There’s an industrial revolution under way in businesses across the world, and it is all about automation. Businesses are embracing machine learning and artificial intelligence to make better decisions automatically. And the reason for this revolution is the comparative strengths of humans and computers.
Computers are strongest at repetitive tasks, mathematics, data manipulation and parallel processing. So long as a task can be defined as a procedure, a computer can do that task over and over again, without getting tired, giving the same results each time. Computers can manipulate numbers and data in volume much faster than any human.
Several years ago I went back to university to do a masters degree, and after a 25 year break from university I was out of practice at mathematics. Imagine my excitement and relief when I discovered that now there is software that will do algebra and calculus for me! And computers can do more than one thing at a time. Have you ever tried to rub your belly and tap your head at the same time? I can’t do both actions simultaneously. But modern computer networks are powerful, able to routinely do dozens of different processes at once.
This does not mean that humans are obsolete. What humans are much more skilled than machines at are communication and engagement, context and general knowledge, creativity and empathy. When I have a frustrating problem, I want to talk to a human. Someone who will understand my exasperation, listen to my experience and make me feel valued as a customer, whilst also solving my problem for me. Humans are much better at common sense than computers, instantly recognizing when a decision doesn’t make sense. And humans can be creative. I recently heard music composed by a computer, and I’m sure that song won’t make it into the Top 40!
Customer Service
Recently I had a conversation with the manager of a call centre that dealt with hundreds of customer service issues each day. In order to ensure the quality of the service and advice, the call centre operators were given scripts and were commanded to follow those scripts without changing a word. The problem was that both staff and customers became frustrated. Staff felt bored and unchallenged, and customers with non-standard problems felt like they weren’t being heard. Staff turnover increased, and customer satisfaction levels dropped.
Customer Satisfaction
The manager then tested using chatbots to answer simpler questions from customers, freeing up the human operators to deal with non-standard enquiries. This was a situation where computers had a comparative advantage over humans. The call center processes were fully defined, operating at scale, and the scripted answers were correct. The results spoke for themselves. Computers were much better at helping with the repetitive enquiries, and humans were better at dealing with the unusual enquiries. Staff engagement increased, as did customer satisfaction.
This has implications for human resources and process innovation. Processes that require humans to do repetitive, well defined tasks can be replaced by artificial intelligence. This frees up staff to do what humans are best at:
asking the right questions,
applying common sense,
creating new solutions,
evangelising new ideas, and
generating sales and profit.
Let your humans be human. Free them from repetitive tasks. Change job descriptions to focus on human strengths, and hire people who best embody the comparative advantages of humans. Look for human processes that are well defined and repetitive, and enhance the process by introducing artificial intelligence. Some ways company have started to incorporate artificial intelligence and machine learning into their processes include:
There are even some companies out there that have started automating the automation, like DataRobot. Instead of hiring and training up a data scientists, the arcane process of building predictive models, once the sole domain of data scientists, can all be automated. The system automatically builds predictive models based on your data, freeing up your humans to be human, to be better conversational AI specialists.
Based in Singapore, Colin is the Director, Customer Success and Lead Data Scientist, APAC for DataRobot, where he advises businesses on how to build business cases and successfully manage data science projects. Over his career, Colin has held a number of CEO and general management roles, where he has championed data science initiatives in financial services, healthcare, security, oil and gas, government and marketing. He frequently speaks at various global conferences. Colin is a firm believer in data-based decision making and applying AI. He is passionate about the science of healthcare and does pro-bono work to support cancer research.
I remember reading an article almost ten years ago talking about how teens were sending over 40 texts a day on average. The tone of the article was incredulous, but the statistic pales in comparison to how we exist online now. Speaking personally, it’s not implausible I send off 40 messages before 10 AM in my morning inbox check in. Sarah Guo, a partner of Greylock, expressed it succinctly when she took to Medium: “More than a decade ago, academics such as Thurlow described a “communication imperative”—human beings are driven to maximize their communication volume and satisfaction. More recently, researchers have described it as compulsion.”
While constant connectedness is old news, technology has finally achieved a point it can leverage this behavior. As with all big shifts, there will be survivors and those who don’t adapt fast enough. Companies will need to change to a conversational mode of thought to maintain the experiences users expect and deliver the individuality anticipated.
People Always Talk
Nearly 25 years ago, Harvard Business Review wrote “today if you’re not on the phone or talking with colleagues and customers, chances are you’ll hear, “Start talking and get to work!” In the new economy, conversations are the most important form of work.” Conversations are how we track knowledge flows. Conversation flows are how people create value, share information, and illustrate how companies operate.
A cited example is McKinsey. McKinsey prides itself heavily on the intelligence of its members, and by an extension the true value of McKinsey over other firms is its extensive knowledge base. That knowledge is curated and developed through internal conversation and shared through internal conversation. In short, McKinsey is conversation.
We are entering a new age for product development – one dictated by the conversational economy. Broadly, the conversational economy is the catchall phrase for companies, products, and ideas built on, alongside, or relying heavily on a conversational interface. More simply, they are services that leverage conversation.
This definition is board, and intentionally so. While some apps like iMessage, Snapchat and email obviously fit into this definition, conversation works as a backbone in services like Facebook, customer service complaints, and online advertising as well. Finding a common backbone helps derive a working model for these services.
Between the myriad of mobile apps used every day, access to the internet, and the seemingly innate human need to feel connected, conversation based platforms are dominating our lives. We have effectively destroyed the asynchronous quality of day to day life. We persist online, and, consequently, our conversations with one another never really begin or end. This data stream is a jackpot for product creation.
Smarter Everyday
Artificial intelligence, in the eyes of the public, has snuck past an important threshold. Presentations by titans like Facebook and Google have assured that we are moving away from the robotic idea of natural language processing in a rigid sense to natural language understanding. In other words, instead of responding to a keyword or a phrase, computers are beginning to be able to understand sentence, paragraphs, and intent.
There are a variety of causes for this – improvement of machine learning and deep learning, Moore’s Law, and rate of mobile and app data collection, to name a few. Algorithms and software are taking on their own intelligence. Just the idea that failed outcomes can make systems better is an astounding twist compared to five years prior.
Additionally, we’re in the middle of the boom of ambient computing, the idea that our environments and surroundings are responsive. We don’t have to open our phone or flip open a laptop to be connected. On the way to work I may pass a few smart cameras, a plethora of listening iPhones and Galaxy phones, an Alexa, Chromecasts, and more. Despite this, I would characterize myself as one of the less connected people in my demographic. At every step of my day my voice can be heard, position tracked, and activity monitored. Being connected no longer has much to do with if our phone is on our person or if we’re behind a keyboard.
