From the process of first hearing about Cuvo to giving my first demo of the product I’d like to walk you through my journey and experience with this product. When first seeing the product I didn’t fully understand the concept behind Cuvo, there are hundreds of companies that exist to create a better customer service experience. But further experimentation with the tool brought me to realize that this is more than just a customer service tool, but a customer success platform for user centric products to revolutionize engagement. It is a machine learning platform at its core that continuously learns user patterns and customer challenges, by listening, learning, and delivering a 360 degree perspective on users. The generative AI engine automatically generates context sensitive and empathetic responses at consumer scale. Some other features that elevate Cuvo are advanced customization and the data organization; these are just a few of the wide features that Cuvo offers.

Cuvo’s use of machine learning has three unique objectives. These are described as: Automation, Unification, and Discovery. Automation illustrates how generative AI would be able to formulate more immediate useful responses to customer reports. These responses are getting more useful and correlated to the size of the company’s knowledge base. The importance of this is realized when understanding that more immediate and useful responses to customer reports reduces overall customer churn. With Unification, Cuvo is able to analyze the incoming responses and do things such as; grouping similar tickets together, correlating data across multiple sources, doing anomaly detection, and reducing time to resolve issues. Lastly, Discovery, which is Cuvo providing well organized AI dashboards to help you better understand the reports you received as well as discover new trends and create alerts. Cuvo’s breakdown of its use of AI into these three categories has helped it maximize the reach of its assistance. In action I saw myself heavily appreciating the data organization and clustering provided by these features.

Aside from machine learning, I noticed that the advanced customization and accessibility help elevate Cuvo allowing for it to act as a chameleon, able to blend into any platform it’s implemented into. The Product can be deployed on virtually any modern device and platform, and can even be modified to activate based on different events. Another positive I noticed is the level of variety doesn’t waiver on the customer end either seeing as Cuvo provides ample space for customers to voice their opinions and other concerns as well as the option to send multiple different media files to better convey your point or issue. Surprisingly, for the level of customization provided, Cuvo can be set up and fully functional within hours and requires zero-coding. 

Cuvo has a long catalog of features that will help customers have their concerns better expressed as well as assisting companies with managing tickets. But the key new ideas and strategies implemented in Cuvo are what I see defining the difference between a customer service tool and a user centric customer success platform that will improve

customer support operations, reduce churn, and deliver personalization.

The introduction of 5G technology brings disruptive changes to customer feedback, particularly in content streaming. The advantages of 5G for streaming are evident including seamless real-time streaming and serving more users at once.

The implications for customer feedback are significant:

• Real-Time Feedback: 5G facilitates immediate sharing of customer feedback via online reviews, social media, and dedicated feedback channels.
• Enhanced Multimedia Feedback: Faster speeds ease sharing of media-rich feedback, improving actionability of customer experiences.
• Augmented and Virtual Reality Feedback: 5G’s low latency and high bandwidth support immersive AR/VR feedback, enhancing product development, and customer engagement.
• IoT Integration: 5G’s connectivity facilitates automatic collection of valuable feedback data, enabling proactive problem-solving and personalized experiences.
• Improved Data Analytics: Faster speeds and larger bandwidth improve efficient analysis of large volumes of consumer 360data, fostering data-driven decisions and enhanced satisfaction.

As 5G evolves, businesses must adapt to leverage its potential, delivering unparalleled content and immersive experiences while harnessing customer feedback at scale. 

Cuvo is a consumer engagement platform that can layer on just about any smart device and collect live feedback from users in-app while they engage in an experience. It is poised to not only receive customer feedback at scale, but to correlate that information to detect signals from the noise, and automate responses to audiences to improve engagement. 

With Cuvo you can reduce churn without increasing customer support costs, and bridge the gap with product development to drive a roadmap that reflects customer sentiment.

Cuvo is a consumer engagement platform that can layer on just about any smart device and collect live feedback from users in-app while they engage in an experience. It is poised to not only receive customer feedback at scale, but to correlate that information to detect signals from the noise, and automate responses to audiences to improve engagement. 

With Cuvo, you can reduce churn without increasing customer support costs, and bridge the gap with product development to drive a prioritized roadmap that reflects customer sentiment.

Here are some use cases on how our customers leverage Cuvo:

1. Live feedback in-app

Cuvo is used by most of our customers to collect live feedback from their users when they are engaged in an experience. For example, FAST channels are able to collect feedback on viewer experience so they can customize ad preferences or when performance degrades, and they get a large number of requests from viewers, they are able to correlate that to information from video analytics platforms all within Cuvo.

