Month: August 2022

Can AI bridge the gap between Travel Agents and Aggregators?

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A holiday can be a life-changing experience for a person. You plan a vacation well beforehand, researching locations, booking tickets, and then getting hyped with excitement. The travel agent has assured a memorable travel experience and has taken care of your itinerary.

The drama unrolls on the day of the travel. Minor irregularities turn into regular occasions of discrepancies like the hotel getting overbooked and a 3-star hotel being offered as an alternative to the 5-star hotel promised at the time of booking. The classy suite turns into a standard hotel room; the all-you-can-eat buffet is anything but that.

A disabled traveler from California was quoted an astronomical price for the only available resort by her travel agent. Some travel agents are all about making high margins and do so even at the customer’s cost.

A UK-based traveler had a harrowing experience when there was a miscommunication between the travel agent and her bank, and even though she had already booked the tickets, she did not get seats on the flight. While she eventually got the refund, she had to book new flight tickets spending thousands of pounds, which she luckily carried at the time. She said she never wants to go through such a pathetic experience ever again.

Most of you have been recipients of such experiences playing spoilsport in your most awaited and well-planned vacation, or at least know of someone who has endured such an experience.

According to the Bureau of Labor Statistics, the number of full-time travel companies has fallen from 124000 in 2000 to 81700 as of 2020-21. It will decline a further 12%, according to the government projections.

The state of the Travel and Tourism Industry

While the pandemic caused an abrupt halt and threw a screwdriver into the travel and tourism industry, it was a rubber band effect. The pent-up frustration created a trend of revenge travel which has reinvigorated the travel and tourism industry, albeit with a few complications.

While things looked gloomy in 2020 when the pandemic hit, it seems like the industry is picking up pace.

A report suggests that flight bookings and hotel reservations are seeing an upwards surge post-pandemic, which is favorable for the industry.

While things look positive for the tourism industry, will the travel agents share the same fate?

The traditional travel agent was a gateway for tourists to travel worldwide. A travel agent takes care of bookings and itineraries and is the sole touch-point throughout the trip.

With the evolution of the internet, things started changing slowly but surely. It has revolutionized our lives and businesses alike. On the one hand, there is the time-consuming, cumbersome process of a travel agent’s methods. On the other hand, with the internet, all information is at people’s disposal; a simple Google search will throw out results and how!

From educating themselves about locations and researching the options to booking flight and hotel tickets, the internet offers the entire process at their fingertips, bridging the gap between standing as a robust alternative to a travel agent.

The probability of the extinction of travel agents has been touted for a while after Expedia entered the industry. The number of travel agents has seen a drastic downward spiral since 2000, but 43% of people still use a travel agent to book flights.

Why?

Convenience. For one, many tourists do not want the hassle of researching, planning, and booking tickets in advance to save money. They would rather have a professional agent take

care of these technicalities than do it themselves and would prefer to look forward to enjoying their vacation peacefully.

Additionally, travel agents can get the best package deals owing to their expertise and valuable contacts across the verticals within the tourism industry. Some travelers are not money-conscious but would prefer a better overall deal.

But the travel industry is seeing many online players like Booking Holdings, TripAdvisor, and Travelocity, among many others, which raises the question- how do online players amp up their game and take the battle to the travel agents?

Because 43% is not a number to scoff at, and with the advanced technology and data available today, the market is up for the taking.

With the availability of data and a device agnostic online digital profile, can AI help online travel aggregators level the playing field in this game?

AI in travel and tourism

AI is making strides across various industries; such is its prowess when combined with quality data. For the tourism industry, there are multiple parameters at play and many areas could see a potential benefit from AI, reaping benefits for both businesses and customers

The cumbersome process of search and research

Even with all the information available at their fingertips, many tourists prefer to go through a travel agent instead of an online travel aggregator because of the better overall deal.

This is an area where online aggregators have much to gain, and AI-based solutions are at home with problem statements like these.

But there is a silver lining for the online travel aggregators here. 82% of all travel bookings worldwide were done with no human interaction, meaning there is enough demand but also scope to improve the process to enhance the experience.

Search, and Recommendation System is an AI solution developed by Affine that uses a recommendation engine to leverage customer behavior specific to engagement metrics in the travel and tourism industry. It uses critical metrics like product Click Count, Click Through Rate, Cart Count, and Book Count of various Customer funnels.

For online travel aggregators, this is highly beneficial as the solution leverages historical data from multiple search options to understand customer intentions and auto-complete suggestions that are most likely to drive bookings.

For customers, by leveraging historical consumer data, the recommendations are always spot on, and the suggestions are always the need-based ideal services. This could be an ace card to compete with the best package deals offered by travel agents.