Although passive collection has subtly pushed past our natural aversion to share information with technologies we don’t understand and people we don’t know, this one-sided trade has come with the expectation of usability. When software doesn’t work or apps crash, we no longer blame ourselves, we blame companies. We are inundated with choices, but that means that we have little tolerance of poor experiences. Users are more empowered than ever in that they don’t have to subject themselves to experiences they don’t want or content they’d rather avoid. We so demand these freedoms that events like net neutrality rapidly cause public outcry and social faux pas by companies like EA tank sales.
Computing, connectedness, and data almost completely undermine how product managers need to think about designing products. The need to leverage conversation to deliver value has emerged as one of the most critical company problems. IDEO acquiring a data analytics company, giants like Apple acqui-hiring boutique companies with human-centric software, and Salesforce pushing Einstein all serve as mine canaries that even the most established companies are racing and struggling to adapt.
Buying In and Cashing Out
As George Box famously cited – all models are wrong, but some are useful. Where is the utility of viewing products as ongoing conversations?
A helpful place to start is in how companies have historically fended off competition. These ‘moats’ include things like brand loyalty, unique data sources, and intellectual property. However, as technologies like AI are more readily available via open source projects, cloud hosting and computing are only a few clicks away, and systems of engagement continually emerge, the traditional ideas of tech defensibility are evaporating. In a Greylock article on Medium, they wrote “In all of these markets, the battle is moving from the old moats, the sources of the data, to the new moats, what you do with the data.”
In another words, companies are now finding defensibility through the experiences they create. To create these experiences for customers in the conversational era, companies will have to harness existing behavior, respond personally, and work faster.
Harnessing existing behavior is an exercise in invisibility. The real frontier for conversational companies to generate solutions for problems before the consumer is even aware. For example, Facebook realized that people asked for recommendations on their newsfeeds. Instead of creating a new service, they had posts automatically update with reviews and locations. They created a new card that changed automatically depending on what a user wrote. As expressed by a product designer at Facebook: “We didn’t try to invent a completely new behavior; rather, we found an existing one and made it way better.”
To cite an example within my own career, food industry companies often lose hours if not days within food recall investigations. Tracking a faulty shipment through several distributors can be tricky. We worked to create a product that reads the complaint before the owner may even be aware it exists and start and investigation. By the time an owner is even aware there is a problem, a report is ready. By approaching complaints, invoices, and shipments and messages between companies, value can be created seamlessly in a second layer.
As I’ve written about before, personalization is an increasingly critical element of producing customer lifetime value. Harvard Business Review started to notice this trend in their research on customer service: “Even as artificial intelligence becomes embedded in everyday interactions; human conversation remains the primary way people make complex purchases or emotional decisions.” The fatal error in a lot of software products is focusing on company efficiency over consumer experience. While these changes may boost bottom line in the short term, they encourage competitor entry and consumer drop off.
Conversational AI apps have an obvious outlet for personalization, and the power behind them allow easy switching between automation and human elements. More simply: “these intelligent agents will facilitate one-on-one conversations between consumers and sales or customer service representatives rather than simply replacing human interaction.” Imagine a case where someone sits on a delayed flight and sends out an angry tweet. A conversational built system could find the message, tag it, and route it to an agent. While the agent delivers a personal response with an update, the system has already sent an alleviating reward of extra miles to the customer. The captain may be alerted of sentiment on the plane and deliver an announcement. While an autoresponder may have been cheaper, the customer will now remember the exceptional level of immediate service and is more likely to return. As information and computing become free, the real commodity becomes the personality of the person on the other end of the line.
In the shorter term, there’s a simpler way to think about AI adoption – people don’t trust what they don’t understand. In the classic product management advice, it’s best to start with a problem and move to solution. Leveraging conversation is a means to building a better product, but that doesn’t change what the bottom lines should be. In other words, “Bots do not need to be human to be human centered.”
Outside of the shift in new product priorities, another major implication is how we use the technologies we use currently. In a blog post, Dan Rover (sp?) declared that bot won’t replace apps, but inboxes were the new home screen. Our email, text messages, and more were queues demanding our intention and driving our usage.
Companies leveraging platforms like WeChat have been able to effectively create micro services and apps for things like ordering that have integrated seamlessly with how we act now. Bot companies that are able to daisy-chain onto conversations to do scheduling and commuter planning have shone in venture capital funding. It’s not inconceivable the next unicorn will have nothing to do with creating a new platform but layering effortlessly onto the ways we talk with those platforms now.
Speak Now
We talk online all the time, but computing has finally let us create value from that. Companies need to invest in ways to leverage these conversations to deliver seamless and personal content. This means focusing on personnel and focusing on alleviating frictions than automation. Companies that don’t value the communication imperative and connectedness of customers will soon find themselves lagging in experience, and, later, sales.
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A prime example of this is Amazon Web Services’ fast climb to dominance. Legacy systems like Oracle required costly deployments and developers, and setting up cloud instances on AWS is only a few clicks away. IaaS records have shown Amazon’s sheer dominance. Oracle, trying to defend by housing data and curating an elite brand, couldn’t compete with Amazon’s engagement accessibility.
Perhaps the most obvious implication of smart conversational apps is efficiency. However, despite all the news and hype around an artificial intelligence singularity, businesses – and their customers – still revolve around the interactions person to person. This means that products needs to be resolved around facilitating conversation, collecting information, and iterating form that information. The AI boom has made it easier than ever to facilitate personal conversations no matter where customers are online.
A must-read guide for enterprises with billions of conversations and millions of customers.
Modern call centre with millions of customers.
Enterprises are much more overwhelmed with conversations than ever before. Not only do they have to actively respond to customers over a myriad of channels like email, phone, social and livechat, they’re expected to give personal, relevant and fast responses. To tackle this problem, many organizations are looking at new technology to help them meet customer expectations. Some of the most notable are AI chatbots, self-service knowledge bases and good old Interactive Voice Response (IVR) systems. The problem? These all aim to lessen the time customers spend with agents.
While people do like self service for speed and convenience, majority still want to be able to talk to a person in times of need, or at important turning points in their life. Curiously, while we’re moving more towards a more digital and self-service world, most consumers still want the ‘human touch’ in their service communications.
Human touch is more important than just automation.
The challenge is to provide highly personalized and relevant offerings to meet both customer and business goals, all the while delivering the experience through the customer’s natural mediums of interaction. Counterintuitively, the likeliest solution to bring the human element back into customer conversations is though technology and big data. So, what should you look for in a technology that will give you both customer satisfaction and maximize revenue?
Multichannel Conversations
At the basics, an organization’s communication channels should be in one view. That means a business should be able to see and reply to customers by email, phone, livechat, social media, forums and wherever they could be talking to you, or about you, on one platform. Why? Convenience and transparency.
