BIA/Kelsey Bytes are excerpts from research reports. This is the latest installment from the recently launched report, Call Commerce: A $1 Trillion Economic Engine. It picks up where last week’s post left off.
Speaking of AI, it could benefit call commerce more than it threatens it. As big data collides with voice, AI will unlock call analytics’ capabilities through underlying technologies like voice processing and advanced machine learning. As these technologies advance, so will call analytics.
We already see this in advanced forms of call analytics that apply benchmarking models to automate lead scoring. This involves scoring large volumes of calls against quality criteria and desired outcomes. Things like keywords and voice tone are identified as quality targets.
From there, machine learning can ingest that qualifying information and apply it to new inbound calls to score them accordingly. The target criteria will generally change for different campaigns, though some universal quality indicators will persist (think: a spoken credit card number).
In addition to scaling up analytics capability, this automated approach has the potential for considerable cost advantages over the current state of the art: human lead scoring. And though voice processing and machine learning still have limitations, their reliability is evolving quickly.
Meanwhile, it is important to note that data alone don’t mean much. Call analytics is only as good as the insights that can be drawn from it. And for the time being that is at least partially a human endeavor. As the saying goes, “We don’t need big data, we need big insights.”