Online travel aggregators can use this solution to optimize their marketing budget and improve overall ROI by reducing search times, increasing their search-to-book ratio, and increasing booking revenue.

Volatile ticket prices lead to toxic experiences

Ticket and hotel booking prices are always volatile. With multiple uncontrollable aspects at play, travelers, and travel businesses both endure most of the finicky prices.

This is an area of demand where online aggregators have the highest probability of making the most with an intelligent price management solution. This opens the door for repeat business and an excellent customer experience while also organically taking care of the branding through word of mouth.

Flight tickets are at an all-time high and up by 25%, which, when paired with inflation, is leaving travelers across the world in shambles. Inflation is pushing people towards saving more, but they are still booking tickets and traveling, which means the online aggregators are at an advantage. But what if both aggregators and travelers could meet halfway?

Price Freeze Technology solution is an AI solution with a strong potential to tackle the pricing issue of tickets.

The solution works on a simple yet robust method that leverages the historical pricing trends of flight tickets from up to 7 years. The agile and accurate predictive analytical framework helps the traveler lock-in a fixed price for up to seven days by paying a small token fee.

Once locked in, even if the prices increase, the traveler has to pay the lock-in price. However, in case of a price decrease, the traveler will pay the lower amount for the ticket!

These are some of the AI solutions that can help online travel aggregators in the upcoming years. There are many other AI-based solutions to help drive customer engagement, bookings, ad revenue, and overall revenue.

Conclusion

As the travel industry sees a boom again, instances of airports getting overcrowded and hotels getting overbooked are a regular occurrence across the world.

The sudden post-pandemic travel rage has surprised the travel industry with a shock and awe effect. The online travel aggregators have changed the game, but there’s still room for improvement, both- for businesses and travelers.

As people have embraced the internet culture and are co-dependent on it for their day-to-day lives, these AI solutions will immensely benefit travel aggregators and provide travelers with the best customer experiences by leveraging data and personalizing vacations down to the last detail as per the customer’s requirements. At the end of the day, an excellent experience and a good deal can keep the customer happy!

What does Affine bring to the table?

Affine is a pioneer and a veteran in the data analytics industry and has worked with space-defining logos like Expedia, HCOM, and Vrbo, to name a few. From travel andtourism to game analytics, & media, and entertainment, Affine has been instrumental in the success stories of many Fortune 500 global organizations; and is an expert in personalization science with its prowess in AI & ML.

Learn more about how Affine can revamp your Travel and Tourism business!

AI/ML for You, Me, and Everyone

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Enterprises are adopting technology at an unprecedented speed as COVID has fast-tracked the digital transformation journey by a couple of years at least. Enterprises are focusing on innovative solutions to enhance customer satisfaction, optimal cost management, planning, etc., to stay ahead in the market; this is where digital transformation plays a critical role.

Where does AI stand in Digital Transformation, and how does it matter to businesses? 

Digital transformation integrates digital technology into different verticals of any enterprise, such as operations, delivery, and management. It is defined in four broader categories: process transformation, business model transformation, domain transformation, and organizational transformation. Process transformation mainly focuses on analytics and artificial intelligence-driven insights to automate processes and robotics, whereas business model, domain, and organizational transformations are centered around strategic decisions. Business model transformation redefines a company’s digital journey and how it adds value to its customers and overall business. Domain transformation fuels company growth by expanding the businesses into new domains, and organizational transformation is about adopting best industry practices within the organization.

The digital transformation market is expected to surpass the 1 trillion USD mark in 2025 from 469.8 billion USD in 2020 at a compounding growth rate of 16.5%.

Machine learning and artificial intelligence are niche technologies, and companies have started thinking about or utilizing these technologies aggressively as part of their process transformation journey. Market experts estimate that artificial intelligence-driven solutions will add approximately 13 trillion USD to the global GDP by 2030 and transform the world as electricity did almost 100 years ago. The research report below supports this prediction by depicting the three key digital transformation statistics that will play a crucial role in transforming an organization’s business.

These advancements have changed the demand curve for data scientists, machine learning, and artificial intelligence technologists. Artificial intelligence-driven digital solutions require cross-collaboration between engineers, architects, and data scientists, and this is where a new framework, “AI for you, me, and everyone,” has been introduced.

AI for you, me, and Everyone framework

Before designing any machine learning solution or application, architects must understand the complete landscape. If they fail to understand it, challenges such as productionize ML pipeline, automated retraining, real-time inferencing, etc., will affect their workflow, which they never experienced outside the machine learning environment. The same reasoning applies to product owners and engineers, and they should be familiar with the areas where AI/ML can be applied or cannot be applied, along with its limitations. COVID has sparked the demand for data scientists at an all-time high, and this skill is in short supply.