Convenient Conversations
A single platform for the entire range of conversation channels is much more efficient for customer-facing agents. Often, they have to switch between multiple channels to check for new customer interactions, and unfortunately, miss some communications here and there. With one view for conversations, they save on time, and reduces the chance they will miss communications from less monitored channels.
The convenience isn’t just for agents. Customers want to interact with brands through their medium of choice. 51% of U.S. consumers are loyal to brands that interact with them through their preferred channels of communication. Younger consumers especially, want to interact with large organizations via instant messaging channels where they can use natural language. Having all channels on one platform allows agents to have visibility across all channels, instead of doing well on a few and lagging on others.
Transparent Conversations
In so many organizations, a different team handles a different channel. They are responsible for that channel, and that channel only. But the customer is dynamic. They might reach out on one channel, and upon finding that it isn’t fast enough or substantial enough to resolve their problems, they will switch channels.
The ‘different team, different channel’ approach doesn’t account for the customer’s flexibility, resulting in multiple replies or inconsistent replies from two different people, both creating bad customer experiences. With multiple channels on one view, conversations are transparent. Conversations from the same customer are stitched together, and the same person can handle issues without making the customer’s journey difficult.
Holistic Customer View
In an enterprise with multiple departments, systems and channels, it’s necessary to have a collective view of the customer. A single customer view (or a 360 degree view) is a complete profile of a customer, created from aggregated data points within an organization’s systems and channels. It collates data from multichannel communications and customer data platforms (like CRMs, analytics, marketing and legacy systems).
Customers often complain about the lack of continuity in their conversations and having to repeat themselves. Problems like this arise because agents have no visibility on what customers have said on a separate channel, or what customer information exists on a separate system. As such, interactions are treated as a completely new “ticket”, and in the worst cases, existing customers are seen as a new customer. With a single customer view, an agent can see a given customer’s conversational, transactional and behavioral data in one place. This not only improves time-to-answer by 20% – 80%, it also ensures customer information flow is consistent and continuous, reducing awkward moments like the ones above.
The use of a single customer view can even go beyond customer care activities. Integrated systems mean that there could be a seamless blend of sales, marketing and service activities through conversation. Having this feature marks the start of being able to use critical sources of data collectively. The key however, lies in how the customer intelligence is used. The following presents ways customer intelligence can be used to take control of conversations in providing exceptional customer experience and maximize revenue.
AI-assisted agents
Use of artificial intelligence (AI) in enterprises is not new. For decades they have been used to automate heavily manual processes to increase efficiency, accuracy and decrease costs. What is new, is the use of AI beyond processes to interactions. Use of AI opens up the potential to deliver personalized interactions and hyper-relevant offerings that are scalable.
Whether it’s the AI itself doing the talking, or an algorithm providing assistance to a human representative, online, or face-to-face, AI holds incredible potential to re-establish the human-to-human connection in an increasingly digital world. Check out some examples below.
Deliver relevant content and information with AI
Many organizations have invested heavily into user experience, self-service and knowledge management tools. Yet, it is still difficult and time-consuming for customers to find the right information when they need it. Companies like Zendesk have developed AI-powered virtual assistants that help customers self-serve. By processing natural language, the technology suggests articles in the knowledge base to help them resolve their problems on their own. Research has found that most people are open to using self-serve AI technology like this, and see it as faster and more convenient.
Other organizations like Woveon have built AI-powered response assistants to help agents have more productive conversations in real-time. As agents talk with customers, the response assistance helps guide conversations so better results can be achieved for both the customer and the business. It would suggest opportunities like ‘other customers like her also bought’, or ‘he mentioned credit cards, link to these articles from our blog to help him decide’.
Speed up resolution times
On average, a customer care specialist spends 20% of their time looking for information and context to resolve a customer’s problem. That’s one whole day in a work week! AI can help organize information so that it’s easily digestible and relevant to a customer’s enquiry. Woveon’s Intelligent Response framework for example, will change the information it displays to assist agents based on the flow of conversation. If a customer talks about their personal loan, their loan details pop up. If the conversation shifts to their lost credit card, their shipping details will surface and agents are prompted to cancel the lost card.
Instead of wasting time looking for information, AI assistance leave agents more time to build a relationship and take up on untapped customer opportunities. Customers also love a quick and productive interaction. 69% attributed their good customer service experience to quick resolution of their problem.
Reduce repetitive admin tasks to open doors for higher value interactions
Administrative tasks like After-call work (ACW) have been a constant headache for employees in customer-facing roles. Though they are necessary, it’s tedious, repetitive and and takes up too much time. Technology can help to reduce time spent on these menial tasks, leaving agents more time to build customer relationships and, in the process, make their jobs more productive and meaningful.
For example, Avaya has a natural language summarization tool to help agents process customer information post-call. Talkdesk automates call routing, where the customer is automatically paired with an agent with the best ability to solve their problem. Woveon can prioritize conversations real-time, based on customer importance, value, urgency, or a mixture of all factors.
Freeing up employee time away from menial tasks allow them to participate in higher-value activities.
Intelligent Analytics
There’s no doubt that data analytics is incredibly beneficial for customer conversations. The trick is knowing what data to use, how, and when.
Whatdata is being used matters because not all data is created equal. For example, rather than looking at metrics at a point in time (customer rated the agent 4 out of 5 for resolution), it’s much more important to look at the larger picture (that it took 3 calls and an hour on hold to get there).
Howdata is used is arguably more critical to conversational success. The key lies in knowing what datapoints to tie together, and what analysis to draw from it. A mesh of marketing and service data can show how a recent marketing campaign has affected conversation volume and NPS. A cluster analysis of related keywords in customer conversations can lead to discovery of a huge logistics flaw.
Whento use what data is of particular importance to customer-facing agents. 74% of Millennial banking customers for example, want their financial institutions to send them information about services exactly when they need to see it. This could be information about personal loans when they’re starting to look for a house, or travel insurance before they intend to travel.
Companies these days have a wealth of data on their customers. In theory, organizations should have the ability to know who they are, what they need and what makes them defect to another company. However, lack of visibility on the holistic customer journey and customer intelligence tools stunt their ability to provide such excellence.
The following section will delve into three types of analytics particularly useful for managing customer conversations — predictive, clustering and revenue-generating.
Predictive Analytics
Predictive analytics provide foresight into potential customer problems and opportunities. Extracted from existing historical conversational, transactional and behavioral data, it can help agents better prepare for customer outcomes and trends.
A pretty common example is prediction of when influxes of customer conversations come in. For eCommerce businesses, holiday seasons generally see a spike in customer conversations and steadily reduces till the next holiday season. In a more complex scenario, predictive analytics can find that customers with a particular occupation, a certain concern and at a similar stage in their lives is actually a niche the organization hasn’t capitalized on.
