5 steps to analyze customer feedback at scale - equip your enterprise to be successful

June 28, 2024
Reading time:
2
minutes

CX could be the biggest growth opportunity in decades. To capitalize on this, enterprises must look beyond a fragmented and incremental approach to CX and build the right capabilities to take advantage of opportunities from customer feedback.

Let’s take a look at the steps that you need to follow to understand what your customers think and feel. This will allow you to act on this information to achieve your business objectives.

How should your enterprise approach analyzing customer feedback?

CEOs are pushing their organizations to prioritize better CX. Now, one-off improvements aren’t enough to differentiate in a market where the bar for experience is constantly being raised. In 2023, a Boston Consulting Group (BCG) survey found that companies leading in CX had 190% higher three year revenue growth and 70% higher customer loyalty.

Analyzing feedback is what allows an organization to understand what the customer thinks and feels throughout their journey. The problem is that most enterprises don’t align their customer feedback with the plans of their operations, product, or digital teams and, as such, fail to deliver organizational alignment at scale. Their data is scattered across multiple platforms, different teams use fragmented tools, and multiple sources are not integrated or unified.

Because enterprise alignment is what provides Return on Investment (ROI) from CX initiatives, businesses must scale customer feedback analytics across their organization and establish a unified strategy that supports growth driven by CX. You need to understand the specific reasons that customers interact with you and create an approach that follows that.

In this article, you will discover why companies need to focus on their customers and the five steps you can follow to collect and analyze customer feedback at scale.

Why enterprises need to make sense of customer feedback

It’s no surprise that enterprises understand that collecting and analyzing customer feedback for customer insights is a top priority. In the last few years, data has exploded in CX and the scale of unstructured data is set to increase. Forrester recently reported that unstructured data managed by enterprises will double in 2024. They found that social posts, reviews, and contact center information currently represent less than a third of all managed data today. But, with feedback comes the expectation from customers that businesses will improve experiences across all products and services that they engage with.

Higher expectations raise the bar. Successfully using customer feedback is a goldmine of opportunity because data helps you to understand what your customers think and feel. But it also presents huge challenges for enterprise businesses as they look to create a complete view of customers. The right technology can unlock this data and surface insights that companies can turn into clear points of differentiation.

Unprecedented advances in CX technology mean that companies have a new opportunity to understand customer perceptions of their experience. For example, a meal subscription company found that the amount of choice is key to CX. In fact, loyal customers require more choices because they get bored, but limiting choices for new customers can impact acquisition.

As such, understanding the holistic experience of the customers enables enterprises to set objectives against specific areas - like retention or differentiation - and connect those with data sources to drive use cases. The results are clear when we look at pure-play digital businesses like HelloFresh or Uber, which have reached a high level of analytical maturity and are leading their markets.

Follow these 5 steps to gather and analyze customer feedback to improve your products and services

Step 1. Map out the customer journey and note where you are currently collecting customer feedback

In a CX context data collection means capturing the breadth and complexity of the customer’s experience at all stages of the journey.

Data collection is best understood as an ecosystem. In addition to any data that you choose to collect using surveys, your customers will provide feedback via a range of other channels (for example, social media and the app store) as well as through their direct contact with your organization, such as via customer service teams. Surveys are one piece of the puzzle, but centralizing multiple data sources is the key to understanding customers. So, the only way to understand customers is to have a complementary architecture of data collection that covers all elements of the customer journey.

The process to achieve this is logical: start by mapping out the customer journey and overlay where you are currently collecting feedback. This will identify which elements of the customer’s experience you currently understand as well as crucially where the gaps are.

Step 2. Don’t let poor data ruin your customer feedback analysis

Most companies start with basic data hygiene factors, such as ensuring that customer attributes - gender, age, and product purchased - accurately match with each piece of feedback. This is an essential element of quality data, but ensuring that the feedback data itself is high quality is something that is often overlooked. However, how and where feedback data is collected is critical to ensure that the data is accurate and can be used to support the right decisions.

Bias or limiting questions can create poor quality feedback data that leaves a skewed or incomplete impression of the customer’s experience. Gaps in where the data is collected (including stages of the journey or segments of the customer base) also unintentionally distort what the data shows and how it can be used.

These pitfalls accumulate to create an inaccurate representation of the customer’s perspective which is then used to shape the customer experience. This is exacerbated by the fact that once high quantities of feedback data have been abstracted into charts, the quality of the data is hidden. This can lead to suboptimal decisions being unknowingly taken based on flawed data. Given that the purpose of understanding customers is to invest time and money to improve the experience, when poor quality data is used, it leads to poor quality investment. The risk of poor quality data going unnoticed can be higher in CX as - unlike some metrics that can be easily checked - there is no independent barometer of what your customers think.

The scale and scope of new AI solutions, combined with the vast quantities of data that these models can analyze, means that enterprises must ensure that best practice data collection principles are in place.

