Founded in 2013, MNC Play has been providing Indonesian customers with IPTV, Broadband Internet and Multimedia Entertainment services under MNC Group. With a large subscriber base of millions of subscribers, retaining subscribers and expanding recurring revenue has become a pivotal part for MNC Play to sustain growth. Moreover, with a wide array of services, upsell and cross-sell are also important to MNC Play’s strategy.
For a long time, MNC Play struggled with its outbound capacity calls to increase retention (payment reminder) and upsell (renewal & add-on) due to the shortage of manpower. Faced with diverse cycles occurring monthly, quarterly and yearly, limited human agents proved inadequate to manually make reminder calls. Moreover, trapped in repetitive retention calls, human agents barely have time to deeply delve into other upsell promotions, which usually requires sophisticated pricing strategies. As a result, the company saw subscribers lapsed, renewal rate dropped and revenue growth slowed down.
In Oct 2022, MNC Play adopted WIZ.AI’s Talkbot as an innovative technology solution to solve its scalability issue, with an aim to boost operational efficiency and recurring revenue.
The implementation was done in phases with increasing needs from MNC Play. In the beginning, 5K reminder calls were automated per month; in six-month time, the automated calls reached 30K per month. Compared with human agents, WIZ Talkbot excels in effortlessly adapting to workload changes by scaling up and down call volume with no resource limitation. Within one year, over 125k subscribers have been connected by WIZ Talkbot, which would have required over 200 human agent force in strength if done manually.
Compared with human agents, WIZ Talkbot is able to extract conversation insights instantly and tag them accordingly. In the payment collection, Promise to Pay (PTP) is regarded as a crucial metric to track payment willingness but could hardly be explicitly asked or recorded by human agents. WIZ Talbots, on the other hand, can record every subscriber’s payment intent with ease. With a PTP rate as high as 41.7%, WIZ Talkbot enabled MNC Play to keep a closer track on each stage of the payment collection, and have a more accurate estimation of the actual payment collection number.
The final collection results were also encouraging. WIZ.AI’s Talkbot delivers far superior actual collection performance compared to human agents, constantly beating human agents with a higher repayment rate up to 28%.
Using WIZ.AI's Talkbot as an alternative digital approach to customers provides outstanding results compared to human agents. Talkbot's voice is similar to a human voice and is not stiff, so customers understand what the robot is talking about.
Making high-volume routine reminder calls could be daunting and emotionally taxing for human agents. Naturally influenced by the emotions and responses of the customers they are calling, their moods can fluctuate throughout the day. This can create inconsistencies in the level of service provided in each call, jeopardizing the customer experience.
In contrast, WIZ Talkbot is consistent, stable and reliable. Trained with large quantities of data such as user responses specific to payment reminder scenarios, and empowered by well-designed dialogue flows, the WIZ Talkbot perfectly encapsulates the industry’s best practices and consistently applies them to every call, crafting a coherent customer experience.
Behind WIZ Talbot’s consistent performance lies the consistent support from WIZ.AI’s responsive team. The Customer Success team provides proactive back-end operation support and monitoring to ensure call efficiency, and regularly reviews the call performance with MNC Play’s retention team regarding business results and actionable insights.
“In terms of the features provided, they are effortless for us to use, and the WIZ.AI team is cooperative in adapting to our needs so that the user experiences are optimized over time, based on our feedback.” noted Budiman.
With the objective of constantly optimizing the conversation experience to achieve best business performance, the Customer Experience team proactively conducts quality checks on the call experience. WIZ’s product managers are available for consultancy anytime should clients require a second opinion on their collection strategy, or a more customized solution.
By automating high-volume routine reminder calls, WIZ.AI’s Talkbot frees up human agents from low-value manual tasks so they may focus on high-value strategic planning and other more valuable work. With WIZ Talkbot relieving the agents of the bulk of such repetitive calls, MNC Play’s renewal team was able to invest more energies in diversifying their renewal and upsell strategies, such as refining the collection touchpoints to optimise renewal rates and implementing diverse promotional schemes to expand revenue growth for the company.
As Budiman adds, “Before we partnered with WIZ.AI, our team was on a treadmill of mundane tasks that ate away their time and energy. Now, things have completely changed. The Talkbots have taken over all our routine reminder calls, empowering human agents to apply their expertise and interpersonal skills to build lasting relationships and foster customer loyalty.”
Following the success of WIZ Talkbot implementation, MNC Play has renewed their partnership with WIZ.AI for a second year. Additional use cases are being explored to further automate operations and boost revenues.
Currently, MNC Play is piloting Talkbots to upsell customers from monthly or quarterly subscriptions to yearly plans before renewal dates. This proactive outreach helps secure advance annual payments while reducing the manual effort required to repeatedly engage short-term subscribers.
Going forward, MNC Play is also keen to automate more processes with WIZ.AI’s latest technology innovations, including integrating Talkbots with their other digital channels like WhatsApp and email to deliver promotions and notifications at scale.