2. Interactive new product or user onboarding

When a new product or feature set is introduced, Cuvo can be used to guide the users to fully onboard the new experience. Additionally when a new user is being onboarded to an existing product, Cuvo can be used to guide the user to successfully start using the product.

3. Automated ticket responses

Cuvo can automatically generate responses to users at scale. It can correlate issues to identify higher level problems and cluster incoming requests based on the problem category. This automatic ticket response and resolution helps our customers scale without increasing customer support headcount or sacrificing customer responsiveness.

4. Augmented support portal

Cuvo is layered on support portals to replace or augment chatbots as it provides much richer contextual information on issues through live screenshots, screen recordings, and voice to text. This increases the actionability of the feedback by 2x.

5. Bridging the gap between customer success and product development

Cuvo can be used to collect feedback from viewers in A/B tests, such as a new product experience. This helps determine choices that are preferred by the user, and to understand feature prioritization based on customer feedback. With Cuvo, you can reduce churn without increasing customer support costs, while bridging the gap between Product Development and Customer Support.

To get started today contact us: https://www.cuvo.io/contact-us and sign up for a free trial: https://dashboard.cuvo.io/sign-up

Generative AI, also known as creative AI, is a type of AI that can create new and original content, such as images, videos, music, or text, that are not based on pre-existing data.

Generative AI can have several valuable applications in media products. In this blog we’ll consider the significant opportunity to transform how streaming media products can fundamentally operate by improving user engagement and reducing churn.

Enhanced Customer Assistance

In the world of media products, switching costs are so low, so consumer experience is incredibly important to reduce churn. The first 90 day retention of a consumer predicts the lifetime value and can make all the difference. Just imagine an autonomous AI agent that interacts with you right where you are, on your media app, whether it’s on a television, smart device, browser, or phone: that responds to your questions, gets comprehensive answers that are intelligent, refines results, and provides inspiration. It speaks to you in the language that’s primary to you, with empathy and relevance.

This is a game changer in increasing user engagement. In addition, the autonomous agent can significantly reduce customer support costs through automated responses, reducing query volume by up to 60%. This is an untapped market opportunity as there isn’t an easy way for people on media products to provide direct feedback other than picking up the phone and calling a support line. The reality is that the majority of consumers never pick up the phone; instead, they simply churn quietly. An autonomous agent that can predict this behavior and proactively communicate with those users can make all the difference.

Ads and Content Personalization

Consumers today are bombarded by the paradox of choice when it comes to media and entertainment. In the small windows of time you get to engage, just imagine a generative AI that gauges your mood based on your likes and dislikes, as well as the time you have available, providing you with content that perfectly aligns with your preferences. And then just imagine if the ads that are generated are so personalized that you can see yourself living an experience that you can identify with it just so deeply, that rather than switch channels or churn, you would click, to purchase.

Generative AI can help media and entertainment companies personalize their content to their audiences’ preferences and interests. For example, a streaming service can use generative AI to recommend content to users based on their viewing history and preferences.

Generative AI is poised to power exactly this sort of personalized audience experiences. Delivering custom-fit, compelling audience experiences, will help mitigate subscribers from churning and viewers from abandoning content experiences for competitive platforms.

Content Innovation

Generative AI can be used to create high-quality and original content, such as music, art, and videos. For example, generative AI can create music compositions, movie scripts, or video game environments that are unique and engaging.

Generative AI can help media and entertainment companies explore new and innovative content ideas that may have been difficult or impossible to create otherwise. For example, generative AI can be used to create virtual reality experiences that are immersive and one-of-a-kind.

Generative AI can save time and effort in the content creation process. For example, a production company can use generative AI to generate multiple concept art pieces for a film in a matter of minutes, instead of having to rely on manual sketches and iterations.

Overall, generative AI can bring significant value to media and entertainment companies by enabling new forms of creativity, personalization, efficiency, and innovation in content creation and delivery.

How do we get there:
It’s all about contextual data

All these use cases mentioned above present highly compelling reasons to plug in generative AI into media products. But what does it really take for this to be beyond a theoretical exercise? Generative AI relies heavily on data to create new and original content. To succeed with generative AI, you need a large and diverse dataset that is representative of the content you want to generate. Here are some key factors to consider:

1. Quantity

The more data you have, the better your generative AI model will be able to learn patterns and generate new content. Generally, more data is better, but there may be diminishing returns beyond a certain point.