Step 3. Be Careful choosing your customer experience ai platform

You can’t turn around without bumping into a new claim about how artificial intelligence is going to revolutionize the customer experience. There’s no doubt it will; new products using Generative AI are appearing on the market every week. The problem is that nearly all these products are focused on applying new technology for the sake of it – rather than the problem that businesses need to solve.

You need the right solution architecture to find the insights that impact business decisions. AI is a tool to solve a problem. As with any tool, its success is based on both choosing the right tool for the job as well, as how and where it is used.

As such, once you’ve ensured data quality, you need to make sure that your data analysis accurately captures what your customers think and feel so this intelligence can be used to take decisions.

Here’s a brief comparison of AI technology used for CX analysis:

Rule-Based Lexicon uses predetermined rules to categorize unstructured feedback responses. Positive and negative sentiments are manually assigned to words and phrases. Feedback text is scanned and sentiment is calculated based on positive and negative word counts.

  • Pros: Simple approach to analyzing text
  • Cons: Limited accuracy as it does not understand nuances or contexts for example irony, or sarcasm; additionally constant manual input and supervision is needed to manage feedback

Thematic Analysis and Neural Embeddings is most commonly found in psychology, sociology, and anthropology. This approach emphasizes identifying and interpreting themes or patterns in data. Human validation is needed to filter and select relevant keyword-based themes.

  • Pros: Flexible, continuous theme creation and surfaces organic patterns
  • Cons: Time-consuming data inspection and theme validation, limited understanding of context

Large Language Models leverage extensive training data and deep learning principles to extract topics and sentiments from customer feedback and these results are then delivered in natural, human-like text.

  • Pros: Sophisticated and outputs appear to be remarkably “human”
  • Cons: Unreliable, outputs can be ‘made up’ rather than being grounded in the data (often referred to as hallucination) while lack of domain specific knowledge causes inaccuracy, often produces different outputs with every analysis

The best platforms for CX integrate multiple AI models for precise classification with LLMs on top to derive actionable, accurate, contextually rich insights that support tactical actions and strategic goals. However, enterprises need to look out for marketing information that refers to all these approaches as AI. Understanding these differences helps you to see the significant variations in the level of sophistication between solutions. Then you can put a customer-centric solution at the center of feedback analysis to unlock the full potential of CX.

Step 4. Putting this intelligence into the hands of decision makers

Once the quality data you’ve collected has been effectively analyzed the next step is using this data to inform decision making and action.

Stakeholders and teams within the business impact the experience of the customer in different ways. Access to role-specific data is what allows individuals and teams within the business to make customer-centric decisions.

Simple decision making can be made at a tactical or operational level. For example, fixing a bug in the product or making instructions clearer on the website. These decisions can often be made within a single team. However other decisions will be more strategic and require the input and collaboration of multiple teams. Strategic challenges include decisions that involve nuance or trade-offs - for example, where different customers have different needs or where making changes will impact other metrics such as cost or conversion.

Changing customer perception in relation to more conceptual elements of the experience such as trust or value for money also typically sit outside the remit of a single team. The ability for the organization to collaborate and align around these crucial but harder to fix elements of the experience is dependent on all teams having access to the right data.

This means that to make the most effective decisions at a strategic, tactical, and operational level, enterprises need to ensure that all teams and stakeholders have access to the information they require. This isn’t just about creating dashboards and alerts. It means identifying what is needed to facilitate customer-centric decisions across the organization.

Having a platform that acts as a single source of truth for all teams makes an enterprise-wide approach to enhancing customer experience possible. This includes both the initial coordinated decision making as well as the subsequent measurement of the impact of changes that are made from the customer’s perspective.

Step 5. Using feedback to deliver business outcomes

Analyzing customer feedback to understand what customers think and feel is not an academic exercise. The goal is to use this intelligence to deliver business objectives.

Once decision makers across the organization have access to accurate customer intelligence they can use this information to make changes. However, developing the experience of the customer is an iterative process.

Customer perceptions are constantly changing, impacted by their latest interactions with your organization as well as their experiences with other products and services across all aspects of their life. Understanding the impact of the changes that you make is fundamental.

The impact of changes should be measured in terms of:

  1. The perception of the customer
  2. The behavior of the customer
  3. The business metrics

While most organizations track changes in customer behavior or business metrics such as conversion or Customer Lifetime Value (CLV), it is equally important to track the specific impact of changes that are made through the eyes of the customer.

Customer perception ultimately drives behavior. It acts as a leading indicator and shows whether the changes that are being made are having the desired effects. The process of analyzing feedback includes understanding the impact of changes that you make from the customers point of view. This ability is what makes it possible to consistently optimize and improve experience and achieve business objectives.  

Conclusion: Future-Proof Your CX Strategy

Analyzing customer feedback represents an enormous opportunity. However, to unlock the benefits, a number of things need to be in place. Each building block needs to be robust for the ecosystem to work effectively. Ensuring that each step of the process is optimized drives solid decision-making that supports profitable outcomes.

See Chattermill in action

Understand the voice of your customers in realtime with Customer Feedback Analytics from Chattermill.