User Experience (UX) Design at its core, ux is about considering the needs of the people who will be using the product (website,app, etc ).
User Experience Design (UXD or UED) is a design process whose sole objective is to design a system that offers a great experience to its users under the theories of a number of disciplines such as user interface design, usability, accessibility, Human-Computer Interaction, etc.
What is UX design and why is it important
User experience (UX) design is the process design use to create products that provide meaningful and relevant experiences to users.
So, during this process user experience is important because it tries to fulfill the user’s needs. It aims to provide positive experiences that keep a user loyal to the product or brand.
What are the most important skills in a UX designer
UX research
many of the decisions UXers make are not just plucked from air but are deep thought out, studied and researched.
App prototyping is a great way to understand key functionality of your design before being built by developers.
3. UX Writing
Writing is the unsung hero of UX. Coding is a skill that shouldn’t be dismissed, but writing is a talent that can be nurtured over less time to create brilliant users experiences.
4. Interaction design
It’s one thing to create an aesthetically pleasing design but it’s another thing to understand how users will interact with that design.
Does UX design require coding ?
Most UX designers have at least a little understanding of code, including HTML, CSS and Javascript. It is best to leave coding to the experts, but knowing code is a great asset for toolbox and makes more marketable.
When I was a young student and short on cash one Summer, I got a job with a lesser-known comparison line. My job was to take phone calls from the irate British public and convince them to change their telecoms package. You would think that this would be difficult, especially as they were normally coming from your main brand providers and all my company had to offer were companies that, up until that stage, I had never heard of, nor the customer. Well, it turns out that it wasn’t, when people want to leave a company, they really just want to leave.
What surprised me though was that most of the callers had been with their provider for a long time and up until this stage had been happy with their service. And, their reasons for leaving were normally easily fixable on the part of the provider. Now if you consider that I was working for a much smaller competitor (really — much smaller), this makes you wonder, how many customers are these guys actually losing each year? And from their perspective, how can they prevent it from happening?
When people want to leave a company, they really just want to leave.
Lost customers are called “churn”. It is difficult to go into a strategy meeting without hearing the word. No matter how great your sales team is, how captivating your product, your business model is failing if you don’t keep on top of your churn. Clients leave for a multitude of reasons, some are unavoidable (especially with the current COVID situation) but identifying the customer’s motivation for leaving allows you to predict and prevent churn.
With my job at the comparison line, most people complained to me about long wait times and bad customer service. If this had been recognised and measures such as extra team members at busy times and additional customer service training put in place then the company could have saved millions in churn. So how can you identify these factors as early as possible?
Our team looked to create a project analysing 7043 current and former customers of a telecoms provider in order to better understand what types of people are most likely to cancel their contracts.
With the intention of showing how this might be done (and easily reproduced) our team looked to create a project analysing 7043 current and former customers of a telecoms provider in order to better understand what types of people are most likely to cancel their contracts. On top of this, we also wanted to know whether there is anything the telecoms provider could do to motivate their customers to stay.
We found a dataset from Kaggle containing 26 variables including the customer’s type of internet service, whether they had churned or not and their monthly charges. We used these variables as factors to create a clustering model using Graphext — a platform for conducting complex data analytics without writing any code.
At this stage, our goal was simply to see the churned customers and to understand them better, so we coloured the graph and filtered it so that we only had the data points for the customers that had left. Then, we used the Graph to zoom in on the 4 communities of churned customers in the dataset in order to further discover the characteristics of these customers.
Cluster 1
Cluster 1 stands out as it is an outlier in the graph and includes only a very small minority of customers. This group were only using a phone line and weren’t paying for internet or TV. They had a low tenure, or to put it more precisely, they had been in the company for only a short period of time. Looking closely at the characteristics of this group, they seem to reject any firm ties to the company. They were on a month-to-month contract, they pay by cheque and their monthly billings are very low.
Cluster 2
Cluster 2 has a little bit more going on and at the very least they have broadband at home. It’s DSL though — if you remember those days. They are equally paying by monthly cheque and they also don’t have any TV packages. Once again we find that these churned clients are on a month-to-month contract and only stayed with the company for a short period of time. Moreover, they opted out of tech support in their package.