In one of the survey reports, I found that over 50% of the workforce will be preparing for artificial intelligence or technologies revolving around data science, and corporates have started investing heavily in upskilling the talent internally. This is where the “AI for you, me, and everyone” framework becomes applicable as it ensures that over 50% of your workforce is upskilled around data science enabling workflows.

Daunting Challenges of business across industries

  • Software companies are finding it tough to onboard data scientists, ML engineers, ML architects, or product owners who understand industry-wide machine learning applications
  • The upskilling resources are time-consuming as they have to go through a completely new technology stack
  • Theoretical knowledge is not sufficient, and people can’t be productive unless they have hands-on experience
  • Lack of bandwidth from office work, perseverance, benefits, and industry trends keep people unskilled or unaware of these technologies  

How does “AI for you, me, and everyone” framework help overcome these challenges?

Companies driving digital transformation should follow industry-wide best practices, and the “AI for you, me, and everyone” framework helps them to upskill their internal talent pool. This framework will not only help companies to ramp up their skills but also help in delivering projects involving trending AI/ML technologies within timelines, increasing market share, mitigating unknown risks, driving client innovations, and many more.

1. Learning paths: Companies must define a curriculum for employees based on their core skills, and enthusiasts must learn artificial intelligence and its enabling technologies with respect to their core skills, as it will help them to get onto the ML track quickly. The below representation is a high-level visualization for Data scientists and ML engineers, which depicts how enthusiasts can transform their career path toward AI/ML or ML engineering. It covers 10 broader areas of AI/ML and ML engineers, and professionals should have a fundamental understanding of these techniques and their applicability.

  • Data Scientists: Data scientists are primarily responsible for building AI/ML solutions and mathematical models and extracting data insights. They should be very well versed in Python, Jupyter notebook, TensorFlow, PyTorch technologies, mathematical concepts used in algorithms, model building, and communicating results to the stakeholders. It is always a good idea to familiarize yourself with at least one cloud AI/ML services, as it gives an edge to your skillset.
  • Architect / ML Engineer: ML engineer or data engineer needs to be well versed with OOP (Object Oriented Programing) concepts in Python, Spark, data ingestion, storage, scalability, pipeline creation, and deployment. They also need to have a good experience in various cloud services, along with their benefits and limitations. ML engineers usually deal with multiple tasks ranging from data acquisition from multiple sources, aggregation, processing, and storage of the data for further analysis. This workflow should be automated by setting up ETL pipelines.
  • Product owners: They should be aware of the latest happenings in the market, including the challenges companies are facing and how you can help them overcome such challenges using AI/ML. In fact, they should also be aware of AI/ML limitations, prerequisites, and areas across industries’ wide applicability as they are going to drive the customer requirements along with a complete review of the client problems, competitor analysis, and designing a comprehensive roadmap for the client.

2. Training: Companies should design a month-by-month training curriculum targeting the business and technical side of emerging technologies or the role of AI in the modern world, which would help them learn these technologies. Such training programs will not only help the people to grow in the learning curve but will also help the company in the long run by having a competitive edge along with credibility and trust. 

3. Certification: People should be encouraged to take AI/ML programs certification as it increases their technical competency. Companies should take the full or partial cost of such certifications and include certification programs in their quarterly or yearly goals. This approach will set the standards and motivate employees to upskill and complete the certification assessment.

4. Mentorship: Training programs are generally centered on imparting theoretical knowledge, but in reality, people come across many more challenges that no book talks about. Companies should assign a problem statement to the employees to work on who are undergoing technical training programs and assign a mentor to supervise them during their solution time. Once the candidate successfully implements 2-3 solutions then they will be comfortable taking on the research themselves and approaching a new problem independently with initial level guidance. 

5. Involvement: Employees should be involved in a project where they will get a chance to work closely with the team on a real-time client dataset and problem. Working on a real-time project allows employees to work with seniors in the team, improves the learning curve, and boosts the employees’ confidence level.

6. Competitions: Employees should be motivated to participate in Hackathons and competitions to improve their skillset. These opportunities and platforms help employees ideate and implement a prototype quickly and get a chance to identify other challenges and find solutions accordingly.

7. Academic Collaboration: The gap between academic institutes and industry is prevalent and needs to be filled in. Companies should leap one step toward and initiate research programs with professors and Ph.D. students. Companies should go back to the institutes with the potential industry problem to find the right solution for it. This way, both professionals and students can learn from each other and solve new problems in their respective industry.   

