There’s an interesting article in the January/February issue of Harvard Business Review by Matthew Dixon and some associates from CEB, a consulting group, about customer service agents that I think is worth a look.
Most of us understand the phenomenon that new customer service channels such as social, email, knowledge bases and the like have left the service agent with harder problems to solve. Those channels are here to stay because they cost pennies to solve problems, when they work well, but conventional agents can cost many dollars to resolve an issue. So there’s a decided bias for self-service where possible.
But as I wrote in “Solve for the Customer,” we need to ensure that there are graceful transitions from the channels to agents or we risk a backlash. As a matter of fact, some amount of backlash in the form of irate customers confronting agents is par for the course, but how are we preparing for this?
The HBR article suggests that we’re mis-staffing the call center for today’s activity. I say mis-staffing rather than anything else because the staffing model is simply old; it’s built on the assumptions of another time that no longer reflects service realities. The article summarizes a study of over 1400 service agents from diverse industries and many countries and its conclusions are interesting to say the least. It groups agents into seven different buckets and characterizes them and in this demonstrates the mis-alignment of skills to need.
The predominant type of agent in the study is labeled The Empathizer and they make up 32 percent of agents covered in the study. An Empathizer says the report, “Enjoys solving others’ problems; seeks to understand behaviors and motives; listens sympathetically.” That’s fine and harkens back to a time when all sorts of easy and hard problems visited the call center. But the agent type that’s more effective for today’s need is labeled The Controller, which the study describes this way—“Outspoken and opinionated; likes demonstrating expertise and directing the customer interaction.”
Unfortunately, Controllers make up only 15 percent of agents in the study and while they are ranked #1 in effectiveness, they are ranked #3 in service manager desirability. The reason is simple. Controllers, like good sales people, go off script; they relish their freedom to roll up their sleeves and solve a problem and they aren’t terribly enamored of following all the rules set down in a manual.
Here’s the rub: by the time a do-it-yourself customer gives up on tracking a problem through a maze of sometimes contradictory or confusing information on the Web, they aren’t likely to want a long empathetic encounter with a service agent. They’re much more likely to want someone to FIX their problem, pronto. That’s where the disconnect happens. Controllers take charge and provide comfort of quick resolution with little fuss that empathizers, despite best efforts, might not.
What to do? The article suggests that we get more controller types into the service mix but that suggests a change of mindset in the hiring process and even in the job description. If you advertise jobs for self-starters or other similar descriptions, you have to be able to let them, well, you know, self-start. That’s a big effort for managers used to controlling and scripting the action but it’s increasingly apparent that the change is needed.
There are approaches for making changes in the service department suggested in the article and I encourage you to read and consider them though I won’t summarize them here. But in an age of job depletion due to automation it’s great to see this article because it pinpoints one area where human interfaces are needed and also a mode of working that shows how technology can augment human actors.
In January, Oracle announced the acquisition of Apiary, a small company that tucks in to its product line and will not make much of a splash in the financial pages or possibly even in tech circles. Nonetheless, it’s important strategic news as I can explain.
The value of the deal wasn’t publicized but given the parameters the dollars will get lost in the rounding of Oracle’s overall revenue and profit numbers. The reason the buy is so important can be found in what Apiary does. As you can guess from the name, Apiary is a framework and tools for developing application programming interfaces or APIs. These interfaces will be used to help create cloud-based applications and services.
Now, I suspect the first thing many people will think about is all the Oracle applications that might need this framework but truth be told, Oracle has been working for years to cloud enable its apps and has rewritten most of them in the process. So I don’t think that’s the intended target.
Oracle’s move to the cloud is stacked with legacy issues which is one reason it has been offering so many types of cloud solutions to customers including infrastructure-, software-, and platform- as a service along with data as a service. The reason is simple, oracle has an installed base of well over 400,000 companies big and small. They’re running many different apps developed in-house as well as by Oracle and third parties. Moving all of them to the cloud will take a lot of time even if some apps eventually get replaced by modern cloud versions.
