Learn How Other Pros Use Data Profitably

Customer data is one of the most valuable types of data that companies can use to optimize marketing and sales efforts. Whether it’s behavioral data—how long a customer was on your website before making a purchase—or identity data, such as which job titles customers have, it can help companies engage better with their customers and offer every customer a personalized experience.

Using customer behavior and characteristics to drive outbound communications can be key to a successful sales and marketing approach. The window in which personalization was considered a nice-to-have instead of a business requirement is already closed. A McKinsey article shared research from U.S. adult respondents noting that 80% of them want personalization from retailers.

The research also noted that deploying personalization programs at scale can reduce costs for sales and marketing by 10-20%. Personalization at scale allows businesses of all sizes to reach their customers in ways that resonate.

Not only does customer data help with personalization, it also gives businesses a roadmap for finding similar customers in the future. Understanding how your ideal customer thinks and acts is integral to reaching them with your marketing and sales efforts.

Gathering, storing, and understanding customer data takes businesses from guessing how to reach their customers to knowing precisely how to do it.

Benefits of Customer Data Management

Customer data management can benefit any business that’s looking to grow its customer base or already has a significant number of customers. While establishing a CDM process is traditionally a focus for B2C or D2C brands, B2B companies can also benefit from CDM to better define their user base and upsell their existing customers.

In research that Treasure Data conducted with Forbes on company data strategies, we found that only 13% of organizations are highly confident they are making the most of their available data. Increasing focus on customer data acquisition, storage, analytics, and use is a great place to start for companies looking to get the most from their data.

Creating or improving a customer data management process has several business benefits, including:

• Keeping your customer data at a high-quality standard. Data that a company is frequently revisiting, analyzing, and reviewing provides much better insights for internal teams.
• Making better decisions. Real-world customer data can help organizations make better decisions on audience targeting. As a result, pay-per-click ads, social media posts (i.e., organic and paid) and email marketing will be more effective.
• Putting all customer data in one place. If you use a software solution to manage customer data, sales and marketing teams can find customer profiles with consistent and accurate data in one location. This reduces the risk of duplicate data within the organization.
• Supporting compliance. Depending on where your customers live, there may be requirements for your business regarding how to collect and how to manage customer data. Compliance with regulations and guidelines prevents your organization from paying potentially hefty fines.
• Supporting scaling. As a business grows, it needs repeatable processes that require minimal manual effort. Employing the right tools for customer data management can automate many of the operations needed for CDM and allow your teams to focus their efforts elsewhere.

CDM provides a better user experience for customers since their experiences are more personalized. Intelligent use of customer data allows businesses to automate the right messages to reach customers at the right time.

For example, a customer of a retail company can receive a discount through email if they placed items in their shopping cart, but left the website without purchasing. This benefits the business with increased sales, while making the purchasing decision easier for the customer.

Best Technology for Customer Data Management

The primary tool for a customer data management system is a customer data platform, also known as a CDP. CDPs act as a customer data management hub, using several processes to connect to different data sources and link data in real-time to customer profiles. This data can range from insights about the customer’s identity to information on actions they take related to your company’s business. The best CDPs have capabilities such as customer data management dashboards and some even generate real-time analytics, for real-time interaction management.

CDPs ingest and display first-party data—data that comes directly from the customer to the business—and third party data—data that comes from a third-party to the organization. While both types of data are valuable, first-party data is always preferred since companies can determine if the customer consented to the use and storage of their data.

CDPs are used primarily by sales and marketing teams for programs and campaigns; however, the data analytics they provide about customers can be used by other internal teams as well.

CDPs are often confused with another type of customer data management system: DMPs. Though they’re similar, there are clear differences between the two. DMPs also collect and display customer data, but the data is anonymous and is stored for a much shorter time than CDP data. The data is usually third-party or second-party data (i.e., data from an indirect customer relationship, such as a partner company).

Unlike a CDP, which can have several use cases within a company, DMPs are specifically used for advertising and retargeting. DMPs can be used as a data source for CDPs.

Lastly, customer relationship management (CRM) platforms collect customer data, but for a different purpose than CDPs and DMPs. CRMs are primarily used by sales teams for tracking interactions, purchase history and upsell opportunities with customers. The data can be used for tracking the effectiveness of marketing campaigns, running sales analytics, and determining revenue forecasts.

A company might employ all three of these martech solutions as part of a larger customer data management strategy.

Customer Data Platform vs. Customer Master Data Management

In the context of customer data management, people often compare Customer Data Platforms (CDM) with Master Data Management (MDM). This is an “apples to oranges” comparison since CDPs are software tools and MDM is a discipline.

MDM seeks to have a master record of data to act as a single source of truth. Its primary focus is on data governance and organization. As a discipline, MDM is a wide umbrella and customer data management (and technologies such as CDPs) fit within MDM.

You may also see the term “customer master data management” used to describe a subset of MDM. Customer master data management, or customer MDM, is similar to customer data management, but with a more limited scope. It refers to the creation and management of master customer records through aggregated data, but it does not include the focus on strategy that CDM has.

The customer master data management process is very similar to the overall MDM process. It involves pulling together data from multiple sources and systems such as CRMs and enterprise resource planning (ERP) systems and assigning customer identifiers to data so that records are grouped by customer. In addition, it brings records in from multiple internal and external data sources to combine the data into a holistic view. Customer MDM also includes removing duplicate data to create one source of truth.

Customer MDM data can be especially helpful for the Sales, Customer Service, Finance, and Legal teams. This data provides another tool to use alongside customer data management systems and is not a competitor to CDP, CRM, or DMP tools.

