Customers want more personal connections with brands. That’s what we’ve come to expect with many of the most respected brands today. Lululemon, Amazon and Starbucks, for example, are building their companies on the ability to forge emotional connections at scale.

As more brands rely on digital channels like chatbots, messaging apps and email campaigns to build relationships, AI and machine learning are critical in being able to use the big sets of data amassed to create more personalized experiences that demonstrate empathy and authenticity.

It almost seems counterintuitive: Automation is required to do more “human-focused” marketing? In a word, yes.

The reality is that empathy happens when brands tap into an individual customer’s wants and motivations continually, connecting with them on a deeper level than just a one-off exchange. That’s just not possible without automatically understanding the context and intention behind each individual customer interaction — and doing that across the entire customer journey.

Customers had sophisticated demands for digital experiences prior to COVID-19, and the pandemic raised those standards even higher.

Brands learned they need to see the world from their customer’s eyes and treat every action as part of a growing relationship, not a series of transactional exchanges. It’s not enough to just have the information about what customers have done in the past and use it sporadically. To build more personal connections, businesses need to step into their customer’s shoes and anticipate their future needs or wants on a regular basis.

Accomplishing this means analyzing customer data to detect wider patterns and changes in preferences. Brands cannot wait around for data scientists to explain what customers are telling them. They need to be active listeners and respond in real time.

Making Big Data Actionable

With more interaction points, content types and digital moments in the mix than ever before, predicting customer behavior and the next best action has also become increasingly complicated. Simply having a large swath of data coming in from different sources isn’t enough to build a valuable and trustworthy relationship with customers.

With billions of potential experiences to choose from, selecting the best sequence of events is an impossible task for a human to accomplish on their own. That’s why AI and machine learning are key to building more personal connections. Whether standalone or within a larger platform like a customer data platform (CDP), AI and machine learning can help brands make sense of enormous volumes of customer data across multiple channels and offer sophisticated recommendations based on past customer interactions.

Putting AI to Work

Blending data intelligence with prescriptive AI and predictive machine learning techniques gives a view of customers that encompasses all of their previous histories with a brand, whether that was via the website, in-person browsing, or leaving a review on social media. Only when brands have built this more holistic view are they able to work toward improving each future interaction through predictive modeling.

I’ve worked with enough companies to know this analysis differs greatly across industry, audience and many other variables, but here are a few examples:

• Question: Is this customer an avid in-store shopper who suddenly has been browsing the e-commerce site, maybe due to store closures?
• Action: Acknowledge their adjustment with a free shipping offer to lessen the inconvenience of needing to change their shopping channel to online.

• Question: Has the customer showed a strong interest in a luxury item, but also failed to pull the trigger on the purchase?
• Action: Use AI to detect their pattern of behavior and alert them to a price drop through SMS notification.  

• Question: Has this customer recently called the customer service hotline to report a delayed package or wrongly delivered item?
• Action: Send them an exclusive free shipping “apology” code for their next order.

All of these actions go beyond simple customer acquisition and help build trust. These kinds of data-driven insights should also be fairly simple to achieve by leveraging machine learning and uniting data from across all customer touch points to shape a vision of customers in real time.

When a person trusts a brand is using data in a thoughtful and intentional way, rather than to just drive a sale, they are more likely to offer that brand more data to inform experiences in the future. Each interaction generates more value for both the organization and the customer, and it’s essential to do this at scale to grow deeper connections and foster customer loyalty in the long term. That’s only possible with AI. 

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NICE has brought its artificial intelligence (AI) technologies to the heart of the contact centre, with the addition of Enlighten AI to the NICE Satmetrix Voice of the Customer.

According to the company, the AI is able to help contact centre agents identify at-risk customers and turn them into ‘loyal net promoters’  through the automated deployment of real-time, hyper-personalised surveys that are contextual and conversational.

According to NICE, consumer habits within digital environments are creating more demand for contact centres. Consumers will engage contact centre services for everything from research to purchases, as well as post-purchase enquiries and renewal.

The company also points to a recent NICE survey, which showed that 92% of contact centre decision makers have noticed increased interaction volumes. Further 84% of consumers are likely to switch brands after one poor customer experience.

Aberdeen vice president and principal analyst Omer Minkara says that organisations typically learn what causes customer churn only after they lose their clients.