In an industry where customer retention and upsell are key to growth, MNC Play’s forward-thinking approach, powered by WIZ.AI’s innovative voice AI solutions, sets a benchmark for others to follow. The future looks promising as MNC Play continues to innovate the way of engaging with customers, increasing operational efficiency and boosting revenue growth.
WIZ Talkbot is WIZ.AI’s flagship product. The intelligent platform facilitates over a 100 million automated customer interactions hourly, enabling exceptional customer service and strong business ROI.
Empowered by WIZ.AI’s proprietary TTS, NLP, and ASR technologies, WIZ Talkbot excels in understanding, listening, and communicating like a local, making it so convincing that 95% of users can’t differentiate it from human conversations. It adapts to a variety of voices, switching from firm to soft, swiftly to cope with the different use cases.
Wondering how voice AI automation can improve your business results? Speak to one of WIZ.AI’s specialists today.
Artificial Intelligence (AI) is revolutionizing how businesses operate. For companies, the path is full of potential yet accompanied by complexities. In this article, we would like to guide you through this intricate AI landscape, by unraveling two key AI technologies, the established voicebot and the rising LLM agents.
When you receive a phone call reminding you about an upcoming appointment or pending repayment, you could already be talking to a voicebot. Voicebots utilize Automatic Speech Recognition (ASR), Natural Language Processing (NLP) and Text to Speech (TTS) technologies. They can really understand how people talk and stimulate human-like interactions based on customer reactions.
A common use case for voicebots is 24/7 customer service. They can handle common inquiries from inbound calls at any time, so human agents can focus on more complex issues.
Voicebot also excels at high volume outbound calls for reminder, promotion and leads filtering. They are effective to help businesses to boost their customer and revenue growth. Voicebot can make customer outreach calls on a massive scale to help remind all relevant customers, or quickly filter and qualify prospects in the entire CRM contact list. With voicebots handling initial outreach, human agents can follow up on qualified, sales-ready leads.
As we introduced in our previous insight article, an LLM agent is a combination of a large language model with three other key components for planning, memory and tool use. We can also refer to an LLM agent as a LLM powered autonomous agent system, in which the LLM functions as the brain.
LLMs have unveiled their powerful Natural Language Understanding (NLU) capabilities. But LLMs alone are like brains in vats, and they can not realize a wide array of real-world applications. To unlock the full potential of LLMs, we need to build systems that can acquire and apply knowledge to solve practical problems. That’s where LLM powered autonomous agent system or LLM agents comes in. LLM agents can perceive, reason and act like our copilot, beyond just conversation capabilities. In theory, when giving LLM agents any general objective and a robust enough agent system, they can plan for task completion step by step. They are also capable of calling available agents or tools for help when necessary.
Voicebots adhere to rigid pre-determined dialogue flows. They only understand the expressions and words they are trained for. For instance, a seemingly simple use case like transferring a hospital patient’s call from the main line to different department extensions could require a matrix of dialogue flow. This could take weeks for conversation designers to develop.
In contrast, LLM agents possess a new edge in their potent Natural Language Understanding (NLU) capabilities. They can understand diverse human expressions, comprehend in context during real-time conversations. If your query includes something LLM agents don’t understand, they can continue the dialogue until they achieved clarity. This closely resembles how a human would ask follow-up questions to understand better.
Furthermore, while voicebots can perform what they are trained for efficiently, they lack autonomous planning or decision-making capabilities. Nor can they adapt to dynamic external environments.
In comparison, LLM agents are capable of self-learning new knowledge across languages and knowledge domains. They possess both short-term memory from in-context learning, as well as long-term memory from search and retrieval. They also can perform reasoning and planning, breaking down large and complex tasks into smaller, manageable steps.
Let’s consider a customer refund request scenario to understand how a voicebot and an LLM agent would act differently.
A voicebot can understand all expressions and keywords meaning refund if trained for them. It can read out the refund process from a FAQ list when a refund is requested. However, a voicebot won’t be able to handle queries beyond the FAQ list.
On the other hand, an LLM agent can call different tools and leverage industry best practices to resolve the issues. Moreover, it is capable of continuous learning and optimization, acquiring new knowledge and best practices overtime. When a client requests a refund, a LLM agent can ask for the purchase ID, then call relevant agents or tools (i.e. logistics tracking sheet) to check delivery status. They can even soothe the client, suggesting waiting for a couple of days before processing the refund. Meanwhile, LLM agents would analyze order time, and assess whether the no-fault refund period has expired, or check the customer’s credibility status to decide if a refund is eligible.
Looking ahead, we expect LLM agents to go far beyond inbound and outbound customer engagement. They will fully integrate with the enterprise software system, and become the core command center of the entire system. By then, LLMs will be industry experts and decision makers. They would understand domain-specific enterprise knowledge and dynamically call different tools to automate task completions. The agents or tools being called by LLMs can vary, including common office systems such as CRM, ERP, OA and PMS etc. We envision LLM agents to be versatile copilots, managing workflows alongside employees and assisting customers.
Despite the promising future of LLM powered autonomous agent systems or LLM agents, their development is still in early stages. LLM agents can alleviate the burden of training and setting up intricate dialogue flows. They have the capability to learn and optimize with minimal human intervention. However, the training of such LLM agents demands significant computational resources and GPU capacity. Therefore, LLM agents might not always be the most cost-effective solution for your business.