Cluster Analysis
Now this one isn’t as common in a conversational technology, but is definitely worth mentioning. Cluster analysis involves conversations and customer information to be tagged, then for similar or related tags to be clustered together to draw insights.
Cluster analysis can draw out how topics in conversations can be relevant, or how particular customer segments can be feel about a product. This customer intelligence can then feed into other parts of the business. It could be used to help create a new automated customer workflow for upsells, or contribute to a new marketing campaign for a newly discovered customer segment.
Revenue-generating analytics
As repetitive and menial conversations are moving towards being solved by self-service solutions, agents must also move from a traditional support role to a hybrid service-to-sales model. This category of analysis is as the name suggests, analysis that serves to generate revenue for the business within conversations.
For example, Woveon’s Intelligent Response Framework suggests ways customer specialist representatives in banks can sell more products to their customers. A customer who fits the profile of ‘customers who typically get a black American express card’ will prompt a suggestion for the agent to talk the customer into an upgrade from their current card. A customer who is at a stage in their life where ‘customers like him are looking at buying a property’ will prompt a suggestion to link some home loan webpages, or a free session with a financial planner.
In the best possible scenario, this analysis is also delivered at the right time for an agent to capitalize on the opportunity, like in an intelligent response framework.
Be a data geek, not creep
Of course, it’s important to know that use of data should be “cool”, not “creepy”. There’s a fine line between the two that should never be crossed. Also, everyone’s fine line is drawn differently, so what one customer may think is cool, can be perceived as creepy by someone else.
Enterprises should have enough data about their customers to track and understand individual preferences, and see how customers respond to different use of their information at different points in the customer journey. Conversational intelligence and analysis tools can help create better relationships without overstepping the customer’s boundaries.
On a whole, customers don’t mind companies using their data for personalizing their experience and suggesting products and services that benefit them.
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While human contact is diminishing in volume, the quality and importance of each interaction increases. Forward-thinking organizations should be balancing quantity with quality to maintain a competitive advantage in customer experience. Technology can be a great booster to that end.
Have more ways you think businesses can improve on their customer conversations? Reach out to us to add to the article. We love chatting to like-minded people!
Artificial Intelligence (AI) and machine learning (ML) is all the buzz right now, and rightfully so with the significant contributions it has made to redefining many aspects of business. However, many people are still skeptical about the application of AI and ML to enhancing customer experience.
Some would argue that machines cannot possibly take over customer service, something that has a heavy focus on human interaction. Machines lack the empathy and emotional intelligence core to providing a great customer experience. On the other hand, many also see the benefit of applying AI and ML to automate repetitive tasks, allowing humans to dedicate more time to, well, being human.
We reached out to some experts from Oovvuu, Canva and The Minerva Collective to pick their brains about the issue.
What is the current state of customer experience, and how do you see it evolve with AI & ML technology?
Present customer experience is “all over the place, with wildly varying results. Two customers using the same service can have completely different impressions of their experience, and in many cases the service is clunky and poorly structured” says Anthony Tockar, Data Scientist and Co-founder of The Minerva Collective. The unfortunate reality is that 78% of consumers have bailed on a transaction or not made an intended purchase because of poor service experience. In fact, companies only hear from 4% of its dissatisfied customers. With so much choice available to consumers, it’s much easier to find another company with similar offerings than spending time complaining or calling about a problem. Which is why there is a very real need to focus on customer experience, a factor that is becoming increasingly important to retain the modern customer.
Paul Tune, Machine Learning Engineer at Canva, believes “there are two trends in improving customer experience:
A trend towards tailoring for the individual, as more data is gathered about each customer at a large scale, and;
A trend towards providing a smooth experience for customers across multiple touchpoints by anticipating their needs. “
To demonstrate how customer experience has evolved, Paul continues with an example. “Early recommendation systems, such as the recommendation engines developed at Amazon and NetFlix in the early 2000s, provided recommendations at a much coarser level, chiefly for specific groups of customers. The granularity of recommendations in the near future is going to be much finer. For instance, an engineer from NetFlix I spoke to recently, mentioned that a subscriber’s favourite character for a TV series would appear in the menu when the TV series is selected. This means having to learn more about each customer and predicting their habits. We also see this in the form of smart personal assistants, such as Alexa and Siri” he says.
Ricky Sutton, Founder and CEO of Oovvuu, adds on that whilst AI and ML “certainly has an element to play [in customer experience], it also lacks a key element…empathy. So my thought is that it will evolve. The more AI is used, the more it learns and the better it gets, but human-level empathy remains a pipe dream for now.”
What is the biggest lesson you have learned from applying smart technology to customer experience?
For Anthony, the lesson has been the need for people using smart technology to properly understand it – “My experience is that people often don’t trust what they don’t understand. The latest technologies have been great for grabbing headlines, but only the most forward-thinking businesses are serious about applying them to derive value. This isn’t necessarily a bad thing – domain knowledge is essential for good data science, and blindly relying on new approaches has many inherent risks. There is a lot that has been learned about customer experience over time and there is a need to explain smart technology to business people using the right language to allow them to fully realise its value.”
To Paul, what matters most, is the customer’s end-to-end experience. Meaning that all the touchpoints with the customer should be seamless. For him, “the challenge with integrating smart technology to improve user experience is similar to managing any other complex system: with more moving parts, there is a higher chance of failure in the system. Naively applying machine learning to improve customer experience is misguided. Machine learning works best if it is complementary to the customer experience, serving to enhance the experience of a great product.”
“At Canva, our goal is simple: we want to give the customer the best experience in empowering them to create and design. To that end, there are two aspects that we focus on. Firstly, how do we make the content that they need for their designs easily accessible. Secondly, how do we anticipate what resources might be helpful for them in the future. We achieve these goals by improving our search and recommendation services to enhance customer experience.”
The biggest lesson for Ricky is that “AI turns humans into super-humans, but only for certain tasks.” – “When we started Oovvuu, we hired editors to read articles and find relevant videos, and they were able to read one publication each and find 40 relevant videos per day. That same person using the AI tools that we created, can now read 100,000 publishers, and 300,000 stories a day, covering 26 million topics and find relevant videos from more than 40 global broadcasters. AI is mind-blowingly powerful for automating manual human tasks, but humans remain better at all the things that, well, make us human.”
What are some challenges for businesses who try to integrate AI & ML technology and customer experience?
Anthony, Paul and Ricky all agreed that a huge challenge for businesses is not having a solid data infrastructure, or a deep understanding of what exactly should be measured to achieve business goals and customer satisfaction.
“Many companies approach us seeking to use conversational AI as a ready-made silver bullet for a business problem. Others come to ask to play with AI, so they can find a business opportunity. Neither really works.” Ricky said. “For us, the solution was to know what business problem we were trying to solve: namely, to put a relevant video into every article being published worldwide. We then used AI to solve it, but what we started with was very basic and not up to the job. We have had a team nurturing the teaching for almost 1,000 days to get it where it is.”
