What is Database Marketing?
Database marketing is the art and science of aggregating and mining consumer data based on their demographic, psychographic and behavioral traits. Its purpose is to help organizations reach their target audiences more efficiently while optimizing the ROI of their marketing investments.
For many B2B organizations, database marketing strategies closely overlap with account based marketing strategies as they provide insights that marketers can use to “hyper-target” key segments, a necessary component of ABM.
Database marketing companies, and their in-house equivalents, have grown in importance recently due to the rise of ABM and the looming ubiquity of artificial intelligence (which performs “human-like” tasks based on data inputs). As technology advances, providing customers with a uniquely custom-tailored experience will likely change from being an applauded experience to the industry norm or minimum requirement for doing business. This evolution from “novelty” to “standard way of operating” will likely be similar to the arc we have witnessed with the rise of the Internet.
Who needs database marketing?
Who can benefit from database marketing? In short, every organization can. But, like any other decision, we must weigh the pros and cons. There are definite costs for implementing and maintaining a database marketing system, and increased profits generated from database marketing strategies must offset the costs, time investments and overall resource allocation. Therefore, businesses that already generate ample web traffic and sell multiple products are the typically the best candidates for database marketing. And B2B-targeted companies are especially great candidates if they have long-term customer relationships and actively engage in content marketing.
While all companies may engage in database marketing activities eventually, right now we are only starting to see these trends rapidly grow. Social media marketing platforms and Google AdWords have arguably mastered database marketing techniques early on by leveraging demographic data and web behavior to target consumer segments more efficiently.
Nevertheless, we rarely see brands accurately track consumer behavior across multiple platforms and browsers, and integrate the data holistically to meet interests and needs more effectively. In order to reach this type of “holy grail” of a customized, automated marketing experience, we must be able to track and identify customers using behavior-based algorithms aggregated from multiple sources. While we are working towards this paradigm, and growing closer each day, these types of technologies are disparate and rarely integrated well.
Examples of database marketing
A great example of a brand that has mastered database marketing is Amazon. While some could argue that Amazon does not engage in heavy content marketing (for even its B2B products), it’s apparent that Amazon leverages and mines customer data very effectively. Amazon closely tracks what customers have viewed, purchased or placed on wish lists, and then cross references this data with what other users have purchased to “cross-sell” and “up-sell” its shoppers (e.g. “You might like this too”). Database marketers often refer to this strategic algorithm as a “recommendations engine”.
Netflix is clearly another example. Netflix uses a recommendations-based algorithm to suggest programming based off your viewing pattern, and then cross-references it with other viewers with similar tastes. Pandora also pioneered a similar type of recommendations engine to suggest music based off of your prior activity. Although Pandora has seen its future dim against the rise of Spotify; there is no denying that it has been an innovative company that carved out a successful niche by using database marketing techniques.
Of course, the distinction between Amazon’s recommendations engine (upsell techniques) for shopping, and the Netflix/Pandora type of recommendations for programming is that their algorithms serve different business objectives. SaaS-based entertainment platforms use recommendations-based marketing to reduce churn and provide a more ideal customer experience. However, online retail distributors employ similar database marketing techniques but adapt them to boost incremental and repeat purchases.
How to begin with database marketing?
Depending on your capabilities from a data science and IT infrastructure stand point, it may be beneficial to outsource database marketing to a software provider or boutique consultant. Regardless of whether you partner with a vendor or to do it in-house, your first step should be to collect as much data as possible on clients and prospects without negatively impacting their online experience. You will want a database that contains at a minimum the name, address, and transaction history of your clients. Any data enrichment you can provide either in house or through a 3rd party provider will also serve you to help fill in the blanks.
Once you decide which data fields are most important to collect, you should ensure the data you aggregate gets attributed to the proper account and interactions. This will be crucial for guiding intelligent decisions as the attribution process lays the groundwork for successful database marketing.
Often the data points you seek entail a mix of business metrics and those that impact the user experience. Sometimes these data needs compete with one another. Therefore, you may to optimize the right balance carefully with A/B testing to see what resonates best with your target audience. If you work with a software company specializing in marketing attribution, they can ensure to that data is collected compliantly and accurately before applying it to any database marketing model.
Once you have defined your data collection methodologies and have infrastructures in place, you will be equipped to build segments and personas for marketing campaigns based off of the data points you are tracking. The more data you collect, the more you can improve your segmentation strategies and processes. For example, did you create a persona that you thought would be tied to a buying activity, but the data does not support it? Look to see if further segmentation is needed. If the assumptions around buying activity for personas are incorrect, and you are confident the data integrity is up to par, you could have a hole in your data bucket. You may be missing a key piece to the puzzle simply because you are not tracking a point that ties to an important buyer behavior pattern. As always, when troubleshooting, start with the simplest possible answer and then move up the chain.
If you would like to learn more about how Mirabel’s Marketing Manager can help support your Database Marketing goals, please contact us for a free consultation.