The Future of Customer Service: Part Four - The Customer Service Agent of the Future
“They look like me, but none of them are me”, I, Robot
In the first 3 tracks we discussed, reframing the role of Customer Service agents into a Personal Concierge. Someone who can step in and anticipate customer needs, moving from reactive, to proactive to predictive. How using CS as a competitive advantage, and removing friction can really improve the overall customer journey. We then went on to talk about true CSaaS, removing Customer Service as a function and focussing instead on the ‘skill’, enabling Brands to put customers at the heart of their business.
In this final track we will discuss putting agents at the heart of your business too. Helping customers with their needs, one does not exist without the other.
Every year around budget setting time, we often hear the word productivity thrown around as a magic bullet to do more with less, or somehow hold to account those who could do more, but just aren’t. Heads and department managers within CS have had to find creative ways to make their budget stretch, and that matched with AI use cases in the contact centre, has oddly the opposite impact on productivity. If you automate the easy and repetitive stuff, you leave behind the trickier and meaty things for agents to deal with. The numbers will then of course show a drop in contacts per agent (CPA).
Did we unknowingly suppress the need to ask the obvious question: will AI take my job or replace me? Not only did we forget about the elephant in the room, but it is now mechanised. How we got here is bigger than this mixtape but let’s take a step back to reality, by drawing the distinction between human-like vs. replacing humans, on behalf of the agents we work with.
As AI improves, there will still be a gap between “human” and “human-like” responses. Great customer service requires true understanding. Imagine your wedding suit is delayed – would and could AI understand the importance and urgency, moving heaven and earth to find a solution? AI can sympathise to a degree but lacks human flexibility and autonomy. The customer service agent of the future is the ultimate problem solver, augmented by AI. When all else fails, the human agent swoops in to save the day, like a goalkeeper making an incredible last-second save. We’ve always seen agents as heroes, in the future this will still be the case.
Where are we now
Scripted responses – AI isn’t human enough and the scripted responses have the opposite but related impact of making humans more robot-like. Restricting the freedom that agents can have, leads to them naturally favouring process over judgement.
Lack of autonomy – Business rules are set that may prevent agents doing everything possible to help customers get what they need. Many agents fear making decisions that will ultimately help customers but which may overstep their authority and cause problems for them personally.
Difficult to find information – what slows agents down is that the information they need is not easily available or easily searchable. Being able to get the context immediately, to read the case history is essential to provide the best response.
Backlog pressure – agents are often targeted with answering a certain number of tickets per hour, even if they don’t necessarily resolve them. That’s because the backlog looks too imposing, and they’re always looking at the queue ahead.
How do we get there
Fully autonomous agents
Too often we treat humans like machines. We set rules and boundaries and insist that they stick within them, and punish them for using their own judgement in a situation. That’s not a great environment to work in, so it’s no wonder there is high employee turnover in customer service.
But as the machines get better, the ability to program them to do tasks that only humans are currently able to do also gets better. Once a large portion of the repetitive tasks that humans have to do is automated, then businesses have a responsibility to think about how best to use humans.
Finding creative solutions to problems, seeing things that machines can’t, and truly understanding the human need behind a customer inquiry. This requires agents to have near full autonomy to solve the problem that the customer presents them with. Do whatever it takes to solve that problem. If brands can let go of some of the control and trust their agents to move between process and judgement, this will positively make customers happy.
Augmented by AI
How would you explain your issue to AI? A superhero agent augmented with AI, could quickly see a summary of the case, sift through the salient points of interest, and get to the heart of the matter. This would negate the need for the customer to take a deep breath and explain their issue for the third time!
Then when the agent sets to work on solving the problem, the AI can step in to find the right knowledge base. A quick query can bring up everything that the agent needs to forge a solution. Any action that has a degree of complexity can be automated by AI, whether it’s generating a returns label, or booking a collection or any other such task like that.
Augmenting allows agents to listen really well without distraction, and using AI to take over routine tasks through commands saves everyone time. Imagine no longer needing your teams to print out returns labels!
Natural problem solvers
They will be the world’s best Googlers, the people who can find a solution to a problem and present it back with a “ta-da”, creating those “Wow” moments that really delight a customer and do wonders for the customer experience.
