The ecommerce boom sparked by the pandemic in 2020 has leveled off, but online sales show no signs of slowing. Overall retail sales continue to increase-– more than $1 of every $5 spent on retail in Q2 of this year came from online purchases. For state governments, as long as ecommerce sales are on the rise, so is the amount of potential tax revenue generated by remote sellers, i.e. businesses that sell into a state but don’t have a physical presence there. While most states have adopted remote sales tax laws that apply to such businesses, actually collecting the revenue can be challenging.

To optimize revenue collection from this growing cohort of ecommerce companies, agencies must:

Identify Noncompliant Businesses

The first step is identifying out-of-state businesses that have nexus but are failing to remit sales tax. But tasking auditors with manually searching for these businesses isn’t the most efficient approach. Many state agencies are leveraging data and analytics to uncover patterns in sales activity and better identify potentially non-compliant businesses. Tools that leverage machine learning and artificial intelligence, like RevHub, have demonstrated results in this area.

Leverage Data and Analytics to Efficiently Drive Voluntary Compliance

Resource-strapped state agencies must use manual outreach methods and enforcement activities wisely. By leveraging AI/ML models to prioritize collections efforts, agencies can more accurately identify taxpayers who are likely to pay after a low-effort interventions—like “nudge letters.” Doing so will drive voluntary compliance and free up staff to focus on pursuing cases unlikely to resolve on their own.

Measure Outcomes to Scale for Success

It’s crucial that agencies analyze their efforts and use lessons learned to improve and expand identification processes. To accomplish this at scale, agencies need a powerful analytics platform with dashboards and reporting tools to track outcomes for key indicators and tax types.