“Modern contact centres don’t need to pay such a high price of frustrating or losing their clientele. Contact centres using AI capabilities achieve 2.3 times greater improvement in first contact resolution rates, 3.5 times greater annual increase in customer satisfaction rates, and an 11.5 times greater annual improvement (decrease) in service costs.”

NICE aims to Bring Enlighten AI to Satmetrix Voice of the Customer to help organisations provide impactful customer experiences that improve satisfaction and customer loyalty, and to reduce churn.

NICE workforce and customer experience group president Barry Cooper explains,  “As part of a holistic Voice of the Customer program, Enlighten AI Feedback identifies customer experience blind spots and enables organisational agility to close the loop. With that, it delivers what customers want and expect: informed conversations that are frictionless, hyper-personalised – and help resolve their problems.”

Enlighten AI with NICE Satmetrix Voice of the customer:

• Listens to and interprets 100per cent of interactions to transform subjective behaviours into objective insights without bias, using no human effort.
• Eliminates the need for human listening to understand agent performance and identify customer churn threats while providing more precise information.
• Based on the context of the interaction, deploys real-time hyper-personalised, contextual, conversational surveys that expand closed loop efforts to improve customer satisfaction and increase lifetime value.
• Uses pre-trained models that drive proactive, predictive, and prescriptive action to improve the operational efficiency of closed loop efforts at scale.
• Accelerates agile actions to resolve customer issues and prevent churn.

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It’s no doubt that artificial intelligence has taken its firm position in every industry, making processes efficient and convenient than ever before; ecommerce of course is no exception. Especially during these pandemic times, buying preference of consumers have witnessed a sharp spike towards digital shopping thereby triggering a tectonic shift in the contours of the age old customer service operating model.

What has worked in the past for customer service teams will not work in the future and several ecommerce brands learnt this lesson the hard way in the recent past. Why?

4X-5X increase in online orders for ecommerce businesses during the global pandemic also means similar increase in call load at the call centers of these businesses. The age old model of call centers manned with trained agents to take incoming calls of customers just cannot support this exponential increase in call volumes and many ecommerce businesses had to look at re-inventing their processes using meaningful technology interventions. Enter AI to the rescue.

AI in ecommerce sector is nothing new though. How many of us actually relied on buying online before the automations, smart recommendations, virtual assistants etc.? Today though we enjoy online shopping more than before, most of us do not realize that we are interacting with an ‘intelligent’ system until we really try to notice it. The pandemic situation has just accelerated the pace in which AI gets adopted by businesses and especially for customer service, AI provides an assortment of benefits that can no longer be ignored or postponed.

Below are some top use cases of AI for customer service and how brands are able to deliver exceptional service quality to their customers anytime and anywhere

Customer needs translated to product suggestions within a second 

Suppose a customer is looking for a specific type of product and is finding it difficult to find the right one from the vast assortment available in the catalog. Instead of having to spend endless hours trying to locate the right product, an intelligent AI that is knowledgeable on the products, can make the customer’s product discovery journey much easier and faster.

Helps customer connect to the brand emotionally and strengthens the relationship

AI’s greatest benefit is that it helps build customer loyalty and hence strengthens the customer-brand relationship. The information provided is more objective and the customer has far greater control over the interaction experience. A study conducted by PwC revealed nearly 80% of online consumers feel that speed, convenience, knowledgeable help, and friendly service are the most important elements of a positive customer experience – something that was grossly lacking in yesterday’s customer service processes but no made a reality with AI based intelligent systems.

Intelligent QnA system to answer customer queries

Question Answering, one of the major Natural Language Processing tasks, has helped us build virtual assistants which can provide answers to user queries/ concerns, generated from all the data it possesses. Best part, this service does not sleep! It is always on, available across any channel and any device.

Automation of Order related Processes

An intelligent AI can be trained to understand the issues of the customer and based on the intentions of the customer it can guide them through the processes such as cancelling an order, changing shipping address, getting refunds all within a few clicks.

Artificial intelligence analysis to understand market demand and customer needs

When consumers interact with the AI, they provide a lot of their context information which can help the ecommerce business understand the needs of the customer better. It is impossible to manually go through these queries to find insights out of these. However AI based analytics made on user queries, can predict consumer demands, forecast sales and also help in competitor analysis as well.

