We chatted to VP of Operations at HumanN, Eric Shaffer to hear firsthand how data matching logic has changed not only his own workflow, but re-shaped the way the entire company uses their data. Today, the company can use data to inform key decisions for marketing and business intelligence purposes.
HumanN is growing fast. On top of their many accomplishments—including a Nobel Prize—the producer of nitric-oxide-based health products was named to the Inc. 5000 List of Fastest Growing Companies in the US for the second year straight.
Employing a complex omni-channel marketing strategy, and with an influx of new customer and activity data, HumanN’s database ballooned. Eric and the HumanN team knew that deriving actionable insights from customer data would be key to sustaining growth, but knew that trying to manage things on their own would be expensive, time-consuming, and (likely) suboptimal.
Take just one example… In order to accurately understand the performance of a radio campaign, HumanN need to understand that from those customers who place an order on the website or via a call center – which are the new and which are existing customers. This data is pulled down in a daily fashion and the results interpreted the following day.
HumanN’s current solution was failing them, and their inability to effectively analyze and derive insights from data was costing the company a significant amount in both time and money.
Adding to frustrations, Customer Service lacked visibility into historical customer data—making it a challenge to deliver the top-tier level of service customers had come to expect. Recognizing their inability to manage this data—ever-growing, filled with duplicated and inaccurate records, and spread across disparate data sources—on their own, the company knew that something had to change.
Said VP of Operations Eric Shaffer, “As we scaled up marketing, the need for accurate campaign and customer data became more critical than ever. We knew our records were a mess, and that manual analysis would be error-prone and costly. We needed to automate.” Without highly accurate data matching logic, Marketing was investing without any real visibility into what was actually working. Fortunately, the data-driven team at HumanN discovered 360Science.
Reliable data quality… with accuracy and speed.
Because 360Science could match and verify records with more accuracy and speed than the competition, HumanN was able to enjoy the results of intelligent data matching right away. “Despite having to process large volumes of data, the integration was smooth and the results reliable,” said Shaffer.
A solution for the people—a more refined approach to data quality.
While alternative solutions offered general data matching algorithms, 360Science specialized in what matters most to HumanN: people. Because 360Science was built to address the nuances of customer data, and able to solve for issues not present in other forms of data, HumanN was able to better serve customers with a 360-degree of profiles and activity.
Trustworthy data enables confident reporting.
“Today, the first thing management does each morning is review reports facilitated by 360Science,” says Shaffer. “Everybody is confident that the numbers are right and there’s no longer the question of data accuracy. A true game changer.” This unwavering faith in data reliability means that HumanN’s teams are more aligned than ever, and the company knows exactly which marketing levers are available (and optimal) to achieve even their biggest goals.
The impact of superior data quality was felt across the organization.
After implementation, the results were felt company-wide. Marketing was suddenly able to identify its most successful campaigns and then make smarter decision, customer service was able to delight customers with improved context around activity, teams were more aligned, and the company had all the pieces in place to not only crush their goals, but realize truly exponential growth.
To learn more about how 360Science helped HumanN overcome their omni-channel marketing challenges and become a truly data-driven organization, read the full case study now.