For them getting to the cloud means packing up and moving apps that can be twenty or more years old. Now, some customers will decide to scrap the old apps and implement shiny new cloud apps so they’ll only need to care about moving their data. But for many others, the first step in moving to the cloud will be economic which means simply moving the data center down the hall to one in the sky because renting infrastructure will be more economic than periodically buying new servers and managing versions of operating systems, middle ware, and applications. To these customers Oracle wants to sell IaaS or infrastructure as a service but to be successful, it has to help out with the move.
More likely than not, these companies already subscribe to a few cloud apps so the challenge now will be knitting together the new and legacy systems and that’s why Apiary is so important. Apiary will be a kind of facilitator for businesses moving to the cloud incrementally. Its value will be found in helping businesses forego the need for more expensive conversions and upgrades as they reach for the cloud. As a facilitator its value will be measured in deals for upgrades and upsells that might not happen otherwise or that would happen with greater difficulty and expense. So I hope Apiary’s owners factored that into their valuation estimates when they set a price. I am sure they did.
Oracle jumped into the AI and machine learning space for its CX products (a.k.a. CRM) and other applications like HCM at OpenWorld with an interesting difference—a huge data store to help educate the algorithms that work for you. Now we’re waiting for products to be delivered this year.
Machine learning depends on data about prior situations that the learning algorithms can use to get smart about a situation. Ten examples are good, 100 are better and generally the more samples there are the more refined a recommendation can be. That’s why machine learning never really ends. Like a great player or team, the learning and practice never stop and neither does the improvement. But it’s worth understanding that improvement beyond a point of basic competency will slow down regardless of what you’re modeling.
When you’re a kid you can make great strides in almost any sport but as you progress those strides become smaller and they’re harder won. Consider Olympic swimming or track and field where athletes try to shave fractions of seconds from world records. Often the difference between gold and silver can be an arcane difference in technique.
In business and machine learning, algorithms don’t stop learning for a very good reason—every new bit of data suggesting some fractional difference could be the harbinger of an evolving trend and the only way to stay abreast of that evolution is to stay current with the data. So you can quickly see that data is critical to the success of machine learning and that’s a big deal because few organizations possess all of the data they would ideally need to feed the algorithms that drive decisions.
Moreover, data quality is also a major issue and while a business might hold a great deal of customer data, its quality or lack—the duplications, misspellings, ambiguous designations, and incompletions—have, for years been the bane of data scientists and analytics users wanting to get information from their data.
Data quality is one thing that will distinguish Oracle’s Adaptive Intelligent Applications. Scheduled for delivery soon, Adaptive Intelligent Applications will work with customer data as well as Oracle’s Data Cloud, a collection of more than 5 billion consumer and business profiles, with over 45,000 attributes. The combination of a business’s specific customer data combined with this third party data will yield important insights that are unique to a business and its customers.
Businesses have always sought out fine differentiators like these solutions can provide to separate them and their rivals. Depending on the stage of market evolution that could mean product differentiation, value added services, product line extension—almost anything. The problem with all of these approaches is that they’re superficial. It’s all vendor, brand, or product centric because that’s all that a business could control prior to the development of very powerful computing and modern analytics and machine learning. If you wanted to peer into the mind of your customers you had to rely on gut feel—usually that of an executive who’d been involved in the industry for a long time.
The trouble with gut instinct is that it’s often wrong. The research that led to a Nobel Economics Prize for Daniel Kahneman—see Michael Lewis’s new book, “The Undoing Project”—shows that the rules of thumb or heuristics that we use in every day fast decision-making are often wrong or reveal a bias. Interestingly, since machine learning is definitely not human, it can avoid heuristics and biases and work the way we work when we concentrate and work slow and perhaps use pencil and paper. But the point of machine learning is to have the benefits of thinking slow and with a pencil but without having to do the work. In the process, machine learning is able to reach more users and prevent more incorrect assumptions from coloring business decisions.