Voicebots, conversely, serve as robust business copilots, and are capable of driving impressive results. They enhance customer satisfaction by providing round-the-clock services, augment payment rates through automated reminder calls at scale, and efficiently qualify leads for human agents to follow up, among other tasks.
Consequently, we recommend businesses leverage voicebots for relatively straightforward applications like sending reminder calls, executing preliminary lead filtering, or addressing routine inquiries. Businesses should consider tapping into the power of LLM agents when they necessitate complex interactions, dynamic reasoning, industry-specific knowledge, or integration with a variety of enterprise systems.
The world of artificial intelligence offers a broad spectrum of solutions for businesses. Whether it’s a voicebot or an LLM agent, the choice depends on your specific needs and resources. The key to success lies in understanding these AI systems, and choosing the right AI assistant to navigate your business towards a prosperous future.
Zhiheng Xi et al. (2023). The Rise and Potential of Large Language Model Based Agents: A Survey. Retrieved from: https://github.com/WooooDyy/LLM-Agent-Paper-List
Today’s customers expect personalized service and quick response at every touchpoint, no matter whether they are purchasing luxury bags or subscribing to SaaS service. Personalized marketing is important not only because it proves how well a brand understands its consumer, but also because personalization helps the customer move along the lifecycle journey.
Through decades of innovation, PLDT has facilitated meaningful connections for its customers through fixed-line, wireless, and information and communication technology services. With a strong drive to elevate the quality of customer experience, PLDT’s customer group is leveraging innovation to expand its traditional contact methods with more personalized ways to individually connect with customers. “Our customers are at the heart of everything that we do. We are always looking for faster, safer, and more reliable channels for our customers to reach us if they need help,” says Jeanine Rubin, First Vice President and Head of Customer Care Group at PLDT.
Apart from some outsourced callouts, PLDT relied mostly on one-way communication, including SMS, email, and APRS (automated payment reminder system) for collections. Dianne Blanco, Vice President and Head of Credit and Collections at PLDT, was looking for ways to enhance two-way conversational interactions with customers, particularly when it came to more sensitive situations like reminding customers of an outstanding balance.
The answer was WIZ.AI’s flagship product, Talkbot Pro. “Many other payment and collection reminder solutions don’t empathize, acknowledge, or respond to customer queries very well. If customers receive a call and realize it’s from a robot, there’s a strong chance they will hang up,” explains Blanco. “The WIZ.AI Talkbot does it all—during our first demo, it didn’t feel like a robot talking. It sounded like a human.” Combined with hyper-localization features, this voice artificial intelligence (AI) was the perfect fit for PLDT.
Singaporean-based WIZ.AI builds intuitive, smart voice AI engagement solutions that are truly human-like. “Unlike traditional turn-based or text-based chatting, our Talkbot listens and understands the intent of the caller’s speech in natural spoken language. In fact, over 95 percent of users are unable to tell the difference between a human agent and our Talkbot,” explains Allan Ong, Head of Regional Sales at WIZ.AI.
Over 95 percent of users are unable to tell the difference between a human agent and our Talkbot.
In September 2022, ePLDT and WIZ.AI worked together to develop and deploy 35 talkbots that handle payment collection, reduce churn rate, and help customer retention. “We’re proud to be the first telecommunications provider in the country that is leading the way in using voice AI to offer better customer service,” says Blanco.
The solution was integrated into ePLDT’s cloud contact center as a service (CCaaS) offering, backed by an Azure Stack Hub which is locally hosted in the ePLDT VITRO data center. WIZ.AI oversaw the application services and customizations while ePLDT managed the required infrastructure, including virtual machines and firewalls. In addition, ePLDT also ensures that the integration of WIZ.AI Talkbots into CCaaS will result in optimal connectivity for PLDT.
For Blanco, ensuring that the project would spark the upskilling of PLDT employees was essential. With this mindset, an internal team led the initiative, from understanding how to work with AI to deploying and managing daily operations. “Our call operators are no longer just making the call but also managing the talkbots doing the callout,” she explains. “Through working on this project, they’ve increased their knowledge of the AI bot and learned the terms that allow them to communicate smoothly with the technical team.”
Over the first six months, the talkbots conducted 3.7 million outgoing communications with minimal supervision, the bulk of which were routine reminders. One year in, PLDT is seeing outstanding results.
“The talkbots added two more hours of call time into the day, which increased our productivity by 33 percent,” says Blanco. “The calls are even concluded quicker—the handling time for most calls are cut in half, from an average of six minutes to three minutes.”
The bots follow a standard script guaranteeing quicker and more consistent calls. Any updates or changes are incorporated by simply uploading a new script. With each call, the voice AI adapts, according to Blanco. “Similar to a person, the talkbot learns with each interaction. Every week, with each new conversation, we add to its knowledge base.”
PLDT was also able to increase revenue generated from collections by increasing contact rates with customers. “The net benefits of this initiative, resulting from the boost in efficiency, are a reduction in manual callouts by 15 percent and a higher collections rate,” says Blanco.