The mindset of the customer service agent will change subtly. Removing repetition and ‘challenging’ them to find creative solutions makes for a far more interesting job. These problem solving skills would then have knock on effects to other teams and departments, as these agents move into other roles, and bring transferable super problem solving skills along with them.
By using good feedback loops into the business, each relevant department can use the data collected by CS to improve the customer journey. This joined up way of working to solve problems for the long term will make a massive difference to the customer’s life.
Boundless creativity
AI image generators are very good at producing creative-looking images, but they still need a carefully constructed prompt to create something truly creative and personalised. If you remove the prompt, you end up with nothing.
Take an AI powered stylist for example. Its algorithms might be trained on patterns it sees in fashion magazines, celebrity red carpets and perhaps choice Instagram accounts. This will most likely mean that a lot of its suggestions tend towards the samey, and may lack the finesse that a real stylist may have.
For our problem-solving customer service agents of the future, they will have access to the best and most creative stylists and artists out there to tap into, and be able to find the right option for the customer who is asking.
Imagine that one of the leading sneakerheads is a globetrotter from Sao Paulo, who really understands what is cool because she is setting the agenda. Being able to connect with her and get ideas would be invaluable to our agents, and that’s something that will be possible with modern technology. Custom curation from the best in the world, wherever they may be at that time.
Conclusion
While AI and automation will take on more of the routine tasks, the human agent will still have to solve the really complex problems that occur. Therefore the agent will need access to a wealth of information that can be easily searched and summarised. But they will also have to do the things that AIs will struggle to do, which is think outside the box.
I’m hoping this rejection of AI as the side show to the main event of Customer Service superheroes, doesn’t come back to bite me, in a Skynet / Terminator movie plot scenario. Even with the amazing recall of some of the best memes on the internet, the gap between AI and agents is very real.
Try asking ChatGPT for a joke. With lots of well structured prompting, it can probably find a good one from the languages that it is trained on, and maybe it could even explain why it’s funny. But could it truly possess the wit to be able to understand a situation and make light of it? The rate of progress may well render this conclusion useless in a few short months!
The Customer Service industry has focussed on control and processes that have slowly killed any creativity that will leave a customer delighted. We have pitted agents against each other in a race to be the most productive, which is not the same thing as being the most helpful. So let’s enable the environment for them to flourish and recognise their differences to unlock creativity and deliver happiness for customers. Because, whilst on paper Customer Service agents might all look like me, none of them are me
The Future of Customer Service: Part Three - True Customer Service as a Service
”You Mustn’t Be Afraid To Dream A Little Bigger Darling” – Inception
Broadly speaking there are 3 models for setting up a call or contact centre for your business.
Inhouse (do it yourself),
Outsourced (use a 3rd party partner), or
Hybrid (bit of both)
Customers don’t often know whom they are speaking to, and as long as the experience results in a happy customer, it doesn’t matter whether the staff member is paid by the Brand or the Outsourced company. Sometimes outsourcing is referred to as Customer Service / Support as a Service (CSaaS). They are paid by the Brand they were contracted to support and pay their own staff (agents) separately.
The rise in online shopping and its global reach, means that sometimes the multiple products you purchase can come from multiple locations and in multiple packages (think drop shipping, direct to consumer etc.). If the retailer doesn’t quite stock everything you need, you may then need to place another order with another retailer and suddenly the number of parcels and deliveries start to stack up. Much like the cardboard boxes in my hallway ready for the weekly recycling trip.
As a customer, when you need some help, should you call the Brand you placed the order with? (most likely scenario) or in the case of mixed basket products: flowers, an iPad and some biscuits, which brand do you call? Perhaps you could contact the carrier making the delivery such as DPD, Royal Mail or DHL? Maybe even try calling Apple directly right after you’ve chased the Florist? I thought shopping in 2023 was meant to be more convenient?
What if, we just had to contact one place and one place only. For ALL of our orders ever. A single point of contact that could meet our customer service needs? It could be like picking up the Batphone and getting straight through to your customer service superhero.
