AI, Customer Life Time Value, Advanced Analytics, Insight360, Predictive Analytics, Customer Centricity | 4 min read
The Challenge and How It Should Be Solved
Not all customers are created equally, companies today are dealing with customers through multiple channels, who is the valuables customers and how can we focus on the right customer?How can a company know its customer equity and how much their customer worth not in the past and present only but in the future as well, knowing your customer equity is definitely the base to confirm and measure the success of sales and marketing strategies.
“Knowing what your customers are worth is the secret to focusing your time and money where it makes the most difference. You can’t be all things to all people, so you need to learn to find out who really matters to your success”
“Customer centricity is a strategy that aligns a company’s development and delivery of its products and services with the current and future needs of a select set of customers in order to maximize their long-term financial value to the firm” Peter Feder
Answering the question of how a company can align its products and services with the wants and needs of its most valuable customers is always a challenging if we don’t use the right metric that allows us to measure the individual value (today and future value) knowing this measure for every individual customer and monitoring it will enable more efficiency :\
The measure that can drive all of the above is the Customer Life Time Value (CLV).
CLV is the base of a customer-centric strategy. It is the unit of measurement that estimates the customer equity, consequently, help to create greater firm value. CLV estimate the value of our customers individually and collectively.
Customer lifetime value is the present value of the future (net) cash flows associated with a particular customer. “Peter Feder- Customer Centricity”
CLV is not concerned with how much value Customer X brought to us over the past year, the main concern of CLV is to know how much customer X is going to be worth in the future (tomorrow, next month, next year…). In other words, we are interested in finding out how much money Customer X could potentially make for our company from now until eternity, and therefore, how much money we should be willing to spend to keep him.
The Traditional Methods of calculating CLV failed because they don’t take in consideration customer heterogeneity. they assume that given a certain amount of information (average customer retention rate, discount rate, net cash flow per period.), and by running some calculations they arrive at CLV of customer X at particular point in time.
These methods assume that new customers should be treated the same as old customers and that Customer X will act the same way as Customer Y and this is completely wrong.
Some other companies as well calculate CLV for an average customer then multiply it by the size of its customer base and consider this as it’s customer equity and this is completely wrong also.
Definitely, it is not right to calculate CLV by treating all your customers individually and collectively, in the same way, no matter their circumstances, their loyalty, and commitment or their tenure.
In order to have a successful estimation of CLV, we need to build it based on individual customer behavior which means that we need to have a model that analyzes the previous behavior of the customer and predict their future behavior based on that.
We can summarize a customer past behavior using RFM characteristics recency (time of most recent purchase), frequency (number of past purchases), and monetary value (purchase amount per transaction) as those 3 characteristics are the tangible results of all other factors influencing customer behavior so they are sufficient to model the customer behavior.
Based on RFM characteristic developing a stochastic model that predicts the future individual value of a customer is the most accurate approach to handle the CLV modeling. This is today the more reliable method to calculate CLV and it is being used today by many companies. CLV is a game-changer. Its use cases are growing, and it has the ability to bridge silos, offering a ‘gold standard metric’ that everyone from marketers, R&D people, HR, and senior executives can share.
When we calculate CLV we want to find a data point that we can actually put to use at our companies with confidence. To have this, these companies need good, solid, actionable data, and CLV-the expected lifetime value of a customer is the most important data they can have because it gives companies a greater understanding of what their customers (as individuals and as a group) are actually worth; by extension, CLV helps those companies to:
If you want help on calculating the right CLV for your company get in touch with Insight360. Insight 360 can assist you in implementing the future looking CLV using Advanced Analytics to reach the most accurate estimation of customer assets at the individual level and collective level.
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Originally published Jun 11, 2019 12:14:00 PM, updated July 15, 2019
2 min read
Artificial Intelligence or AI is one of the most disrupting technologies of the 2000s. Machines are getting smarter each day. While AI can create tremendous time and cost savings, it promises even bigger, more meaningful returns in another area: customer experience.
Businesses across the world are discovering new ways to leverage AI and their enhance customer experience and engagement.
In recent research, Gartner indicated that “in a few years from now, 89% of businesses will compete mostly on customer experience”.
In this post we will discuss some ways AI is already disrupting customer experience across the industries.
