Customer experience (CX) analytics has become a critical aspect of modern businesses, as companies strive to understand and improve the interactions they have with their customers. 

However, despite the growing importance of customer experience analytics, there are several limitations to existing solutions. These alternatives often fail to provide an accurate and comprehensive picture of the customer journey, leaving organizations with gaps in their data and an incomplete understanding of their customers' experiences. This lack of actionable insights can make it difficult for companies to make informed decisions about their customer experience strategy and can result in missed opportunities for improvement. 

Traditional Market Research

Traditional market research involves gathering data through methods such as surveys, focus groups, and interviews. This data is then analyzed to gain insights into customer attitudes, perceptions, and behaviors related to a company's products or services. Other methods used in traditional market research include observational research, ethnography, and secondary research. The goal is to provide a comprehensive understanding of customer needs and preferences, which can be used to inform business decisions and improve the overall experience.

This can be a useful tool for customer experience initiatives but can also be a hindrance. Here are some reasons why traditional market research may not be the best option as a CX alternative.

• Traditional market research takes a long time to complete, from designing the research plan, collecting data, and analyzing the results. 
• Next, it relies heavily on the researcher's interpretation of the data and can be subject to researcher bias. 
• Also, traditional market research can be very expensive. It requires resources such as hiring a research agency, purchasing necessary equipment and software, and paying for data collection. 
• The research is limited to a small range of data and cannot capture the full range of customer experiences. This means that the data collected may not tell the full story. 

Social Listening Tools

Social listening tools involve monitoring and analyzing social media conversations and mentions of a brand or company in order to gain insights into customer sentiment and feedback. These tools can be used across various social media platforms to analyze the sentiment of those mentions and identify common themes and topics that customers are discussing. 

• Social listening tools require an expert to configure the keywords and phrases that they want the tool to listen to. This means that it is important to have someone knowledgeable and experienced in the field of digital customer experience set up the tool correctly. If this is not done correctly, then the tool will not be able to provide the desired insights. However, without an expert, there is a risk of missing out on valuable insights as you don't know what to search for. This can lead to a lack of actionable insights from the data. 
• Also, these tools are often used to track campaigns and for online reputation management (ORM). 
• if the tool is not configured correctly, then it is possible to get non-actionable insights from the data. This means that the insights may be too broad or vague to be used to make decisions or take action. 
• Lastly, they are usually limited to data from social media platforms. This means they cannot provide insights from other sources, such as customer feedback surveys, customer service calls, or other forms of customer engagement. Again, this can limit the insights gained from the data. 

Data Science Teams

Data science teams use various statistical and machine learning techniques to analyze customer data, such as demographic information, purchase history, website behavior, and survey responses. They also use natural language processing, text mining, and sentiment analysis for customer feedback and reviews.

It may also use predictive modeling to identify patterns in customer behavior and predict future customer needs and preferences. They can also use A/B and multivariate testing to experiment with different design and messaging strategies to optimize the customer experience.

The insights and recommendations generated by data science teams can be used by other departments, such as product development, marketing, and customer service, to improve the customer experience and drive business growth.

Data science teams can be an attractive alternative as they can offer a more personalized and tailored approach to customer service. However, using data science teams as a CX alternative can be challenging. 

• Firstly, data science teams usually require deep technical skills to build and maintain the system necessary to generate the desired insights. This means that the team must either have the necessary skills in-house or hire staff and train them in the relevant areas of data science. This can be a lengthy and costly process. 
• Secondly, it takes time to get the data science system right. This is because the team must go through the process of collecting, cleaning, and analyzing the data and implementing the validations, tests, and training needed to ensure the system is running accurately. This process can be time-consuming and costly, and the team may need to iterate multiple times before the system works as desired. 
• Thirdly, these specialized teams are often highly expensive. This is due to the cost of hiring staff and developing and maintaining the system. Additionally, the teams typically only support a limited number of data sources, mostly social media platforms, which may limit the insights generated. Finally, they often have shared resources, making it difficult to allocate them when needed.

Manual Tagging

Manual tagging involves working with a third-party agency to manually categorize and label customer feedback and data to gain insights into customer sentiment and experiences. For example, the process can classify and categorize customer feedback, survey responses, and other data into different topics, themes, or categories.