Anthony went on to add that “there is no silver bullet – good data scientists are required to translate these algorithms into business value. Having a solid data science strategy is essential, and through good leadership, increased data literacy and an understanding of how to build a high-performance data science team, businesses can harness these technologies to forge a competitive advantage.”
Paul concludes with another common challenge many businesses face when adopting AI & ML into their processes – the volume of data. “Present machine learning techniques rely on a relatively large amount of data to provide good predictions” he says. “While there is fundamental research being carried out presently to (hopefully) reduce the amount of data required to train these machine learning models, the current main technological limitation of requiring a huge amount of data is here to stay for the foreseeable future.” But “fortunately, this effect can be mitigated if the data collected is of sufficiently high quality.”
Are you implementing AI and ML technology in your business? Share your story with us in the comments below!
About the Contributors
Anthony Tockar
Anthony is a leader in the data science space, and has worked on problems across insurance, loyalty, technology, telecommunications, the social sector and even neuroscience. A formally-trained actuary, Anthony completed an MS in Analytics at the prestigious Northwestern University. After hitting the headlines with his posts on data privacy at Neustar, he returned to Sydney to practice as a data scientist while co-founding the Minerva Collective and the Data Science Breakfast Meetup. He also helps organise several other meetups and programs for data scientists, in line with his mission to extend the reach and impact of data to help people.
Paul Tune
Paul Tune is a Machine Learning Engineer at Canva, responsible for developing solutions for tailoring and personalising content for Canva’s customers. He has several publications in prestigious computer science conferences and journals, including the ACM SIGCOMM conference in 2015. His interests include deep learning, statistics and information theory.
Ricky Sutton
Ricky is founder and CEO of Oovvuu, an IBM and Amazon-backed start up that uses artificial intelligence to match videos from global broadcasters with publishers worldwide. It’s mission is to use AI to insert a relevant short form and long form video in every article. In doing so, it aims to tell the news in a new and more compelling way, end fake news, and in doing so, repatriate billions from Facebook and Google back to the journalists and broadcasters who make the content.
Recently I wrote an article for LinkedIn titled “Can we maintain the human touch with customer service?” I couldn’t help think about how fast we are moving with Artificial Intelligence that the question still remains, I am not worried about 5 years from now or what new customer interactions will be digital, but how will businesses maintain the reality check with their customers? Surely digital chat bots and automated ticketing systems will ask random customers surveys about what they thought about their service response and the level of happiness to refer another customer. To implement is very easy but to keep the human connection with your customers will be the challenge.
Deliver Smarter Customer Service Solutions
At Woveon, we watch and analyse through thousands of conversations all uniquely handled by diligent customer service agents who, assisted with technology, work tirelessly around the clock to acknowledge, understand, listen to and resolve the incoming customer conversation. Clearly customer service has the human touch here! Even with today’s conversational AI technology surpassing standards in reliability, accuracy and now business intelligence the human touch in AI must not be far away? This is an important consideration looking at the technology landscape today, companies are working on delivering smarter customer service solutions, from chat bots that understand your sentiment and can adapt to your tone and writing style to automated enquiry systems that can help recommend products while you shop online. Yet still, customer service and particularly conversation management is still a human “touch”, something that is defined intrinsically in the term “customer experience”.
Let’s take the example of creating an outstanding customer service experience. Data tells us that outstanding customer service increases brand loyalty. Examples include begin a conversation with a podcast, send personal messages, create a lifestyle and get back to your customers. We’re not talking about getting back to your customers via a bot or automated reply email, but rather using an actual person who understands your customers and can understand the fine details and semantics of human feelings. Remember, customer service is all about listening to your customers and putting yourself in their shoes. Great customer service professionals can quickly adapt and understand the customer’s frustrations and calm their emotions. Being present and responding quickly in human is very different to doing this via a scripted automated response. However, in the enterprise world, a study by Oracle put it at 8 out of every 10 businesses who are already implementing or about to implement AI as a customer service solution by 2020. Nearly 40% of all enterprises are already using some form of AI technology with Forrester predicting a 300% increase in AI investments, the disruptive power of AI will impact every part of the business from customer service to sales and support. So are businesses going ahead at this the wrong way?
Having interviewed several CTOs and CMOs working with the technology, there is no rushing into the game looking for the holy grail. For most, the best step moving forward is in assistive and adaptive technology or to assist with data collection and analysis. AI technology is encapsulating more and more human qualities as technology advances. Bots are often deployed to collect data based off human input and use it to optimise the customer’s experience. This is particularly applicable to personalisation. Human teams then need to help filter, sift through and make sense of all the personalisations so the system can make better judgements in the future. Artificial intelligence predicts and prioritises the user’s interests according to their searches and similar inputs given by other users. This, when compared to the pros of human service, has similar benefits to empathy and experience. For example the human touch can continue on more serious, complicated customer challenges whereas standard, mundane everyday enquiries can be handled by AI bots. An example is AI assistance to lessen waiting periods for customer inquiries. KLM, the flag carrier airline of the Netherlands, used DigitalGenius’ AI system to answer customer’s questions faster. The AI units interpreted the questions and answered them with a quick edit of the preformed answer to relate directly to the language used by the customer. It was also able to adapt to the platform for the inquiries, pumping out longer responses to emails but limiting Twitter responses to 140 characters. Digital customer service seems to be directed towards matching human interaction but with the removal of prominent flaws.
So can we maintain the human touch in customer service? Having been a product manager and worked in technology since the first dot com (no I am not that old, I was just young when I first got into technology), we can expect to see customer service significantly enhanced with AI bringing the human touch to a new level. The amount of data that AI and ML will help sift through to help “advise” and “suggest” to a customer service team will break new boundaries. Customer service teams can then be deployed to work on escalated or prioritised items that result in a big sale or help close the deal. Customer service, intuitively is tied closely with the human touch, a computer cannot learn years of successful customer interactions without first being taught and guided by humans. This is a realistic fact.
The hype around Artificial Intelligence technologies is at its peak. According to the 2017 Gartner Hype Cycle, emerging technologies such as deep learning, machine learning and virtual assistants are at the “peak of inflated expectation”. Cognitive expert advisors have passed this peak and are now descending towards the “trough of disillusionment”. This occurs when interest wanes as experiments and implementations fail to deliver.
The benefits of AI for customer experience management are potentially game changing. AI has the capability to analyse vast amounts of data in real time from various sources, including human behaviours and emotions. Expectations are high because this capability can then be used to create seamless and personalised customer experiences that are optimised to the device and channel of choice.
