Customer Data Quality: How to Solve Your Top 3 Challenges
[Estimated read time: 6 minutes]
Robust voice of the customer (VOC) information, personalized service, and omni-channel engagement are among the most important factors in an exceptional customer experience strategy. But that’s not the only thing they have in common. They all rely heavily on accurate and complete data. In other words, customer data quality needs to be one of your top priorities.
Let’s take a look at why data hygiene is so critical and how to overcome the top three challenges to collecting and storing customer data.
The Importance of Customer Data
Just as high-quality, robust data is important to internal business operations like finance and HR, it’s crucial to your customer-facing functions as well. To meet today’s best practices for voice of the customer insights, personalization, and omni-channel experiences, you need to be collecting and storing data from every interaction.
Voice of the Customer (VOC)
Companies with VOC programs outperform all others in cross-sell and up-sell revenue, return on marketing investments, customer win-back rate, annual company revenue, first contact resolution, net promoter score (NPS), average response time to customer requests, and average cost per customer contact.
To hear the customer’s voice loud and clear, you need a complete picture derived from multiple data sources, including:
- Internal operational and quality metrics
- Customer complaints and questions
- Customer surveys
- Social listening
- Reviews and other unstructured data
- Employee input
Research has uncovered a strong correlation between highly personalized offers and increased revenue. To provide a truly personalized experience, you need to collect and record as much information as possible about every interaction across all channels. Almost three-fourths of consumers expect agents to know their contact information, product information, and customer service history from the moment their support interaction begins, and that’s just the tip of the iceberg.
Data like past purchases, cart contents, viewing history, loyalty program membership, demographics, GPS location, and even social media posts—when analyzed and presented as useful information—can let you provide a level of personalization that differentiates you from your competitors. It may sound a little Big Brother-ish, but customers are actually willing to share some pretty private details to ensure a personalized experience.
Over two-thirds of consumers expect that the information they give to an organization through one channel will be available in another channel. They don’t want to have to retell their stories. When agents have access to comprehensive interaction history, they can ensure a seamless, omni-channel experience.
For example, a customer sent you a Facebook message two weeks ago and called your contact center earlier today. In an omni-channel experience, when he now starts a live chat on your website, all of those previous interactions would be displayed so the agent could pick up the conversation where it left off.
The Importance of Quality Customer Data
But what good is all that data if you can’t trust it? A study by Experian found that 91% of companies suffer from common data errors, including incomplete or missing data, outdated information, and inaccurate data. And 77% of those companies believe it affects their bottom line, wasting an average of 12% of revenue.
Just as damaging is the hit your brand’s reputation will take. Customers will be frustrated when speaking with agents who have incorrect data and irritated by having to correct information they’re already provided. Or worse, they may think their personal information has been compromised and stop providing it, leaving you in the dark about who your customers are and how best to serve them.
Three Common Roadblocks to Customer Data Quality
The Aberdeen Group discovered that 85% of companies are not satisfied with their ability to use data to make informed decisions when managing customer conversations. Almost all companies face at least one of the following challenges in their efforts to collect and store accurate data.
1. Decentralized Collection and Storage
The average large organization has eight different databases, which likely doesn’t include other one-off spreadsheets or sources of data. In fact, Forrester analyst Brendan Witcher puts the number at up to 30! It shouldn’t be a surprise, then, that 66% of companies lack a coherent, centralized approach to data quality.
2. Integrating Structured and Unstructured Data
About 95% of all customer data is now in an unstructured, text-based format—usually from emails, social media, and free-form text survey questions. While structured data fits neatly into pre-defined data fields, unstructured feedback is more like human conversation, with grammatical errors, lack of punctuation, and so on. Both are important to track, especially for VOC programs, but the challenge lies in integrating the two types to provide a complete picture.
3. Lack of Automation
According to Experian, the main cause of data inaccuracy is human error introduced by manual entry—whether by employees or consumers. On top of that, 53% of companies perform manual data cleansing tasks, such as reviewing data in Excel or making manual corrections to exported data but not the original record.
Ensuring Customer Data Hygiene in Three Steps
Although those roadblocks are significant and widespread, they aren’t as difficult to overcome as they first appear.
1. Integrate Your Customer Engagement Solutions
Top-performing companies are 30% more likely than all others to focus on building a unified, 360-degree view of your consumers’ data across all enterprise systems (CRM, SRM, marketing automation, etc.). When you integrate your customer engagement solutions through a central CRM, you’ll end up collecting more complete data. And centralizing your storage in one database connected to that CRM will have a big impact on your data accuracy.
2. Use Text Analytics to Process Unstructured Data
Natural language processing (NLP) and sentiment analysis tools can help you pull meaningful insights out of unstructured feedback and turn it into quantitative data. Many companies use sentiment tracking to determine customer satisfaction levels from text-based communication, but combining that technology with NLP helps you understand the cause of those levels.
This process allows you to store your unstructured data in the same format as your structured data so you can look at it as a complete picture. Best-in-class companies are 45% more likely than all others to standardize customer data across the organization, regardless of its original format.
3. Automate Your Data Recording and Cleansing
If the main cause of data inaccuracy is human error, then a simple solution is to reduce manual entry and manual QA processes as much as possible. The most powerful way to do that is by connecting your various customer engagement solutions through a central CRM, as we discussed above, in order to automate data transfer across channels. Top performing firms are 92% more likely than all others to use an automated process to integrate feedback and sentiment data captured across multiple channels within their CRM.
Some manual entry will always be necessary, but you can still maintain accurate data by using an automated QA tool, which—unlike manual data cleansing—catches errors without introducing new ones.
How Astute Can Help
Astute’s smart consumer engagement software is integrated through our ePowerCenter CRM, allowing you to centralize the collection and storage of your customer data and reduce human error. And our patented approach to natural language processing delivers accurate interpretations regardless of slang, nuances, or other complexities of human speech. Request a demo to see our software suite in action.