Cluster 3
Cluster 3. We are beginning to see some patterns emerging. These customers are our first cluster with fibre optic but once again we see that the majority are paying by monthly cheque and are mainly on a month-to-month contract, and still no tech support. These customers have been with the company for longer.
Cluster 4
Cluster 4. Our youngest cluster seems to have a better package with TV streaming and monthly spending a bit higher. However, we still see them without tech support, month-to-month contracts and once again, paying monthly by check. Furthermore, they churned quickly having spent a relatively short amount of time with the provider.
All this clearly indicates that the three characteristics common to all of our churned clusters are; paying by check, month-to-month contracts and no tech support. Once these are identified it is much easier to gather solutions that could help you retain these customers. Despite the fact that there are communities of customers that didn’t churn also possessing these characteristics, the prevalence of these features inside churned communities is significant.
Once you identify why customers are leaving you, it is much easier to take appropriate action.
I have since moved on from my job with the comparison line but still, this analysis exposes clearly ways to mitigate this volume of churn no matter which sector you are working in. Taking into account the reasons these customers abandoned their contracts, the company could start offering add-ons to directly combat the movement of these customers away from their business.
Offers such as free tech support in the first year, discounts for direct debit payments and strategies such as up-selling those month-to-month customers and getting them on year-on-year rolling contracts with exit fees would be relevant and informed action that the company could take.
Understanding churn is a crucial method of stopping money from draining out of your business. It seems counter-productive to spend time developing new campaigns and targeting emerging markets when you can’t keep the customers that you already have. This kind of trend is cyclical and downward. It needs attention and action. Analysis tools like Graphext allow you to quickly find where your strategy is leaking and identify the appropriate tools to plug those holes. This kind of analysis is no longer as hard as it once was and can make the difference between red and black accounts.
Customer success is the measure of how much your product offers value. Take an example of a SaaS software company that builds custom software for a logistics firm. The number of successful delivery orders fulfilled will be an indicator of the success of the software company.
According to a survey, 93% of respondents believe that customer intelligence is the secret to customer success, while 70% of site data capture challenges to be massive. So, you need a reliable analytical solution for all your customer intelligence needs.
Since the pandemic impacted the world economy, businesses are trying to offer resilience to economic downslide. However, there is no denying the fact that customer success can be critical in times of uncertainty. So, you will have to configure a way to optimize resources for tremendous customer success.
If you want to maximize conversions and help improve customer success, monitoring key performance indexes or KPIs is essential. However, you will have to pre-define each metric that needs monitoring before analysis. So, here we are with some of the top customer success metrics that you need to track.
#1. Monthly Recurring Revenue (MRR)
Monthly Recurring Revenue is a basis of how much revenue your organization earns each month. This is a crucial metric to measure not just for financial purposes but also to improve the conversion rates. MRR allows organizations to leverage financial forecasting, planning of revenue sources, growth measurement, and momentum.
Take an example of planning a B2B sales appointment setting. It is a fundamental activity for any organization’s sales team, yet calculation of MRR can help them understand the need for an increase in the intensity of efforts.
You can calculate MRR by,
Monthly Average Revenue per User (ARPU) X Total Number of Monthly Users = MRR
It is a business-performance metric that helps measure the product’s ability to impact customer success through revenue generation. However, MRR is dependent on the average revenue per user or ARPU.
ARPU or Average Revenue Per User is also known as the Average Revenue Per Unit. It is a measurement of how much revenue an organization generates from each user. SaaS-based organizations, telecommunication companies, and digital media firms use ARPU as crucial customer success metrics.
ARPU can help you gain essential data regarding,
Capacity to generate profit
Customer needs
Forecasting financial gains
Competitor analysis
Calculation of ARPU depends on specific factors like total revenue, time for which the sale is considered, and an average number of user interactions.
Total Revenue / Average Users = Average Revenue per User
Here, for calculation of ARPU, many organizations consider the time monthly due to the subscription model of revenues. ARPU also relates to the churn rate, considering the average number of users for calculations.