The talkbots added two more hours of call time into the day, which increased our productivity by 33 percent.
The localized AI talkbot, which can communicate even in Taglish (a mix of English and Tagalog, which is among the most commonly spoken languages in the Philippines), has significantly reduced the high volume and manual tasks for PLDT agents. “The real-time, automated Smart Hashtagging feature identifies customers in need of assistance, such as for billing disputes or service termination requests. This allows the agents to prioritize and serve them quickly, which results in higher customer satisfaction,” shares Ong.
Most of the follow-up work required after each call now happens automatically, further reducing arduous manual work. This includes the automatic transcription of each call and more accurate data for auditing purposes. “In the past, collectors would compile tally sheets manually. Now we have a database with all the information. We spend more time reviewing and analyzing the data instead of manipulating sheets of data to identify trends,” explains Blanco. PLDT uses these actionable insights to further enhance customer experience.
Having already met the targets for 2023, PLDT is ready to take the solution to the next level, to deliver even better customer care. This includes adding more talkbots to assist with the inbound call queue, promotional campaigns for pre-paid customers, and finding ways to increase personalization. “Right now, a lot of our calls are in Tagalog but we’re working to expand the regional bots so that conversations in Mindanao and Bisaya can also happen. We want to make our calls even more empathetic and authentic so customers can connect with and understand the talkbot immediately,” says Blanco.
Recent advances in large language models (LLMs) are renovating the development of intelligent AI agents with powerful natural language understanding capabilities. This has sparked rising interest in LLM powered autonomous agent systems, or LLM agents.
According to Insignia Ventures Partners, the market for autonomous AI agents is estimated to grow at a CAGR of 43% (from USD 5 billion in 2023 to USD 29 billion by 2028), catalyzed by the democratization of LLMs. In this article, let’s explore what new possibilities LLM agents have unlocked and what are some feasible applications we can envision in the future.
*NOTE: For the simplicity of the article, we treat “LLM powered autonomous agent systems” and “LLM agents” as two interchangeable concepts. They belong to a bigger concept called AI agents. AI agents refer to Artificial Intelligence systems that can perceive environments and take autonomous actions to achieve goals.
To answer the question, we can imagine LLM is the brain, and it can call different agents or tools, which are its hands and feet, to automatically perform a diverse range of tasks.
As Open AI’s Lilian Weng describes, a LLM powered autonomous agent system is comprised of an LLM functioning as the brain, and three other crucial components for planning, memory and tool use.
Planning
LLM agents can mimic human thinking patterns and proactively plan for task execution. During planning, LLM agents can break down large and complex tasks into smaller, manageable steps. They are also capable of self-reflecting and learning from past actions and mistakes, so as to optimize for future steps and improve final results.
Memory
This encompasses both short-term memory from in-context learning, as well as long-term memory from search and retrieval. Memory helps LLM agents to learn between context in real-time and recall information over extended timeframes.
Tool use
LLM agents can proactively call external APIs or vector stores for additional information, based on dynamic decision-making. By calling different tools and using semantic search and vector databases, LLMs agents can provide precise answers according to search results. This also avoids common LLM issues such as inaccuracy and hallucinations.
LLMs have brought a new edge through their Natural Language Understanding (NLU) capabilities. This makes real human machine interaction a reality, and in natural languages! With LLMs, we can now communicate with machines just as we would with another human.
But LLMs alone can not realize a wide array of real-world applications. To unlock the full potential of LLMs, we need to build systems that can acquire and apply knowledge to solve practical problems. That’s where LLM powered autonomous agent system comes in. Without agents or tools, LLMs are like brains in vats-impressive but isolated from the real world.
Integrating LLMs into autonomous agent systems unlocks greater possibilities. Let’s look at two examples of LLM agents in action:
Here the LLM agent must first understand what information is needed, like the airline’s change policy and available alternative flights (planning). It can then call tools like documentation APIs and flight databases to gather the necessary details (tool use).
The LLM agent needs to comprehend what department the patient requested (short-term memory: in-context understanding). It can then check office hours, on-call numbers, and other relevant information (planning and tool use). This allows dynamically connecting the patient to the right destination, i.e. department extension during office hours, or the doctor on call during non-office hours.
Intelligent LLM agents are taking a huge leap towards an era known as “Software 2.0”. This is a concept proposed by former Tesla director of AI, Andrej Karpathy. In Software 1.0, human engineers write codes and programs to complete tasks. These codes and programs are like individual dots with some desirable behavior.
While Software 2.0 refers to a new generation of software that leverages machine learning algorithms and neural networks to build intelligent, self-learning systems. Software 2.0 can analyze data, identify patterns, and constantly optimize its own code without human intervention.
Applying the above concept into today’s enterprise software system, we see AI capabilities are currently serving as supporting tools to perform isolated tasks. These AI capabilities can include recommendation algorithms, Natural Language Processing (NLP), Text to Speech (TTS), Automatic Speech Recognition (ASR) and so on.
