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Data Transformation: 4 Steps to Better Understand Your Customers

How to Transform Your Data in 4 Steps

Using data transformation methods to improve customer understanding

Customers and prospects are everywhere. Unfortunately, for most organizations, so is their data.

To adapt to user preferences and stay top of mind, organizations are continually expanding the number of channels they use to market and communicate with clients. But all too often, each data stream exists in its own vacuum, resulting in data that are both underutilized and not representative of a customer’s experience with a business or brand.

This presents a pressing challenge: turning their ever-growing, disconnected mass of user data into meaningful customer understanding that leads to growth

What are the four steps your organization can take to achieve data transformation and better customer understanding?

Related Reading: Digital Transformation: Why High-Quality Data is Do or Die

Step 1: Onboard Your Data 

A critical first step towards customer understanding is to bring all the data together—to onboard it—into a centralized location. Given the continual introduction and adoption of data sources, it’s inevitable that data management and onboarding is an ongoing process for an organization. 

The best practice is to start with a two-year plan based on current and anticipated data sources. The schedule should align with business needs and also account for the required human and financial resources. 

And be sure to prioritize work according to the anticipated impact—digital channels are usually high on the list—and focus on the sources that are most critical to get into the data supply chain.

Step 2: Create a Unified Data Layer 

Raw data is messy. And it can be a struggle to connect disparate data sources, especially for large organizations with a widespread digital and physical footprint. A unified data layer can help you maintain consistency, scalability, data privacy, and speed when it comes to analyzing and generating actionable insights from your customer data. 

An open framework for connecting data channels, a unified data layer neutrally defines the data important to your business in language meaningful to your organization. This framework should be technology agnostic and serve as a central repository for other data layers. Most often, it’s a cloud-based system. 

While you’ll need to dedicate concentrated resources to create the parameters that ensure the data layer’s success, the investment is absolutely worth it in the long-term. You’ll have smoother onboarding, fewer bottlenecks and a system flexible enough to meet both current and future business requirements for user data. 

Related Reading: 6 CRM Best Practices for Improving Your Data Quality

Step 3: Determine User Identities 

By design, your various data sources contain different customer- and prospect-identifying features. But all that complexity can make it challenging to deliver a consistent omnichannel user experience.

The technologies pulling in this data play a key role in identity resolution by providing context between the relationship of the records from all the data sources over time. However, the onus is still on the company to initiate the correct activities that will generate meaningful and accurate data. 

Identifying and understanding users is a delicate balance between knowing and guessing because users change as they move through the stages of the buyer’s journey—from anonymous, to unknown, to known, to a prospect and, finally, to a customer.

However, there are a lot of different tactics you can take throughout that journey to determine identities. Website cookies are an example of a tactic to use early in the journey, often followed by prompts to provide identifiers (like name and email) in exchange for gated content, perks, loyalty rewards, access to competitions, or a trial account. 

A new foray into the field comes in near the end of the buyer’s journey: relationship intelligence automation. Often known as RIA, this new technology fills out and corrects contact data already housed in your CRM, ERM, and other business data systems. 

Related Reading: What is Relationship Mapping and How Does it Work?

Step 4: Map Your Customer Personas

A customer or buyer persona is a representation of your ideal customer based on research and data about your current customers.

Creating a few key personas can help your organization in a host of ways, from helping you focus your time wisely to guiding product development, and more.

Relationship intelligence tools, like Introhive, can help organizations get to know their users even better. The degree of captured information grows along with your relationship to the contact. In the beginning, relationship intelligence pulls data from social media profiles and other sources. 

Once emails are exchanged, relationship intelligence also extracts information from the email signature and stores the email for later reference. Then, when a user becomes a customer, the tool tracks relationship trends to ensure that organizations are taking the right actions to ensure customer satisfaction and retention.

Grow Business-Building Relationships with Data Transformation

When your user data is a mess it’s hard to achieve true customer understanding. Taking critical steps towards data transformation can help organizations deliver the right message across platforms regardless of whether the user is unknown, known, a prospect, or a customer. 

Relationship intelligence automation improves the effectiveness of customer outreach and the strength of relationships by improving the quality of data and expanding what is known about customers and prospects.

To learn more about how Introhive can help your organization connect disparate data to reveal valuable customer insights, check out our brochure. And for more help with your transformation project, download our step-by-step Digital Transformation Journey Playbook.

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