"Intelligence is not the ability to store information, but to know where to find it."
~ Albert Einstein
Analytics is the action of making sense of customer data and uncovering meaningful trends. Insights are the value obtained through the use of analytics. Insights are incredibly useful in identifying areas of opportunity that can tune to growth and enhance experience delivery.
Selecting and interpreting the most relevant information from the sea of data is both a science and an art form.
CUSTOMER DATA STRATEGY
You might already have a data analytics strategy at your organization. It's crucial to have an inventory of what you have, what you collect, what you are missing, and what you want to capture. This information will provide you with higher quality analytics and create insights that enable you to make decisions, improve the overall experience, and drive growth.
As customer feedback flows from different parts of the organization, creating centralized data management is essential to get an understanding of customer insights.
Data strategies help gather disparate data resources into a common place where analysts have the customer view and type of feedback. For example, if a customer left a positive review on a support website but a negative NPS on their last interaction with field delivery, bringing together the feedback gathered from different resources allows analysts to get insights into the customer journey at different touchpoints. This way, NPS analysts are given a holistic picture of the customer's sentiment with the organization.
DRIVERS AND INSIGHTS
Analytics is a critical part of the engine that systematically analyzes customer feedback and associates it with other customer data to inform teams and management what the drivers are positively or negatively affecting NPS. It highlights the gaps and allows the teams to perform further root cause analysis to ensure the issue is resolved.
For enterprise organizations, there might be multiple segments, products, services, and geographies that take into account the level of analysis to determine how to improve the experience.
At one point during my previous work with technical support, management wanted to examine the negative impact on the NPS score. We analyzed NPS scores and verbatims of customers who responded to the surveys and associated both quantitative and quantitative feedback with case resolution time. The results highlight that when the customer representative solved customer issues within one or two days, the NPS score tended to be high. NPS score dropped when resolution time got pushed further to day three or four. However, if the customer representative informed the customer that there would be a delay in solving their issue, the NPS score tended to be higher. The insights allowed management to train their customer rep to set expectations if the solution was in the backlog for more than two days. Uncovering these important results helped NPS move an astounding 12 points in three months.
The same can be applied to different attributes by products, user knowledge, or region.
As excessive surveying became a burden on customers, businesses began collecting customer data and feedback from various types of interactions to understand customer behaviors, habits, usage of products and services, and social influence. Predictive modeling seeks to reveal what is most likely to happen in the future, based on past patterns and trends. The predictions allow companies and their NPS teams to scale up consumer observations and improve their ability to discover fractions in the CX, and predict the likelihood to recommend their organization to others.
Furthermore, when your NPS analyst team generates this impact data and applies analytics using artificial intelligence (AI) and machine learning (ML), these capabilities enable your organization to generate intelligence, highlight insights and predict the next action to enhance the experience to a personalized level.
To drive data decision action and improvement:
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