Pragmatists and battle hardened cynics will recall that when automation was first introduced into customer service channels, the results were often spectacularly underwhelming. So, is the application of AI to customer experiences destined to fall into the trough of disillusionment before climbing the slope of enlightenment? Or is there a path to follow to avoid the pitfalls of unmet expectations?
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Intelligently using Artificial Intelligence for Customer Experience
In order to find out whether the application of AI to your business’ customer experience will take a downturn, it is necessary to first ask yourself: What is driving your organisation’s AI strategy? Is it because:
AI is all the rage in your industry and your organisation is fearful of being left behind?
If you take the lead in implementing AI, it will make you look smarter/cooler than your colleagues?
It sounds like a cool and fun toy to experiment with?
Your organisation needs to catch up with your competitors who have been early adopters of AI?
AI is a great opportunity to reduce the cost to serve our customers?
If the answer to any of the above is Yes, then the trough of disillusionment beckons.
Alternatively, if you are deploying or considering AI because…
AI can enable your people and optimise your processes to operate more intelligently and efficiently, in order to provide individualised and predictive experiences for your customers at scale
…..then a brighter future awaits.
For these technologies to have any chance of success you should have a clear sense of purpose of how to you intend to deploy AI to drive CX in your business. Here are three ways you can use AI in a purposeful way to create meaningful customer experiences.
1. Use AI to Enhance your Knowledge of the Customer
An example would be using data analytics to anticipate the needs of individual customers at each moment of truth and key stage of their journey. Some specific examples oh how businesses are using AI to enhance customer knowledge:
2. Use AI to create stronger emotional connections with your customers
Using AI to recognise a customer’s emotional state helps agents better respond to the customer during an interaction, thereby creating stronger emotional connections.
Not only can AI empower agents with emotional intelligence to reply appropriately to customers, it can be used as a tool to connect service agents with the right information in the organisation’s knowledge base in real time. Examples of why this can be powerful to a business:
As a result, nearly two thirds of CX leaders say their organisation’s revenue growth outperforms their industry counterparts, compared with only a quarter of CX laggards. The proof is therefore clearly in the pudding: when applied in a purposeful and meaningful way, AI technology can enable organisations to increase agility and overcome competitive threats and leverage this advantage to drive acquisition.
Written by Dr Steve Nuttall – Head Of CX Research, Fifth Quadrant. Steve has worked in various leadership roles as a market research insights professional for over twenty years in Europe, Asia and Australia. He leads Fifth Quadrant’s program of CX strategy research and is an international speaker and presenter on best practice customer experience. Steve assists organisations to deliver their customer-centric strategies and business performance goals including designing and implementing programs to help optimise the customer experience.
The success of any business, irrespective of what industry or market it belongs to, is measured by how they treat their customers, and smart technology helps make a difference. The products or services you offer are irrelevant when it comes to moving customers down the funnel. The ability to attract customers to your business holds the key, and you will only be able to do that if you treat them as valuable assets to your business.
Wondering how to measure the worth of customers for any business? The customers provide the company with direction and purpose, as they supply valuable feedback, which is used as a springboard for new ideas and concepts. Customers hold all the keys to success since they are the ones who bring in the revenue for the business.
That is why businesses today are focusing more on offering exceptional customer satisfaction because if the business appreciates the customer, it will result in better conversions. Most businesses struggle with moving customers down the funnel, which means turning website traffic into solid leads and sales. However, smart technology has helped make a difference as it allows businesses to understand their customers to identify gaps in the buying process much faster.
Before we move on to discuss the benefits of using smart technology to move customers down the funnel, we must look at why you need to understand your customers. It is important that as a business you cultivate a relationship with your customer since it will determine the success of your company. This can only happen if you know your customer, and this is where smart technology has made things so much easier for businesses.
It has made it easier for businesses to acquire data about their customers, which helps them better understand the likes, dislikes, and needs of their customers. The importance of customer data can’t be understated, which is why it is important to keep accurate records of all customer transactions. This will help the business keep a flourishing relationship with customers, and will profit both partners.
The Benefits
So how does smart technology assist in helping businesses move customers down the funnel? This all comes down to the collection of customer data, which is where smart technology has helped businesses in improving customer satisfaction. We will cover all the benefits that it offers in detail below, along with helping you identify the key points that will help your company achieve success.
1. Improved analytical data and reporting
Miscalculated data can demolish your chances of success, which is where smart technology helps. There are different CRM tools and systems available, which help store information about all customers, and leads to enhanced data analytics. The best part about these tools is that they can be integrated easily, and can generate automatic reports, which helps save time. With improved data reporting, any business can make effective and resourceful decisions to achieve greater customer loyalty.
2. Heightened informational processes
The key to success for any business trying to move customers down the funnel is knowing their customers. The better a business knows their customer, the easier it will be for them to provide them with an experience that results in conversions. This means that the business must record, document, or identify all customer data for future reference, and thanks to smart technology all of this is possible. Having access to customer data always plays a crucial role in helping move them down the funnel.
3. Increased efficiency for teams
Smart technology allows you to easily access stored information about customers, which can be shared with multiple teams across the business. This improves efficiency for all departments, mainly sales, customer service, and marketing, and helps them funnel customers down the pipeline to close sales. All departments can share information with each other, which ensures that the organization improves its operations and boosts the bottom line.
4. Improved communications
As discussed above, smart technology helps all departments in the organization to function at the same high level of service, as they all have access to customer data. This helps the entire business to stay on track and improve their daily functions, resulting in more client retention. The happier clients the clients are, the better the chances of the business moving customers down the funnel.
5. Automating tasks
Completing sales are never easy, and isn’t the same thing as getting a customer to commit. There are lots of things that go under the radar of every sale, which needs to be completed in the proper order for the sale to be completed. This includes forms being filled out, addressing legal issues, and sending reports to different departments. All of this takes time, and involves a lot of people down the chain, which is where mistakes can be made. Smart technology takes that out of the equation, by ensuring that all these tasks are automated. This helps your representatives to deal with closing leads, and moving customers down the funnel faster, while smart technology automates the rest.
I was in a conversation with friend this week. He’s an avid murder mystery and investigative reporting fan. He loves shows like Making of a Murderer, 60 Minutes, 20/20, and True Detective. I asked him if he was a podcast fan. He said he wasn’t. With a grin, I leaned in and said, “I’m about to change your month. Have you heard of Serial?”
We need to know our customers like we know our friends.
Serial revolutionized podcasting, coming in with over 40 million downloads in its first season as it followed a murder mystery story week by week. My buddy devoured all 12 episodes and spent countless hours reading on the web about possible theories.
And I had no doubt he would.
We need to know our customers like we know our friends. The question is, how?
