Build Customer Loyalty With Customer Insights
Getting a new customer is 7 times more expensive than retaining existing customers. In fact, retaining customers increases investment by 5%. In other words, a satisfied customer who is loyal to the brand will repeat their purchase. For this, there are numerous marketing strategies, but it is also crucial to have solutions such as Customer Insights to achieve this goal.
Customer Insights provides the knowledge necessary to create personalized strategies and gain customer loyalty. Combine all the data and use Artificial Intelligence and Machine Learning to obtain a 360° view of the customer. It takes into account three points with which it handles customer data, from its ingestion, through its management and creation of unique customer profiles, to the transport of marketing lists to other systems.
First, Ingest Data
The first step is to collect all the data. Customer Insights aggregates this data through connectors that may or may not be predefined by the system. You can ingest data from various sources such as:
- cloud services
- CDS (Common Data Service)
CDS is a proposal from Microsoft that consists of a data repository and offers us a layer of semantic metadata. That is, it tells us what meaning the data has and recognizes unique customers.
The advantage of Customer Insights is that it uses a data loading interface based on an evolution of the Excel interface. This is also used in Power BI apps , which seamlessly integrates with Customer Insights and tracks operations more thoroughly. In addition, it allows programming automatic updates of the ingestion of all data sources and transmitting it in turn to the construction of the profiles.
Second, The Data Washer
Once the data has been ingested, Customer Insights, following the parameters entered, manages and groups them, unifying them into unique profiles for each customer. This profile contains the customer’s sociodemographic, behavioral and transactional information.
In this sense, the advantages offered by this solution are numerous:
- It deals with internal and external duplications of data , unifying them according to the parameters that the client gives them or based on established parameters.
- Allows you to define a map to link specific fields with profiles found in other repositories. In other words, it brings together information from different repositories and data sources to form the unified customer profile.
- It enriches the unified profile thanks to additional information such as the customer’s interactions with the brand, the campaigns in which they have participated, their purchases, their responses to surveys, etc.
The last step, Segmentation And Analysis
The objective is that the information gathered in each customer profile is transformed into relevant actions of the company. This is why it is so important to segment audiences and create analytics. In this way, we will be able to increase sales and reduce the costs associated with communication with the client thanks to a high rate of loyalty.
We define 3 types of parameters:
- Client measures : They are multidimensional and allow advanced calculations that analyze a data in different dimensions. They can define parameters such as the customer’s purchase year after year or the behavior of said customer in the stores and their frequency, etc.
- Customer Attributes : These fields are simpler. They show the client file with parameters such as the number of visits of the client to the page, to the physical store, online purchases, etc. This especially helps physical businesses.
- Business measures : They are used to make a quantitative and aggregate analysis. In this sense, the parameters can refer to the total sales volume, the total sales per store, etc.
These actions allow the company to get to know its customers and drive meaningful and personalized actions that increase customer loyalty.