Originally Published in Customer Think | By Alex Weihmann | September 12, 2017
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.
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