Study of customers’ needs to make relevant decisions

According to Narrative Science, 61% of those who are using technology to implement innovative strategies, said they are using AI to identify opportunities in data that would otherwise be missed. Customer behavior has been changing expeditiously and companies, who care about their customers’ demands, know that they need to provide an amazing experience. A great advantage that AI offers is the ability to constantly learn from customers’ behavior and using that learning to take decisions cutting across diverse areas such as merchandizing, assortment, price match, new product ideas, geography expansion, inventory and price optimization and many more.

Conclusion

AI is the future and in order to survive this challenging and innovating environment, companies that haven’t implemented Artificial Intelligence yet, should consider conversational commerce in their roadmap. Ultimately, making an investment for the sake of great customer service and personalized marketing strategies is not at all a bad idea.

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The term “digital transformation” has become so ubiquitous that it can mean almost any change from a manual process to an electronic one. But why do we have to think of change in terms of digital transformation? Few would argue that replacing an inefficient manual task with automation is a “transformation.” However, I think of change in terms of innovation, in terms of altering how we do something or the way we behave—in terms of disrupting an ecosystem. Innovation isn’t just automating processes that already exist, but rather applying technology to solve a problem in a different way.

In my view, one of the best ways organizations can approach a given problem space is by leveraging the myriad of data they collect every day. Data analytics comes to mind, of course — crunching a sea of data to find correlations and insights we can use to make a process better. How then do we decide what to do with those insights? You develop and train machine learning (ML) models to make more accurate, unbiased decisions based on the available data. Then you apply artificial intelligence (AI) to suggest the best way to act on those decisions to improve the chance of a successful outcome.

Using AI/ML for innovating the customer experience

One of the most visible targets of transformation initiatives is to improve the customer experience. The internet has removed geographical distance as a barrier between you and your competitors, so a company’s online presence is more important than ever. That’s why everyone is rushing to provide ever more-engaging online experiences, to hold a prospective customer’s interest.

Numerous companies provide website plugins to track a visitor’s clicks and actions, analyze them to intuit intention, and determine, for example, what content, advertisement, or offer to display next. Going beyond that, today’s most successful e-commerce sites also use AI/ML to personalize each shopper’s experience, like the order and presentation that will most likely result in another click or a purchase.

AI can anticipate with near certainty—based on past and present action, search patterns, profiles, external demographics and more—what a customer wants to see now and will do next. If successful, your website visitors will come to feel at-home, excited, and perhaps even brand loyalists. They’ll buy more and return more often.

But digital innovation shouldn’t stop with customer experience

There is nothing wrong with applying analytics, AI, and ML to create a more innovative and engaging customer experience. Not doing so can put you behind your competition. It’s all about building customer loyalty and boosting revenue.  

No matter how important customer experience is, however, it is a mistake to believe it is the only operational area that can (and should) be transformed using technologies like these. After all, today’s enterprise amasses data about more than just customers and orders. Your company, product, and delivery must broadly innovate — and all these happen on the backend. The efficiency of your internal operations — your support team, supply chain, production, inventory, quality control, human resources, and so on — can all benefit from applying AI and ML technologies. Consider just a few of many possible examples.

• Motivating a remote workforce – With so many teams working remotely, first-hand observation of employee engagement is next to impossible today. AI can analyze which applications employees use most, possibly even judging their levels of efficiency or frustration. Organizations can understand how happy, motivated, and engaged teams are so they can maintain or increase efficiency and productivity.
• Refining a business model and marketing – Beyond mere numbers, AI can analyze which products in your online portfolio work best and for which shoppers. Yes, this can help you shape the online customer experience. But it also lets you adapt which products you choose to keep or eliminate from your lineup (your business model) and adapt your offers based on observed customers’ choices or preferences (your marketing strategy).
• Protecting intellectual property – Organizations can even protect their patents, intellectual property, and product uniqueness by using AI, ML rules, and image recognition to smartly crawl the web to identify look-alike products and would-be theft.

The possibilities for internal process improvement across the enterprise are endless.

AI/ML isn’t just for large technology companies

In short, companies should apply AI/ML innovation to their operational processes as much as they do to the customer experience. Artificial intelligence isn’t just for technology companies, nor is it for analyzing and solving only technology problems. Organizations can use it to better understand their customers. They can use it to automate inefficient internal processes. They can leverage it for improving online security, boosting employee engagement, and reducing theft and risk.

Why aren’t more companies using AI? Frankly, they do not know how to start. They know they need to use it, but they don’t know where to “plug it in” to their systems first. Or they think they have to hire a team of AI engineers and build their solution from scratch.