2. Quality

The quality of your data is crucial to the success of your generative AI model. The data should be accurate, relevant, and representative of the content you want to generate. Poor quality data can lead to inaccurate or biased content.

3. Diversity

Your data should be diverse to ensure that your generative AI model can generate a wide range of content. For example, if you want to generate music, your dataset should include different genres, instruments, and styles.

4. Annotation

Data annotation is the process of labeling data to provide additional information for the generative AI model. Annotation can help the model learn more quickly and accurately by providing additional context or insights into the data.

5. Continual Improvement

As your generative AI model generates more content, it can be used to create a feedback loop. The feedback loop can help you identify new patterns in the generated content, which can then be added to your dataset to further improve your model.

In summary, to succeed with generative AI, you need a large and diverse dataset of high-quality data that is representative of the content you want to generate. Additionally, you should continually improve your dataset to ensure that your generative AI model continues to generate high-quality and relevant content. Specifically for media products, generative AI has the potential to revolutionize the industry on its head.

Cuvo is positioned to take the lead in all of these areas. We have a series of products that use generative AI:

1. Cuvo Automate delivers real-time responses in customer success, to reduce the customer operations cost.
2. Cuvo Unify correlates tickets across your existing systems, analytics frameworks, and reputation dashboards to escalate alerts that need immediate attention.
3. Cuvo Discovery provides dashboards that deliver in-depth analysis to uncover new patterns and detect anomalies.

To learn more on our AI/ML offering that reduces churn through generative AI, contact us at info@cuvo.io.io and get started today with our product demos: YouTube Playlist of Cuvo Demos

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Customer service is important for any product, whether it is a software product or physical product. We’ve all had experiences with customer service, where we were made to wait in line to speak to a representative and the wait time may go for 30+ minutes. For those who don’t have much time to wait on the phone, they often opt to use online chat, automated messages, or system FAQs. But the quality of support from these static or rules-based support systems is very general and does not solve customer issues effectively.

Exceptional customer support can significantly contribute to brand building, customer identification, increased sales, customer retention, as well as uncovering opportunities for upselling and cross-selling. With good customer support, customers will not hesitate to give valuable inputs that can lead to product ideas. It can lead to building new or enhancing existing products. This will save product builders a lot of time in doing customer study. The feedback and input from current customers complement external customer studies and research effectively. Good customer support also helps to make better business decisions based on data.

With the advancement in technologies, the support has come a long way. Now with the Large Language model (LLM) such as GPT, LLaMA, Google Flan, etc., taking over the world, everybody is looking towards the next phase in customer support.

With LLM models, the customer support can be personalized and unique as per the user. AI based systems are going to be advantageous in many ways.

1. Chat and email replies can be automated and personalized.
2. Issues can be correlated and grouped.
3. Anomalies can be detected faster and accurately.
4. It can provide better insights on product performance and feature usage, allowing teams to focus on building features that truly matter to customers.
5. Available 24x7x365 days.
6. It can be deployed at a faster rate than training a human being.
7. Will reduce the number of people needed to provide the support over the phone, chat, or email. The time saved on this can be used by humans to do more innovation for their product.
8. As the models mature, it will get better. The solution will become cheaper over a period.
9. Language translation becomes easy.

Training these LLMs is time consuming, costly, and not many companies can do it. But there are models such as Meta LLaMA, Google FLAN that are pre-trained on various sizes. LLaMA is available trained with 7B, 13B, 33B, and 65B parameters. FLAN is trained with up to 137B parameters. Systems learn from each interaction and learn more with each interaction over course of time, this makes responses/interaction a lot more accurate and advanced. It is impossible for humans to get trained with this much volume of information. In order to get a better result from these LLM systems, the prompt generator (input) to those systems must be meaningful. The more personalized the prompt generator, the better the results from the LL systems. The companies that find a niche with prompt generators will be in an advantageous position. This can make a real difference in customer support with the AI based systems that will help them with customer acquisition, retention and lower churn.

Cuvo is committed to making a difference in customer support and product insights. Its leading AI based customer service solution can help companies and product builders make a difference. Companies can focus on building their businesses and Cuvo can help take care of the customer support. If you would like to explore how AI can help with your product building or customer support, we are eager to help you and be part of your success journey. Let us talk.

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Customer success and product development are two crucial components of any successful business. Customer success is focused on ensuring that customers have a positive experience with a company’s products or services, while product development is responsible for creating those products or services. Bridging the gap between these two teams is essential to ensure that a company can continue to innovate and grow while also meeting the needs of its customers.