Exploring the AI/ML use cases:

Every industry is leveraging machine learning to optimize internal and external processes, and it is helping them to make data-driven business decisions. There are many use cases where artificial intelligence (AI) or machine learning is one of the crucial elements. During their training, mentorship, or certification program, AI enthusiasts can pick any use case from the below themes:

  • Personalization in media, entertainment ecommerce
  • Forecasting in supply chain management
  • Cost / Resource optimization
  • Root cause analysis for machines
  • Chatbot for interactive query resolution
  • Defect detections in manufacturing units
  • Sentiment analysis for any product, policy, content
  • Fraud / Anomaly detection
  • Object detection in an image or video
  • Image / Audio / Video Analysis
  • Language translation

Final Words!

AI/ML isn’t a silver bullet. While it can be a powerfully transformative technology that provides enormous value, getting started and learning how to implement AI/ML in your organization doesn’t have to be overwhelming and burdensome. If you’re intrigued by using AI/ML in your organization, this is where you start. Dive into small, manageable pieces to see what works for your business. Bet on technologies aligned to the business context and solve your critical challenges. Schedule a call today to know more about our success stories and AI capabilities.

Can AI ease the messy chaos of Revenge Travel? 

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Recently Heathrow Airport saw incidents of mass flight cancellations, delays, and baggage issues thanks to the resurrection of the zeal for traveling amongst people, owing to the bottleneck caused by global travel restrictions. Such is the effect of the revenge travel phenomenon.  

Tired of being locked down for over a year due to the pandemic, people started storming to nearby holiday destinations to break free from the humdrum activities and routine life.  

The travel industry was subject to unavoidable impact due to the Covid shutdown. According to Statista, the worldwide travel and tourism GDP saw a 50% freefall from 10% to 5% in 2020.  

With any unnatural imbalance, an adverse effect is imminent, and in this case, a new trend emerged – Revenge Travel.  

New work trends have paved the way for Revenge Travel

The exhaustion of staying inside their homes for a continued period led to this reactive global phenomenon. Once the cases started to decline and countries across the globe began easing travel restrictions, the vacation-starved populace rearing to make up for lost time and confinement started the trend of revenge traveling. 

While traveling was always an option for people, the revenge travel phenomenon saw its inception as animosity towards not having a choice of leaving their homes.  

As with contemporary trends, revenge travel saw an immense foothold, and people started booking airline tickets like there was no tomorrow. Staycation and workcation trends have emerged amongst organizations across the world, opening possibilities to travel more than usual. People even preferred domestic traveling, and domestic flight bookings beat international flight bookings in July 2021.

So, what exactly is the solution? Like other industries, can technology play an aiding role in easing these issues? Can it help accelerate the performance of the travel industry?

Travel and Tourism –can AI be beneficial? 

Messy travel experiences are an issue for customers, while businesses cannot afford to lose face. Everyone has been the recipient of a messy travel experience at least once in their lifetime. Being allocated a different room and tickets booked for the wrong date or time is something everyone has faced. The classic story of a travel agent messing up one of the most important adventures of people’s life is not something new.  

But travel aggregators have changed the landscape for travel and tourism businesses. AI has made the life of travelers a lot easier by being able to book without visiting travel agents.  

For businesses, AI offers to increase profitability in many ways. Pioneers in AI and data analytics have designed and developed solutions specific to the Travel & Tourism industry, benefiting both businesses and customers. Let us explore some AI-based Travel & Tourism solutions that can drive growth for the industry.  

Managing heavy demands & cancellations 

One of the major effects of the rise in revenge travel is the volatile demand. Flights, hotels, and tourist destinations were overwhelmed at once and the unpredictable nature of this demand brought instability and took the travel and tourism industry by surprise. 

The availability of big data is such a valuable potential to tackle this challenge for many of the players in the industry. Leveraging data to forecast demand based on several factors like customer behavior, price trends, and upcoming events can be the game-changer and help ease the unforeseen demand and excessive cancellation situation that plagues the industry.  

Demand & Cancellation Prediction & Management is an analytical OTA solution from Affine that does this along with predicting inclement weather and the resulting flight delays. By doing this, the solution also helps OTAs equip themselves to handle and assist customers, resolve queries, and manage rebooking in case of cancellations. 

This data powered analytical solution helps OTAs predict demand, reduce cancellations and manage refunds, while improving cash-flow for the business. Effectively managing cancellations and refunds also result in a smooth customer experience and increased brand loyalty. 

Automated query handling – the need of the hour for both OTAs and customers 

With the revenge travel chaos and ever rising flight and hotel bookings, customers have many qualms and queries. The sheer volume of queries paired with the skyrocketing number of customers makes this a herculean challenge for OTA players. 

While agents are necessary to solve certain queries and issues, manual efforts simply can’t hold up to this excessive number of requests and a sea of travelers. 

OTAs need to automate the initial levels of travel queries for a smoother process. Furthermore, chatbots are far superior to manual labor in terms of time management and efficiency in handling the sheer volume of customers.  

