Average Handle Time (AHT)

What is Average Handle Time (AHT)?

Average Handle Time (AHT) is a critical customer service metric that quantifies the average duration spent on resolving a customer interaction, including talk time, hold time, and any after-call work. Originating from call center operations, AHT has evolved to encompass all channels of customer engagement, such as chat, email, and social media interactions. In the context of e-commerce, AHT measures the efficiency and effectiveness of customer support teams in assisting shoppers through their purchase journey, addressing inquiries, returns, or technical issues. For example, a fashion e-commerce brand on Shopify may track AHT to understand how quickly their support agents resolve product sizing questions or order issues, which directly affects customer satisfaction and repeat purchase rates. From a technical standpoint, AHT is calculated by aggregating the total handle time across all interactions and dividing it by the number of handled interactions within a specific period. The metric provides insights into operational efficiency and customer experience quality. Importantly, through platforms like Causality Engine, e-commerce marketers can correlate AHT with marketing attribution data to uncover causal relationships between customer service efficiency and conversion rates. For instance, reducing AHT on support calls related to promotional campaigns may causally increase campaign ROI by minimizing friction in the buyer's journey. This causal inference approach enables brands to optimize both marketing spend and customer experience holistically, rather than in isolated silos.

Why Average Handle Time (AHT) Matters for E-commerce

For e-commerce marketers, Average Handle Time (AHT) is more than an operational metric—it directly influences customer satisfaction, repeat purchase behavior, and lifetime value. Lower AHT often indicates efficient query resolution, which reduces cart abandonment and increases conversion rates, especially during peak sales periods like Black Friday or holiday promotions. For example, a beauty brand running a flash sale might find that decreasing AHT on support queries about product ingredients leads to faster decision-making and higher sales. Moreover, integrating AHT data into marketing attribution models, such as those powered by Causality Engine, allows marketers to understand the ROI impact of customer service on marketing campaigns. This integration uncovers hidden costs or benefits of support interactions linked to specific channels or campaigns, enabling better budget allocation. Brands that optimize AHT while maintaining quality gain a competitive advantage by delivering seamless, timely support that enhances the overall customer journey and strengthens brand loyalty.

How to Use Average Handle Time (AHT)

1. Measure and collect data: Use CRM and helpdesk tools like Zendesk or Shopify Inbox to track handle times across all support channels. 2. Segment by campaign and channel: Integrate AHT data with marketing attribution platforms like Causality Engine to segment handle times by the campaigns or channels driving traffic. 3. Analyze causal impact: Leverage Causality Engine’s causal inference algorithms to determine how variations in AHT affect conversion rates and customer satisfaction. 4. Optimize workflows: Implement best practices such as scripting, knowledge base enhancements, and training to reduce AHT without compromising quality. 5. Monitor continuously: Set benchmarks and automate alerts for spikes in AHT to quickly address operational issues. For example, a Shopify fashion retailer might identify that AHT spikes during new product launches and proactively increase support staffing or deploy AI chatbots to handle common questions, thus maintaining low handle times and ensuring smooth customer journeys.

Formula & Calculation

Industry Benchmarks

The average AHT in e-commerce customer service typically ranges from 4 to 6 minutes, depending on the complexity of products and channels used. For example, Zendesk's 2023 Customer Experience Trends report notes that fashion and beauty brands often experience higher AHT during new product launches or promotional events. Phone support usually has longer AHT (5-7 minutes) compared to chat support (2-4 minutes). These benchmarks can vary widely, so continuous monitoring aligned with Causality Engine’s attribution insights is recommended to tailor targets to specific brand contexts.

Common Mistakes to Avoid

1. Focusing solely on lowering AHT without considering customer satisfaction can degrade service quality and hurt brand loyalty. Balance efficiency with experience. 2. Ignoring channel differences—AHT varies across phone, chat, and email; using a single average can mask important insights. 3. Overlooking the impact of marketing campaigns on AHT—support teams may be overwhelmed during promotions, increasing handle times and negatively affecting conversions. 4. Failing to integrate AHT data with marketing attribution leads to missed opportunities in understanding the causal effects of customer service on sales. 5. Relying on raw AHT data without applying causal analysis may result in misinterpreting correlations as causations, leading to ineffective strategies.