Making self-service more efficient
Everyone knows how excruciating it can be to just stay on hold for several minutes when we call a customer care number for a simple issue.
One of the most favourable aspects of AI is that it eliminates the need to put the customer “on hold” for the next available agent to help them with a simple problem like billing, booking or any trivial question.
AI allows companies to use chatbots to answer common customer service questions, and let customers resolve their trivial issues in a jiffy.
Back in 2011, Gartner predicted that by 2020, 85% of customer relationships will be managed without human interaction.
Besides this, most customers don’t mind chatting with customer care chatbot. As long as they answer their question quickly, they leave as a happy customer.
Improving Personalisation for online services
Another important way AI is transforming the customer experience is by providing personalised content. Now a days we rarely have to search for the products that would fit our preferences. It’s so easy now to get a recommendation based on your past history.
For example: Audio & videos streaming services like YouTube, Netflix or Spotify or concerts and performances on Bands in Town. And anyone who has shopped on Amazon has seen products recommendations.
It is now possible for products and services providers to use technology to make intelligent and accurate recommendations to their customers based on their past purchase or browsing history.
Round-the-Clock Availability
AI never needs to sleep. Similar to empowering customers via self-service, AI also offers customers 24/7 support — something most companies would not be able to afford or staff just a couple of decades ago.
What’s more, 24/7 support isn’t just “good CX”— the stakes are high in today’s market. Most customers are not willing to wait until you open your doors to get answers to their issues. AI makes that possible.
Automated day to day assistance
Many households now a days use Alexa to turn on music or dim the lights. But more and more, companies are using Alexa, Google Home and other robotic AI to make their customers’ lives easier.
E-commerce stores have started to link Alexa and Google Home to allow customers to order products using voice commands. People can also purchase movie tickets and pre-order snacks before they even leave the house. These are just a few ways the AI is transforming the customer experience.
It can save significant costs
Our partner, Ubility AI recently supported two companies in France — DataSolutions & Retail Explorer in optimising their services using AI.
In the past DataSolutions and Retail explorer used to take all information in newspapers and magazines, scan them and identify prices and promotions from major retailers, enter it in their systems and then using the insights generated support their customers on how should they adjust their prices and offerings.
Until now this was a manual process. They used to scan all the pages and have people handling data entry manually loaded them into their system. This used to take on average 2-3 weeks costing EUR 50,000.
Using Ubility's image and text recognition they were able to automate 90% of the data entry tasks, which reduced the time required to do the job from weeks to days translating into EUR 40,000.00 savings per month.
Ubility AI is a leading provider of AI Solutions helping organisations optimise their work processes using technology.
Related: Meet the Experts: Khaled Dassouki, CEO, Ubility
As we can see, the entire goal of using AI to improve customer experience is to help your customers feel known and valued. Business should test the processes, re-test, and test again— from the customers’ viewpoint. The only thing worse than not using AI for CX today is using it inefficiently.
2 min read
We live in a time that is characterised by a major technology takeover, a time experiencing the 4th industrial revolution. Companies that want to survive and evolve must keep track of technology breakthroughs, because as we’ve come to know, technology can make or break a company’s success.
In light of that, it is imperative to always look forward in anticipation and not just wait for a trend to start “trending”. We have created a list of what we speculate to be the major technology trends of 2020 that everyone should keep an eye out for.
No matter how much technology advances, it is agreed that no single tool can replace humans. Most organisations out there are already familiar with automation, which involves automating simple tasks that require processes with predefined rules and structured data. The idea of HyperAutomation, on the other hand, involves a combination of tools that together result in the creation of an organisation’s digital twin, which allows for the automation of more complex work.
According to Gartner, combining robotic process automation, intelligent business management software, and AI enables organisations to visualise how functions, processes, and key performance indicators interact to drive value.
Allowing this digital twin to become an integral part of the HyperAutomation process as it provides real-time continuous intelligence about the organisation will enable more informed decision making. Successful automation involves several key factors: discover, analyse, design, automate, measure, monitor, & reassess.
An example of a tool that is designed based on these factors would be Exceed’s ESP.
While Blockchain was first developed back in 1991, it came to life with the introduction of Bitcoin in 2009. The idea of bitcoin mimics printed currency in the transactional sense, but instead of being regulated by a central bank or government, bitcoin is regulated by a network of computers. Blockchain is the protocol on which bitcoin is built.