For the brand, they could have access to a supercharged customer service offer. The best of the best service personnel, not limited to a location or a single company’s contract. Think Uber meets Customer Service. Your Superhero would be connected to all systems, with deep knowledge bases and brand agnostic. A fully augmented Personal Concierge spreading joy and happiness for customers. No longer exclusive to a select few.
Where are we now?
Extra work for customers – when a problem occurs, customers may complain to the ‘wrong’ company. This creates extra work for customers trying to speak to a brand who passes them to a carrier and so on.
Unrecognised CS talent – customer service rockstars can be limited to the brands they work for, the location they are based in and the basic systems they have access to. It can be hard for great talent to rise to the top and be seen as the best of the best across the industry (the awards industry helps, but not accessible to all).
Siloed access to information – even within brands, it can be difficult for agents to find all of the information that they need to be able to answer questions fully. Time is wasted searching for the right answer, transferring the call to another colleague / department or having to escalate to a more senior team member.
Contact centres as a cost – focused on driving down cost to serve. Little investment and as a result little engagement, feeds the high turnover of talent in the sector. Today, the debate seems to focus on where the team member is based, and for as many studies that show an increase in productivity from home, there is an equal study showing the same for working onsite in a building. Who really knows? And does it even matter?
How do we get there
Brand agnostic customer service
The first step to creating true CSaaS and enabling this version of the future, requires us to separate the agent from the brand. This means that the customer service agent is able to work across multiple different businesses at any moment. Taking their true CS skill with them to any and every business in the world (I’m not talking about bureau services).
This means that when a customer has a query, they call upon their dedicated agent (or agents) who can then set about finding the answer for them. Their personal concierge would look for the answer, relay it back to the customer, in the right language or tone of voice (not always the brand’s tone of voice – think cultural differences). AI’s role here would be to let the agent know if a particular brand is more formal than another, it can translate what the agent has said to suit the brand experience (or not).
Common knowledge sharing platforms
Think of Neo in the Matrix learning Kung Fu. He essentially “downloads” the knowledge from the cloud enabling him to fight more effectively. While customer service training is not quite kung fu and while I’m not currently suggesting “downloading” information into people’s brains, this is the model you should be thinking of. An agent could plug into Sainsbury’s knowledge base (KB) and find the right information for one query, then jump to the Tesco KB and do the same thing for that query.
Brands would need to get comfortable with making information available – most likely in a standardised way – perhaps it could be a LLM or GPT for their products.
All knowledge would then become accessible on a common platform. Of course, there would be a need to understand the information security risks but I’m convinced that we can figure out how to protect customer data (addresses and payment), and not let that get in the way of progress. Many SaaS providers already have ways to integrate order data, carrier data, product information etc. This would be a natural evolution to the current offerings.
In this version of the world, all it would take is a simple command to get under the bonnet and find almost anything and everything an agent could need to deliver the best ever first contact resolution (FCR).
One portal for customers
The Batphone or portal would keep things simple for customers as a single point or place of contact. You won’t need to figure out who to call first, should I try Nike? DPD or someone else? I should be able to drop in my query via voice, text or my thoughts (neuralink anyone?).
The ticket or query would be assessed with AI and triaged. If it could easily be solved through customer service automation, driven by AI, then the answer would be presented back almost instantaneously.
If the triage determines that the ticket cannot be solved automatically, it then gets passed to a human in order to answer the question. A personal concierge would pick it up and can dig around to find the answer, or provide advice based on what they know.
Either way, the customer gets a response to their query, via an alert appearing on their futuristic augmented reality goggles! Taking it in their stride and daily flow.
Nomadic customer service
With this kind of on-demand brand-agnostic customer service, we can reimagine the traditional contact centre. No longer bound by a fixed location – contact centre or current remote working environment, to anywhere in the world! Our Uber for CS would do away with the traditional fixed hours and could enable queries to be picked up as they are needed.
We are closer to this way of working than you think. Check out how Limitless are disrupting the target operating model for CS, and how large outsourcers have sites around the world.
There is and will always be a place for oversight and ensuring quality is maintained. AI could comfortably rate and assess the sentiment for every query and self learn / correct any issues with this way of working. We would end up with a global workforce of CS heroes helping customers around the global clock.
