Editorial
Think you know how much customer data issues are costing you? You’re probably underestimating — by a lot.
Customer data is important. Most marketers can agree on that.
Rich customer data can improve our results, but we don’t know how to connect all of our data. And we don’t know how to improve our data quality so it can work for us, not against us.
Bringing Customer Data Together From Siloed Applications
The number of technologies now available to marketers are overwhelming. We've all seen the whopping 3874 technologies Scott Brinker, author of the Chief Marketing Technologist Blog, documented in his Marketing Technology Landscape Supergraphic 2016.
And with a proliferation of technologies comes a proliferation of data. We have A LOT more data to deal with.
While more data is good in theory, siloed data doesn’t deliver a lot of value. Neither does poor quality data.
The number one data challenge marketers face is that customer data is fragmented across:
- marketing applications (50 percent of companies are using 21 or more marketing technologies),
- sales, order management, billing, and CRM or call center or support applications,
- social media channels,
- legacy systems and
- third party data providers
And bringing data together — unifying those disparate sources of customer data and ensuring it is of high quality — is daunting. But until we do, we can’t use the data to improve our marketing results.
Bringing data together from disparate sources was named biggest marketing data challenge, closely followed by the quality of their data
How Customer Data Issues Impact the Lead-to-Revenue Process
Donlon was a featured speaker at a recent Chief Marketer webinar that I moderated. In the “obvious” category are easily detectable issues such as poor customer segmentation or a lack of market sizing data — things Donlon referred to as “everyday headaches.”
“The vast majority of issues are either hidden or suffer in silence,” said Donlon. “These are things that are either hiding under the surface, such as reporting gaps due to incomplete records, or they are things we aren’t even trying to do because we know we don’t have the data.”
Most customer data issues are concealed or suffered in silence as opportunity costs, but impact the lead-to-revenue process
Learning Opportunities
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6
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The Building Blocks of Personalization
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Attracting—and Keeping—Your High-Net-Worth Investors
Join PwC to dissect their recent survey findings on a specific customer subset — high-net-worth investors.
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The Future of Contact Centers with AI at the Helm
Learn how to balance human intuition and interaction with the potential uses of AI to create delightful and lasting CX
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3 Steps to Unlock Your Customer Data and Drive Revenue
Discover how Twilio Segment’s #1 Customer Data Platform helps implement data-driven strategies.
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AI for Marketers: Optimize Personalization Across the Funnel
Understand the benefits of utilizing AI across your entire marketing stack
The High Cost of Poor-Quality Customer Data
Regardless of whether it’s detected or not, poor and missing customer data leads to substandard performance in five critical marketing processes:
- Market intelligence: Lack of insight to the size of the market, market penetration, etc.
- Personalization: Inability to give a dynamic, customized experience.
- Lead scoring and routing: Inability to optimize timing for sales contact.
- Outbound outreach: Lack of segmentation; inability to distinguish between leads and customers.
- Reporting and analytics: Ineffective, actionable results from analytics efforts.
By making an effort to improve the overall quality of customer and prospect data, organizations can significantly improve these processes.
“If you’re collecting — but not connecting — data from these systems, your insights and execution is only incrementally improved,” said Donlon. “But if you’re able to connect and unify the data from siloed systems, each bit of data you add can dramatically increase your ability to execute.”
Unifying the data from just three systems — Marketing Automation Platform (MAP), social listening and customer care — will give you immediate insights and the ability to execute in these areas:
| Insights | Execution |
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How to Connect and Improve the Quality of Customer Data
In short, by bringing customer data together and improving the quality of customer data you can make a significant impact on your marketing results. So how do you do it?
Customer data exists in three levels, explained Donlon:
- Core marketing technologies: For example, Marketing Automation Platform (MAP) or Sales Force Automation (SFA)
- Extended marketing technologies: For example, social media and content marketing
- Technologies from elsewhere in the business: For example, billing, customer care, or a product portal
Few people know that they don’t have to spend their valuable time fixing customer data issues using a time-consuming and manual process. Even fewer know there is another option besides outsourcing customer data clean-up to a third party, which is a temporary, tactical solution.
Donlon offered three different strategies that work:
- Lean on an existing operational system, often a marketing automation platform or sales force automation, and feed other clean data into it, making it the “single source of truth” for marketing. This strategy offers low incremental cost and low training requirements, but it lacks scalability.
- Create a data hub. Bring fragmented, incomplete, inconsistent data from your applications into the data hub, which is a central location where you can manage, improve the quality of, validate, and enrich your data on an ongoing basis in an automated way. The hub ensures your customer data is accurate, complete, de-duplicated and related to other valuable data. Then you can share that data with your marketing applications. You can also share it with systems and applications across your business. This takes a bit longer and requires some IT expertise, but it gives you all the features and functionality you need to gain a customer 360 view that’s clean and connected.
- Implement a data aggregation tool that proactively spiders source systems, pulls the data out and gives a virtual look at data across disparate sources.
Donlon made a point of noting that a data management platform (DMP) cannot fill the role of data aggregation on its own. “A DMP is a good tool for ad tech and ad purchases but it isn’t optimized for other sources of data,” said Donlon. “However, it’s a great source to feed into a data hub.”
The full on-demand webinar is available here.
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