In the simplest terms, Investopedia defines Blockchain as “a distributed, decentralised, public ledger”, which translates to digital information (blocks) that are stored in a public database (chain). While blockchain is beneficial in peer to peer transactions and small-scope projects, it remains immature for enterprise deployments due to technical issues.
However, market speculations anticipate it to be fully scalable by 2023. According to research conducted by Gartner, “true blockchain will have the potential to transform industries, and eventually the economy, as complementary technologies such as AI begin to integrate alongside blockchain.”
Can Machines Think?
AI involves designing “human-like” machines that are able to perform tasks requiring intelligence. Machines are built to mimic processes and tasks that involve recognition of images, speech, or patterns & decision making. Those processes include acquiring information and rules, using those rules to reach conclusions, & self-correction.
Unlike traditional coding, the computer creates instructions for itself using machine learning algorithms rather than having humans write those instructions. To demonstrate the effect of AI, take google translate for an example.
When it first went live, google translate used to have more than a million lines of code (human-created instructions). Currently, google translate has 500 lines of code due to machine learning. However, while it is expected to overtake every industry, one must understand its limitations.
Knowledge in AI comes from data, and for the machine to be accurate, it must read from accurate data. While businesses have been understanding what AI can and can't achieve for the past few years, it expected that the future points towards a time where machines are appointed not only all of the physical work, as they have done since the industrial revolution, but also the mental work involving planning, strategising, and making decisions.
Sources:
https://www.investopedia.com/terms/b/blockchain.asp
https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2020/
https://www.simplilearn.com/top-technology-trends-and-jobs-article
1 min read
You see the end product but we’re bringing you closer to the people behind it !
Among our recent partnerships, we joined forces with Ubility.
To bring the service provider closer to YOU, we sat down with their CEO Khaled Dassouki, to gain firsthand insight on the things they do and the types of human capital profiles that drive their success.
Khaled is a Lebanese national with an engineering background and PhD in Artificial Intelligence & Security obtained at Université de Technologie de Troyes, France.
As a result of over 20 years in the field, Khaled decided to establish Ubility in France and build it on a base of Artificial Intelligence in Customer Experience, which was considered to be highly innovative as it was a niche field.
Khaled was quick to notice that there had been a shift in the way business was carried out, the shift had been made to be more customer-focused. With that came new challenges, the ease with which customers can connect with the business led to a huge influx of queries and requests for support, which was very time consuming, tedious, and required businesses to grow their customer support teams leading to higher costs.
While growing their teams did solve some challenges momentarily, organisations were quick to realise that the complexity and average time required to resolve customer challenges was growing which eventually led to dissatisfied customers. Not only that, customer service agents were quickly burning out as they were answering an ever-growing list of routine questions rather than helping solve complex problems and offering innovative solutions.
Taking all that into account, Ubility offered an AI-powered platform that learns from interactions between an organisation’s customers & their customer care team to enable quick suggestions of appropriate solutions that could solve a customer’s challenge in seconds rather than minutes.
In addition to that, Ubility also offers AI training for professionals in the IT industry, a sales chatbot that is based on a recommender system and knows the sales cycle, and AI Consultancy in which they are recruited to build and implement AI services in response to an assessment of an organisation’s processes.
Success Story
Ubility was commissioned by a Telecom operator in the gulf to assess and analyse their IT processes. After a thorough analysis done by their team of experts, it was concluded that a huge portion of their IT processes had an opportunity for automation.
After studying the best ways in which they could automate their processes, they determined that incorporating AI would allow the elimination of a manual process by looking at historical data. Instead of their operational costs raking up to 50,000 USD a month, they were now cut by 80% to be only 10,000 USD.
Intrigued and want to learn more?
Artificial Intelligence or AI is one of the most disrupting technologies of the 2000s. Machines are getting smarter each day. While AI can create tremendous time and cost savings, it promises even bigger, more meaningful returns in another area: customer experience.
We live in a time that is characterised by a major technology takeover, a time experiencing the 4th industrial revolution. Companies that want to survive and evolve must keep track of technology breakthroughs, because as we’ve come to know, technology can make or break a company’s success.
You see the end product but we’re bringing you closer to the people behind it !
