Discover the best practices to starting a data-driven marketing strategy that reduces implementation risk and unlocks analytics’ potential organization wide.
All signs point to data-driven marketing as the way of the future. Organizations looking to modernize their marketing approach and upstart businesses looking to scale are all pursuing smart investments in data sampling, data storage and analytics in the hopes of getting even smarter.
However, like any transformative process, adoption can pose difficulties that make positive progress come only in fits and starts for some. The key is for businesses to anticipate these challenges early on while adapting their data-driven marketing implementation strategy to best fit their organizational structure. To help them accomplish this, we are proposing these three critical data-driven marketing best practices so that organizations can minimize risk while increasing their chance of success.
1. Realize That the Quality of End Analytics Hinges on Your Data Architecture
“Big data” processing approaches can handle massive informational floods, but they cannot spin wool into gold. The quality of inputs and the right data framework are what allow us to partner with any software, do any sort of analytics and deliver on our client’s business goals.
Maintaining data quality involves more than simple auditing and “scrubbing” tasks. Your entire structure — from how initial data is captured to how it is amassed in a centralized system or preformatted for processing — can eliminate many of the headaches adopters face when their data signals become a cacophony. Instead, arrange the data in symphony with conscious organizational standards that make the most sense for your goals.
For instance, allowing customer service reports to tie each complaint to an actual purchase from POS system data can transform an abstract result, “We had X percentage of complaints per purchase,” to a much more actionable result, “People who bought items at X store had the most complaints and were willing to spend more than the average.”
2. Start with a Pilot Program and Think Agilely
Organizations can flatten their learning curve by starting with the “training wheels” on, so to speak. Rather than giving analytics access to all your data, start with a small experiment and a set goal. You should feed limited sets of data into this system through intermediaries and retain backups so that there are multiple fail safes. Then, see if your inputs can lead to an actionable observation that impels decision-making. The lessons you learn from starting small allow you to focus on problem-solving and pursuing goals rather than tackling technical issues of scale. According to big data evangelist Harphajan Singh, pilot programs can also provide a proof of concept to stakeholders and help them “determine where to make the next incremental investment prior to making larger commitments.”
In the software world, “Agile” project structures go through a similar trial run process before adding complexity in iterations. After each major addition, the project integrity is tested and measured, and then it informs the next iteration.
3. Move Towards Self-Service
Once analytics program heads have most of the program’s kinks ironed out, their organization should shake off the notion that analytics is something that must be kept under lock and key. By giving everyone in the organization access to data:
More value can be leveraged from your investment
Data-driven thinking becomes the prevailing attitude
“Gut feelings” are weeded out in favor of data-backed decision-making
Most importantly, everyone participates in making analytics processes more usable and accessible through their feedback and personal user experience. As the volume of available data intensifies exponentially, so too does the need to have everyone committing to using it in the most effective way possible.
Soon, technology, like business analytics, will become as commonplace in the office setting as a keyboard and mouse. Those who do not keep pace risk technical illiteracy in an arms race that has become more about 1s and 0s than the ability to make brilliant decisions extemporaneously.