#3. Churn Rate
Customer churn rate is the measure of how many users you have lost. These customers don’t use your services and products anymore. This is the reason why it is also known as the customer attrition rate. Churn rates are one of the most significant customer success metrics.
According to a report, a SaaS company can have a churn rate of about 5–7% annually; at the same time, a small business may have to deal with 58%. There are several indicators to an increase in the churn rate,
Subscription cancellation
Accounts getting closed
Recurring value loss
Contractual losses for recurring business
If you want to calculate the churn rate for your business, you need to divide the total number of customers churned during a specific time by a total number of users.
Total churned customers/total no. of all customers = Customer Churn Rate.
Churn rates are closely associated with the detractors and promoters, leading to another vital customer success metric- Net Promoter Score(NPS).
#4. Net Promoter Score
NPS is a business metric that helps you gauge customer relationships. When you want to measure NPS, there are two types of customers: a detractor and the other is the promoter. A detractor is an unhappy customer that is unlikely to buy or experience your product again. At the same time, a promoter is a loyal customer.
However, some passive customers may or may not rebuy your product. To calculate the net promoter score, you need to subtract the percentage of detractors from promoters. It will help you understand the level of customer attrition and the reasons for higher numbers of detractors. Another critical factor that can help you determine detractors and promoters is customer satisfaction.
#5. Customer Satisfaction Score (CSAT)
NPS is a measure of how your customers may or may not promote your services; at the same time, a customer satisfaction score can help you understand how satisfied they are. It is one of the easiest customer successes that you can track through several touchpoints across the user’s journey. In addition, it offers feedback on parameters that you need to improve on for customer satisfaction.
For CSAT, you can use short surveys that offer ratings from customers regarding your product or services. The survey can be sent through different channels of interactions like emails, chats, social media messengers, etc.
First, you need to divide the positive responses by the total number of responses you receive and then multiply by 100 to get the rate in percentage.
All these customer metrics are a great way to understand the bottlenecks in your products or services. However, once you find these issues, there are several ways to improve on them. One of the most effective approaches is to use a chatbot. A chatbot is a computer program that can help you mimic the human to human conversations for enhanced user experience and customer interactions.
Chatbots are a great way to simultaneously improve your content marketing efforts and business performance through users’ rich experience. For example, you can employ chatbots that target customers based on their position in the sales funnel and improve conversions. This way, it can enhance your CSAT, reduce the churn rate, and increase NPS.
Conclusion
Customer success is a major aspect of any business performance. It means that your customer’s success is directly proportional to your organization’s performance. Tracking these metrics will help you gather vital data related to product performance which you use to improve your offerings.
At the same time, advanced technologies like chatbots, Artificial Intelligence and Machine Learning can help you aggregate customer success data, analyze it, and offer intelligent recommendations.
Just getting started with chatbots and found some old(ish) tutorials that begin with rivescript.
The website seems down though for already couple of hours. Was the project abandoned if anyone knows, or am I just being impatient hehe? Any other beginner resources to making a chatbot you would recommend?
Hello! I’m making a chatbot as a personal project which intends to have human-like conversations. However, mainly due to the lack of training data and all the possibilities of conversation topics, I’m finding it hard to get that human-like conversation flow.
Does anyone know of any online resource which could help me in learning how to design better chatbot conversations? My aim is not to make this a customer support chatbot, but just to have a friendly conversation with it.
Side note: I’m using DialogFlow for the chatbot, just in case you were wondering.
With nearly 1 in 4 U.S. adults already having a smart speaker in their home, voice assistants and conversational AI are quickly increasing in popularity in most major markets and becoming a normal part of people’s lives around the world.
Systems like Alexa and Google Home have created a new field of research in cognitive science that examines the effects of conversational devices interacting with users. The widespread availability and increasing adoption rates have also contributed to consumer behavior trends and purchasing patterns — from voice assistants becoming ubiquitous to people spending more on home improvement projects and the growing use of mobile devices as digital assistants.
In this article, we’ll take a deeper look at this field and explore 9 of the most important predictions for voice assistants and Conversational AI.