Looking ahead, we expect to enter an era we call “Enterprise Software 2.0”. In the era of Enterprise Software 2.0, LLM agents will become the core command center of the entire enterprise software system. In the system, LLMs are industry experts and decision makers. They would understand domain-specific enterprise knowledge and dynamically call different tools to automate task completions. The ecosystem is with close-loop communication and operation. The agents or tools being called by LLMs can vary, including common office systems such as CRM, ERP, OA and PMS etc.
By then, LLM agents will have the ability to assist in solving intricate issues across diverse industries, and self-learn from their own experience. We envision LLM agents to be versatile copilots, managing workflows alongside employees and assisting customers. AI accessibility will also be further democratized by LLM powered autonomous agent system, enabling anyone to copilot with a multitude of LLM agents to manage as many tasks as possible, and amplify productivity.
The road ahead remains long, but the disruptive potential is immense. LLM powered autonomous agent system has displayed transformative impact in dynamic, unstructured environments. Although limitations exist, steady progress in machine learning algorithms and design patterns will empower LLM agents to tackle ever more complex challenges. The future of AI has never looked more promising!
Lilian Weng. (2023). LLM Powered Autonomous Agents. Retrieved from: https://lilianweng.github.io/posts/2023-06-23-agent/
Wechat official accout. (2023). AI Agents大爆发:软件2.0雏形初现,OpenAI的下一步. (The rise of AI agents: first glimps of software 2.0 and what’s next for OpenAI). Retrieved from: https://mp.weixin.qq.com/s/Jb8HBbaKYXXxTSQOBsP5Wg
Andrej Karpathy. (2017). Software 2.0. Retrieved from: https://karpathy.medium.com/software-2-0-a64152b37c35
JAGADIRI mempunyai tujuan untuk melindungi aspirasi nasabah Indonesia di bawah naungan PT Central Asia Financial (CAF). JAGADIRI menyediakan asuransi jiwa, kecelakaan, dan kesehatan melalui saluran pemasaran digital, pemasaran telepon, dan pemasaran langsung.
“Intinya, asuransi itu berbeda dengan perbankan, ” tutur Ibu Fitriah Betan sebagai Head of Customer Experience di JAGADIRI. “Sebagai perusahaan yang menyediakan asuransi, JAGADIRI perlu melakukan panggilan pengingat premi kepada pelanggan dan terus memberikan layanan, bahkan hingga lewat jatuh tempo 90 hari.” Karena kebutuhan asuransi terjadi dalam situasi dadakan, dan perusahaan berusaha semampunya untuk melindungi para pelanggan. Menurut mereka, pelanggan perlu segera membayar premi untuk memastikan bahwa polis mereka aktif dan bisa melayani insiden yang tidak terduga.
Kebutuhan untuk memastikan cakupan yang berkelanjutan adalah alasan mengapa JAGADIRI berupaya keras untuk mengingatkan pelanggannya akan pembayaran premi. Namun, tantangannya adalah waktu. Menghubungi pelanggan untuk pengingat premi bisa menjadi hal yang sulit dilakukan karena pelanggan bisa saja sibuk beraktivitas di siang hari. Faktor lainnya adalah kebiasaan setiap pelanggan ketika berinteraksi – suasana hati dari agen manusia atau caranya berkomunikasi bervariasi dari satu panggilan ke panggilan yang lain. JAGADIRI menginginkan cara yang lebih konsisten untuk terlibat dengan basis pelanggannya, dan memastikan bahwa para pelanggan setia bersama perusahaan.
Pada Juni 2022, JAGADIRI memutuskan untuk mengalihkan panggilan pengingat pembayaran premi ke Talkbot WIZ.AI. Pelaksanaannya dilakukan secara bertahap; pada September 2022, semua panggilan pengingat pembayaran premi sudah otomatis. Hasilnya sudah bisa membuktikan. “Sebelum Talkbot, kami memperoleh kurang dari 10 persen panggilan terhubung untuk pengingat pembayaran premi. Sekarang, secara rata-rata, kami mendapatkan sekitar 50 persen,” kata Ibu Fitriah. Talkbot juga 5.3x lebih hemat biaya dibandingkan menggunakan sumber daya manusia; perbandingan biaya untuk Talkbot sebesar enam persen, dibandingkan dengan 32 persen untuk agen manusia.
Penggunaan solusi WIZ.AI dapat memberikan hasil yang nyata ketika menerapkan pengingat pembayaran premi kami. Sebelum Talkbot, agen kami mendapatkan kurang dari 10 persen panggilan yang terhubung. Sekarang, secara rata-rata, kami mendapatkan sekitar 50 persen.
Di luar hasilnya, JAGADIRI menyukai kebiasaan WIZ.AI yang rutin memeriksa cara kerja Talkbot dan memberikan rekomendasi tentang cara mengoptimalkan solusi kecerdasan buatan. Dari tim Sales hingga Project Management, kemudian ke Customer Success dan bahkan Customer Experience, tim WIZ.AI siap membantu JAGADIRI untuk memaksimalkan penggunaan Talkbot. “WIZ.AI Customer Experience Designer membantu kami untuk menyempurnakan strategi kami dengan meningkatkan naskah perbincangan berdasarkan data yang kami dapatkan,” ungkap Ibu Fitriah. “Ada komunikasi dua arah yang sangat baik antara kami dengan tim WIZ. WIZ terus memberikan saran mengenai cara memperbaharui Talkbot, dan menerapkan saran tersebut akan meningkatkan hasil kami.”
