November 3, 2020
Someone was asking me about the techniques that in time have been used to qualify contact center calls – to understand what the caller wants and so route the call to the best group of agents in the contact center operation. I must say I wasn’t there for the beginning of this story, but certainly I am there now, so here it goes…
One can say that call centers got started with the widespread availability of touch-tone enabled handsets. DTMF (dual tone multi-frequency) was invented by Bell Systems for signaling in the early ‘60s and the first DTMF enabled handsets were sold to the public in 1963. But it took many years before touch-tones telephones were widespread enough for use in applications other than calling. It also took many years for computers to become powerful enough to run software able to distinguish the tones, while also managing other aspects of the calls and the database queries and screen pops that allow the agents to be productive, at a manageable cost.
All these technologies converged in the 1990s. Several companies started to develop PC boards that could connect with the telephone network, using analog or time-division multiplexing (TDM) adaptors to talk with a switch. These boards communicated with other boards were packed with digital signal processors (DSP), programmable chips that could independently detect signals like DTMFs on the telephone line and provide the relative events to the software running on the host. At the time, I was working with Natural Microsystems, a Massachusetts company that was a pioneer in computer telephony.
Since this architecture was geared towards PCs, whose cost was falling while their power was raising fast (following Moore’s law), companies were able to set up contact centers at a reasonable cost. It was the dawn of industrial customer service: people started to be able to use their phone to try and resolve issues or set up services that previously needed an in-person visit to an office, or sometimes writing letters with no guarantee of a response.
However, while technology certainly enabled the birth of call centers, the humans work to make them function was also completely new. Agents had to be recruited and trained, a new organization geared to make answering calls as efficient as possible had to be developed. As with other sectors of industrial work, there was a need for specialization: agents could not be experts of everything and so a need arose to qualify calls for routing to the groups of agents in charge of specific topics.
The Automatic Call Distributor (ACD) was born, fronted by an Interactive Voice Response system (IVR). This was the beginning of the new century, the golden age for companies like Avaya and Genesys, innovators in the call center software field: every airline, every bank, every telephone carrier suddenly wanted a call center and business was booming.
(Call center is now an old term. Successive generations of marketing lingo have changed the name of the thing, first to contact center – with the addition of messaging and chat to the voice channel – then to customer experience (CX). We’ll change terminology as appropriate…)
So, companies wanted a call center, but to do what? This was a new capability and initially it was driven by technology (because we can) and by competition (because our competitor is doing it). Consumers were probably pleased but the ability to call a company was not seen by the public as a reason to buy its products or services more. Since calls were mostly to complain about a troubled offer, it was rather a double negative: mitigating problems, try not to lose customers. This does not bring in new money, so for companies, the call center was a cost, not an investment.
But they couldn’t go back: the genie was out of the bottle and the public was used to it. And so, in order to reduce costs, contact centers were operated on a shoestring budget, with the bare minimum of agents. At the same time, callers needed to be filtered: only the most motivated, and persistent, could be allowed in and IVRs were the perfect tool to provide this filter. And so, the menus got longer and more confusing, music on hold was invented (it could be seen as an advance, but for the low bandwidth of telephone lines that are geared to human voice and not music. This is why it sounds horrible.) and the wait and bad experience necessary to reach call center agents became a widespread meme.
Occasionally, there were attempt at improving the situation. In the late 2000s, texting and messaging app started to be used more and more as people switched from fixed phones and mobile “feature phones” to the first smartphones. Call center software vendors incorporated messaging into their product, which became “contact centers”. Also, the first limited voice recognition software started to appear and suddenly people were asked to “press 1 or say “sales”, press 2 or say “support” – not very user-friendly actually since the time it took to read a menu grew substantially. But something was also changing in the companies’ attitude towards their customer support function.
Slowly, customer support was seen as more of a competitive advantage. The first step was the recognition that, with products and services largely equivalent between different players, consumers could switch allegiance quickly and easily. Customer service was an area of possible improvement, and one in direct contact with the public. I was working at Genesys then, and I remember the spiel we adopted in customer conferences: how customers were a lot more likely to churn after a bad experience, so it was imperative to avoid one.
But this was still a negative approach: the real turning point was when companies started to factor in the customer experience not only as a cost, but also as a way to increase revenue, switching the contact center from a pure cost to be a revenue-generating activity. The switch is all in the accounting, but it’s fundamental: now there are reasons to invest more in the contact center and measure the overall revenue brought in by a better customer experience.
Which brings us to the latest development: the rise of conversational AI for customer service. Conversational AI was made possible by advances in natural language processing, understanding, and rendering, and the advent of Cloud architectures and the abundance of data available to “train” the Artificial Intelligence algorithms. Conversational AI changes the paradigm of how calls (or text-based interactions) are qualified, and in many cases allows customers to self-serve. The key is to understand the natural language that people use. This allows the computer program (Virtual Agent) to find out the intent of the call very quickly because the service domain is treated as a flat field and not a tree. Language is much more expressive than tones or single words and so an interaction goes through the qualification phase in 1, 2, rarely 3 question-answer exchanges. At this point, the Virtual Agent has enough information to either engage a computer application to satisfy the customer or forward the call to a human agent.
The customer experience is much better, and costs are also reduced for companies. This is because the biggest cost in contact center operation is the agents. Conversational AI has been proven to help boost agents’ productivity, and increase their job satisfaction: routine, boring interactions are best served by the Virtual Agents, which also collects information for the human agents to use. Agents are left with more interesting and engaging conversations.
But having more productive agents means that fewer agents are needed; happier agents means that turnover is reduced, with less need to train new agents since old hands are more able to solve customers’ problems faster. At the same time, fast, frictionless experience is what customers want when they contact companies: the best customer experience is when problems are services are provided quickly and easily.
Conversational AI is the present of call qualification, and its near future too. In the next few years it is easy to predict that contact center will rely more and more on a mix of humans and bots – the bots dong the grunt work, the humans taking advantage of it to deliver better and better experiences to customers. Beyond that, who knows? Making predictions is hard, especially about the future (Neils Bohr, various attributions).
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