Explaining the Shift Towards Conversational AI and Voice Assistants
The consumer shift to voice, driven by evolving user demands, is causing change across the customer service space. Voice user interfaces, or VUIs, offer highly effective means of communicating and interacting with consumers. As users become increasingly comfortable with digital interactions in real-time, brands can use conversational interfaces for faster response times and increased customer satisfaction.
Due to these reasons, voice assistance is growing at a tremendous rate and it’s highly likely that nearly every app will be using AI-based voice technology in some capacity in the next five years. The emergence of voice assistants will also be helped by the fact that voice applications are becoming significantly more intuitive, responsive, and simpler to use in the future.
Top 9 Predictions for AI-powered Voice Assistants and Conversational AI
Personalized Experiences
Personalization is more than names at the top of emails, it’s staying in touch with customer tastes and preferences and actively including them in the conversation. Personalization is essential for building meaningful relationships that last. Businesses can use machine learning (ML), in particular, the subset of ML known as Natural Language Processing (NLP) along with Sentiment Analysis to identify the true meaning of customer requests and queries. By identifying the Intents in those requests, brands can generate accurate responses to customers instantaneously.
For example, Pillo health helps users stay on top of their medication — measuring when it should be taken, keeping it stored, and dispensing it at the right time. When a user adds a new medication to their Pillo account, the robot politely reminds them to take it regularly before the date they need to administer it.
Voice Push Notifications
Voice notifications are a valuable tool to engage users within the application. Notifications can be helpful in reminders, promotions, and information. 55% to 60% of all mobile users opt into push notifications which means that businesses have a stronger chance of reaching their audience with relevant and timely messages.
Voice assistants are also designed to connect to third-party apps for voice push notifications, for instance, both Google and Alexa have this functionality, allowing them to notify users about everything from calendar appointment reminders to music streaming services.
As adoption rates among online shoppers continue to rise and voice search continues to be at the top of the eCommerce sales funnel, eCommerce sites must ensure that they have the tools necessary to capture information and engage customers. By engaging customers, brands can develop long-lasting relationships with customers.
According to Juniper Research, consumers will spend $19 billion on voice-enabled products by 2022. If voice search models are successful enough, this will introduce a new advertising gate for brands that want to keep their messages prominent.
Inbuilt Security Features for Users
The latest trend in the voice assistant market is built-in security features, aiming to help users feel safer when using voice assistants.
Once again, mega-corporations like Amazon and Google are taking charge here, having released updates that put security measures in place like speaker verification and ID confirmation.
To further resolve users’ privacy concerns, Amazon has published several more comprehensive documents about the Echo’s recording capabilities and how it preserves users’ data.
If you’re concerned about your data being recorded by your Echo (or lack thereof), Amazon added several significant new features to help ensure that personal information is never stored on the device.
Voice Assistance in Mobile Apps
Apps with integrated voice assistants have improved usability and make app navigation easier. With voice-activated apps, users can control nearly all of an app’s functionality through voice commands. In many ways, this is similar to text-based chatbots or GUI-based conversational agents that allow users to navigate enter websites through a single element in the website. But, voice-based navigation is even faster and easier. This is a game-changer for end-users who are less tech-savvy and want to use apps while spending less time and energy.
Inbound Calls and Smart IVR with a Natural Language Understanding (NLU) Feature
An advanced Interactive Voice Response (IVR) and a call tracking system can significantly improve sales and customer satisfaction. Businesses can use an intelligent virtual agent powered by an NLP engine to answer customers’ questions in real-time or create outbound calls with the click of a button. A smart call tracking system integrated into a business’ IVR lets them monitor and record every phone call from prospects or customers, creating robust data that can be used to generate outbound sales campaigns.
Increased visibility into your leads and contacts will give you a brand-new approach to sales, allowing you to optimize efforts immediately — giving your business a competitive advantage and improving overall performance.
When mentioning Conversational AI’s use in gaming, we can’t ignore the importance of text-to-speech as well as voice recognition in creating a more immersive gaming experience. This is not an easy feat, especially when considering the vast possibilities of different types of voices, including synthetic voices and generative neural networks.