Today AI is available for any company to use and benefit from, even smaller companies, without the need for a team of experts. There are many commercial apps and solutions in the marketplace that readily adapt to an organization’s existing processes. Some are even SaaS- and cloud-based solutions, meaning they do not require a big infrastructure investment to get started. The important thing to know is that any company can start small and scale up their AI solution in their own time — but getting started is the only way to stay competitive.

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The world today is bombarded with continuously growing big data sets. This large amount of data is being produced every minute by businesses as well as individuals. Processing such voluminous data requires advanced analytics solutions. This is where big data analytics comes in, playing an indispensable role in analysing big data sets to uncover information.

So, What is Big Data Analytics?

Big Data Analytics refers to a form of advanced analytics and helps excerpt meaningful insights, such as unexplored correlations, hidden patterns, market trends, and customer preferences. It involves complex applications with elements like predictive models, statistical algorithms, and analysis powered by analytics systems.

How Big Data Analytics Can Help Improve Customer Experience?

Effective and successful delivery of customer experience relies on data. This data is typically collected by companies to understand their customers’ behaviours and deliver personalized, on-demand offerings to make consumers happier. This collection of customer data helps businesses with market competitiveness, user productivity, sales revenues, and eventually the customer experience.

Here are some ways how big data analytics can help improve the customer experience.

Personalization: Big data analytics can find out hidden patterns present in the data. It enables businesses to get to know their customers and effectively facilitate and transform customer-company relationships.

Build Customer Loyalty: Big data analytics allow organizations to actively build loyalty rather than just respond to customer queries. As it provides a quantitative and qualitative view of business, enterprises can pull out meaningful trends that can be translated into better products, services, and operational activities.

Track Customer Behaviour: Big data analytics is constructive in identifying what is behind customers’ behaviours. It allows firms to get personal with their customers and drive the quality of customer experience.

Data Collection: Today, every search, voice command, and major purchase may be recorded as people go about their professional and private lives. In the era of digital marketing, many companies now maintain profiles with thousands of data points about each individual.

Customer Retention: Data has maximum potential that aids businesses in planning ahead based on specific users’ interests.Data-driven customer experience is imperative in improving customer retention, engagement, and growth.

Predictive Analysis: With an optimized solution and predictive analysis, data can be much more useful, especially when it comes to determining what might interest a business customer now and in the future.Predictive analytics solutions help companies get to know what might happen, and prepare a response in advance, get ahead of the risks and influence the outcomes.

More Convenient Support: Once trained, data-driven systems can work with all existing channels, including voice, email, SMS, and more, often without human oversight. With big data analytics, customers even get more convenient, get responsive support across preferred platforms.

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Leading Mobile Cloud-based Business Operating system provider, Domo, is taking the Connected Devices route to bridge Sales and Marketing processes. In an exciting development for Marketing and Sales Technology aficionados, Domo has announced a strategic partnership with MarTech giant, Zendesk. This partnership with Zendesk will help Domo customers to better manage their IoT solutions and offer a better, proactive service experience to their end users.

How IoT App is a Value-Add to Domo Customers?

This new IoT app extends the Domo IoT Cloud provisions, enabling a better, proactive customer service experience with Zendesk. This is particularly focussed at creating a self-service autonomous workflow from device data to issue resolution.

Domo is already recognized as a leader in Self-Service BI Market by independent market research firms and analysts. This new IoT App further strengthens the Company’s BI product roadmap with Zendesk as a key partner.

Why you’ll love your Zendesk data in Domo

Modern customers expect an unprecedented level of experience delivered on every device they access. It’s easy to promise on Customer Experience dreams than to truly deliver on these. This Domo IoT App allows customers to move out of the vacuum created due to Marketing, Sales and Customer Service silos.

Intricately tying into every business operation, Zendesk’s Domo integration allow Domo customers to connect and manage all your other sources of customer data within Domo. This means you can get a holistic view of your customers’ needs and habits. With that kind of information, you won’t merely respond to helpdesk requests.

Role of Zendesk in Domo IoT App

With this new app, companies can merge their Zendesk data with data from other sources, all in the Domo platform. Once this data is in Domo, companies can get a holistic view of its customers’ needs and habits, and see how business decisions are affecting customers in real-time.

Benefits of Working with Zendesk within Domo

With the new IoT app, you can merge Zendesk data with data from any other source, all in one platform.