I’m not sure we would get to an either or situation, for some brands, a centralised team or in house systems will still be the way to go. As long as your target operating model is aligned with your brand’s vision and mission, you can find your own way to put the customer at the heart of your business.
Conclusion
Customer Service has always been thought of as a function within a brand or outsourced to a CS partner. But why? Separating the need for a ‘function’ from the ‘skill’ is hard. It immediately makes you think of the process steps involved in passing over too much information, and the risks of things going wrong are too high. But with AI and proper security there is no reason why this cannot be a possibility.
We have to reshape our thinking of customer service as a function to make things easier for customers. That means: them knowing a single place to go to for help, with access to the very best CS talent wherever they may be, and augmented to answer any and all queries, even the ones they don’t yet know to ask about.
Every business leader ever will tell you that they put the customer first and at the heart of their company. So here I am asking you to do exactly that, and you might be justified in responding with: If it isn’t broken, why fix it? Or why would you need a sledgehammer to crack a nut? You could even tell me that we don’t want to risk over engineering things, that taking the obvious and easier route to something is just fine. Whilst that is fine most of the time, if we want progression or better still, evolution, I would conclude that we need to dream a little bigger darling.
The Future of Customer Service: Part Two - Predictive Customer Service
“I skate to where the puck is going to be, not where it has been” – Wayne Gretzky
In the first track from the mixtape: The Future of Customer Service, The Role of Customer Service, I laid the foundation for moving from a reactive CS proposition to a proactive one. Today, we are going to build on this solid base foundation by introducing a new kid on the block, step by step (ooh, baby – if you know then you know), Predictive Customer Service
Much like Wayne, we need to anticipate where customers’ needs will go and be there ahead of them. Yes, even before they know what they want. If this sounds a little like the Precogs in Minority Report, predicting crimes (against service) before they happen, then you’re on the right sort of track. But rather than a group of people in a pool having visions, this predictive behaviour will be supported through data (both structured and unstructured).
Incoming generalisation, If you’ve always done something a certain way, you’re likely to do that same thing in the same or similar manner in the future. A subtle nod to how habits might be formed. Therefore the products we’ve purchased, when we purchased them, and what we’ve looked at since among many others – we can start to make predictions about what comes next for you.
What does this look like? Say Adidas launch a new collaboration with Kendrick Lamar. They know you’re a fan of some of their previous collaborations based on your purchases and viewing habits. Combined with multiple other data points, it wouldn’t be the biggest leap in assuming you would want exclusive and early access to the new launch. This almost works today via their Confirmed app.
Why not go one better and send you a box of trainers that arrive automagically on launch day? If you like them, they’re yours to keep and your account will be debited. If you don’t – send them back, free of charge. That’s predictive customer service in a nutshell – we anticipated that you’d like something and so we’ve sent them to you.
Where are we now?
Reactive customer service – Agents are effectively “waiting” for customers to contact them, and would rarely think to or have planned time to contact customers directly. Instead dealing with where is my order (WISMO) or where is my return / refund (WISMR), adding little value to everyone involved.
Lack of complete joined-up data – Retailer A doesn’t know that you bought product X from Retailer B, so they keep sending you adverts and offers for it and similar products. This leads to wasted effort and spend for retailers, and a worse experience for customers.
Unpersonalised recommendations – current recommendation engines are generic and don’t fully understand what your previous behaviour says about what you are going to do next.
The unknown unknowns – customers don’t always know the information and updates that they need. AI could anticipate what customers need and offer it to them before they ask.
How do we get there?
Reactive to Proactive to Predictive
Customer Service is a reactive function. Customers raise an issue, customer service addresses it. This has always been the case, and this will always be a model in the future for at least some occasions.
However there is a fundamental problem at the heart of this: the customer has an issue. That means the customer has noticed something wrong, or has a problem and has been unable or not willing to find a solution. Self-serve isn’t for everyone.
Brands have figured this out and through experience we’re seeing a move towards proactive customer service. This means detecting problems as they happen, or just before and reaching out to offer a solution.