Affine’s Contextual AI – Chatbot & analyticsis an AI-based chatbot that handles major customer queries and manages them. Live agents are necessary to solve certain issues but this chatbot only transfers the customer to the live agent when it is absolutely necessary, thus easing the load on agents while efficiently handing most mundane queries thanks to its intelligent capabilities. 

For OTAs, this solution helps improve operational costs and reduce customer service costs by having fewer agents as the chatbot handlesthe majority of the traffic. It also helps understand customer interactions helping improve customer experience and overall customer satisfaction. 

These are just examples of a few solutions, and there are tailor-made solutions to improve almost every aspect of the travel & tourism industry like  

  • Conversion rate 
  • Acquisition cost 
  • Ad impressions and many more. 

 As people are getting more dependent on technology day by day, providing a smooth customer journey is essential in the long run for players in the travel industry. Leveraging the abundance of data and the excellence of AI and ML technology provides an airtight business practice headed towards sustainability & success. 

Conclusion  

The post-pandemic era has brought some drastic changes to the lifestyle of people all over the world. The innate yearning for traveling has burst and traveling has become the de-stressing factor for the majority. Hybrid working models for offices and work from anywhere trends have opened the possibilities to travel with just a laptop and an internet connection. 

Revenge travel may be a one-time phenomenon, but it has awakened the deep desire to travel within the populace across the world.  

Revenge travel is just a setting stone for what is in store for the travel and tourism industry. The travel and tourism industry needs solutions that will help them operate efficiently and rake in higher margins. Booking agents are history and travel aggregators are competing across the industry, but AI-specific travel solutions will help travel and tourism businesses equip themselves with the future-ready foolproof tools required to sustain.  

What does Affine bring to the table?   

Affine is a pioneer and a veteran in the data analytics industry and has worked with space-defining Logos like Expedia, HCOM and Vrbo to name a few. From travel & tourism to game analytics, & media and entertainment, Affine has been instrumental in the success stories of many Fortune 500 global organizations; and is an expert in personalization science with its prowess in AI & ML.   

Learn more about how Affine can revamp your Travel and Tourism business!  

What are legacy systems? How Can Modern Data Platforms Bring Revolutionary Change?

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Affine’s Analytics Engineering Practices is kicking off a new series on “All that you need to know about Modern Data Platform.” Read the second part of the series here. You can also read part one of the series here.

What are Legacy Systems? Does your business have one?

 Legacy systems comprise ETL systems, data warehouses, and other traditional software/hardware data architectures. Organizations retain legacy systems if it is expensive to transition to modern data platforms because of data migration or if the legacy system is critical to the business.

Why organizations need to adopt Modern Data Platform?

Investments in modern data platforms can transform business practices in the long term. The following are four fundamental reasons; why an organization should adopt powerful modern data platforms.

Modern Data Platforms – 4 Potential Reasons Why They Are Revolutionary?

1. Enhancing data discovery efforts

A robust modern data platform can synthesize different data types. It can parse through structured or unstructured data in the cloud or organize it according to user requirements.

Legacy systems are less effective in handling advanced data discovery and processing. They store data in isolated silos that neither the system nor a user can reconcile. This flaw makes legacy systems less efficient since the user will have to manually manage or organize the data with other tools and then process it. Only after they complete these steps can they obtain insights from the data.

On the other hand, a modern data platform will jump right to the last step to generate insights for business.

2. Promoting Data Democratization throughout the organization

The idea behind data democratization is to enable the business user a smoother way to access staged data so businesses can leverage data to transform the workflow by unleashing the value of information locked up in the data store. A modern data platform facilitates effective data democratization while making accessibility easier to empower users to obtain the relevant data points and insights independently and quickly.

A transparent process and platform to access data enable domain experts such as data scientists to skip logistical hoops and effortlessly home in on the data points they need. Thus, it might not be the case for legacy systems, which often have redundant interim steps, such as report request processes.

3. Prioritizing data safety and privacy

Modern data platforms are equipped with multiple layers of security to prevent data breaches. Most organizations follow the regulations such as CCPA, HIPAA, FCRA, FERPA, GLBA, ECPA, COPPA, and VPPA. These data protection laws provide governing frameworks for data usage, storage, and deletion, which are easier to process in modern data platforms to ensure data security and privacy.

Most businesses that use legacy systems find it difficult to implement the regulations required to meet security and privacy standards.

4. Ensuring self-service of data

A streamlined modern data platform smoothly enables self-service of data to your internal customers. It is well equipped to identify various data points and efficiently cater to internal customers’ requirements. Thus, a modern data platform reduces the complexity and allows users to access the data swiftly when they need it most. Legacy systems lack self-service, and all data requirements must be routed through IT and data teams.