Additionally, you can create real-time alerts and receive instant notifications when Zendesk metrics hit thresholds that you determine.

The most satisfying benefit of having Zendesk work within Domo is the availability of Instant insights. Domo customers can now bring Zendesk and other data sources together to see how business decisions affect customers in real-time.

Marketing and Sales will Relish this Fluent Combination of CRM Data with IoT Functionality

Companies get real-time alerts based upon changes in the data coming from a device, and Domo pushes that data back to Zendesk Sunshine, the company’s CRM platform built on Amazon Web Services (AWS), to automatically initiate a ticket for Zendesk support. This app offers an end-to-end solution for companies to take action based upon the data from their IoT machines.

At the time of this announcement, Jay Heglar, Chief Strategy Officer at Domo., said, “Domo is helping customers evolve their IoT data from an operational tool for the shop floor to a valuable asset for the entire company.”

Jay added, “With this new App, we are providing our mutual customers with a seamless experience for taking data from machines and initiating (a) workflow to solve problems. Zendesk Sunshine makes it easy to deliver this integrated experience, which we believe is the future of IoT.”

Domo Customers Enjoy An Upper-Hand in Dealing with Heightened Customer Expectations

Tech does all the talking in this Zendesk+Domo IoT app function. Customers can leverage Domo’s new IoT App to merge an organization’s most relevant Zendesk data points and turns them into dynamic visualizations. These include:

Meet Customer Satisfaction Objectives

This feature allows users to easily analyze customer survey responses and breakdown the data by marketing channel, manager, location, teams, and individual agents, to identify trends and improve customer satisfaction scores.

Support Ticket Activity

This feature allows customers to view all of their support ticket data in one digestible dashboard, seeing opened and closed tickets, the number of escalations, and support team efficiency with a variety of filters and segments.

Autonomous Zendesk QuickStart

The Zendesk QuickStart provides a comprehensive summary of team performance, including backlog management, ticket handling, agent leaderboards, and much more. This feature allows customers to optimize their customer support and streamline their organization’s operations.

Norm Gennaro, SVP of worldwide sales at Zendesk, said, “The best customer experiences are built on Zendesk. Our customers are looking to drive excellent customer experiences and increased business value through IoT data, our new partnership with Domo helps us deliver on that opportunity.”

Norm added, “With the new Domo and Zendesk app, organizations can gain a holistic view of their customers’ needs and habits, and anticipate helpdesk requests that are affecting their customers in real-time.”

What Domo’s Loyal Customers Have to Say about  the new IoT Applications for Their Marketing, Sales, and Service

Companies like SharkNinja will be able to use this new solution to provide a new level of customer service to its install base.

“When SharkNinja launched the new Ion robot, they had hundreds of thousands of robots in consumers’ homes. And it would take them a whole week to analyze data from just 20 robots to learn things like ‘is the battery running low,’ or ‘is the robot completing its mission.’

Domo already offers its unique Data Science suite to customers. Adding its IoT App to the marketplace will heighten its sellability quotient, especially with Zendesk as its tech partner in the ecosystem.

Domo currently has over 30 IoT Integration Apps including AWS IoT Core, Azure IoT Hub, Raspberry Pi, and Soracom.

Besides the Zendesk integration, the newest in the fleet of IoT Integration Apps is the MQTT data and connectivity App. MQTT is a machine-to-machine, Internet of Things connectivity protocol. This protocol is lightweight and is suitable for constrained environments. This protocol is deployed in many IoT devices globally. MQTT is useful for connections with remote locations where a small code footprint is required and/or network bandwidth is at a premium.

For example, MQTT has been used in sensors communicating to a broker via satellite link, over occasional dial-up connections with healthcare providers, and in a range of home automation and small device scenarios. It is also ideal for mobile applications because of its small size, low power usage, minimized data packets, and efficient distribution of information to one or many receivers.

Currently, Domo’s mission is to be the operating system for business, digitally connecting all your people, your data and your systems, empowering them to collaborate better, make better decisions and be more efficient, right from their phones. Domo works with many of the world’s leading and most progressive brands across multiple industries including retail, media and entertainment, manufacturing, finance and more.

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Big Data is very important, and there is no doubt about this fact. Big Data has proven its mettle over the period of time. It has definitely become one of the most valuable, innovative and exciting technological offerings for the world.

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