Did you know that our brains are biased towards the negative? That negative orientation is probably why we are more familiar with a vicious cycle than a virtuous one. Yet, a virtuous circle also known as the snowball effect is a recurring cycle of events that reinforces itself through feedback resulting in continuous improvement. It is the process that underpins Amazon's Flywheel.
In 2001, Jeff Bezos sketched what he called the virtuous cycle on a napkin. Data is collected during each customer experience. That data is used to refine the customer profile. The more a consumer uses Amazon, the more data is assembled ultimately creating a personalized experience for millions of users. Each iteration moves a potential buyer through the sales funnel until Amazon is recommending products customers didn't know they needed.
Amazon's feedback loop allowed the company to inform its product design. As the company interjected data earlier in its customer experience design, the better the end results and at a lower cost to the company. Without data, Amazon would not realize its virtuous cycle.
Although the mantra for product design has been customer-focused, its implementation has not always produced a positive outcome. Companies need data coupled with a customer focus if they want to ensure financial success. A recent survey found that organizations with a data extraction strategy to support their data-driven designs added 66% more profit to their bottom line in the last 12 months. These businesses established a process to collect, organize, and analyze data that was used during the design phase to deliver a data-driven, customer-focused product.
Knowing how to manage the collected is the first step in data-driven design. Companies collect data on their customers from a variety of sources. Because that data may contain personal or confidential information, they have a responsibility to regulate what data is collected and stored. They need to delineate the standards for data collection by:
Not only do organizations need to standardize data collection to protect confidentiality, but they also need standards to minimize the need for data manipulation. Through proper governance, enterprises can deliver data earlier without incurring added risk.
For data-driven design to work, everyone needs access to the same data. Collecting consumer information from an advertising campaign should not be restricted to the marketing department. Designers, developers, and engineers can benefit from knowing how potential customers responded to a specific product line. However, democratizing data requires data governance to ensure that protected information is not accessible to everyone.
Data collection can benefit from a virtuous data circle. As data is collected, it should be evaluated to ensure data quality. Through a continuous, iterative process, data consistency can be ensured and more accurate data can be applied earlier in a product's lifecycle. Constantly measuring data quality provides transparency in data collection and instills confidence in the results.
For example, engineers create a bill of materials for a product. Procurement is tasked with finding the parts at prices that do not exceed the projected production costs. As procurement agents go through the list, they find suppliers with supply chain issues. This event requires the engineer to adjust the design to compensate for the supply issues.
If the data contained in the procurement department were available to the engineers designing the product, they could have identified the potential bottleneck and selected a different part from the beginning. Depending on the criticality of the part, the product delivery could be delayed.
Organizations with a data strategy that includes governance and democratization have the foundation of a virtuous data circle. However, they need technology to fully realize their data's potential. There's simply too much data for humans to process. How many decades would it take a person to analyze a single Amazon customer's buying history to deliver a personalized experience?
Artificial intelligence (AI) is essential for a functioning virtuous data circle. The process of collecting and democratizing data becomes automated through AI and machine learning (ML) algorithms so the data quality and quantity improve continuously as the processes repeat. As the circle gains momentum, it becomes the Disneyland of data where dreams can come true.
As Tom Davenport of Babson College commented, data collection improves AI models, which improve services that reduce customer friction and help acquire more customers who contribute more data. The circle's value becomes substantially greater than any one cycle.
With more reliable data injected as early as possible in product design, organizations gain valuable insights as each cycle completes which leads to innovation. Black & Decker's understanding of how their power tools were used at construction sites resulted in a cordless product line that generated over $300 million in incremental sales in under three years. Because power sources are often limited to generators at construction sites, the battery-packed tools made it easier for professional contractors to work. Like Amazon, Black & Decker created a product that contractors didn't know was possible.
Amazon created a new marketplace with its flywheel model. Black & Decker developed a solution that professional contractors didn't know they needed. As Matt Ashare reported, even Netflix used its data algorithms to fund its hugely popular House of Cards.
Having data at your fingertips makes innovation possible. Whether it's understanding supply chain disruptions or market fluctuations, virtuous data circles enable companies to pivot quickly to capitalize on valuable insights generated through AI and ML. Using ChristianSteven's Power BI Report Scheduler (PBRS) ensures that information is available quickly and seamlessly. Automating delivery with Power BI Report Scheduler allows businesses to capitalize on the results of a virtuous data circle.
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