Top 5 Tips: Transforming Marketing Data Into Customer Insights

Categories: Data Warehouse Enterprise Data Hub General

Many marketers face a common problem: we collect and store tons of data, but are we able to access and analyze all of that data to make smarter decisions?

I work on the marketing operations team at Cloudera, THE big data company, and we too are challenged with marketing data problems daily. It’s ironic, I know. On the marketing operations team we work closely with all of our data sources, and it’s our job to equip the marketing organization with breakthrough analytics, reliable reporting, and ultimately, meaningful insight to drive critical business decisions.

Having experienced this journey first hand, I know this process is easier said than done. That is why I want to share a few tips and tricks that I have learned along the way.

1. Get the right tools in place Before you begin to build your marketing tech stack you first need to understand what you are trying to accomplish. We’ve been building our arsenal of data tools over the past years around the need for visibility, forecasting, and predictability within our marketing department. Here are the core components that we have implemented:

  • Marketing automation platform
  • Social Listening and Analytics
  • Content Management System
  • Web Analytics
  • Webinar Management Platform
  • Marketing Revenue Attribution and Forecasting
  • Lead Scoring / Predictive Analytics
  • Account-Based Marketing, Targeting and Personalization, Data Enrichment
  • CRM platform
  • Sales Enablement Tools
  • Customer Advocacy Insight
  • Enterprise Data Hub (Apache Hadoop)
  • BI Visualization

I would recommend starting with the core platforms (CRM and Marketing Automation) and find an expert who understands data management, scalability, and API integrations to ensure your foundational system is future-proofed. Having a technical marketing operations professional will allow you to deliver richer customer analytics to your marketing team and executives without having to rewrite the system for every new use case.

2. Start Collecting Data It’s often helpful to think of your data in terms of volume and completeness. At every event registration page, gated resource, or paid media touch-point, we’re capturing a prospect’s main contact information, and allowing our back-end data enrichment tool to auto-fill their associated account information (company industry, revenue band, size, geo-location, and other criteria we may use for segmentation).

The trick is to implement fill-in-the-blanks automation so you can get a more complete picture of who’s interested and who you’re actually marketing to without making the form burdensome to the buyer. The more we know about our prospects and customers, the more accurate our audience segmentation will be.

Data enrichment tools help us build a richer customer 360 profile without fatiguing the prospect with too many form fields to fill at any given time. Progressive profiling is a tried and true solution for capturing customer data, and has allowed us to do so over a period of time, without negatively impacting user experience. If you are interested in enriching a profile with social, make sure you implement social authentication in order to get social IDs.

3. Unify Your Fragmented Systems Data fragmentation is a problem everyone has. At one point, we found ourselves piece milling data from our predictive analytics tool, external web data (whose consuming what content based on keywords), and account behavior from our account-based marketing tool. Effectively, this was an attempt to understand the complete customer journey.

With prospect behavior and buyer persona data housed in your marketing automation platform, and opportunity and customer data stored in your CRM, you may have the data, but the insights can be fragmented. And this is only the tip of the iceberg. If you recall our technology stack (see tip #1), any organization is subjected to ten or more additional data sources. Not to mention structured and unstructured data unique to the architecture of each system. Unifying fragmented data that marketers require is key in order to derive analytic value.

4. Take Control of Your Data  Here’s the reality: just because you’re collecting more data, doesn’t necessarily mean you’re understanding customers better. The truth is, if you can not properly manage this information it will remain useless.

As a big data company, we eat our own dog food. Our journey to building our internal enterprise data hub has provided us a centralized repository to properly manage our customer data. In order to effectively manage customer information you must have processes in place to normalize disparate data source, understand trusted dimensions (machine vs. human generated), and make sure the data is prepared in a way that is immediately accessible for marketers to ask the questions they require.

5. Start Small with Customer Analytics In order to provide your marketing team the analytics they require you first need to understand all of their needs. Having a conversation about their visibility and forecasting requests is a great way to start this journey.

The senior director of your demand generation team may be wondering about weekly inbound leads while a campaign manager may only care about individual webinars. It is important that you show quick wins to the key stakeholders in order to ensure the metrics you are providing helps them achieve their KPIs.

Conclusion: Every team in your organization will have pressing insight questions that need to be answered. You may not have all the answers now, but the journey in seeking them will ultimately help influence a data driven culture. If you are interested in learning more, I would recommend checking out our upcoming webinar with Tableau, “How Big Data Can Help Marketers Improve Customer Relationships”.

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