Dengan keberhasilan pengingat pembayaran premi secara otomatis, kini JAGADIRI ingin lebih banyak menggunakan Talkbot WIZ.AI untuk berbagai macam sektor. Saat tulisan ini dibuat, tim sedang mengembangkan naskah penagihan premi untuk akun jatuh tempo dan tunggakan. JAGADIRI juga berencana untuk menggunakan Talkbot untuk panggilan selamat datang ke para pelanggan baru. “Talkbot telah mempermudah panggilan kepada pelanggan kami untuk tim Customer Experience kami,” tutup Ibu Fitriah.
Hal terbaik tentang pengalaman kami menggunakan Talkbot adalah bahwa WIZ.AI Customer Experience Designer selalu membantu kami menyempurnakan strategi kami. Ada komunikasi dua arah yang dijalankan dengan sangat baik antara kami dengan tim WIZ. WIZ terus memberikan saran peningkatan kinerja Talkbot dan dengan menerapkan saran tersebut, hasil kami akan meningkat.
Link Net telah menghadirkan konektivitas internet dan hiburan rumah bagi masyarakat Indonesia selama lebih dari 20 tahun dengan First Media, i-solution, dan Link Net. Saat ini, mereka melayani hingga 2,8 juta rumah di 23 kota di Indonesia. Untuk mengelola akun pelanggan, terutama saat penagihan jatuh tempo tagihan, hal itu merupakan sebuah proses yang cukup panjang.
“Awalnya kami menggunakan SMS untuk penagihan, yang kemudian kami tambahkan dengan panggilan keluar,” kata Pak Ikhsan Kurniawan, Head of Printing and Collection Department di Link Net. “Secara bertahap, kami beralih ke email dan sekarang, kami juga menggunakan WhatsApp. Kami juga mencoba Smart IVR, tetapi ternyata tidak cocok untuk penagihan jatuh tempo. Jadi, kami berhenti menggunakan IVR karena tidak memberikan hasil.”
Link Net juga menghadapi kesulitan dalam merencanakan kapasitas panggilan keluar untuk penagihan. Ketika tim penagihan mengalami kekurangan tenaga kerja, perusahaan akan sementara memindahkan orang dari departemen lain untuk membantu mengelola beban kerja. Hal ini mengganggu pekerjaan sehari-hari. Link Net menyadari bahwa mereka memerlukan cara yang lebih efektif dan efisien untuk melakukan tindak lanjut terhadap akun-akun yang jatuh tempo dan mulai mengeksplorasi pilihan yang dapat membantu kinerja penagihan.
“Kami menyukai solusi yang diberikan oleh WIZ karena Talkbot merupakan interaksi dua arah, tidak seperti IVR yang sangat terbatas,” kata Pak Ikhsan. “Ini juga membantu kami dalam menyeimbangkan beban kerja. Jika seseorang dari tim penagihan mengambil cuti melahirkan, kami dapat segera menambahkan lebih banyak bot untuk mengisi kekosongan.”
Solusi tersebut memberikan hasil yang nyata. “Ketika kami mencoba WIZ, hasil kami meningkat, terutama dalam tingkat koneksi,” kata Pak Herri Dwi Prasetyo, MIS Leader Head of Printing and Collection Department di Link Net. “Tingkat koneksi untuk agen manusia sebesar 25 persen, tetapi dengan Talkbot dapat meningkatkan menjadi hampir 50 persen.” Penggunaan Talkbot juga membebaskan agen tim penagihan dari tugas-tugas mudah namun repetitif. Serta, mengurangi beban panggilan keluar bagi agen tim penagihan menjadi separuhnya. “Ini memberikan waktu lebih bagi agen kami untuk fokus pada melayani pelanggan dengan lebih baik dan menangani keluhan yang lebih kompleks. Penggunaan bot membuat peran agen manusia menjadi lebih strategis,” tambah Pak Herri.
"Talkbot adalah strategi penagihan terbaik yang pernah kami implementasikan."
Ketika membicarakan pengalaman mereka dengan WIZ, Pak Herri memiliki hal-hal baik untuk dikatakan. “Aplikasinya Wiz sangat mudah digunakan dan memiliki sistem validasi yang baik,” katanya. “Misalnya, jika seseorang mencoba mengunggah nomor yang sama dua kali, sistem akan segera menghapus salah satunya.”
Bekerja dengan tim WIZ juga memberikan pengalaman yang positif. “Semua anggota tim WIZ sangat mendukung dan membantu,” lanjut Pak Herri. “Apapun yang kami butuhkan, proses eskalasi sangat cepat. Sebagai pengguna, hal ini sangat membantu untuk mengetahui bahwa segala sesuatu diproses dengan cepat dan semua anggota tim dapat dihubungi.”