For example, a crucial delivery is late, so a customer service agent reaches out to the customer to apologise, update and refund the shipping cost. The customer has had to do nothing, and has a reasonable outcome.
Or, a customer has purchased a wooden table from a furniture brand. Customer Service reaches out after some time to ask if the table needs treating, and offers the products or services to do that.
This kind of process can be run via AI with machine learning. Understanding when there is a breakdown in patterns, such as delivery procedures, or when tables start to show wear and tear (time based) is something that AI can learn, and then adjust based on how customers respond.
But AI can ultimately be helping right now by automating responses to the reactive customer service issues, freeing up human agents to be proactive. Then over time, the human agent, augmented by AI can start to be predictive in their customer service.
Ultimately the goal is to be predictive, and to reach out with a solution before a customer realises they have a problem. The journey from reactive to predictive runs through proactive.
Going beyond simple recommendations
Recommendations are part and parcel of every retail experience, and customers are used to them. Sometimes they are spot on and at other times they can be poor.
As Justin Shanes shared on Twitter: “Amazon thinks my recent humidifier purchase was merely the inaugural move in a newfound hobby of humidifier collecting.”
Major life changes such as moving home, having a baby, starting a new job seem to trigger and drive adverts that put more ‘relevant’ products and services in front of you. Sometimes following you around the internet or via push notifications from Amazon offering me new toys (my son using my account to browse!)
This points to a level of capability of retailers and advertisers making good recommendations when they have the right data and are processing it in the right way. Whilst this could give Big Brother vibes, in reality you can opt out.
For future customer service, this will mean using AI models to make actual predictions for what a customer might like and then recommending them. Using machine learning, these recommendations would get better over time. A combination of self learning and direct feedback to optimise the model.
Customer Service agents could quickly and swiftly swipe left or right on which recommendations are most suitable, with the occasional wild card thrown in for good measure. Who knew I would want a single cable dock setup for my home office? Turns out Amazon did.
Taking autonomous action
Building on our personal concierge model in track 1, in the future, customer service agents will use recommendations from advanced AI models to actually order items on behalf of customers.
When Ocado started doing home food delivery it had to work out what to do with items that were not available at the time of packing the order on the day. Rather than leave an item out, substitutions offered a suitably close / similar alternative. The default position started with a customer having to opt-in to substitutions. But a move to opt-out soon demonstrated that more often than not, customers accepted the substitutions they were given, even when they had the option to reject them at the doorstep.
By the same logic, retailers could move towards anticipating orders and shipping them to customers before customers want them. An early and simple example of this is the move to businesses adding a subscription option. The conversation would be as follows: Hey customer, you purchased this item twice in the last month, shall I set up a subscription for you? This could save you money and it would be more sustainable. Or, I noticed you purchased this product recently, it is a known subscription product, shall I get that setup for you?
A bigger and bolder step would be to order an item that customers may not know they want for them. The customer would then be free to keep the item, and have their account debited, or return it at no cost.
There are a few logistical challenges to make this process seamless. First of all, customers would have to allow it (more on that below), but also the recommendations would have to be so good that customers would accept the item more often than not. Finally the returns process would have to be incredibly simple, otherwise retailers are burdening customers with a box they’ll have to find time to return.
All of these challenges are surmountable for the bold retailer, with a customer service team of Personal Concierges supported by AI.
A journey to AI acceptance
The major obstacle to this is accepting AI’s role in customer service. Surveymonkey revealed that 90% of Americans prefer humans to AI, claiming that humans understand their needs better.
It’s possible that a lot of customers have been burnt by poor customer service chatbots that aren’t built to address and resolve needs, but instead send customers down a variety of rabbit holes and dead ends.
But with advances in generative AI, the ability for customers to detect the difference between AI and humans will grow smaller.
As AI improves and tackles more use cases, and as long as humans are in the loop, ready to step in when the AI fails, then customers will accept it more.
Top tip: don’t attempt to hide your bot (I prefer virtual assistant), be upfront with customers and make it really easy for customers to find human help. Customers will thank you for it and trust will continue to grow.
