Digitalization has drastically changed the way people shop and how they interact with brands. In this digital age, everyone is a potential customer who can research and compare products from different retailers. To nurture your brand and capture new customers, you need to adopt customer-centric marketing strategies.

According to the Gartner, only 14% of organizations have achieved a 360-degree view of the customer. However, 82% of businesses still aspire to attain this goal in a Gartner survey of 402 marketing, IT and other enterprise leaders.

The two major hurdles that organizations face when attempting to achieve a 360-degree view of the customer are poor customer data quality and the lack of consensus on what exactly a 360-degree customer view is,” said Lizzy Foo Kune, vice president analyst in the Gartner Marketing practice. “Digital marketing leaders hope that this view of the customer will unify cross-functional data in their organizations to drive personalization and retention. However, few are actually successful."

Creating a customer-centric digital marketing strategy that accurately targets and addresses your customers’ needs is essential for any business. However, this can be challenging when you lack the right tools and data to help you do it. In this article, we’ll be focusing on how you can use data analytics in your digital marketing efforts to acquire new customers and retain current ones.

What is customer data analytics?

Customer data analytics can be described as the process of using data to improve your digital marketing strategy. It involves obtaining and analyzing data from your channels and the customers who use them, so you can improve your customer engagement and conversion rates.

How to capture customer data with analytics?

The first step in data analytics is to capture data from your channels and customers. The type of data you capture will depend on your channels and customer types. For example, you might want to capture the email address of customers who sign up for your newsletter.

Start with clear objectives

It’s important to start with clear objectives so you know exactly what data you need to capture and why. For example, if you want to increase the number of customers who sign up for your email list, you need to decide what number you want to increase. Once you have decided your end goal, you’ll know what data you need to collect and create marketing campaigns that are tailored to getting that result.

Identify the necessary data

Once you have your clear objectives and number of customers in mind, you can start to decide which data you need to collect. There are a few different types of data you can choose from, including: -

Demographic data - This includes data such as age, gender, marital status, income, education, and interests.

Behavioral data - This includes data such as what customers do and don’t do, such as how often they open your emails, click on your links, and purchase your products.

Customer interactions - This includes data such as how often customers interact with your brand, such as how many times they click on your links, open your emails, and make purchases.

Customer engagement - This includes data such as whether customers like your content, your products, and your brand as well as how often they interact with your content.

Use the right customer data analytics tools

Once you’ve collected your data, it’s time to start analyzing it. Before you do this, you should make sure you’re using the right tools. It’s also important to consider the type of data you’re analyzing, as well as the context in which you’ll be using those data.

Espire’s integrated Analytics services cover Diagnostic/Descriptive Analytics to AI/ML powered Predictive and Prescriptive Analytics offering industry specific expertise in diverse domains such as Utilities, Customer Communications Management, Insurance, Education and Manufacturing.

 

Advisory

Domain specific consulting, Business maturity assessment, Strategic roadmap

Design

Diagnostic/Descriptive Analytics - Develop Bus Matrix, Business Process Elaboration, Conformed Dimension Model. Predictive and Prescriptive Analytics - Feature Engineering

Architecture

Diagnostic/Descriptive Analytics - Component Analysis, Data Flow, Data Integration, Data Lake Development, Presentation Layer, Data Strategy & Governance Predictive and Prescriptive Analytics - Diversified Algorithms Models, What-if and sensitivity analysis, & A/B testing, Assess and Deploy Analytics Models

AI/ML

Text, image, video, sentiment analysis, threat detection, model reports, solution deployment

Customer data analysis drives revenue through insight

Now that you have your data, you can start to put it to use. The first thing you can do with your insights is to create reports. These allow you to create summaries of your data and what it means, allowing you to track your progress and improve your marketing strategy. You can also use insights to start creating targeted marketing campaigns.

Summary

Organizations are facing headwinds in customer acquisition and customer retention as almost all the markets are nowadays buyer’s markets. Executives expect analytics to deliver information on potential customers/markets to channel resources and capitalize by acquiring customers/market. At Espire, we are assisting companies implement state-of-the-art technology solutions to achieve their business goals and build resilience.