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The Future of Cognitive AI in Customer Experience

Alex Weihmann | Sep 12, 2017 827 views No Comments

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The ability for companies to collect, store, and manage vast amounts of digital information has paved the way for big data to shape corporate strategy for a variety of departments. The big data push is particularly big within customer experience space, where countless customer touchpoints can be analyzed to improve interactions and increase loyalty. Today, Chief Customer Officers are able to harness data through the use of cognitive artificial intelligence programs that take their data capturing and analysis a step further. According to Consero Group’s 2017 Customer Experience Report of Chief CX Officers, 48% of CX executives are considering the implementation of cognitive artificial intelligence technology within their operation—all within the next several months. While in it’s infancy, the adaption of this relatively new technology will grow quickly and can have a significant impact on the evolution of the CX department.

The Opportunity Of AI Within CX:

Cognitive AI offers several noteworthy opportunities in CX for organizations that capture a variety of customer touchpoints, including the ability to gain a holistic view of their customers. Traditionally, organizations with large customer bases struggle to understand the needs of their individual customers—a gap that cognitive AI can fill as it allows for segmentation, identification, and scoring of customers using previously under-utilized data. Pairing artificial intelligence with such rich information allows for enhanced understanding of buying behavior, preferences, and loyalty—all of which can unlock actionable insights. Most importantly, it allows brands to anticipate customer needs and to go the extra mile, all while delivering the personalized experiences customers have come to expect.

Another positive characteristic of cognitive AI is its ability to constantly adapt and learn in real-time. Cognitive AI can leverage information from customer conversations, learn from previous interactions, and automate common responses to common requests. This can also take the shape of reframing responses based upon their context or even sifting through large knowledge bases in order to present the most relevant answer. Real-time learning paired with the instantaneous nature of AI permits for a more in-depth understanding of the customer, taking into account the channel or time of day. Ultimately, AI will be able to analyze customer interactions and adjust the customer’s journey based upon current sentiments. In an environment where customers are more demanding than ever, AI’s ability to respond quickly and dynamically regardless of platform can take customer service to a whole new level.



Finally, AI allows applicable cross-channel insights to be gathered in real-time and applied to make better business decisions. Data points like customer wait times or balk rates can all be utilized as actionable resources that bolster customer service. For example service delivery and agent availability can be optimized based upon historic workflows. Further, high-priority cases can be proactively addressed or escalated through the use of predictive analytics. While the technology continues to evolve, as time goes on organizations who refuse to use AI as a tool may miss out on capabilities make faster and better informed decisions.

Challenges Involved With Implementation:

Putting raw data to work through a cognitive AI program is not without it’s challenges. While cognitive AI technology programs—recently launched by 14% of companies, according to findings from the 2017 Customer Experience report—are beginning to emerge, none of the surveyed executives reported a fully mature program. Practitioners attribute this gap in maturity to a few problem areas, including lack of CX program maturity. I spoke to a director of Consumer Experience at a major California health insurer, who explains, “some companies are further along than others. The concept of Customer Experience is still early for most industries, and this idea of using AI is still not understood. There hasn’t been real proof of concept where everyone is jumping on the bandwagon.” From this practioners’ perspective, organizations that do not have a mature program may risk muddling their CX operation should they not have a clear understanding of what customer insights they hope to uncover.

Which leads to another challenge surrounding cognitive AI—knowing what data to collect. Often times, companies make the broad assumption that big data should be all encompassing. It is important to identify what questions you want your data to answer and what data is of no value to your organization. By doing this you can avoid unnecessary ‘noise’ from data that doesn’t align with your departmental objectives. The Consumer Experience director warns that “a lot of companies think about big data and they just want to go and get everything. Then figure out what to do later.” This can be compounded by the fact data is often siloed within separate areas of the same organization, leading to challenges in data analysis. Rather, CX executives should make efforts to understand which information is currently accessible, and what data gaps must be filled to fulfill certain objectives. They should work across functions to gain these insights to make the impact more powerful.

Finally, practitioners should be aware of how much consumer information is too much. As AI programs evolve and can anticipate customer leads, much like organizations like Amazon already do, executives must be conscious of avoiding a ‘creepiness factor.’ While some generations may be more tolerant to the use of their personal information, others may be less so. CX departments must understand not only how to properly leverage AI, but also when to properly leverage. Organizations that refuse risk alienating large portions of their customer base.

Jumpstarting Your Cognitive AI Program:

As cognitive AI programs become more common, opportunities for companies to leverage their data to deliver personalized customer experiences through the use of such technology will grow. So what does it take to jumpstart your organization’s foray into AI? Here are three tips gathered from speaking with practitioners in the space on how to jumpstart your cognitive AI program.

1. Reflect if your organization is ready for AI
Customer experience is relatively new, with few organizations that can consider their programs very mature. While it may be tempting to immediately jump on the AI bandwagon, make sure your current customer experience tactics reflects your present business objectives. AI is not a replacement but rather a supplement for a well-run CX program.

2. Understand what data you require
Avoid redundant data capture by auditing useful customer information already in-house. Remember that this data may be warehoused in separate areas and unable to interface together—you may be surprised to discover you have more data than previously thought. Avoid falling victim to data overload; while it may be tempting to capture as much information as possible, focus on collecting data that bridges current gaps.

3. Ensure your AI program focuses on 2-way communication
In the age of the chatbot and smart assistant, it’s easy to frame cognitive AI as a primarily outbound CX tool. Rather than just pushing information out to consumers, ensure your program focuses on two-way communication. Being a groundswell and listening to how your customers respond is one of the biggest benefits of implementing cognitive AI. It is what allows for your AI program to adapt, learn, and automate common requests.

Through careful considerations of how cognitive AI can be leveraged to improve the customer experience, CX executives will be in a better position to implement an AI program that will increase customer retention and have a positive impact on the financial performance of their organization.

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