Conclusion
For retailers competing to win over customers and increase orders, customer service will be a key battleground. There is a real opportunity here to make or break the relationship one conversation at a time. Don’t waste it on poorly thought out customer journeys and reactive transactional queries.
Wayne Gretzky is perhaps the most successful ice hockey player ever. He saw a competitive advantage in anticipating what was going to happen next – adjusting his game and going on to score a record 215 points in 1985. A NHL record that stands today and considered unbeatable by most.
As customer service shifts over to be proactive, and then predictive, a new competition ground will be created. Customers will gravitate towards brands that make their lives easier – brands where shopping is simple and pain free. A real incentive to stop overlooking the competitive advantage of what great customer service can do for your business.
Although you’re not going to get it right every time, by using AI to spot patterns, augmenting your customer service teams and removing friction from the Customer journey, more often than not, you will find yourself where the puck is going to be.
The Future of Customer Service: Part One – The role of Customer Service
Image: Futurama
‘When you do things right, people won’t be sure you’ve done anything at all.’ - Futurama
If we fast forward, 10, 50 or even 100 years into the future, what role does customer service play? To answer that question, it’s important to think about what the perfect Customer Service experience is.
The perfect state for customer service is for the work to be invisible and happening in the background, 98.3% of the time. Always there, but you never notice it. Picture yourself in Dishoom working your way through those Okra fries as your waiter tops up your drink, bringing out your mains as you take a breath after finishing your starter, or appearing with the bill as you look up, but before you even ask for it 🙂 anticipating what you need next, subtly and swiftly.
Fast forward, think of Customer Service like your very own FRIDAY, Iron Man’s natural language interface from the Marvel films. FRIDAY is an advanced AI which anticipates threats, offers solutions, and explains potential problems – all of which allows Tony Stark to use his genius in the best possible way: saving the world. Yes, Customer Service done well can save the business world. As leaders look to cut costs, drive revenues and increase profit, the power of great Customer Service can not be underestimated.
The future of Customer Service is a personal concierge (play superhero cinematic sound clip). In the background, and augmented via AI to supercharge the customer experience. This means that customers get precisely what they need when they need it. This may mean that the work of Customer Service goes unrecognised, and taking care of customer hygiene needs may feel basic. However, the route to customer self actualisation centres around doing less better. Let’s look at how we get there.
Where are we now?
To look forward, it’s worth recognising where we are now.
Being reactive
Imagine a scale, from reactive on the left, to proactive on the right, and a bit of both in the middle. A number of brands are playing in the reactive space limiting their contact centre to a purely reactive function. Agents deal with customer issues as and when they are brought to their attention. How can we reposition Customer Service from reactive to proactive.
Limited to working hours
24 hour customer service is often a facade and not the norm. The pandemic made leaders look at their offer and question everything. We saw operating hours cut and herding or nudging of customers into certain contact channels. The experiment shed a light on the need for a 24 hour service, that ends up being expensive or an answering service that doesn’t resolve queries – given that almost every other department in the business is asleep and not able to help. Customers may have to contact customer service during their own working day, or wait till the next day for a contact form or email to be picked up.
Long waits for responses
While most SLAs expect response times to be 60 seconds or less, it seems to be normal for customers to wait on the phone or in a live chat queue for far longer than that. Sometimes you can get a pleasant surprise when the phone is answered immediately and catches you off guard! And we won’t go into why the Covid-19 pandemic is not an excuse for why response times might still be slow today.
Lack of personal customer service
Bill Murray is stuck in a Groundhog Day loop dealing with customer service, having to explain the situation over and over to different agents who struggle to find the right context. Clicking through multiple systems and screens looking through order history, previous conversations and notes, whilst trying to get up to speed quickly enough to offer a bespoke service. We haven’t made it easy for agents to do their best work.
How do we get there?
If customer service is going to rise and meet the future it has to embrace a world where ambition meets budget and produces an experiential customer service baby.
People x Process x Technology = Personal Concierge
Here comes the hotstepper (Ini Kamoze). AI is the hotstepper in this story, an enabler. Artificial Intelligence or generative AI is NOT about replacing people or removing jobs, but about clearing away the repetitive non-value adding tasks involved in doing the actual job of customer service: helping people. The agent is therefore Ini Kamoze in this story.
