers are in their journey and how to characterize them.
2. Personalization: How to communicate in a way that resonates with your customer.
3. Automation: Finding customers by life cycle stage and communicating with them in a way that educates and incentivizes them to engage more with your brand and product.

These core capabilities start with data.

Segmentation relies on broad access to shopping history and behaviors. Personalization depends on a complete customer profile, including interactions with your brand. Automation might be the most challenging because it requires integration with real-time behavioral event streams to understand and respond to customer actions.

Fundamentally, data-driven marketing relies on using insights to drive business outcomes. As CRM experts, we’re tasked with enabling core capabilities based on data analysis and insights to serve customers better and boost performance. But what does “enabling” really mean? 

The Secret Is Out: Most Marketing Automation Solutions Don’t Actually Automate

It’s true. Though the C-suite may regard data scientists as rock stars, data enablement is about enabling the marketer, and that piece still depends on IT, data, and integrators to make the magic happen. Ironically, the tools that drive marketing automation ultimately limit workflows and prevent organizations from being truly data-driven.

To make automation possible, we must rethink data ownership and associated workflows. The most effective approach should focus on three areas: people, processes, and systems.

Just as tracking customer behavior across platforms should integrate seamlessly, data and IT teams should support creating internal workflows for marketing without blocking daily operations.

For instance, segmentation tooling is only as good as the data behind it, and if the right data isn’t available, you’re not data-enabled.

The same goes for manual processes that enrich a campaign with personalization or that feed into reporting on and optimizing campaign experiments. These workflows, done manually, represent marketing’s biggest bottleneck. Each of these workflows needs to be a seamless part of your marketers’ day-to-day.

Data enablement also requires organizations to rethink their process. CRM should be able to effect segmentation, personalization, and automation without having to work cross-functionally. Efficiency demands that core processes run independently and in real time. If processes are slow, people will naturally avoid them, resulting in CRM activities that are, at best, merely data-informed.

Suppose you’re only getting a snapshot of your customer by relying on the narrow readily available fields instead of looking at the complete customer profile. In that case, you’re not getting the best insights to drive optimal business outcomes.

Bringing all of the data together requires reliable data systems. Snowflake is widely regarded as an industry leader for cloud-based data warehousing, and its strengths are well-understood. A critical but poorly understood challenge with data and CRM is one of unlocking this core data. Even if your CRM team knows Structured Query Language (SQL), it’s still a long way away from operationalizing data in your enterprise data warehouse or cloud data environment.

For example, your team likely has access to your model of a user or contact lists. Now, what if you want to derive behavioral aggregates from this data for segmentation. Maybe you want to target users by how often they engaged with a specific feature of your app last week. Your team may not know how behavior events tracking is laid out or how to appropriately join it through your identity model into the user table.

Further, let’s say you need both the aggregated behavioral properties and specific raw events contributing to the behavior. Maybe those interactions contain item SKUs you need to further join in content from a product catalog to produce the ultimate marketing message. Now you’re faced with integrating the same data in at least two distinct ways, but you need to keep these consistent, updated, and available for your single downstream marketing campaign.

From a systems perspective, the goal should be to support self-reliance and to enable CRM to access new fields and new data in the right context and format.

With all of the strides made within data science, it is still contributing to a larger problem when it comes to successful data-driven marketing and CRM integration. Organizations should view their data science initiatives in a way that promotes enablement and is brought to the forefront by business users. As we continue to see innovation in data science, we need to master the basics of people, processes, and systems before discovering the next hot trend of the future.

Jason Davis is cofounder and CEO and Co-Founder of Simon Data. A data scientist-turned-entrepreneur, he previously founded Adtuitive, a retail adtech platform that was acquired by Etsy in 2009. While at Etsy, he led several engineering teams including data science, analytics, and big data infrastructure. Davis holds a Ph.D. in machine learning from the University of Texas and spent time developing search algorithms at Google.