AI-driven customer sentiment analysis can help CMOs

• A study by Gartner revealed that 50% of the respondents can track the financial benefits of their customer experience projects.
• With search engines opting to eliminate their third-party cookies soon, CMOs need to look at alternative tools to track data
• By turning unstructured data into actionable insights, AI-driven customer sentiment analysis delivers complete transparency of their agents' interactions with their customers.

Delivering an enhanced customer experience has become a top priority for all brands. Providing a great customer experience not only helps brands build a positive image of their product or service, but also helps drive their revenue goals. In fact, according to a study by Gartner, around 50% of respondents stated that they track the financial benefits of their customer experience projects.

Delivering a greater customer experience requires brands to understand the sentiments of their prospects and customers towards the brand image. Also, today’s increasingly competitive marketplace and the elimination of third-party cookies demand brands to track what people say about their brand.

Using customer sentiment analysis, powered by Artificial Intelligence (AI), CMOs are empowered to track their customers' feelings, both positive and negative, and allow them to intervene whenever needed. While many brands have already incorporated AI-driven customer sentiment analysis to understand aspects such as indirect communications, its potential goes beyond common use cases.

AI-driven customer sentiment analysis is not only about analyzing customer reviews and feedback. Turning unstructured data into actionable insights, customer sentiment analysis provides CMOs with complete transparency of the interactions of their digital platforms and live agents with customers. Such an in-depth level of analysis allows CMOs to quickly identify customer frustration while simultaneously improving these direct interactions moving forward. A few ways that brands can capitalize on AI sentiment analysis include:

Helps to monitor efforts that are often overlooked
Measuring KPIs based on call duration is not an ideal way to measure the impact of their support staff. For instance, a long conversation between the agent and customer may reveal that the agent is tackling a complex issue and having trouble resolving it. CMOs can use AI-driven sentiment analysis to identify the agents that are consistently involved in calls having positive sentiment. This will enable them to reward their agents while asking them to mentor newly joined or less experienced team members.

Provide data for root cause analysis
By extracting data from the KPI reports of the IT department, CMOs can determine correlations that might otherwise be hidden from them. Using visuals such as line charts, they can better examine their rate of customer retention plotting versus the number of calls of negative sentiment. Using these, marketing leaders can listen to the recording of the phone calls between customers and agents that have a negative sentiment. This allows them to correlate a decrease in retention to identify why their customers may be leaving.

Enable quality assurance auditors to navigate areas that require immediate attention
With some of the calls happening on a day-to-day basis, quality assurance auditors are swamped with calls to listen to. But many simply do not have enough time to listen to every interaction as well as track its quality. With AI-driven customer sentiment analysis, they can detect the interactions that had negative sentiment. This also provides auditors with a good starting point for their reviews.

Choosing the right customer sentiment analysis is critical
With customer sentiment analysis playing a key role in staying competitive in today’s marketplace, it is crucial to choose them wisely. Choosing a tailored AI-driven customer sentiment analysis tool requires CMOs to consider the below factors:

Cloud-based solutions are the easiest to start with. Most cloud vendors provide tools that they developed and trained on various datasets of their choice. Brands can submit their sentiment extraction via an API and receive a response. Ensure that your cloud provider comes with the right technology, platform, regulatory requirements, and support services.

The other option is to work with a vendor to build a Custom AI-enabled sentiment analysis model for your business. Marketing leaders have a better say in this scenario as they can present their requirements to their vendors. In return, the vendors can build a model that corresponds to the business demands and train it with the brand’s datasets.

Driving powerful customer experience
With demand and customer expectation continuing to witness an upward trend, it is vital for CMOs to understand every aspect of their customer journey. The Gartner survey revealed that 80% of the organizations expect to compete mainly on CX. It means that the skill for realizing the benefits of customer sentiment will skyrocket. Therefore, by opting for tools based on AI, CMOs can scale and identify what triggers positive and negative brand perceptions that provide them with insights into what was otherwise undetectable.


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