How to overcome legacy systems confrontations, and where should you invest?

Legacy systems are in rapid decline. Organizations are choosing to store a significant chunk of their data on modern data platforms to optimize their data processes and decision-making. Modern data architectures need to keep up with the rapidly growing data-driven needs of businesses. As a result, modern data platforms have emerged as the most efficient solutions and promise to take the business world by storm.

However, before making a significant financial investment in the space of Modern Data Platforms, organizations must find a suitable, competent technology partner to partner with them on this journey. An expert like Affine’ s Analytics Engineering practices can make this transition seamless and effective. Are you ready to begin your journey to true data centricity? We are here to help. Schedule a call today!

This blog is the second episode that signifies “All You Need to Know About Modern Data Platforms.” In the next episode, we’ll compare traditional vs. cloud hosted data platforms to determine which would be better for your business.

Comparing the Big Three – AWS Vs. Azure Vs. GCP – From the PoV of an AI & Analytics Services Partner

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Unless you have been living under a rock or are completely alienated from the tech world, you’d know that Amazon Web Services (“AWS”), Microsoft Azure (“Azure”), and Google Cloud Platform (“GCP”) are the three major public cloud providers in the world today, with a combined Market share of approx. 64%. Trailing behind them are Alibaba Cloud, IBM Cloud, Oracle Cloud, and others, who, in my opinion, need to either go niche or take a big bang approach if they want to catch up with the dynamic requirements across industries being catered to by the big three. But have you ever thought about what the big three bring to the table from the point of view of an AI & Analytics services partner? Let’s take a closer look at how each of these service providers differs in its value proposition.

What makes AWS, Azure & GCP the same, but still different from each other?

Talking about the three biggies, while AWS is the oldest of the lot, a lot mature in multiple aspects such as ease of integration & technical prowess of their products and kicking it out of the park in terms of their market share, their partner programs can learn a thing or two from Azure’s playbook. From strategic sales relationships with partners & joint GTM to general sales teaming to grow partners’ business, AWS is slightly behind. On the other hand, given they have been longer in the market and are a lot bigger in size, the sheer volume of demand that they see could potentially more than make up for this lack.

It’s not all bad for AWS, though – Irrespective of the size of the partner, they provide a personal touch during partner onboarding and help navigate the ocean that’s AWS, which is more than what I can say for Azure. Although a leader in profits & pricing for partners, Azure is notorious when it comes to personal touch & handholding for new partners, unless you can show the size and $$$$. Even for lead sharing and demand generation, Azure is known to prefer existing & known partners to new ones (not that I blame them!). I like to call Azure the “Business” cloud, as it has an unequivocal focus on improving its customers’ business rather than focusing too much on the technology perfection side of things.

All said, Azure today is one of the leading choices for companies as an enterprise cloud due to its native integration with other Microsoft products such as M365, Teams, Dynamics, ERP, etc., and superior support for Hybrid Cloud infrastructure. On the other hand, AWS, with its whim to stay away from being a hybrid or private cloud, is lagging in this area, and with a bulk of Fortune 500 companies moving to the Hybrid model, it may hurt AWS in the long run and may knock a few points off its market share.

Just like Azure, GCP also offers great support for the Hybrid Cloud model through its offering, Anthos, but is currently being considered, primarily, as a support cloud and not an enterprise one due to the low maturity of larger product capabilities (both IAAS & PAAS), low ease of integration and less evolved documentation, processes & features. From a partner’s standpoint, GCP is actually not behind on demand generation & lead sharing (as compared to Azure), and that is saying something, given that GCP came into the enterprise limelight only in 2019-2020 timeframe. However, due to less evolved partner programs, lower profitability, and limited sales relationship & support, GCP and its partners have not seen the kind of growth they set out for.

That said, with Mr. Kurien at the helm, GCP is very quickly making a name for itself as a huge proponent for its customers & their businesses, even with a highly developer-focused tech stack, which is open-source friendly and DevOps centric. They have also started ramping up their partner programs & strategy, which has grown by more than 400% in the last couple of years.

Capabilities of AI, ML & Analytics in AWS Vs. Azure Vs. GCP 

Talking about Artificial Intelligence, Machine Learning & Analytics capabilities, despite its slow growth, GCP is coming out as a “hands down” leader with its powerful infrastructure, low latency & superior performance for high-end computing workloads. This is further strengthened by its Data based DNA – Due to their other free product offerings, GCP has had access to tons of data which has allowed them to create best-in-class AI capabilities. On top of this, GCP has further distanced itself from AWS & Azure by unveiling the Vertex AI workbench in 2021, which brings Google’s ML services under one roof to simplify the process of building, training & deploying ML models at scale.