Bagi pelanggan Link Net, perubahan strategi tersebut hampir tidak terlihat. “99% pelanggan tidak menyadari bahwa mereka dihubungi oleh robot,” kata Pak Ikhsan. “Dari 20.000 yang dihubungi, hanya 10 yang mengeluh karena dihubungi oleh bot. Ketika itu terjadi, kami menghapus pelanggan tertentu dari database panggilan Talkbot selanjutnya.”
"Semua anggota tim WIZ sangat mendukung dan membantu. Apapun yang kami butuhkan, proses eskalasi sangat cepat. Sebagai pengguna, sangat membantu untuk mengetahui bahwa segala sesuatu diproses dengan cepat dan semua anggota tim dapat dihubungi."
Dengan keberhasilan strategi penagihan baru, Link Net bersemangat untuk mencoba Talkbot dengan skenario lain. Perusahaan memutuskan untuk mencoba otomatisasi retensi pelanggan atau perpanjangan kontrak. Bersama tim WIZ, Link Net membuat skrip baru untuk menghubungi pelanggan yang tidak membayar langganan mereka selama tiga bulan. “Tingkat koneksi untuk kategori pelanggan ini lebih rendah,” jelas Pak Ikhsan. “Kami berharap dengan skrip retensi baru, kami dapat mendapatkan kembali pelanggan dengan menawarkan promosi.”
Masih dalam tahap awal strategi retensi pelanggan dan hasilnya masih harus dilihat. Tim saat ini sedang menyempurnakan skrip berdasarkan umpan balik, untuk menyampaikan pesan yang tepat.
Link Net ingin menetapkan standar yang lebih ketat untuk proses penagihan mereka di masa depan. “Kami menuju ke arah digitalisasi, segalanya harus menerapkan otomatisasi,” ungkap Pak Ikhsan. “Jika seseorang gagal membayar atau melewatkan pembayaran, mereka harus dihubungi secara otomatis. Segala sesuatunya harus sudah terotomatisasi, termasuk panggilan ulang. Kemudian hasilnya dapat diperbarui secara real-time di sistem Link Net.”
Bagi Pak Herri, data adalah kunci rahasia untuk kesuksesan yang berkelanjutan dan masa depan. “Di masa depan, kami berharap dapat mengunduh transkrip percakapan langsung dari sistem,” katanya. “Dengan fitur ini, kami dapat menggunakan semua informasi untuk mendapatkan umpan balik dan keluhan dari pelanggan. Jadi ketika agen manusia melakukan panggilan tindak lanjut kepada pelanggan tertentu, mereka akan segera mengetahui apa yang dikeluhkan oleh pelanggan. Kami akan memiliki riwayat pelanggan dan dapat memberikan pengalaman yang baik.”
Tim koleksi sangat puas dengan WIZ Talkbot sehingga mereka merekomendasikan solusi ini kepada tim penjualan Link Net, untuk mendapatkan pelanggan baru. “Sebelumnya kami tidak pernah menghubungi tipe pelanggan seperti ini,” akui Pak Ikhsan. “Ini adalah sesuatu yang akan kami eksplorasi dan bahas lebih lanjut dengan Tim WIZ.”
Link Net has been providing Indonesians with internet connectivity and home entertainment for over 20 years under the First Media, i-solution, and Link Net brands. It currently serves 2.8 million homes across 23 cities in Indonesia. Keeping track of all those accounts – especially when they fall past due – is a whole operation in itself.
“Previously, we used SMS for billing, which then was supplemented with outbound calls,” begins Pak Ikhsan Kurniawan, Head of Printing and Collection Department at Link Net. “We gradually moved to email and now, we also use WhatsApp. We also tried smart IVR but it turned out not to be suitable for overdue payments. So we stopped using IVR because it did not bring results.”
Link Net also struggled with capacity planning for its outbound calls for collections. When the telephone collections team is experiencing a manpower shortage, the company would temporarily reassign people from other departments to help manage the load. This disrupted day-to-day work and workload management. Link Net realized it needed a more effective way to follow-up with overdue accounts, and started exploring options.
“We liked WIZ’s solution because it was a two-way interaction, not like IVR which is very limited,” shares Pak Ikhsan. “It also helped us with workload balancing. If someone from telecollections takes maternity leave, we can immediately add more bots to fill the gap.”
The solution brought tangible results. “When we tried the WIZ solution, our results improved, especially in the connection rate,” shares Pak Herri Dwi Prasetyo, MIS Leader of Collection & Recovery Department at Link Net. “The connection rate for human agents was 25 percent but with the Talkbot, it increased to almost 50 percent.” The use of Talkbots also freed up human agents from many of the easy, repetitive tasks. It also reduced the call load for human agents by half. “This gave our agents more time to focus on retaining customers and handling more complex complaints. The use of the bot made the role of human agents more strategic,” Pak Herri adds.
“Talkbots are the best collection strategy we have implemented so far,” confirms Pak Ikhsan.
"We stopped using IVR because it did not bring results. Talkbots are the best collection strategy we have implemented so far."
When talking about their experience with WIZ, Pak Herri has good things to say. “The application itself is very user-friendly and has a good validation system,” he begins. “For example, if someone tries to upload the same number twice, the system will immediately eliminate one of them.”