That said, generative neural networks are machine learning tools that are making this possible. Developers can create dynamic verbal dialogue for video games with far less manual labor.
As neural networks and artificial intelligence engines become more advanced, game designers can create NPCs with current voice-acting tools and use them to create a more immersive storyline. The next innovations in AI engines will allow bots to develop a custom personality based on player action, producing more realistic conversations. The NPC responds according to how the player has acted throughout the game. Considering that video games have become the biggest sector in the entertainment industry, it’s promising to see voice technology being a core part of its innovations.
Voice Cloning
Voice cloning is a process that uses machine learning along with neural networks to generate realistic human speech Neural network-based text-to-speech platforms mimic how the brain functions to process language and exhibit outstanding efficiency at learning patterns in data.
Deep learning comes into play when it’s time to generate human-like speech and is particularly effective at capturing nuances such as speed and intonation.
Through the power of artificial intelligence, deep neural networks, and cloud-based GPUs, new startups can create a computerized voice that modifies your own and make it indistinguishable from the voice of a natural person. Voice cloning will certainly be one of the biggest drivers in the entertainment industry, very similar to early CGI. The realistic nature of voice cloning is already creating a buzz in Hollywood. To a lesser extent, voice cloning may see consumer uses, especially in privacy-focused online communities.
The Rise of Enterprise Voice and Chatbots
Brands like Starbucks, Spotify, and eBay have built intelligent customer service into their online presence. One of the most innovative chatbots is the Bank of America’s Announcement bot by the name of Erica. Erica uses artificial intelligence, algorithms, predictive messaging, and many other advanced techniques to help customers make payments, check balances, and new products.
On the other hand, Amazon voice assistant continues to extend its lead over the competition by announcing its Alexa Skills and Alexa Capabilities. Amongst other new features, Amazon has given developers the tools to build their own Alexa skills (apps) — a unique feature that’s not available on any other device.
Some ideas for using Alexa skills include: improving the user experience, providing information, and improving productivity. For instance, a customer can experience a new product through Alexa’s customer-centric approach — with questions like “Alexa, how is this product made?”
Challenges
Conversational AI and voice assistants have improved at communicating with humans across a range of situations. However, voice recognition and natural language understanding aren’t perfect and there is still room to improve. For now, experts are innovating to combat a few key challenges, including:
Language Input
Although voice recognition has advanced in leaps and bounds, AI still needs to continue improving — especially at recognizing minorities, as voice assistants today are disproportionately better at recognizing white male voices. Rather than a technological flaw, this is an indication of the lack of sample data that AI models can be trained against.
Additionally, inputs that are not appropriately processed can lead to frustration and a loss of customer trust across the board. To ensure a better experience, it is essential to develop AI that recognizes different dialects, accents, background noises, slang, and even nicknames.
2. Cybersecurity Concerns
The key to success with any conversational AI app is building trust and confidence among end-users. End-users can have high-security protocols, and despite recent advancements in privacy and security, privacy concerns are still present.
3. Apprehensive Users One of the early expectations from voice assistants was that it would be the younger millennials and Gen Z accepting voice assistants the most. However, the older generations (ages 55 and above) seem to like the idea of voice assistants more than the younger generation. According to a survey by Think with Google, the adoption rates for voice-activated speakers are surging among baby boomers. Google found that 51% of Baby Boomers use voice assistants as an informative companion and not just as a tool to play music or make a quick shopping list.
Furthermore, as employees begin using voice-based automation in their workplace, they far likely to adopt the same technologies in their homes and personal lives. Therefore, It’s important to understand that customer hesitation doesn’t reflect poorly on your brand. Instead, it is an indication of the voice technology gap that is getting smaller every year.
Challenges of Voice Assistant Technology
The Future Conversational AI and Voice Assistants
The future of conversational AI, and particularly voice assistants is very bright. About 60 percent of smartphone users have tried voice search at least once in the 12 months; while they might not engage with it every day, they are beginning to see the convenience and accessibility it offers. By 2024, the global voice-based smart speaker market could be worth $30 billion, which is another indication of the vast market of voice assistants. But with every untapped opportunity comes a ticking clock, to capitalize on it before it loses its competitive advantage. With these 9 top predictions for voice assistants, we’ve tried to help businesses like yours find the right opportunity in this promising new world of voice assistants.