Peter McCarthy, founder of The Logical Marketing Agency, has laid the groundwork for us. He describes three buckets that help you identify your customer: (1) Demographics, (2) Psychographics, and (3) Behaviors. You can read more of his work here. For the exercise, take your customer and create the below three buckets and then start asking questions. Here’s a start:
Demographics:
Where do your customers live? What’s their age? occupation? income? political affiliation? urban or rural? gender? ethnicity? These are general questions that you might find on a census. However, you must dig deep. It’s laborious, but it will serve you in the long run. At the end of this exercise you should be able to picture your customer when he/she walks through the door, and that’s huge.
Psychographics:
How do your customers think? What do they believe in? What are their attitudes towards this or that? What are their preferences? What do they love? hate? crave? What are their emotions towards a given topic? What do they value? What gets them excited?! Make a list of emotions and attach a description of your customer to each. Use what you learn to write better copy or apply it to the design of your website or, better yet, your product! When we say “we feel,” we attribute a cognitive value. Learn what your customer feels.
Behaviors:
This is where things get exciting, especially if you have access to a large data depository. What does your customer do? What do they purchase? read? use? crave? search? How do they engage with social media? Instagram more than Facebook? Twitter more than Snapchat? What are patterns that you find with your customers? Why do they drop out at point of purchase? Use A/B testing as a tool to discover behaviors.
Next time: Identifying your customer can be laborious, but it’s crucial for risk mitigation and customer growth. I’m going to introduce you to tools that will help you to identify your customer in 30 minutes. These tools are free and fun to use!
How the Job-To-Be-Done theory can help you understand consumer behaviors and provide you an innovation compass
There are zillions of words written about how customer centricity leads companies to success.
However, the “customer-centric” term is sometimes misused as a catchall for customer feedback or customer satisfaction results, but making people happy is not enough. To have sustained success, companies must genuinely understand what the customer wants and needs, and implement the right internal and customer-facing processes, strategies and marketing actions to satisfy them.
Hands down, the best example of customer centricity is Amazon, whose mission says it all: “to be Earth’s most customer-centric company”.
“We see our customers as invited guests to a party, and we are the hosts. It’s our job every day to make every important aspect of the customer experience a little bit better.” Jeff Bezos, Founder & CEO of Amazon
Last month, when struggling to define our customer personas at Virtual Lab better, Fede Boero recommended me to read Competing Against Luck by Clayton Christensen. This read introduced me to “Jobs-To-Be-Done” Theory, a way of looking at customers with a completely different lens which shed a lot of light on my discovery process.
Jobs Theory
“Job-to-be-done” involves a mindset change since it forces you to look at our product the way customers do. Clayton Christensen described this concept back in 2006, in this paper he wrote together with Intuit founder Scott Cook.
The theory just asks, “What job your product is hired to do?”.
People buy services and products to get specific jobs done; and while products come and go, the underlying JTBD doesn’t go away. A commonly used example is that people do not want a quarter-inch drill; they want a quarter inch hole.
JTBD theory is a change in the lens we use to understand customer needs. The Job, not the consumer, is the fundamental unit of analysis.
To understand what’s the Job our product is hired to do, we need to find which progress the customer is trying to make in a given circumstance — what the customer hopes to accomplish. This growth or accomplishment is the job-to-be-done.
All of us have endless jobs to be done in our lives. Some are small (pass the time while waiting in the supermarket line); others are big (seek a more fulfilling job). Some are regular (pack some lunch early in the morning before leaving to work); some show up unpredictably (find a nearby auto repair shop after our car broke up during a trip). When buying a product, we’re mainly “hiring” it to help us do a certain job. If the product does the job well, we tend to hire that product over again when we confront the same job. On the other hand, if it does a crappy job, we “fire” it and look for a replacement.
The Milkshake Example
The canonical example used by Christensen to explain Jobs Theory is a milkshake.
Some years ago, McDonald’s was trying to increase the sales of their milkshakes. So, they would interview milkshake customers and explain to them that they were trying to improve the milkshakes to increase the sales. They would ask them if they would like bigger milkshakes, or new flavors (like root beer or orange), chocolatier or thicker milkshakes . However, after improving the milkshakes with the interviews results, they found that customers didn’t buy more milkshakes.
Christensen was hired as a consultant to help McDonald’s to nail this problem, and he applied his brand new Jobs Theory to solve it. To understand which job arose in the lives of some customers that caused them sometimes to hire a milkshake, Christensen studied a McDonald’s restaurant for 18 hours one day. It turned out that about half of the milkshakes were sold before 8:30 in the morning. It was the only thing the customer bought, they were alone, and they always got in the car and drove off with it.
To figure out what was that job, Christensen went back the next morning and positioned himself outside the restaurant so to confront these milkshake customers as they came out. He asked them: “What job are you trying to get done that cause you to come to McDonald’s to hire a milkshake at 6:30 in the morning?”
It turned out that they all had the same job to do. That is, they had a long and tedious drive to work. And they just needed something to have while driving to stay engaged with life and not fall asleep. The customer wasn’t hungry yet, but they knew they’d be hungry an hour later. So they needed something they could hold with their right hand while driving, and keep it for the whole commute.
This analysis showed that McDonald’s milkshakes were not competing against Burger King’s milkshakes. They were competing against bananas, snickers, donuts or even bagels to do the same job. However, milkshakes were much more convenient than their competitors since they were easier to consume, only one hand was needed, and they were so viscous that it would take them the whole commute to finish them up with that thin straw.
Customers didn’t care about the ingredients. All they cared about was to be still full at 10 am and have something to entertain them throughout their trip.
Unveiling what the job was, put McDonald’s on a very different trajectory. It explained the reason why there were no results after improving the milkshake on dimensions of performance that was irrelevant to the job-to-be-done
To improve the milkshake for the morning JTBD, McDonald’s moved the milkshake from behind the counter to the front. To help them not to be late for work, they also gave people a prepaid swipe card so they could just dash in, gas up, and go without being caught behind a line. They also made milkshakes thicker to take longer to suck them up, and so on.
When McDonald’s understood that they were competing against bananas, the sales of the milkshakes increased by 7x.
Four Takeaways
JTBD theory tells us how managers should think about customers, strategy, products, growth, and innovation. Here’re some pieces of advice:
1. Design a business around a JTBD
Products become obsolete, and this is why companies should instead create a business around the JTBD, which will lead them to sustainable success. This is why Ford moved from an auto company to a mobility & transportation company, opening the doors to markets such us ride-sharing.
2. Let customers get their entire job done
People do not want to have to put together diverse services or products to achieve their needs. They want only one service/product that helps them get the entire job done.
3. Target those customers who will pay the most to get the job done best
When a market is highly underserved, the fastest way to profits is first to target the people who will pay out the most to get that job done the best.
