How to detect weak signals : user guide

Published on November 29, 2023  - Updated on December 19, 2023

How to detect weak signals : user guide

In a world where competition is increasingly fierce, companies must find new ways to stand out and retain their customers. That's why optimizing the customer experience is a key factor for many businesses. One of the most effective approaches to achieve this is by identifying weak signals in the customer journey. Indeed, identifying weak signals can be a valuable tool for companies looking to enhance their understanding of customer needs and expectations. By identifying weak signals, companies can take corrective measures before it's too late.

What is the difference between weak signals and irritants?

Weak signals : Weak signals are pieces of information that, individually, may seem insignificant but, when analyzed together, can reveal important trends. For example, a customer leaving a negative review about a product can be considered a weak signal. However, if multiple customers leave negative reviews about the same product, it may indicate a deeper issue.

Friction points or irritants : Irritants are obstacles or frustrations that can more significantly impact the customer experience. They can be caused by factors such as the complexity of a process, the slowness of a service, or a lack of clarity in instructions. The main difference between weak signals and friction points is that weak signals are generally early indicators of an irritant, while friction points already have a significant impact on the experience. Weak signals can be used to identify irritants, but they are not necessarily synonymous.

In conclusion, weak signals and friction points are two complementary concepts that can be used to improve the customer experience.

Steps to detect weak signals

Data collection:

The initial step involves collecting data on the customer journey from various sources such as online customer reviews, survey verbatims, sales data, customer service, or marketing data.

Analysis and recognition of weak signals:

Once the data is gathered, it needs to undergo analysis to identify different weak signals. Various data analysis techniques can be employed, such as semantic analysis of textual data, behavioral data analysis, and predictive data analysis. Utilizing tools such as Q°emotion proves effective in comprehending and processing these data. Thanks to our technology, it is now possible to highlight emotions behind the words. Behind these emotions lies an intrinsic need. To address this need, a deep understanding of the signals given by customers, known as "emotional intelligence", is essential.

Understanding that a customer expressing fear will not require the same response as an angry customer is crucial. Without this emotional layer, such as having only sentiment analysis, it would be extremely challenging to respond adequately to the customer's expectations.

Interpreting weak signals:

To better interpret the importance and impact of potential weak signals, it is essential to cross-reference information from different sources, identifying recurrences, patterns, series, evolutions, or ruptures, as well as convergent or opposing information. Below is an example of the "product" page of our Q°emotion dashboard. This module detects and highlights the main topics addressed in verbatims, categorizing them by themes in a graph. This graph enables companies to visualize product reviews, identifying strengths and weaknesses for improvement.

Prioritizing weak signals:

This step involves categorizing and prioritizing weak signals based on criteria established by your organization, such as impact importance, signal frequency, or relevance. As seen earlier with the interpretation of weak signals, temporality and frequency can play a significant role. The collected information may represent a small number of mentions at a specific time, but when compared with other subsequently collected information, it can gain significance and reveal an increasingly impactful irritant.

Below is an example of the "key insights" page of our dashboard. This module detects and highlights key information from verbatims, showing success points on the left and the most mentioned improvement areas (irritants) on the right. This information helps visualize key points and determine actions to enhance customer satisfaction. The analysis identifies points that evoke more customer reactions and prioritizes them for a better response.

Why are weak signals more relevant than strong signals?

Weak signals are more relevant than strong signals because they can indicate emerging trends that are not yet visible to the naked eye. Strong signals, on the other hand, often indicate established trends. For example, several potential customers expressing the need to buy a projector may not look alike and may not buy the same product. However, the strong signal "I'm looking for a high-end projector" remains the same. It does not allow distinguishing them or understanding their real needs efficiently.

If the business approach relies solely on strong signals, the signal will be the same for 99% of potential projector customers. This would imply that recommendations based only on strong signals work a priori for 99% of these potential customers. However, this is not the case. While the expressed need is common, the real need and the suitable solution are individual. This is why strong signals are lacking.

Weak signals are essential because they can help companies identify opportunities and threats before they become obvious. By taking action to address these opportunities and threats, companies can position themselves favorably for the future.