Azure, with its suite of cognitive services and other AI/ML offerings, does not have as broad a spread as AWS or GCP, but the ones available are much more function-specific, which is in line with their “Business” first approach. However, from a performance standpoint, neither Azure nor AWS can match up with Google’s Tensor Processing Unit (TPU).

AWS has the widest variety of services available under the AI/ML & Analytics banner as compared to the other two but offers less flexibility and out-of-the-box algorithms, which makes it less favorable in certain cases.

At an overall level, while the differences in AI/ML & Analytics capabilities are more than noticeable between the three biggies, this area is seeing a continuous infusion of investment from all three service providers and the gap between them is bound to narrow down in the not-so-distant future.

Some of the key AI/ML & Analytics suite of offerings provided by these public clouds are ML as a service, language capabilities, speech-to-text & text-to-speech, vision-based recognition of images and videos, Anomaly Detection, NLP & Text analysis, Conversational AI, and many more.

Summing Up!

With a major pivot in IT & Data strategy from complete On-Prem to Cloud/Hybrid approach, it has become more important now than ever for companies to make the right choice of cloud. Building and operating your systems in-house is no longer necessary. As technology progresses, becomes more versatile and Opex driven, cloud adoption has become an integral part of any company’s IT strategy. With this new paradigm, there has been a shift in how businesses allocate resources across platforms that are most suited to their specific needs. Affine can assist you with this as we have vast expertise moving between all three major cloud providers. Schedule a call today and talk to our cloud experts.

Stop! This next-gen AI satellite image segmentation solution could solve your business problem in 12-20 seconds.

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Satellite remote sensing has become one of the most efficient solutions for surveying the earth at local, regional, and global spatial scales. The technique segments the satellite images to develop the topographic details that can be used for various business applications. The implementation of the segmentation depends on the region of interest, size, and resolution of the satellite images complemented by the technology used to process the image. If you’re looking for a quick and effective way to extract the details of any location on the earth’s surface, Telescope is your one-stop solution.

What is Satellite Image Segmentation, and how does it matter to your business?

Let’s break it down into two parts – definition and overview!

Definition: Satellite image segmentation is a process of dividing an image into smaller regions or segments. This is often done to improve the image’s clarity and to make it easier to analyze. The satellite image segmentation solution can be used to improve the accuracy of land surveys, track the movement of objects, and identify changes in the environment.

Overview: For those who are unaware, satellite image segmentation is the same as image segmentation. It uses landscape images taken from satellites and performs segmentation on them, and provides details like greenery, land, buildings, water bodies, and other details of the specific location on the earth’s surface.

The satellite segmentation process includes two steps: segmentation and classification. Images can be created from image type categories, or the image scene itself can have a variety of structures and textures. Segmentation is the process of dividing a digital image into multiple segments. The objective is to simplify or transform an image’s representation into a more meaningful form and simpler to analyze. In a nutshell, it is the process of labeling each pixel in an image so that pixels with the same label share specific visual characteristics. The fundamental application of image segmentation is used to find objects and boundaries (lines, curves, and so on) in images. 

Why/which business would desperately need a satellite image segmentation solution?

With increasing spatial, spectral, and temporal resolutions of earth-observing systems, geospatial and remote sensing solutions are moving toward a new paradigm of business applications. As a result, satellite image segmentation solutions are gaining popularity. 

Businesses can leverage the satellite image segmentation technique to extract quick results of site/land analysis automatically by eliminating manual efforts and enhancing the accuracy of the survey or analysis of a given location. Below are the few use cases of satellite image segmentation solutions that would be gamechanger for businesses across industries.

Real-estate: For instance, let’s assume a real-estate professional wants to survey multiple lands to know the plot size so that he can evaluate the price of each property, it would take weeks together to evaluate it manually, plus it requires additional resources. The satellite image segmentation solution uses advanced technologies and spatial analysis to provide important details of the location, such as buildings, roads, grasslands, etc., in just a few seconds while enhancing the accuracy of the survey results.

Agriculture: Farmers strive for more sustainable agricultural practices, whether it is crop management or resource planning in warehouses based on yield estimation. Satellite image segmentation can help farmers to understand riparian zones and areas of natural shelter for livestock and wild animals, allowing them to fence off environmentally sensitive areas and reduce the risk of inter-species disease transfer. This technology also reduces the manual efforts in crop management by providing topographic details in seconds.

Mobile tower setup:  The installation of a mobile phone tower is an intricate process that demands extreme precision. A team of technicians working together from different locations conducts the feasibility analysis to identify the right location for the installation process. Also, installation engineers must be able to measure the distance between the equipment and the target surfaces. Satellite image segmentation minimizes most of these manual efforts, especially in the feasibility analysis process. It provides a detailed view of the site specifying the percentage of buildings, greenery, water, utilities, soil, etc., which will drastically reduce the human resources and the cost incurred to a great extent.

