As marketing has evolved from the nebulous intuition checks of bygone eras to the current rigorous testing and analytics processes, it has become ever more reliant on customer data. Many companies now stockpile customer data for database marketing, so we wanted to examine how this practice can be used most effectively.
Database marketing involves collecting, analyzing and interpreting the data of current and prospective customers in order to create more relevant and effective marketing. It was first introduced as a form of direct marketing, with data warehouse companies compiling the demographic details, contact information and transaction histories of consumers, then using that information to create databases for marketing purposes. Access to these databases was sold to direct marketers as a lead generation resource.
Like marketing as a whole, today’s database marketing has evolved to be analytics driven and focused on data science. Brands now collect consumer data from an increasingly wide range of sources, including:
They use this data to accomplish a number of business objectives. Here are a few common database marketing examples:
To execute on these objectives, modern database marketing must be very data-driven. It relies on statistical techniques to create predictive models of customer behavior, which are then used to send the right communications to the right consumers, and to tailor offers to customers’ needs and wants. This is a labor intensive process; brands need data science teams to collect all the data and build the models, then their database marketing managers and marketing teams must figure out how to act on the models to create personalized marketing communications.
Data-driven marketing is how many brands try to personalize their marketing content to consumer preferences. However, the way some companies execute it creates a roadblock to success, because while their data science methods deliver insights, they then require human decision making to act on those insights. If you think about how much data brands are collecting across their websites, CRM software, loyalty programs and social media platforms, you can see why a company would need a nearly infinite amount of human resources to make decisions about all that data—and since that’s not possible, this process often creates a bottleneck.
Brands are better off using their database marketing software to enable a data-led approach. The difference is that while many data-driven strategies rely on people to be the final decision makers, a data-led strategy simply chooses the right technology, then allows that technology to automate the infinite number of decisions needed for 1:1 personalization.
The solutions that can make these advanced decisions utilize AI and machine learning. Humans are still needed as decision makers to set up strategic goals and review results, but no longer need to make the hundreds, thousands or millions of creative decisions it would take to develop individual marketing offers for every customer.
Before successful data-driven or data-led strategies can be implemented, database marketers will need to overcome a few hurdles:
As mentioned earlier, companies capture customer data in a number of ways, but not all of these systems capture the same data types or data points. So it’s difficult to assemble all of this disparate data into a single view of the customer without the aid of a data science team, intelligent technology, or both.
Data isn’t valuable if it’s no longer valid. Just as marketers can’t make use of an old geographic or email address, they can’t make use of old behavioral data like browsing activity if the customer has since had a change in motivation.
Lack of resources
If you’re planning to use your data for marketing that takes a data-driven approach, you’ll need a lot of human effort on both the data science side and the marketing side. Even with these teams in place, a data-driven strategy like microsegmentation can take several months to execute a strategy around.
Building up your human and technological resources is a significant investment, and one that not every company is ready to make. You may need to build a business case through small scale experiments in order to get leadership buy-in on the expense. But, because of the high volume and fragmented nature of data being collected on external platforms, it can be hard to know what experiments to run to prove your case.
If you’re interested in implementing database marketing, these are a few action steps you’ll need to start taking.
1. Collect all your customer data. The basics of customer data start with collecting demographics, such as the customer’s age, gender, marital status, education level, and location. But gathering data goes well beyond these categories—we’ve listed several examples below.
2. Find an automated technology solution that can connect to all your disparate data. Ideally, this solution should have a machine learning and AI component that can both analyze your data and act on it automatically to achieve business results. The automation function is key to saving the large amount of human effort we discussed earlier.
3. Keep data updated and secure. Choose a solution that keeps your customer data updated, and make sure the updating goes beyond contact information and demographic data to capture all of the customer’s behavioral changes and changing preferences. To secure your data, back it up regularly and make sure your infrastructure protects it from power outages and technical glitches.
4. Share data with other teams based on what each one needs to be effective. Request access to data from your data science team, and share access with other divisions that contact customers, such as sales and support.
Database marketing is a valuable tool that will only grow in popularity as marketers strive to be more relevant to consumers. To be successful at it, you must ensure your data is accurate and actionable. That will allow you to keep providing value to customers throughout their journey.