4. Let Jobs Theory guide the future of your company
The only products that will win the future are those that help customers get the job done better. Understanding where customers struggle today to execute the JTBD indicates what a particular product needs to do in the future to win in that market.
No matter what field you work in, customer engagement is just as essential as lead acquisition. In fact, 68% of marketers today say that their companies compete on the basis of customer experience.
However, many marketers still struggle with figuring out the best ways to keep their customers engaged; holding the attention of customers long enough to make an impact can be hard, time-consuming work. While old-school newsletters still have their place, they alone can’t cut it anymore in today’s competitive environment.
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Here are the Best Strategies to Ace Customer Engagement
So, what can you do to differentiate yourself from the competition and keep your customers coming back for more? Let’s take a quick look at a few options that you can consider. Not all of these are easy and some may not suit the kind of business you run, so pick what works for you. While some options may not be cheap, losing loyal customers will be far more expensive in the long run.
1. Start an Employee Advocacy Program
Your employees are the face of your brand, and their interactions with your customers can go a long way in retaining them. If they’re excited by your brand and believe in the products you have to offer, the customers they talk to will feel that. Conversely, if they are demotivated or unhappy, your customers are definitely going to pick up on that negativity.
An employee advocacy program can help shape your company’s reputation and culture online. By having your employees share snippets of their work life on social media, they can help to build your company’s image as a great workplace with an honest, hard-working team. Not only that — you can even leverage the knowledge of your subject matter experts to create content that has depth and authority.
To encourage participation, you can maintain leaderboards and offer incentives to employees who post the most or get the most engagement. Shoe retailer, Zappos, is well known for their employee advocacy program — snippets of which you can see on the official Zappos Culture Twitter.
2. Keep Customers Emotionally Connected
The best way to keep a customer engaged is to find ways for them to interact with your brand, even when they aren’t necessarily interested in buying one of your products. You can do this in a number of ways.
Build a Community
Build a sense of community around your product or service using free tools, such as a designated Twitter handle, Facebook Group, or other online forums where people can meet and exchange ideas.
The idea is to draw your customers into a social circle that is based on the love for your brand or products. Not only are people in these groups more likely to continue buying your products because others are passionately sharing about them, but they’re also likely to recommend them to their other social circles. Their involvement in the community will ensure that they are fully aware of your latest offerings with little marketing effort needed from you.
As an example, BMW manages official “Owners’ clubs” that help their customers connect and learn from one another. Their clubs have grown a big reputation for offering exclusive content, advice, and rewards for car lovers who buy from their brand.
Host Live Events
Hosting events,conferences, or webinars that will be of interest to your customers is a great way to engage them in a non-transactional setting. Many gaming studios take advantage of in-person events to meet and get feedback from some of their most loyal fans. For customers, it’s an opportunity to see what the company is working on, meet like-minded individuals, have their voices heard by game developers, or play demo builds of upcoming games.
BlizzCon by Blizzard-Activision and QuakeCon by Zenimax Media both draw in thousands of attendees each year. And tens of thousands more people watch the live-streams of the events online. Attending larger conventions is also an effective (and much cheaper) way to engage with your fans. However, hosting your own event is a great way to keep the focus entirely on you and your products.
3. Gamify the Customer Experience
While gamification of a customer experience can be difficult, it allows you to engage customers using the sense of instant gratification.
Digital contests and giveaways are great ways to spur customer engagement for mutually beneficial results. For instance, hair accessories company Whirl-a-Style hosted a web contest asking customers to share their #WhirlMyStyle hairdos for a cash prize. The company earned tons of customer testimonials and marketing UGC at a very low cost.
Similarly, you can introduce badges or achievements for customers who’ve hit a certain milestone or completed a particular task while using your products. This is incredibly useful when it comes to making sure they use your product or services often.
Gaming is already well known for the concept of unlocking achievements. Companies like Fitbit and other fitness tracker makers have had good results by introducing similar aspects to their products.
4. Target “Whales” for Customized Services
Loyalty programs are a great way to help keep customers engaged with your brand. But you can take it one step further with a tiered loyalty program. Identify customers who spend significantly more than average on your products and consider giving them some sort of V.I.P. status.
While more loyalty points or discounts are nice, customized services are even better. For example, access to a personal stylist at a clothing retailer who offers expert advice in styles suiting body types and personalities.
A personal touch is extremely valuable when it comes to instilling a sense of loyalty in your customers. While you may not be able to offer that to all your customers, make sure your “high-rollers” feel like they’re getting V.I.P. treatment.
5. Be Socially Responsible
Often maligned by older generations, millennials tend to be more environmentally and socially conscious than any other generation. (And considering that millennials are already the largest generation in the labor force, their loyalty will soon determine whether your business lives or dies.)
Millennials are far more likely to support brands that not only offer valuable products but also support social or environmental causes.
Consider picking a cause that you are interested in and promoting it regularly in your marketing efforts. It can be anything you want as long as you care deeply and sincerely about it. The sincerity is important because millennials are better than previous generations at spotting disingenuous marketing.
For example, Leesa donates a mattress to a homeless shelter for every tenth mattress sold. They even plant a tree for every order received.
6. Streamline Your Customer Experience
This option probably takes the most effort to put in place. Depending on how your business operates, it could require small changes or a total overhaul. The key here is to reduce the number of steps needed to complete a transaction. This applies to both physical and online stores.
Every step required to complete a purchase increases the likelihood of customers walking away from a purchase. In stores, these could be long lines, a slow checkout process, or lack of assistance to help make choices.
Fast food giants like McDonalds have attempted to improve the experiences of their customers with the addition of self-service kiosks. These help to drastically cut wait-times without needing to increase staffing requirements.
Online, Amazon introduced 1-click ordering, allowing existing customers to purchase items with a single click. This uses billing, delivery, and payment information that’s already linked to their account.
Another great way of improving your customer experience and managing excellent relationships is to use a CRM tool. Tools like Salesmate allow you to keep all of your customers’ contact information in one place. You can use it to run email campaigns that include informative and highly-targeted content. You can even do timely follow-ups for any sales queries or interest they may have shown.
Orginally published on Zoomph.
About the Author
Shane Barker is a digital marketing consultant who specializes in influencer marketing, product launches, sales funnels, targeted traffic, and website conversions. He has consulted with Fortune 500 companies, influencers with digital products, and a number of A-List celebrities.
Based in Singapore, Colin is the Director, Customer Success and Lead Data Scientist, APAC for DataRobot, where he advises businesses on how to build business cases and successfully manage data science projects. Over his career, Colin has held a number of CEO and general management roles, where he has championed data science initiatives in financial services, healthcare, security, oil and gas, government and marketing. He frequently speaks at various global conferences. Colin is a firm believer in data-based decision making and applying AI. He is passionate about the science of healthcare and does pro-bono work to support cancer research.
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