Manual tagging with agencies as a customer experience (CX) alternative is not helpful. 

• This is mainly because there is no holistic picture, as only part of the data can be considered. The main issue with manual tagging is that it is not feasible for a CX team to manually tag every piece of customer data. This is because of the sheer number of customer data that needs to be reviewed and labeled. Moreover, it is difficult to accurately label customer data as it takes time to review and label each piece. 
• In addition, manual tagging is error-prone. This is because it is difficult to accurately label customer data as mistakes may be made when manually assigning labels. 
• Lastly, manual tagging also requires the decision of sampling size. This is because the accuracy of the results of manual tagging depends on the size of the sample being labeled. As such, it is important to decide on the sampling size to ensure the results' accuracy. 

Analytics Module of Software Tools

The analytics module of software tools is used to collect, process, and analyze customer data in order to gain insights into customer behavior and sentiment. These tools can be used to track customer interactions and transactions, such as website visits, purchase history, and survey responses.

These tools typically include various features, such as data visualization, exploration, and modeling. The software tools may also include machine learning algorithms that can predict customer behavior and identify potential areas for improvement in the customer experience. They can also use natural language processing to extract insights from customer feedback, such as reviews, survey responses, and social media mentions.

These analytics modules of software tools can also be integrated with other systems, such as CRM, Marketing Automation, and customer service management systems, to provide a holistic view of the customer experience. 

• It is not a very helpful CX alternative as it requires expertise to obtain actionable insights which might not be available in organizations.
• This module is also known to offer sub-optimal analysis as the tools used by the software cannot provide insights that are required to make meaningful decisions in the customer experience. 
• The main issue with this module is that different tools analyze customer feedback differently. For example, some tools use sentiment analysis to determine the emotions associated with customer feedback, while others focus on customer experience journey mapping. Therefore, it is difficult to align analysis by different tools to the same scale. This can be problematic when making decisions based on customer feedback, as different tools provide different results. 
• Another issue is that it is not based on proven market research models. Market research models ensure that customer feedback is analyzed consistently and reliably. Without these models, it is difficult to know if the analysis is accurate and reliable. This means that decisions based on customer feedback may not be as effective as if the analysis was based on a proven market research model. 
• Finally, this module does not allow users to quickly and easily customize their analysis. This can be problematic when trying to analyze customer feedback in specific ways. For example, suppose a company wants to analyze customer feedback regarding the customer journey. In that case, it may have to manually enter data into the software in order to create the desired analysis. This can be time-consuming and difficult, leading to inaccurate or incomplete analysis. 

Advanced CX Analytics Platform Helps Overcome Challenges

Customer experience analytics offer several advantages over traditional methods of collecting and analyzing customer feedback, such as surveys and focus groups. Some of the challenges posed by old methods that customer experience analytics can overcome include:

Limited Data

Traditional customer feedback methods often provide limited data, making it difficult to understand customer experiences comprehensively. Customer experience analytics, on the other hand, provide vast amounts of data from multiple sources, including online interactions and call center logs, enabling a complete understanding of the customer journey.

Time Lag

Old customer feedback methods are often carried out at fixed intervals like quarterly or annually. Customer experience analytics provide real-time customer insights, allowing organizations to identify and address issues as they arise quickly.

Inaccurate Data

Traditional methods can suffer from self-selection and social desirability bias, resulting in inaccurate or misleading data. Customer experience analytics use objective data sources and statistical methods to minimize these biases and provide more reliable and actionable insights.

Difficulty in Measuring Customer Emotions

Surveys and focus groups may need to capture the customer experience's emotional aspect effectively. Customer experience analytics use techniques such as sentiment analysis to measure and understand the emotional impact of customer interactions.

Overall, customer experience analytics offer a more comprehensive, real-time, and accurate approach to understanding and improving the customer experience, overcoming the challenges posed by traditional methods.

The Bottomline

In conclusion, while existing alternatives for CX analytics provide valuable customer insights and recommendations, they also have limitations. Therefore, it is important for organizations to carefully evaluate the alternatives available and choose the one that best fits their needs and goals. By doing so, they can ensure that the insights and recommendations generated are accurate, actionable, and truly helpful in improving the customer experience.

Read More: Uncovering New Possibilities with Data-Driven Product Performance Analysis