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What it Takes to Deliver Data-Driven Customer Service

6 minute read
John Horodyski avatar
For AI and machine learning to truly support and improve customer service, we must feed them good quality data.

“I’m sorry, Dave, I’m afraid I can’t do that.” – HAL 9000

Customer service is the support offered to customers both before and after they buy and use your products or services. It’s all about the experience customers have and maintaining your brand's promise to them, not just answering good questions with good answers. Customer service is so important because when you maintain that good experience and keep customers satisfied, the result is their loyalty to your products and services. 

Artificial intelligence (AI) and machine learning have moved from the realm of science fiction to become an applied practice and discipline in business. Machine learning is the ability for computer systems to “learn” with continually supplied data without an explicit programmer telling the computer what to do. AI is when a computer displays the apparent cognitive ability to think, learn and problem solve in real time. This is both exciting and concerning. It forces us to realize that data is the foundation upon which these types of computer intelligence can be built. We want the machines to learn and do more, but we must provide them with good, quality data in order for them to do that. Customer service is no exception.

What’s your data-driven customer service strategy? We want the machines to learn and do more, but we must provide them with good, quality data for them to do that. We need to get our metadata house in order to support this AI-based technology.

Good data = smart data = happy AI = good learning = happy customers

But if the data delivered does not match the user expectations, then the efficiencies of a personalized, and meaningful consumer experience are lost. What’s a robot got to do to get some good data in this town?

Data Is the Foundation

Data is the foundation for everything that organizations do and every interaction with customers. Data is proliferating, and that growth will only continue exponentially. As it multiplies, organizations need refreshed, enterprise-level approaches to systematically create, distribute and manage data. Hand-in-hand with this expansion comes increases in regulation on how organizations must manage and protect the privacy of their and their customers’ information. Data is intimately associated with business transactions and in turn, those associated actions by people; it demands everyone's attention. The struggle in managing content within the digital world is as complex as the digital workflows underpinning the efforts. This provides the link allowing processes and technology to be optimized, and hopefully where learning and intelligence may begin. The best way for AI to “learn” is by working with good data.

Related Article: What Data Will You Feed Your Artificial Intelligence?

Know Your Customers, and Know Their Data 

After all, data isn't just data. We have structured data, those identifiable pieces of data, such as name, address, location, etc., that are found in database fields and structured for use. The problem is businesses are full of unstructured data.

An intelligent and robust metadata model and data dictionary can provide the necessary structure to put that data to work. Data, information and content feed business — CRM data, finance, HR, and customer data can all contribute to growth and innovation. Attention to how this content has been created, captured, leveraged and how it creates value is the key value proposition of a business’s digital strategy. A strong data foundation provides the groundwork for a transformative digital strategy that expands markets and manages complex, consumer-centered supply chains.

Learning Opportunities

The strategy is never finished but is a continual process of leveraging the collective intelligence of a network of consumers and providers for rapidly cycling invention. As long as change exists, a strategy will change. Success starts by defining what your customers and business aim to achieve and then creating a strategy that is flexible and well governed.

Some of the key principles of good customer service include:

  1. Personalized: Sometimes known as the “human touch” which is interesting when think of automation … do AI and ML allow for caring and personalized interactions?
  2. Competent: This is where data can play a big role in offering a high level of service so long as you have good, accurate, and authoritative data to use.
  3. Convenient: This is also a great example of where automation and even AI and ML can provide value by offering many methods, formats, and even the time of interaction with customers so as to be truly connected.
  4. Proactive: Data is able to help here by knowing what is going on with a customer's order and letting them know the latest news when they need it the most. Proactively providing the right information, to the right person, at the right time will go a long way in delivering effective customer service.

Related Article: Combine AI and the Human Touch for Exceptional Customer Service

Future Forward Customer Service

What about other factors that affect your customer service, such as patience, active listening, the ability to read customers and empathy? The costly misstep of miscommunication, poor listening skills or lack of patience can result in loss of loyalty and customers. Communication is so important in the experience and knowing when to ask for someone to help. I question if that is even something that can be automated … who is going to teach the “robots” patience?

I would like to see more work on identifying use cases where empathy can be helpful in our AI work to allow for greater emotional intelligence when dealing with customers. Perhaps one day it will change from, “I am sorry Dave, I’m afraid I can’t do that” to “I am sorry Dave, I acknowledge and understand you. I am here for you. Let me help you find a solution.”

The future of customer experience needs to adapt to the customer beyond a one-size-fits-all approach in both content and context. Adaption is the key and the realization that both robots and humans have a role to play. If data integrity is critical to AI and machine learning, then trust and certainty that the data is accurate, usable, and responsive is also critical. Make the data meaningful, manage it well, and adapt to change through continuous learning and improvement. We can do that and need to do that for our customers.

About the author

John Horodyski

John Horodyski is a Managing Director with Salt Flats for the Insights & Analytics practice with executive management strategy experience in Digital Asset Management (DAM), Metadata and Taxonomy design, Data strategy, Analytics, Governance, MarTech, and Marketing Operations.John is a world leading expert and has provided strategic direction and consulting for a variety of Fortune 10, 50, 100, and 500 clients from Consumer Packaging Goods, to Media & Entertainment, the Pharmaceutical industry, and Insurance.

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