Connecting systems
If Tony Stark was facing down a global threat to humanity, the idea that FRIDAY would not have access to a key piece of information would be unthinkable. So why should the customer service team of the future be any different?
For Customer Service this means bringing together order history, browsing data, previous conversations, delivery information from carriers, warehouse and stock availability and many more systems.
Of course this is easier said than done. Most of this information exists in the tech stack, but there are multiple layers and APIs that may not be talking to one another or are poorly built over time. Cracking this will unlock a lot of value, we need to engage our systems architects to realise this value.
Bringing together all these systems gives agents a wealth of data to be able to tackle any problem they are faced with. This means customers get faster resolutions, and first contact resolution rate (FCR) will go up, because agents don’t have to escalate as many problems and can address problems fully at the first attempt.
Immediate information extraction
FRIDAY would have access to the information needed, but more importantly would be able to automagically and precisely pinpoint the relevant piece of information at that moment.
Current AI natural language processing can show us the way here, being able to extract meaning from text and images, and other data. The rate of improvement in AI suggests that being able to pull any information at a moment’s notice is not that far away.
For example, today AI can use image recognition to detect damage to products on arrival meaning that brands won’t have to rely on human eyes to spot damage before a customer gets a return or replacement processed. Removing days from a rubbish customer journey.
An agent should then through a simple voice command or push of a button be able to ask any question and get the information they need summarised. Imagine answering the customer enquiry this way without having to search and speed read a static knowledge article.
Detecting and anticipating problems
Ideally this shouldn’t even happen, because customer service should anticipate what issues customers are facing before the customer realises themselves.
Take delivery issues. Once an order has been placed, a team member in a warehouse packages it up, and gets it shipped. At which point a carrier picks it up and delivers it to the customer. Simple, and yet so much goes wrong.
In future customer service would be able to detect when something has gone wrong in the process and would step in to provide a solution (Dishoom waiter-esque – those Okra fries are really good!). If a package gets lost, a new one would be sent out. If it is delayed, an investigation would be raised immediately to find out what’s gone wrong
If there is a choice that the customer needs to make, like whether to get a new order or cancel the order entirely, then customer service would make contact in the channel of the customer’s choice. Catching a customer off guard with proactive customer service is a pleasant surprise and the type of thing they will tell their friends (real world NPS – much better than a survey)
Blurring the gap between CS and Sales
But the wonderful sales person told me I would save a bajillion in my business case for implementing AI. Or my finance team is asking for ROI following the implementation of AI. Rather than take this short term approach to save short term cash, reinvest the time saved to accelerate the blurring of the gap between CS & Sales. Your personal concierge would spend this newfound time anticipating what the customer wants, and that is exactly where you find the best type of service and ROI to boot. Win win right? CS can be connected to CRM data and customer loyalty platforms and start to reach out to customers to check how they are getting on with their order, offer personalised recommendations, and look for opportunities to upsell and cross-sell to customers.
Conclusion
If you wait for customers to get in touch with you, you are almost certainly looking backwards and not to the future of customer service. You will not be reimagining or reinventing your business, you will be missing out on the added value of a well thought out CS proposition.
A constant improvement in processes, combined with ticket deflection and automatic resolutions to common problems will mean that much of the reactive work will either be much faster or taken away completely. You don’t want your team to play here, you want to use technology at a fraction of the cost instead.
As more and more of the repetitive work gets taken off their plates by AI (looking up information, copying information from one portal to another, waiting to hear back from the warehouse), the more that they can focus on interesting and challenging problems. This results in a more fulfilling job overall. Team retention in this industry is such a big challenge and compounded through seasonal trade. Making the work more fulfilling will go some way to helping solve it.
So, we have to reimagine customer service as something that steps in, often unseen, to take action when something goes wrong, but crucially before the customer notices it. This is where the idea of a personal concierge comes from.
Today, great customer service is one that solves the problems that arise quickly, efficiently and to the customer’s satisfaction. In the future, it’ll be one that does that without the customer really noticing, subtly and swiftly. So that “When you do things right, people won’t be sure you’ve done anything at all.” – Futurama
