Customer Data Management: 5 Key Elements to Build Your Strategy
What is Customer data management?
How much data does the world generate in a single day? The answer through a simple Google search is a mind-boggling 2.5 quintillion bytes! This data is valuable to organizations because it helps to answer questions on how customers make buying decisions. So customer data is collected, stored, and analyzed in an organized manner by companies and this process is called customer data management.
There are three reasons why customer data management is necessary
- A lack of control over customer data leads to inefficiencies and poor decision making.
- Applying data management practices regulates how companies are using information because of its sensitivity, which helps build trust with customers
- Using CDM guarantees you a consistent way to generate the most valuable insights and maintain data quality.
This is why it’s become important to have a process with a set of internal and external policies to govern the use of customer data. That’s what customer data management is – a safe and trustworthy way for companies to collect, analyze, and manage customer data.
There are several layers to understanding and establishing a CDM system, and we are going to help you understand these layers. We have broken it down into 5 dimensions:
- Types of customer data
- Customer data platform vs Data management platform
- Steps involved in the Customer data management process
- Best Practices
- Strategic uses of customer data management
1. Types of Customer Data
The first aspect of your customer data management strategy is to understand the different types of customer data. Only 20% of the data generated is structured and easy to analyze, which means 80% is most likely subjective and unstructured. But both are worth something to your organization which is why it’s important to understand what each type of data means:
#1 Identity data: Identity data, is personal information about the customer. It is the most unique part of your data set since it contains details that are different for each persona you create. The importance of this data lies in the ability to create highly specific personalizations.
Data sources: Identity data is usually obtained from direct customer transactions with your organization, such as purchasing a service or product, completing a sign-up form, downloading a content asset, etc.
#2 Behavioral data: As the name implies behavioral data is information collected from any type of customer interaction or activities with your organization as well as the actions they took to reach you. This data is used to better understand different customer touchpoints and intentions during their journey with your company.
Data sources: Sources include data on how the customer is using your products and services, cookie information, IP address, etc.
#3 Quantitative data: Quantitative data is hard customer metrics that you measure to analyze both performance and gaps in your business operations. Some examples are customer service metrics, online/offline transaction metrics, campaign metrics, etc.
Data sources: These hard numbers are collected by helpdesk solutions, Social Media monitoring tools, Google analytics, CRM systems, and marketing automation tools.
#4 Qualitative data: Qualitative data consists of customer feedback, reviews, and opinions on products and services. Any type of information that cannot be directly translated into hard numbers falls under this category.
Data sources: Qualitative data can be collected using feedback mechanisms, group discussions, personal interviews, etc. where descriptive answers are usually the format of the information.
2. Customer Data Platform (CDP) vs Data Management Platform(DMP)
When taking stock of how to handle your customer data, there are two types of platforms that are popular: CDP and DMP. But what are these platforms and which one best suits your needs? The answer is usually both.
A Customer Data Platform allows you to aggregate personally identifiable information from all channels into a singular database. Personally Identifiable Information(PII) is a form of 1st party identity data that includes details such as Customer name, email address, phone number, occupation, etc.
On the other hand, a Data Management platform deals with third party performance data such as audience and campaign data, data from cookies, IP addresses, etc.
So what’s the key difference in using CDP vs. a DMP?
Finding out which platform is more important to your organization comes down to answering the following questions:
- Do you need 1st party or 3rd party customer data?
- How are you going to use your data?
Since DMPs deal with 3rd party data, the user or customer profiles you build from this data are completely anonymous and can be broadly segmented or categorized, but not into identifiable customer personas or descriptions. Primarily this data is used for targeting activities in advertising.
However, a CDP gives you a 1st person view of your customers. This means understanding the A-Z of a customer’s preferences and then the ability to segment them to your choosing. This detailed level of profiling can be used to create highly personalized marketing campaigns.
Most CDPs these days also help collect and manage some form of 3rd party data which is why it is more popular and reliable.
