You can’t take an enterprise approach to the customer without taking an enterprise approach to customer data—and with an unprecedented volume and variety of data to manage, companies today must develop a CDM strategy that incorporates data governance, data access, master data management, data catalogs, metadata management, and more. As you build a CDM infrastructure to help you better understand and deliver on what your customers want and expect, keep these eight best practices in mind.
A customer who’s making regular purchases from one of your lines of business may still be a prospect to another line of business. One department may consider anyone who’s made a purchase in the last five years an active customer, while another may drop them from the list if their last purchase was more than a year ago. As you determine which customer attributes to track and manage, use your enterprise-wide business goals to come to an agreement about how to identify and define customers.
Using AI and natural language processing (NLP) across systems lets you automatically combine transactional data (orders, quotes, incidents, assets, entitlements) and interaction data (web chats, call notes, etc.) into an intelligent omnichannel customer view that’s searchable across all data, structured and unstructured. The goal is to create a consistent 360-degree view of the customer across marketing, sales, customer success, finance, and corporate teams for more effective analysis, strategy, and execution.
Customer data can quickly become outdated. According to one source, up to 25% of the average B2B marketer’s database is inaccurate, affecting costs and customer confidence. Along with having a data backup plan, plan to clean up your data.
What does that mean? Validate and update information such as email addresses, phone numbers, and home addresses. Remove duplicates, and delete contacts who are wasting your resources. This kind of data cleansing enhances the value of your current data.
There are several things to consider with data cleansing:
There are also many automated data-validation tools that can be integrated with your CRM, making it easier than ever to ensure that your data is up to date.
Data cleansing helps you avoid mistakes and ensures that you have high-quality, accurate information to analyze about your customers. Think of data cleansing as part of your quality control.
If you can’t trust your data, you can’t expect it to deliver solid analytics on which to base your actions, so manage it like the strategic asset it is. Collect all your important customer data in a data lake or other repository that works seamlessly with analytics tools and other applications and can handle almost limitless simultaneous tasks or jobs. Implement privacy measures throughout the data pipeline. Transform, cleanse, enrich, and standardize your data and ensure that it is fit for use before you share it across applications. Take full advantage of new AI and machine learning capabilities while automating data management tasks as much as possible to boost efficiency and productivity.
By paying close attention to data quality and ensuring they use clean data, companies can make smarter decisions—and they’ll see the improvement in their bottom line.
The reason these companies have dirty data in the first place is that data collection can get confusing very quickly. Companies often have multiple departments collecting many different data points on individual customers, from demographics to individual data . That can lead to each department collecting the same data point as another department, collecting data in the wrong ways, or collecting more data points than they need.
The end result is too much useless data, which often leads to data security issues and confusion about what your company is doing with the data it’s collecting.
A good customer data management strategy can help you avoid confusing data like this. You’ll get a lot more functionally out of your data if you have set guidelines for customer data management. The first step to building your guidelines for customer data management is understanding what customer data management is and the principles behind it.
Providing customer-data training to your employees can save money over the long term. Having big ideas about how to collect and protect customer data doesn’t mean much if your team can’t put them into action. Invest time in training and education so your employees know how to handle and interpret data. Creating a data-driven culture benefits your business in the long run.
This is especially true if your business has a BYOD policy that allows employees to do work on their personal electronic devices. You’ll need specific policies in place to protect sensitive information. Make sure those policies are going to be easy for your team to follow, but comprehensive enough to be effective.
A range of cloud-based solutions are now commonly used for customer data management that serves as the centralized, beating heart of the effort to improve customer acquisition, satisfaction, and retention; improve customer visibility and targeted communication, and boost data quality.
While solutions such as a CRM may overlap, customer data management platforms go well beyond sales: They include a unified database that can be accessed by other marketing technology systems and integrate behavioral, transactional, structured, and unstructured data from multiple sources into a single repository that allows for customer profiles that provide a holistic, 360-degree view of customers.
Too much data (also known as data saturation) can overwhelm your company with information and hinder decision-making. Hackers can also gain access to data you shouldn’t necessarily have. On top of that, customer data is often not even utilized. In fact, “between 60% and 73% of all data within a company goes unused for analytics.”
Determining what data to collect requires strategic thinking. The data you gain from your customer needs a purpose. Sit down and decide what information you actually need. To get you thinking strategically, here are some questions to answer:
If you’re collecting information about customers using marketing-automation software, think about the data you really use. For example, do you need both the customer’s email address and phone number? Don’t collect data just because you can.
Adopting a systematic, transparent approach to gathering data helps cut down on unnecessary collection. If you obtain only the information that your business really needs to close the sale, you’ll be able to streamline the processes. A cleaner sales process can mean lower overhead and a boost to your overall financials.
Essentially, metadata is descriptive information about the data you are using. It should contain information about the data’s content, structure, and permissions so it is discoverable for future use. If you don’t have this specific information that is searchable and allows for discoverability, you cannot depend on being able to use your data years down the line.
Catalog items such as:
This information will then help you create and understand a data lineage as the data flows to tracking it from its origin to its destination. This is also helpful when mapping relevant data and documenting data relationships. Metadata that informs a secure data lineage is the first step to building a robust data governance process.
Gone are the days when a spreadsheet was the best method for storing customer data. Businesses that take data seriously will store their customer information in a secure database, aka customer relationship management (CRM) tool that’s also GDPR compliant. These tools are designed to make organization and collection of important intel safe and easy. They can also provide segmented customer insights based on the data, which is a great way to identify opportunities for improvement.
And having a CRM has even more benefits:
Unlike past decades, when channels were separate and siloed, today’s path-to-purchase is a long and winding road. Multiple touchpoints, both online and off, need to stay connected— including email, CRM, e-commerce, social media, and retail POS.
The customer journey may include several devices and applications, as well as demands for 24/7 communication and personalization. For example, the vast majority of customers say they find personalization appealing or that they are more likely to do business with brands that provide personalized experiences.
That means it is essential for organizations to deeply understand the customer at every stage. Customer loyalty will soon depend on a company’s willingness to protect customer information with the same fervor the pursue revenue.
Why Certain Product Led Growth SaaS Companies Are Crushing It
The ultimate guide on how to adopt a PLG strategy for saas businesses.
Product Led Growth (PLG) is a go-to-market strategy where the product is used to acquire, activate and retain customers. This strategy allows SaaS companies to create a pipeline of users that can be converted into paying clients without a human touch.
Changing the way you do business, within the tools you already use.
Create your trial account and start driving a product-led growth strategy.