Every support call your team handles carries a cost that most operations heads underestimate. The salary on the payroll is visible. The infrastructure, supervision, training, repeat calls, and idle time that sit beneath it are less visible. Together, they produce a cost-per-call figure that is significantly higher than most finance teams realize and almost entirely reducible for the call types that make up the majority of e-commerce support volume.<\/p>\n
Let’s breaks down the real components of cost-per-call, shows where AI voice bots change the model, and walks through a sample calculation that illustrates what the reduction looks like in practice.<\/p>\n
Cost-per-call is not just agent salary divided by calls handled. It is a blended figure that includes every input required to produce a handled call.<\/p>\n
For a human agent model, the components are:<\/p>\n
When all of these are factored in, a cost-per-call figure of \u20b9120 to \u20b9180 for an in-house Indian e-commerce support operation is a realistic working range for manually handled calls. This is the number a voice bot model is being compared against.<\/p>\n
An AI voice bot does not eliminate cost. It replaces a high fixed-cost model with a low variable-cost model. The components look like this.<\/p>\n
The resulting cost-per-bot-call, at typical e-commerce automation rates, falls in the \u20b940 to \u20b960 range depending on call duration, platform costs, and escalation rate.<\/p>\n
The single biggest lever in the cost reduction model is automation rate, the percentage of calls the bot handles without human escalation.<\/p>\n
For e-commerce operations, the call types that drive the highest automation rates are:<\/p>\n
Call types that lower the overall automation rate include complex complaints, payment disputes, and anything requiring empathy or resolution authority. These are precisely the calls that should reach human agents. Freeing agents from the high-volume repetitive calls means they are available for these conversations without a queue.<\/p>\n
Cost-per-call reduction through AI voice bots is not a theoretical benefit. It is a numbers exercise. The inputs are your call volume, your current fully loaded cost per call, your target automation rate, and your escalation handling cost. The output is a monthly saving figure that finance heads can evaluate against platform investment.<\/p>\n
The \u20b9150 to \u20b950 model is illustrative. Your number may be higher or lower. But for any e-commerce operation handling thousands of repetitive voice interactions every month, the direction of the reduction is consistent, and the levers are well understood.<\/p>\n
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