Get in Touch with Our Conversation Design Experts for a Consultation Today!
However, if you’re unsure of how to proceed with developing and deploying voice assistants of your own, consulting with an expert like Master of Code Global is the best way to go.
Welcome to Lesson 4 in our “Lessons from Our Voice Engine” series, featuring high-level insights from our Engineering and Speech Tech teams on how our voice engine works. This lesson is from Dr. Amelia Kelly, VP of Speech Technology.
What is debiasing?
Artificial intelligence systems will reflect the conscious and unconscious biases of their creators and create poor — and often prejudicial — user experiences for underrepresented users. Machine learning algorithms are unique in that they carry out decisions based on what they’ve seen within a supplied dataset, rather than being explicitly programmed using a predetermined set of rules. Building a system based largely on data from one demographic will result in accurate performance of the speech recognition system for that sub-group, but inaccurate performance for all others.
Debiasing is the conscious and intentional process of counteracting inherent biases that are found in databases and artificial intelligence systems.
Intentional processes, such as utilizing a varied, diverse, and proportionately representative dataset of voices to train machine learning algorithms, can reduce or remove the presence of unintended bias in voice technologies.
Why is debiasing important for our voice engine and AI systems in general?
A biased system can amplify and propagate deep-seated prejudices held by the designers of that system, be they explicit or unintended. The effects of such biases in the context of educational technology, assessment platforms and learning tools for kids can be disastrous.
For example, if a biased system fails to understand a child’s accent or dialect, it can consistently tell that child he or she is a poor reader when, in fact, they are reading correctly. An unbiased system, on the other hand, can offer fair and uncompromised information to facilitate edtech platforms and services. AI companies need to make a concerted effort to debias their technology.
Catch up on our previous “Lessons from Our Voice Engine”:
With the evolution of digitalization, people are now well-connected with each other through various modes of communication on different devices like computers/laptops, smartphones, and other electronic equipment. Companies are also taking advantage of such digital platforms and developing more robotic customer service centers to help users and solve their queries as per their needs and feasibility with a quick response time.
Artificial intelligence (AI) and Machine Learning (ML) based business applications are now widely accepted at all types of AI-enabled devices and machines like smart devices, drones to self-service kiosks and self-driving vehicles, etc. With the time being, people are becoming more habitual at the same time comfortable with AI and its benefits especially when it comes to the customer service experience.
AI based Virtual Assistant services and Chatbots are taking place of human customer service to assist users anytime as per their ease. Chatbots are specially designed to automatically chat with clients to answer their queries or receive feedback towards a particular product or service. The main motive of developing Chatbots is to minimize human interference and right here under this blog, we would discuss how AI-based ChatBots enabling businesses to perform better.
Natural Language Understanding Bots
One of the most interesting parts of Artificial intelligence and Machine learning as a service service-enabled chatbot is that such customer-oriented applications can understand natural languages like speech or texts used by humans for normal conversation. Natural language processing (NLP) equipped chatbots can provide an interaction just like humans do to make B2C engagement managed without human interference. NLP-based ChatBots lead to a conversational workflow with around-the-clock customer support service.
Interacting with customers directly is now changing its dynamics. Nowadays, making direct contact with a customer becomes crucial, as they need quick answers to their queries. And if you do not interact with your customers timely your business or website might be criticized or will have negative ratings. Chatbots can provide real-time communication to understand the problems of the customers and respond to them instantly with the right answers. With this attitude and applicability, such an application is gaining traction and offers businesses a great opportunity to improve their relations with their customers.
Scheduling the Important Tasks
Hiring many employees for customer service is costlier than implementing a Chatbot that can attend to multiple types of customers at a time with the same efficiency. As Chatbots are fully automated they can allow administrations to switch many consumers simultaneously. By integrating Chatbots in the place of human agents you will not only able to save employee overheads but will also avoid the mistakes and technical errors done by the human agents. It is also useful in scheduling meetings and assigns similar tasks with good results. The right mix of Chatbot training data set is used to develop such applications so that they can learn while interacting with customers and respond accordingly.