Working with the WIZ team has also been positive. “All the WIZ team members are so supportive and helpful,” Pak Herri continues. “Whatever we need, the escalation process is so fast. As an end-user, it is very helpful to know that everything is processed quickly and all team members are contactable.”
For Link Net’s customers, the change in strategy was barely noticeable. “99% of customers do not realize that they are contacted by a robot,” says Pak Ikhsan. “Out of 20,000 contacted, only 10 complained about being called by a bot. When that happens, we exclude the specific customers from the next Talkbot call database.”
"All the WIZ team members are so supportive and helpful. Whatever we need, the escalation process is so fast. As an end-user, it is very helpful to know that everything is processed quickly and all team members are contactable."
With the success of the new collection strategy, Link Net was keen to try the Talkbot in other scenarios. The company decided to try automating customer retention or recontracting. Together with the WIZ team, Link Net created a new script for contacting customers who were recently lost, like those who have not paid for their subscriptions in three months. “The contact rate for this category of customers is lower,” explains Pak Ikhsan. “We hope that with the new retention script, we can win back customers by offering promotions.”
It’s still early days in the customer retention campaign and results are still to be seen. The team is currently refining the script based on feedback, in order to get the right message across.
Link Net wants to set more stringent standards for their debt collection processes in the future. “We are heading towards digitization, everything must be automated,” begins Pak Ikhsan. “If someone defaults or misses a payment, they should be called automatically. Everything should already be automated, including redials. Then the results can be updated in real-time on the Link Net system.”
For Pak Herri, data is the secret ingredient to ongoing and future success. “We expect in the future we can download the transcript of the conversation directly from the system,” he says. “With this feature, we can use all information to gain feedback and complaints from customers. So when a human agent conducts a follow-up call to a certain customer, they will immediately know what the customer complained about. We will have the customer history and can give a seamless experience.”
The collections team was so satisfied with the WIZ Talkbot that it recommended the solution to Link Net’s sales team, for customer onboarding. “We never contacted these type of customers before,” confesses Pak Ikhsan. “This is something we will explore and discuss further with the WIZ Team.”
JAGADIRI works to protect the aspirations of its many Indonesian customers under the umbrella of PT Central Asia Financial (CAF). JAGADIRI provides life insurance, accident, and health insurance through digital marketing, telemarketing, and direct marketing channels.
“The thing with insurance is that it’s not like banking,” began Ibu Fitriah Betan, Head of Customer Experience at JAGADIRI. “As an insurance provider, JAGADIRI needs to make premium reminder calls to existing customers and continue providing service even up to 90 days past due.” Because the actual need for insurance happens in urgent situations, and the company does all it can to keep customers protected. On their part, customers need to pay their premiums promptly, in order to ensure that their policy remains active and can cater to unexpected incidents.
The need to ensure continuous coverage is why JAGADIRI puts a lot of effort into premium payment reminders. But the challenge is timing. Contacting customers for premium reminders can be tricky, as customers may be busy with their activities during the day. Another factor lies in the personal nature of the interaction – the mood of the agent or the way he or she communicates varies from one call to another. JAGADIRI wanted a more consistent way of engaging with its customer base, and ensure that these customers remain with the brand.
In June 2022, JAGADIRI decided to transition its premium reminder calls to the WIZ.AI Talkbot. The implementation was done in phases; by September 2022, all premium reminder calls were automated. The results spoke for themselves. “Before Talkbot, we get less than 10 percent of connected calls for premium reminders. Now we get, on average, around 50 percent,” explains Ibu Fitriah. Talkbots are also 5.3x more cost-efficient than using human agents; the cost ratio for Talkbots is 6 percent, compared to 32 percent for agents.
Using WIZ.AI’s solutions brought very tangible results in our premium reminder deployment. Before Talkbot, our agents get less than 10 percent of connected calls. Now we get, on average, around 50 percent.
Beyond the results, JAGADIRI loves that WIZ.AI regularly checks on how the Talkbot is working, and provides recommendations on how to optimize the AI solution. From sales to project management to customer success and even customer experience, the WIZ.AI team was ready to help JAGADIRI make the most of its Talkbot. “WIZ.AI CX Designers help us refine our strategy by improving the scripts based on the data we get,” shares Ibu Fitriah. “There’s excellent two-way communication between us and the WIZ team. WIZ continually suggests how to update the Talkbots and implementing the suggestions improves our results.”
With the success of automated premium reminders, JAGADIRI is now looking to deploy the WIZ.AI Talkbot for more use cases. As of this writing, the team is developing debt collection scripts for due and overdue accounts. JAGADIRI is also planning to deploy Talkbots for Welcome Calls to new customers. “Talkbot has made contacting our customers much easier for the CX team,” concludes Ibu Fitriah.
The best thing about our experience is that WIZ.AI CX Designers are always helping us refine our strategy. There’s excellent two-way communication between us and the WIZ team. WIZ continually suggests how to improve the performance of the Talkbots and implementing the suggestions improves our results.
CEO & Co-Founder