3. Customer Data Management is a 4-Step Process
According to Forrester, around 60-73% of data goes unused -2(source). In an era where most decisions taken are data-driven, it’s quite a surprising statistic. This is why customer data management is necessary so that companies don’t lose out on crucial insights. There are 4 steps in a customer data management process:
#1 Data Collection: The first step is to have processes in place to collect customer data. You need a single database to collect and store all your data. Information can be collected from various channels based on the different mechanisms your organization has put in place. For example, it could be data collected from forms submitted by the customer or behavioral data that you have been tracking using monitoring tools.
Once you have the data in place, it has to be vetted or ‘cleaned’ to check and enhance data quality. Since data can be structured or unstructured, what is usable and what’s not becomes critical to the next steps of your data management process. Through a business intelligence procedure called ETL which stands for ‘Extract Transform Load’, you can convert all the information into formatted, structured data sets that are ready to be processed.
#2 Data Segmentation: Segmentation is a very important part of data management because it helps create unique and identifiable data clusters that become important in the last step of this exercise. With the help of tools and segmentation techniques, you can create detailed customer profiles and data stacks.
Sales, marketing, and customer service teams have different customer information requirements since each function has a different purpose for the data. Without segmentation, it is difficult to ascertain what exactly the teams that have access to this data can do with it.
#3 Data analysis: Once the data has been segregated and detailed customer profiles have been defined you can start probing the data for valuable insights. The purpose of analyzing your data is to create specific initiatives that address individual customer needs. With the help of analytics and other software at your disposal, you can generate team-specific reports that will aid decisions taken by various departments.
#4 Data Validation: Once all the pieces of information are in place the final step is to move these pieces into the right systems for use. Your customer data will be accessed by several teams using different platforms to enable their data-driven decision making.
This requires you to integrate the systems used by customer-focused teams with the central repository and have protocols in place to manage that data efficiently. This integration helps marketers, salespeople, and other teams to use this data in real-time for optimizing their campaigns.
4. Best Practices in Customer Data Management
Now that you have the process of customer data management in place, it’s time to take a look at the policies and protocols that govern this process.
Storage: How do you store your data? This is the first question you should ask, and it goes back to the debate about using a customer data platform. If you want your customer database to be centralized, then a CDP makes sense, but if you want to distribute customer data across various tools so that multiple teams can access and use the same data, then you’ll have to look at other options. But the short answer is that a CDP that facilitates a centralized system of storing data is much more beneficial because it’s easier to manage. You can also use a data warehouse or other internal sources depending on how much data you are collecting.
Governance: Data governance is the policies put in place to ensure there is no misuse of customer information. Given how sensitive customer information can be, before taking any action on the data, you need to set up rules such as access management and duplication control.
Technology: There are many products and platforms you can use to effectively manage customer data. Here are three main ones used by different teams in a company:
- The most important tool you need is a Customer data management platform that centralizes and organizes all your information. A couple of companies that offer CDP software are Exponea and Microsoft
- Customer relationship management(CRM) tools can be used by sales and marketing teams to store and manage data belonging to different stages of the customer lifecycle. Freshsales is an example of a CRM software
- Helpdesk software like Freshdesk can be used by support teams to handle information originating from customer queries and feedback.
Security: Data breaches are one of the most serious attacks on businesses today. It can cause major financial and reputational damage. With all the publicly known incidents that have happened to the biggest names in the corporate world, security concerns become a top priority for businesses. With GDPR rules also now in place there are several security accreditations companies have to obtain to ensure compliance. Ensuring that there is no chance that your customer database can be compromised should be a key element of your CDM strategy.
Recovery: It’s necessary to have a plan B when handling customer data. That’s what makes disaster management for customer data all the more crucial. Have relevant and robust backup facilities in place to ensure you can recover the data any time in case of a mishap either hardware or software-related.
5. Strategic uses of Customer Data Management
With your data now in place, it’s time to explore how a customer data management system can help improve business performance. Customer data will primarily be used by these 3 teams;
#1 Marketing: The challenge for 81% of marketers when it comes down to using a data-driven strategy is the lack of resources or access to the right data – 3 (source). The goal is to improve awareness and generate demand for your company with well thought out campaigns. It’s not an understatement to say how important data management practices are.