The synergy

While there can be some overlap between these two functions, they are generally complementary. Effective product management requires a deep understanding of customer needs and feedback, which can be provided by the customer success team. Similarly, customer success can provide valuable insights into areas where the product can be improved or expanded. Customer success is often an extension of the sales team when a deal closes, in ensuring the customer is satisfied with the product, but can also be instrumental in renewals and up-sell/cross-sell opportunities.

The challenge

However, there can be conflicts between customer success and product management if they have different priorities or goals. For example, product management may prioritize developing new features or products, while customer success may prioritize providing high-quality support to existing customers. It’s important for both teams to communicate and collaborate effectively to ensure that the company is meeting the needs of its customers while also driving product innovation and growth.

Process breakdown

Customer success teams are the front line of your business, interacting with customers on a daily basis. They have valuable insights into customer needs, pain points, and opportunities for improvement. It is crucial to share this feedback with product development teams so that they can use it to inform product decisions. Today this is done through regular meetings, sharing reports and data, and providing access to customer feedback tools. A lot is lost in translation with the current method of operating – just imagine instead that the product could provide the customer feedback directly to product managers instead.

Fragmented tools

Based on the function within an organization, different tools are used to manage customer information and product data. This creates silos making it difficult to bridge the gap between what the customer wants and what product delivers. For instance, the customer feedback tools used may be SurveyMonkey and Qualtrics, while the product team uses Jira and Trello; the customer success team uses Zendesk and Salesforce, and the customer data resides in MixPanel and Amplitude.

The solution

Just imagine one platform that collects user feedback directly within the product, with screenshots and recordings of the user experience, correlates that with information in all the other tools existing across the organization, and provides priorities to the product team to drive the roadmap.

In today’s competitive marketplace, delivering a great product is no longer enough to guarantee customer loyalty. Customers now expect a holistic experience that goes beyond the initial sale and extends throughout their entire lifecycle with your company. This is where customer success comes in – a proactive approach to ensuring that customers are achieving their desired outcomes with your product. Cuvo offers a consumer engagement platform that bridges the gap between customer success and product management. It delivers a solution where consumers can directly communicate their requirements in-app through the product itself, right where they are. These responses are collected and correlated across all tools in the organization before it is sent into a product dashboard for feature prioritization.

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The media and entertainment industry is in a very crucial period of flux. In just the last decade, the number of cable network subscriptions have gone down by nearly half, as streaming media providers take over. Rising digital literacy, the rollout of superfast broadband and the launch of mainstream direct-to-consumer (DTC) services from major networks have driven a major transformation in consumption habits.

While streaming media providers have seen unprecedented growth, taking over from linear, profits have been hard to come by. This is because the economic model of distributing content has fundamentally changed with moving from cable to streaming.

Linear cable had a subscription based model which basically delivered a buffet to eat-all-you-want, live or on-demand, all from the same cable provider, and capitalized on the fact that there is just one cable provider per household. This model has gone for a toss as households prefer multiple OTT channels. Today, nine out of 10 households in the United States have a subscription to an OTT channel, and the average number of OTT providers per household is 6.8. The same entertainment dollar that was spent on a single cable provider yesterday is now being distributed across multiple OTT providers.

How do OTT providers make money? There seems to be three primary ways:

1. Subscription: where original content seems to be king, increasing cost of operations to be so high that churn needs to be at about 3-5% to be profitable. The industry norm seems to be closer to 35% So most subscription based businesses seem to be losing money.
2. Ads based revenues: FAST channels seem to be the route where users willingly trade their time for money as they see scheduled programs with ads (as against AVOD which is video on demand interspersed with ads). The churn rate is closer to 65% and interestingly FAST channels seem to be profitable despite the high churn. The competition is fierce here as all providers including the big names in subscription such as Netflix and Disney are diversifying in FAST.
3. Sponsorships: This one is a strange beast. Regional Sports Networks (RSNs) are losing money as royalties are high and churn increases if these costs are pushed on to the consumers. These are seasonal events typically being live streamed to specific audiences. There is not a clear way to profitability and are being dropped by the larger players. A new economic model is emerging that requires a lot more experimentation on what would encourage longer periods of engagement from users.

In all three cases, churn seems to be the leading indicator of profitability. And a strong 360 degree perspective on consumers is a necessity for rapid experimentation to improve engagement.

Here at Cuvo we doubled down on understanding churn in media products and have come up with a unique consumer engagement platform that runs on any smart device, can layer on top of a media product with the same look and feel as the base product, providing valuable insight into consumers. To learn more about Cuvo read the next blog in our series and get started today.

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