To be clear this does not amount to a one size fits all approach to analytics. The Adaptive Intelligent Applications that Oracle has built also come with supervisory controls that enable users to fine tune their analyses to the specifics of a business’ needs. So the power of Oracle’s Adaptive Intelligent Applications will come from its well-crafted algorithms but also its Data Cloud. But the fact that it might prevent users from using an estimate or rule of thumb might turn out to be just as valuable.
In the last decade or so, the tech industry has become increasingly political which is different from being politicized. If I had to guess, I’d say that for the most part everyone is on the same page. However, many of the largest technology concerns have come to the realization that to protect their outlook, they need representation in Washington in the form of lobbyists in addition many successful entrepreneurs began contributing to parties. That political awareness became obvious over the weekend as the new administration’s ban took ham-handed hold of immigration policy.
The reporting has been robust on the subject. A story in the New York Times from January 30 noted numerous Valley headliners taking stands notably, “Netflix’s chief executive, Reed Hastings, wrote on Facebook that Mr. Trump’s actions “are so un-American it pains us all” and that “it is time to link arms together to protect American values of freedom and opportunity.”
But also, “Sergey Brin, a Google founder who immigrated from the Soviet Union when he was 6 … attending an impromptu protest on Saturday evening at San Francisco International Airport…“I’m here because I’m a refugee,” Mr. Brin said, according to a Twitter post by the Forbes writer Ryan Mac…
Of course Mr. Social Media also made a point, “Like many of you, I’m concerned about the impact of the recent executive orders signed by President Trump,” Mr. Zuckerberg wrote on Facebook on Friday…
And cautionary sentiment was even felt by those more likely to support some of the administration’s views. “Even some of those working closely with the Trump administration were critical. Elon Musk, the chief executive of Tesla and SpaceX, who sits on two of Mr. Trump’s advisory committees, wrote on Twitter that the ban was “not the best way to address the country’s challenges.”
All of that might only be preamble if the story form Bloomberg gains steam which said in part that the “…administration has drafted an executive order aimed at overhauling the work-visa programs technology companies depend on to hire tens of thousands of employees each year.”
Work visas are another name for the H1B visa. There are 85,000 H1B visas awarded each year and there’s always a scramble for them because they enable companies to import talent that they simply can’t find domestically. It doesn’t mean Americans are stupid or lazy but sometimes if you want the expert in a small field to work for you, the field is rather sparsely populated and you hire where you can.
Tech executives are getting creative. For example, Techcrunch wrote that “Early Twitter investor Chris Sacca, for example, was an early one to start the trend and offered to match donations to those who would direct message or respond with receipts.” About a dozen tech executives and venture capitalists followed the lead.
And that “Google has created a $2 million “crisis fund” that can be matched by up to $2 million in donations from employees.”
All of this might seem like a small disruption to some people especially if they wish to believe in the need for the clampdown. But even though the immigration ban appears to be a temporary disruption, we don’t really know what form any H1B reform might take.
Disruptions drive uncertainty and that drives other decisions. If you put enough disruption driven decisions together you can have a movement or a trend. That’s what the tech world is all about and it’s what we innately understand. So the events of last weekend are at least troubling; they’re certainly troubling to get billionaire Brin to SFO on Sunday to take selfies with arriving passengers.
It’s hard to say what forms the next actions and counter actions will take. Certainly, there are options and one of the easiest and best would be for the sides to begin listening to each other. But it takes two sides to have a conversation and so far each has been speaking mainly with itself.
I am of two minds about this award. I am happy to have received it in a field that includes so many friends who are really smart, good writers and analysts, and nice people. But I also feel sheepish about advertising it. You know? Many thanks to all those people who read this, give me good ideas, and stay friends through thick and thin. All right, this party’s over. Back to work!