Data Types to Include in Your Customer Data Management Strategy

With customer data management, companies have several types of data for analysis and decision-making. It’s tempting to pull in all of the available customer information, but providing too much data can make it challenging to derive insights.

Start by understanding the different types of data available to you. Then, determine which data is most effective for your short-term and long-term business objectives.

Identity Data

One type of data to consider is identity data. Customers usually give you this data when they make a purchase, or you might obtain it through a form submission on your website. Examples of identity data:

• Name
• Date of birth
• Gender
• Phone number
• Email address
• Physical address

Attribute Data

Attribute data describes your customers’ characteristics. Companies can collect this data from customers using several methods, such as surveys, interviews, or focus groups. This can include:

• Job title
• Annual income
• Education
• Marital status
• Number of children
• Type of vehicle you drive

Behavioral Data

Behavioral data tells you how a customer interacted with your company. If you’re using a CDP, these details can be brought into the platform in real time. They can also come from direct interaction with customers. Examples of behavioral data include:

• Website visits
• Website form-fills
• Emails opened or read
• Click-through rates
• Products purchased and amount spent
• Products left in online shopping carts
• Number of product returns
• Customer support log details

Not all of these details need to be tracked. If you have a marketing goal to deliver more MQLs to your sales team through email nurturing campaigns, you will probably need to focus on behavioral and attribute data.

Behavioral data will give you insight into how people are engaging with your emails and related content, and attribute data will tell you common characteristics of the people who are already engaging. You could try new targeting efforts to acquire new customers who fit those common characteristics and focus on personalization efforts to speak directly to your target customer.

Customer Data Management Best Practices

Implementing best practices for customer data management helps your business use data effectively. It also instills confidence in your customers that you respect their privacy rights and handle their data in compliance with local, national, and global regulations. If customers don’t feel that you safeguard their data and use it ethically, they have no reason to continue sharing it with you.

Businesses have a steep hill to climb to achieve this level of trust. According to a report from SAS and Futurum Research, just 54% of consumers say they trust brands to keep their data private. The same report also found that 73% of consumers are concerned with how brands use their data.

Creating and communicating a set of best practices can help establish the necessary trust with customers. This, in turn, will make them comfortable sharing their data. Here are five customer data management best practices to consider adopting.

#1. Develop a CDM strategy. Implementing CDM processes and technologies without first developing a strategy can lead to disorganized efforts and an inability to measure success. It also means the business has no standards, guidelines, and goals with which to be held accountable. The documented strategy should answer these questions:

• Who will be responsible for managing the different aspects of your CDM process?
• Which data sources will be used to build customer profiles?
• Where will you store your data?
• Will you store data in one location (e.g., in a CDP)?
• Data governance: how will data be standardized and validated and who will make sure these processes occur?
• What steps will be taken to ensure that CDM practices are compliant with European Union’s General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA) and related regulations?

#2. Train your team. A strategy will only work if team members know how to use it. Make sure anyone who will have access to customer data understands the strategy and knows how to handle customer data accordingly. Developing the training will likely require collaboration between IT, Legal, and Marketing teams.

#3. Be specific about what you want to track. Decide early on what data to track, and optimize your data sources to meet that goal. You can update your CDM profiles in the future based on the effectiveness of what you start with.

#4. Implement technologies to automate efforts. CDM can be filled with manual processes, but that will create more work for your internal teams. Instead, implement technologies that can automate efforts like data integration, profile creation, and data analytics.

#5. Improve your customer journey. Use the customer profiles to influence your campaigns, and deliver personalized experiences for your customers. Make use of unified customer data to inform email, advertising, direct outreach, and website interactions.

Integrate Your Tech Stack for Maximum Performance

For customer data management, businesses need to connect to any data source that collects customer data. Determining compatibility between different systems can be challenging if the right technology isn’t used.

In a 2020 study titled “Trends in Data Management,” CompTIA found that 37% of IT professionals cited data integration as a top challenge. According to the research, solving the challenge involves the right storage solutions and the right workflow. The study notes, “Across infrastructure, skills and policies, there are a range of issues that businesses have to solve to properly manage their data.”

Implementing a CDP as part of a CDM strategy can break down silos and add a layer of simplicity for bringing together multiple customer data sources. CDPs have native connectors that can be used to integrate data, and provide SDKs, webhooks, and APIs.

With multiple options for ingesting data, all of your data can be included in your customer data management practice. Data from the rest of your tech stack—including mobile apps, CRM, ERP, DMP, website, and marketing automation—can be added to enrich your customer profiles.

Avoid Data Overload

Being intentional about the data you want to integrate into your customer profiles is key, but it also isn’t enough to avoid data overload, or a barrage of data that might not be useful or easy to interpret for decision making.

Here are a few customer data quality management steps you can implement to make sure your data empowers your team to effectively analyze customer data:

• Keep your data clean. Perform regular quality checks on your data to make sure it is clean, categorized correctly, and up-to-date. You can make this check part of a regular maintenance cycle for your CDM technology or more frequently for critical data that won’t automatically update in the CDP.
• Validate your customer data. Make sure you have the correct information from your data sources to include within the customer platforms. If not, you will need to correct this through the connection that the data platform has to your CDP.
• Watch out for duplicate data. If the same data is coming in more than once from either the same source or multiple sources, ensure that you’re not ingesting duplicate data.

Including these steps in your company’s CDM strategy and making sure they are a regular part of data and system maintenance will reduce the likelihood that your team is burdened with data from which they can’t gain accurate insights. Instead, they’ll be much more likely to get value from the technology solutions your company invests in.