Analytics & Insights

Selecting and interpreting the most relevant information from the sea of data is both a science and an art form. Many organizations have an existing analytics strategy in place. But most of them lack the expertise required to identify trends and understand the key drivers that impact the customer’s experience. 

First, you need to understand the impact of the data you collect today, map what you are going to use for your experience projects, and understand what other data is missing.

Four types of common customer data collected today:

1. Basic or Identity Data: Basic, personal, and demographic information.
2. Interaction or Engagement Data: The data that gets collected from many of the touchpoints that customers have with your organization. Think clicks on your ads, visits to your website, or scrolling through a blog.
3. Behavioral Data: Associated with the product and services usage and transaction data collected during purchase of a service or subscription. 
4. Attitudinal Data: Related to the customer’s perception of your product and services, which includes customer feedback, reviews, opinion, sentiment, desirability, and motivation. 

Data stacks need to be carefully managed. Customer data architecture reveals customer profile information, transactions, operations, history usage, and what customers like and dislike from their past interactions with your organization, as well as other behavior that customers share with your brand. While you want, and in some places legally need, to have your customers’ permission to collect their data, let them have control, adjusting according to their preferences when they want to. This allows you to know when it's appropriate to use the information on their experiences with your organization.

You also must understand what type of analysis you want to perform in each scenario or case in which you want to gather in-depth insights and trends. 

Here are the four data analytics types: 

1. Descriptive Analytics: What happened? This describes past results and is the most frequently used type of analytics. 
2. Diagnostic Analytics: Why did it happen? This helps troubleshoot issues and drill deeper into understanding anomalies, positive or negative, so the organization can take action to address them.
3. Predictive Analytics: What is likely to happen in the future? This attempts to predict or forecast what might happen or is likely to happen based on past patterns and trends.
4. Prescriptive Analytics: What is the best course of action to take? This looks at what has happened, why it happened, and what might happen in order to determine the best course of action for the future. This recommends actions for your organization’s next steps.

Descriptive and diagnostic analytics go hand in hand, and they involve past events, whereas predictive and prescriptive analytics involve the future. 

When you have impact data and apply analytics using artificial intelligence (AI) and machine learning (ML), these features enable your organization to generate intelligence, highlight insights, and predict the next action. 

In the not-so-distant future, the data analytics role won't just be about collecting data and analyzing customer feedback in order to create appealing metrics and data correlations. Rather, it will be a combination of data science and psychology. Teams will be expected to use AI to analyze a complex and large set of data and then correlate it with a set of personalized habits and behaviors, which feed into the personalized experience design.