Data-driven marketing isn’t new, but does it still have the same meaning? Historically data-driven marketing was synonymous with applying database analytics to create more meaningful communications. Data-driven marketing typically used customer-level information.
Customer relationship marketing (CRM) was generally described as communications aimed at entrenching customers with a brand or company. CRM has always had a close association to data-driven communications. Customer relationship marketing utilizes segmentation and analytics as a foundation to create tailored, customized or personalized communications. Versioning is based purchase behavior, appended characteristics, modeled attributes or other data variables. Data-driven communications are a fundamental response best practice and a proven practice to enhance marketing program performance.
Fast forward to the era of digital marketing and technology enhancements, and marketers suddenly have easy access to more data sources than could possibly ever be mined. CRM has taken on a new meaning, most often the language is used in reference to sales contact information and systems.
What role does aggregate level data versus customer level data play?
Much of today’s digital data is aggregated at some type of group level (versus customer level). The accessibility via technology enhancements makes aggregated information very attractive for informing decision-making. Marketers can now access self-serve information via hundreds of digital marketing analytic tracking capabilities, social media tools, marketing automation platforms, and other online resources.
The question becomes what do today’s marketing and business leaders believe is the definition of data-driven marketing? And what role does aggregate data play in decision-making and marketing application versus customer-level data?
I recently completed my Google Analytics and Hootsuite Certification Courses. There is no doubt about it, these tools are impressive. The analytics are well equipped to determine performance between different scenarios, such as various campaigns, keyword triggers, content that drives conversion, A/B landing pages, effectiveness of channels, messaging that drives social engagement, geography of web visitors and other like information. The information can help steer strategy decisions and doesn’t require significant analytical capabilities or resources.
For example, search keyword analysis can help inform email subject line copy (and vice versa). Social data can also be utilized to inform website FAQs and sharable content development. There relationships between channels, and testing in one channel, can help optimize marketing performance in another channel.
Is customer-level data still needed?
Aggregated data is useful for driving strategic direction and understanding marketing performance. Furthermore, it is nearly required for tracking pull-oriented online advertising. However, customer-level data increases relevancy. Modern marketing automation enables efficient versioning and delivery of customized communications.
Inbound marketing is predicated on using landing pages to generate hand-raisers by asking for content information to “ungate” content. Once users download a piece of content, marketers have an opportunity to engage, leveraging behavior by tailoring future communications based on the initial engagement. Applying behavioral information to personalize outbound and follow-up communications takes the entire customer experience to a more actionable level.
It’s likely today’s marketers have more data and information than they could ever harness. Marketers that are able to harness both aggregated data and customer-level information are likely to have the greatest chance of success.
I’m interested in how your organization defines data-driven, please leave me a comment and provide me a sentence or two of context. Does your company use aggregate level information, customer-level data, or a combination of both? Which data have you found useful and for what purposes? What type of tools and marketing automation platforms are being used for analytics?