We’re experiencing a revolution in how businesses approach risk and revenue. Companies of all sizes are hiring in Trust & Safety, from powerhouses like Amazon to up-and-comers like Getaround, HelloFresh, and Harry’s.
For most companies, growing the business is the number one goal. But growth often happens at the expense of customer experience. Many businesses invest in expensive, cumbersome anti-fraud infrastructure like rules-based systems, which create friction for honest users. As a result, they face high customer churn, high overhead, and the inability to scale.
That’s why businesses across all verticals are switching to Digital Trust & Safety: aligning risk and revenue decisions, which are underpinned by machine learning technology and cross-functional collaboration. But why make this change?
For one, companies that invest in Trust & Safety are guided by a critical insight: trust underpins their entire business model. Rently, for example, developed a self-showing platform so renters can use their phones to schedule a time to tour a rental property. The company has invested in Digital Trust & Safety to keep bad users off their platform and ensure honest customers have a safe, seamless experience. Smart-mobility provider Lime has adopted a similar model. Their Digital Trust & Safety professionals help scale the business by ensuring the safety of Lime’s users.
In addition to trust, cross-functional collaboration is a cornerstone of Digital Trust & Safety. Under the legacy approach, risk and revenue are siloed. This creates a tension in which product, marketing, and finance teams work to deliver great customer experiences…and fail to align with the fraud team, which designs anti-fraud measures that might hamper those experiences. Businesses that adopt Digital Trust & Safety integrate risk and revenue decisions, so that Trust & Safety teams are stakeholders in growth and product decisions.
Twitter, for example, hires Trust & Safety managers who work across the entire organization to scope out and track projects. The company’s forward-thinking fraud-fighting ecosystem has allowed it to grow despite persistent challenges like content fraud and account takeover.
But it isn’t enough to collaborate cross-functionally and prioritize trust; businesses must also invest in critical fraud-fighting technology. Fraudsters are developing increasingly sophisticated weapons such as bots and machine learning-based phishing tools. Rules-based systems can’t keep up with new forms of fraud like content abuse, and they can’t scale in response to automated attacks.
By contrast, Digital Trust & Safety is underpinned by machine learning technology that customizes user experiences. The rules-based approach tries to apply a one-size-fits-all model to all users, often forcing even honest customers to prove their identity. But under Digital Trust & Safety, fraudsters experience friction and good users don’t.
To protect users from fraud and abuse, Google hires Trust & Safety teams with technical expertise, excellent problem-solving skills, and insight into the user base. Like other industry giants, Google’s machine learning technology gives them an edge against competitors.
In short, businesses rely on Digital Trust & Safety to level up. In a crowded, challenging market, Digital Trust & Safety helps leading innovators earn users’ trust, foster collaboration, and protect their customers and bottom line.