Here are the primary applications of customer data for marketing:
- Targeted campaigns (Personalization): Data plays a huge role in designing any marketing or advertising plan. By using data points from a CDM system marketers can create detailed personas and design tailor-made marketing campaigns for each of them. This helps raise the probability of success of their activities and also reduces cost.
- Create better retention programs: With the power of targeting and personalization, concentrated effort can go into retaining your most valuable customers. This boils down to using data to create the right type of loyalty programs that incentivize your customers to continue doing business with you.
- Optimize customer journeys: Recording behavioral data at every touchpoint is made easy with the use of popular monitoring tools like google analytics. This data is extremely helpful to marketers when it comes to understanding the customer journey and in the process, optimizing each touchpoint using the data collected.
#2 Customer Support: Customer service is a high touch, high impact core function that can benefit from a data-driven approach. 58% of enterprises experience a significant increase in customer retention and loyalty as a result of using refined customer analytics. (4-source) Here are ways support teams can take advantage of customer data management practices
- Streamline support: Customer data management helps you track and understand their activity in a customer service journey. By analyzing a customer’s issue history you can set up several measures to improve service experience in a way that makes it frictionless for the customers. Setting up self-service and automation are great examples of allowing customers to help themselves. Using data you can also predict beforehand issues that might arise for customers and take proactive action.
- Channel optimization: An omnichannel approach is a great way to improve customer convenience. Prioritizing support resources on each of these channels by analyzing the ticket volume is a great use of data. For instance, if emails are the most common way your customers reach out to you, use data to understand why that is happening. Then you can analyze whether you should focus efforts on improving email support or try and redirect some of that ticket volume to other channels.
- Feedback mechanism: Feedback is a big part of your customer data ecosystem, and support teams are generally the owners of this domain. Since feedback is required by most functions in the company, customer service teams should continuously improve how feedback is collected and the insights generated from this information.
#3 Sales: Sales teams rely on customer data to help them push through deals and understand customer buying behavior better.
- Forecasting models: Customer data helps the sales team understand buying trends and patterns. This aids in creating better demand forecasting models and relay the necessary information to the teams that can help them be prepared for the upcoming sales cycles.
- Discount pricing: Like with marketing, sales teams need customer data to adjust their pricing of products. Discounts are usually valuable incentives sales teams use to drive their numbers and hence customer data becomes important to understand what worked for each deal that gets closed.
- Sales strategy: A big part of sales strategy is going after specific buyer personas. So customer profiling which is a byproduct of the customer data management process helps sales teams categorize these personas by industry, geography, etc, and build a strategy to achieve better results.
How Freshdesk Helps Customer Service Teams Manage Data
A customer support software solution like Freshdesk can help customer service teams provide seamless support for their customers while tracking important customer complaint information. Here are some ways Freshdesk enables better customer data management:
- The ticket portal makes capturing complaint information simple
- The Omnichannel capability integrates all information from phone, email, Social media, and chat into one system in order to offer a 260 degree of each customer with all previous interactions and necessary context.
- Access to the same data by different teams, or sharing access to other teams allows for easy collaborations between teams
- It stores a customer’s issue history allowing any agent to establish context easily during follow up conversations with customers
- Reporting and Analytics that helps you derive detailed insights and create customized dashboards to drive your decision making
- Powerful integrations open up possibilities of what you can use customer data for
These are just a few examples of how Freshdesk empowers support teams to handle data efficiently and deliver better customer experiences.
Customer Data Management is an Organizational Responsibility
The case for having a strong customer data management strategy is a simple one – it’s a win-win situation. Why? Because both you and your customers benefit equally from implementing the practice. Organizations have an optimal way of making business decisions based on useful information provided by customers. And customers will trust you to have their back when it comes to handling their data responsibly while getting a personalized customer experience.
So it’s the responsibility of companies to ensure that they understand the importance of customer data. The best way to handle that responsibility is by creating an exceptional customer data management system.