Dealing with people is hard. Ask your sales team and customer reps.
Sales and customer service demand exceptional people skills — attentive listening, empathy, articulation.
Although, both teams solve complex customer problems, the two sets of problems are distinct, if only slightly: sales is about converting consumers into customers, while customer service is about ensuring those customers are always happy, so that they keep contributing to sales.
Sales lead to profits; customer service leads to re-sales, which lead to more profit. A neat cycle.
Is one more important than the other?
If you are tempted to say sales, then you would be wrong. Both are equally critical to running and sustaining a business. (In fact, today, it seems the contrary. Customer service arguably eclipses sales. Even Jeff Bezos urges Amazon’s stakeholders to shift their focus from the competition, and instead focus on the customer.)
In any case, both sales and customer service have been transformed in the last 18 months. Teams are working from home. Meetings are virtual. Sales are uncertain. Inquiries have shot up.
Businesses that use Robotic Process Automation (RPA) are aware of its advantages. But it is time they take the next step: combine the execution of RPA “bots” with the analytics and insights derived from AI to better manage risks and solve customer problems more efficiently.
The result is something of a surprise.
When businesses leverage the combination of RPA and AI, they not only make their customers happy, but even employee happiness increases. And, as we will find out, the powerful combination has other value-adds that accelerate business growth and innovation.
The future of customer service
Customer service is critical to upholding brand reputation. But the reputation of customer service itself needs upholding.
Despite big banners on websites and daily assurances on social media, popular culture, which more or less reflects public consciousness, has always portrayed customer reps as either dull, almost indifferent, or overly, almost disturbingly enthusiastic. Both portrayals are mechanical, depicting real human beings with real feelings and aspirations turned into well-instructed, corporate robots.
Of course, many depictions are exaggerated for effect, parodying our customer-comes-first times. But how many times has your call been transferred from one rep to another, each repeating the same elaborate greeting before it is routed to the admin who can actually solve your problem?
And when customer experience suffers, so does brand loyalty and reputation. The bottom line takes a hit.
Most businesses use some form of RPA to automate simple, repetitive tasks like updating contact details. But RPA still requires a light touch. Instead, businesses could combine their RPA software with AI to make customer service nearly hands-free. At least for the customer.
Instead of pressing numbers and repeating details, what if a speech-based AI could identify relevant keywords and display customer details and solutions on a rep’s monitor in near-real-time?
In other words, the combination of RPA and AI could make customer support as easy and pleasant as having a conversation — more meaningful, less mechanical — as it was supposed to be.
In fact, the introduction of AI not only makes customer support more convenient, but more personalized.
Yes, first, AI-driven data analytics solutions enabled businesses to offer personalized recommendations, which make their products more likely to be purchased. Now, AI-driven analytics could enable businesses to personalize customer support, generating unique feedback and insights, while RPA does the rest.
Now, how would you rate the customer experience, when reps are much more agile, and solutions are delivered much more engagingly and efficiently?
Done well, very highly. Forbes recently discovered that 45% of customers switched providers during the pandemic. The reason? Not just low convenience, but also the lack of deeper engagement.
Poor customer experience, not product, was the reason they renounced their loyalty.
It matters more than you think.
Happy workforce = driven workforce
Converting or servicing, your workforce engages in two kinds of problems.
The first kind of problem is bigger and responsible for creating the most value for both company and customers. The second kind of problem is smaller and creates the least value.
The problem is, bigger problems cannot be solved without solving the smaller ones.
By now, it should be clear what we are referring to.
Bigger problems involve making deep connections with your customers and actually solving their problems, while the smaller problems involve meaningless but necessary tasks like recording contact details.
The term Robotic in RPA refers to the software “bots” that aid work, acting as our digital assistants. But remember that while RPA can automate simple, manual, and repetitive processes, it still requires inputs now and then. The introduction of AI then does not just make customers happy, but also your workforce.
The powerful combination could minimize — perhaps eventually, eliminate — the time and energy your workforce spends on low-value, simple problems, instead, allowing them to focus their limited resources on solving high-value, creative, and complex problems.
Unlike registering refund requests, listening and empathizing are meaningful. They are also challenging. And a lack of meaning, purpose, and challenges makes work dull, which leaves your workforce dissatisfied.
And if a happy customer is a loyal customer, then, according to KPMG, a dissatisfied employee is an unproductive employee. Combining AI with RPA makes for happier employees, which makes for more productive, driven teams.
Refund requests, for example, can be registered simply via voice commands. Admin intervention reduces, making more time for instead working on, say, customer strategy.
Making more time is rather an understatement. RPA innovator UiPath recently conducted an internal study to determine the time its workforce saves by adopting RPA and business intelligence. The tools reportedly saved them more than 300,000 hours. For a typical day of 8 hours, UiPath saved almost 40,000 days. And that is just with RPA. What happens when we factor in AI?
In fact, customer voice recordings and insights generated by AI-based data analytics could also be leveraged to develop better surveys and solutions.
RPA+AI could take productivity, resource management, and creativity to a whole new level.
So, what’s the catch?
Challenges to adopting RPA+AI
The biggest challenges to implementing RPA+AI are the biggest challenges to implementing any data analytics solutions: data literacy and obtaining high-quality data.
In the beginning, we mentioned that the combination of RPA and AI helps businesses better manage risks and make customer service efficient.
That is because insights derived from AI analytics help businesses gain a deeper understanding of their customers, enabling them to anticipate future needs from historic trends.
What that means is businesses can anticipate the emergence of new markets, new opportunities, exploring which, diversifies risk.
And, as we have learned, consumer insights enable businesses to deploy highly personalized solutions, which lead to higher customer satisfaction, and hence, re-sales.
However, forecasts are only as accurate as are the insights, which are only as accurate as is the data on which they are based.
And data can be really inaccurate, or it could be misinterpreted, in case the interpreter is data illiterate.
Yes, RPA has the added advantage of removing errors, like duplication. And the advantage will become twofold once AI is integrated. But while it can remove impurities, it cannot elevate quality. That is up to the enterprise.
Here is where data literates excel: knowing what is measured, and what is not. And how the measurement itself takes place. And when.
But that is not enough.
Integrating AI with RPA might be the next step, but it is certainly not the last. Enterprises must not just collect and leverage data. They must also innovate.
There is always room for improvement.
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SGA Knowledge TeamApril 14, 2021
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