Conclusion
Chatbots can provide a convenient platform for users to ask their queries and get the most suitable answers. No doubt it has become a powerful tool for businesses to keep them engaged and improve their customer relationships. Moreover, it can perform repetitive tasks without fatigue like humans and it can perform each task efficiently in less time than is possible only when you choose the right bot builder AI platform.
Every technology has pros and cons, as it is an AI-based system, hence such models need a huge amount of machine learning chatbot data that can cost you in terms of GPU cost increase making chatbot more expensive. However, with the help of suitable technology and the right resources, you can produce highly interactive and cost-effective Chatbots to get quality results helping you improve your overall business performance. originally Click
This post is part of a series of articles. It comes as a reflection of my digital journey with passion for application, automation, and the benefits they can bring. As I stay tuned to discovering (or rediscovering) how customer channels are evolving to impact our customer’s experience — either simplifying their interaction or making a bad service experience. These articles have been written with the aim of re-experiencing some of the key accomplishments from the past, that gives newfound meanings today.
When did it happen?
In 2008, I was working for one of the largest telecom service providers in India (present day Vodafone Idea). I was driving a large and complex business transformation program in customer experience area. During the ideation workshops, me and my colleagues were applying all our learnings and experiences to good use. The program was introducing a state-of-the-art customer relationship management (CRM) platform which was integrating all known and popular customer engagements channels.
After contact center, Short Messaging Service (SMS) has emerged as one of the most popular channels during the feature phone era (like WhatsApp in the smart phone era). In my earlier role, I had experienced the pain one had to go through to read and analyse customers’ free text messages that used to come on a common number (short sender code: 12345). These customer messages were of varying spectrum: some genuine requests or complaints, and rest either vague, incomprehensible, comical, or even a touché inappropriate. And this was my opportunity to bring a change to this practice or ways of working.
MANUAL PROCESS OF HANDLING CUSTOMER MESSAGES
Note: Above graphical excerpt has been taken from the original concept note
What did I do?
I applied my empathetic perspective and ideated with the team to propose a structured approach (keyword based) and intuitive automation to manage customer’s query, request, or complaints (Q/R/C) using the SMS channel. The offering was named MySMS, and was the first conversational automation solution that was introduced in the market and was quick to gain popularity. It solved multiple pain points: auto-filtering of invalid messages, automatic assignment of customer Q/R/C to the relevant backend team, reduction in overall turnaround time. It also resulted in call deflection from the contact centers (a more costly channel). As the business owner for this application, I defined the roadmap to include new enhancements and make the dialogue (or asynchronous messaging) more intuitive: auto-responding with keywords matching customer’s text or providing help options so that customers are not required to remember the keywords. Also, automating the end-to-end request fulfillment process, making the experience more seamless with zero manual interventions.
Note: Above graphical excerpt has been taken from the original concept note
What impact did it make?
The firm was receiving approx. 40,000 SMS daily (across all regions). Only 30% of these messages were valid and the activity used to consume a vast amount of human effort (wastage). MySMS automated this end-to-end journey and above all improved everyday work life of 23 employees — who could focus on more qualitative work.
MySMS is a good use case of present day, chatbot or Conversational Artificial Intelligence (AI). This new age term represents messaging and speech-enabled applications that offer human-like interactions between computers and humans.
The key drivers instrumental in making MySMS initiative a huge success was:
§ In the middle of difficulty lies opportunity (Albert Einstein): Remember the things that made life difficult; so that you can make it right or better one day.
§ Stay hungry, stay foolish (Steve Jobs): Stay curious and never be shy to try new things
§ Human factor: Leverage your experience
§ Simplification: Make use of Technology
As an endnote, I wish to summarise with the thought: disruption or innovation can happen anywhere or anytime, by chance or by design. It happens when we apply clear conscience and empathy to solve a problem. It’s still magic even if you know how it’s done.
