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how to detect weak signals

Detect weak signals in customer journeys using AI

Dive into the fascinating world of weak signals and their impact on the customer experience, where artificial intelligence plays a crucial role: these subtle indicators can reveal emerging changes and latent issues, essential elements to grasp for anticipating customer expectations thanks to advanced AI-based analysis.

In this article :

  • The relevance of weak signals in the customer experience
  • AI as a catalyst for change in customer experience and the detection of weak signals

The relevance of weak signals in the customer experience

Weak signals are subtle indicators in the customer experience that reveal emerging changes or latent issues. Spotting them is essential to proactively improve customer interactions and satisfaction.

What is a weak signal?

Let’s start with a simple definition of the term: “weak signals” in customer experience. It refers to subtle clues or early indications that may reveal emerging changes or latent issues in the way customers interact with a company or perceive its products and services.

Here are a few examples:

  • A delivery tour with a specific service provider regularly leads to dissatisfaction.
  • A customer portal page has poor ergonomics, hampering the ordering process on certain devices.
  • An agency displays incorrect opening hours online, causing customer dissatisfaction on a daily basis.
  • The absence of an offer proposed by a competitor leads to a decrease in orders.

The importance of weak signals in the customer experience

Companies that manage to capture and analyze these weak signals effectively are better positioned to respond to changing customer expectations, proactively improving the customer experience.

In practice, adequately considering weak signals enables:

  • A quicker reduction in costs related to dissatisfaction (loss of revenue and customer support time).
  • Mobilization of operational teams by providing them with direct access to action plans specific to their domains.
  • Faster innovation thanks to early detection of changes in customer needs and behaviors.

In conclusion,

The ability to detect, process and act on weak signals represents an undeniable competitive advantage for companies.

Why is it difficult to detect weak signals?

Detecting weak signals often proves complex, as they can easily get lost amidst the ambient noise (customer feedback, NPS scores, tickets, online reviews, etc.). What’s more, these subtle signals can include :

  1. Changes in purchasing behavior not explicitly expressed by customers,
  2. Occasional references to specific issues in customer feedback,
  3. Or even changes in how customers engage with the brand on social media (in the case of B2C).

In addition, weak signals are often associated with feedback that receives the least attention from CX, Quality or Operational Excellence teams. It’s partly for this reason that they are distinguished from irritants. Let’s take the example of a Net Promoter Score (NPS) segmentation: irritants are identified during the analysis of detractors and generally clearly expressed by customers. In contrast, weak signals appear subtly in the neutral and promoter segments.

In practice, detecting weak signals involves :

  1. Centralize all customer feedback (customer experience signals)
  2. Process all customer verbatims (unstructured data) associated with these feedbacks
  3. Correlate this data with those relating to sales activity, to establish a connection with operational behavior/context
  4. Implementing appropriate internal processes to identify weak signals and develop precise action plans.

At first glance, these four measures may seem complex without resorting to suitable technologies.

AI as a catalyst for change in customer experience and detection of weak signals

What is the impact of LLMs on the detection of weak signals?

Compared to traditional machine learning technical, which were often costly and challenging to deploy without a team of data scientists, Large Language Models (LLMs such as Mistral, OpenAI, etc.) offer 4 technical advantages in detecting weak signals:

  1. Improved analysis capacity : LLMs can process and analyze large volumes of textual data (customer reviews, feedback, tickets, etc.) quickly compared to human methods (internal or external), which are much slower. Thus, the monthly analysis of thousands of customer reviews now takes a few minutes instead of tens of hours.
  2. Highly skilled in natural language processing : These LLM models excel in natural language understanding, making them useful for interpreting complex feedback and extracting subtle meanings revealing an emerging change or new development. Each verbatim is managed as a vector including all the context of the message (language used, meaning conveyed, intonation) stored in a non-relational vector database.
  3. Multidimensional detection : LLMs can integrate and analyze data from different sources and formats (CRM, customer reviews, tickets, flat files). They thus offer a holistic view of weak signals hidden within a multitude of disparate company-specific data. This multiple consideration facilitates correlation between data and increases the number of detectable weak signals.
  4. Deep context-based personalization : LLMs can be trained or adapted to focus on specific sectors or areas of particular interest to the company, enhancing their ability to identify weak signals relevant to these specific contexts.

How does AI improve anomaly detection?

After addressing the 4 technical points in the previous section, there are various business implications related to the operational processes used to detect weak signals.

  • Firstly, AI enables faster identification of anomalies. Thanks to its advanced analysis, its almost complete understanding of human language and its ability to take account of the company’s context, weak signals are identified much more quickly. Thus, the time needed to identify these signals is considerably reduced when using AI. Depending on use cases and analysis processes, the time needed to identify these signals can be up to 50 times shorter.
  • Secondly, thanks to the integration of operational data in the analysis, AI enables automatic correlation with the source of the weak signal (purchase journey, customer typology, etc.). This corresponds to what is called “root cause analysis”. For example: 87% of dissatisfied customers with online offer X in week S-1 belong to the 65+ category.
  • Thirdly, as part of anomaly identification, AI provides a better understanding of the root issue through several automated tasks such as verbatim synthesis and detection of improvement points.

All these actions were previously carried out quarterly or annually by consultants or specialized marketing research institutes.

Going beyond simple anomaly detection: fast, effective resolution

By fully exploiting the capabilities of AI and its data, it is now possible to go beyond the simple detection of anomalies and weak signals. The goal is now to accelerate the resolution of anomalies at the operational level by business teams (in the agencies / at head office for the product / etc.).

  1. (not related to AI) Anomaly impact assessment : To stimulate real change internally, nothing beats visibility of the cost associated with each anomaly, so as to prioritize their treatment appropriately. By integrating this information into the CRM, it becomes possible to quantify the financial impact of sales potentially lost if anomalies detected in weak signals are not treated.

Financial impact of anomaly ($) = Average revenue per customer ($) x number of customers affected by anomaly

  1. Action plan : The use of AI enables the automatic generation of action plans (what we do at Feedier), based on the impact of anomalies, user feedback, and business data. These plans offer clear and easily applicable operational actions in the field.

In conclusion, in this constantly evolving context, weak signals are proving to be crucial indicators for anticipating expectations and improving the customer experience. The integration of artificial intelligence to detect and analyze these signals offers a competitive advantage by enabling rapid identification and effective resolution of emerging issues. This strengthens the flexibility and proactivity of companies.

About Feedier

Do you want to improve customer feedback management? Feedier’s Customer Intelligence platform simplifies the utilization of customer data by combining unstructured feedback data with structured business data. It accelerates operational decision-making by providing our clients with impactful insights to guide their actions. With Feedier, they fully leverage their customer data through AI integration and automated reporting, allowing them to stay ahead of their competitors while acting with confidence and clarity.

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