Smart City Planning: A smart city is a broad concept that includes both technology and social and human capital development as fundamental components. As a result, feasibility analysis using structured ad-hoc models appears to be an important factor. To avoid inefficient resource allocation, the approach should consider both the project’s smart characteristics and the city’s actual needs. That’s where satellite image segmentation solutions come in, providing effective and quick ways to assess the given area and provide topographic detail, reducing operating costs while instantly preserving on-ground utility information and other data.

Area assessment: Businesses can use satellite image segmentation to evaluate changes in water bodies or land shapes such as dams, rivers, deserts, and mountains. The extracted topographic details can be used to identify and describe the various types of elements in satellite imagery. It can locate and analyze the given location by comparing them to known features in ground control to see if any surface changes or new features have occurred. This capability largely helps in area assessment and reduces human resources efforts.

Armed Forces: Satellite image segmentation solution provides a detailed topographic analysis to track vitally critical developments for defense and security prerequisites. These quick details help armed forces to monitor particular areas at regular intervals. This solution also paves remote monitoring of major construction projects, infrastructure development, power generation facilities, mining activities, and so on.

Natural Catastrophic Event: Identifying the regions impacted by a disaster is critical for effectively mobilizing relief efforts. Satellite image segmentation solution with its unique capability of analyzing vast coverage of the ground surface is a valuable resource that can be leveraged to monitor disasters such as volcanic eruptions, wildfires, and floods, among other things, in an attempt to improve life safety, reduce risk, and build resilience to natural catastrophes.

How can Telescope solve your business problems?

Telescope is a next-gen AI solution hosted on the AWS marketplace as a SaaS offering. It delivers automated, fine-grain image segmentation driven by our exclusive deep learning technology, which can help you reduce 40% of the survey cost and 70 % of the reduction in planning time.

Telescope is capable of producing highly intact topographic details using a cutting-edge combination of Computer Vision and GIS technologies. It allows you to automatically retrieve high-resolution satellite images of sites up to 100 square kilometers. You can analyze any location on the earth’s surface and get instant results to find the percentage of greenery, land, buildings, water bodies, etc.

AI to fuel the Film industry’s future

Posted on by Website Manager

The worldwide revenue for theatres fell from an all-time high of $41.7 billion in 2019 to a jaw-dropping $11.9 billion in 2020. The film industry took a deadly hit from the pandemic, and the following lockdown brought the industry to its knees and raised questions about its future.

Source: Statista

Ever since the onslaught of OTT platforms, the media and entertainment industry has shaken up, and a new form of revolution has set the foundation. The film industry is one such domain that has been the recipient of the adverse effects of this revolutionary transformation in the past decade.

While the big screen and an unparalleled cinematic viewing experience are still unchallenged to an extent, access to home entertainment and content on demand is a dent to the box office.

The Pandemic Saga

One of the biggest jolts for the film industry to date has been the pandemic, which brought things to a screeching halt and left the industry high and dry. Movie theatres had to shut down due to lockdown measures, and people confined to their homes took an interest in gaming and streaming shows on their couches as alternatives.

The result? Box office revenues plummeted to an all-time low!

The challenge lies in the future

The 2020 numbers look dreary, but as lifestyles return to normalcy again post-pandemic, the film industry still has a challenging task. Consumer behavior has changed. The average content consumer has seen value from OTT platforms that provide quality content on tap, and film as a product has deteriorated in value. Video on Demand offers immense value, and this is a critical film industry challenge that needs addressing.

If the five-year forecast from 2020 to 2025 is anything to go by, it is not going to be a smooth journey for the film industry. The OTT platforms have wreaked havoc with value entertainment at their tap and dethroned the film industry, aided by the unforeseen pandemic.

Source: Statista

But the charm of watching a movie on the big screen is unparalleled. The industry needs to revamp its practices in the process of film production. While a passion for the craft fuels the art of filmmaking, the technical and strategic processes stand to immensely benefit from AI practices explicitly designed for the film industry.  

Production and promotion- areas that need efficiency the most

A film’s success or failure has always been a gamble, but the production effort and cost are constant across most film titles. Solutions implemented right from the pre-production phase can result in substantial, measurable impacts.

Many studios spend an insane amount of funds on marketing and promoting their movies. With the current advertising landscape seeing a transformation, thanks to the latest content consumption habits, promotional budgets need to be scrutinized irrespective of the production scale.

Source: Statista

Save for the slump brought by the pandemic, the promotional budget for movies has seen an upward surge in the previous years and is back on track for 2021, which means higher spending and a bigger overall budget. While this amplifies the reach of the film across the globe, there are two main challenges here:

  1. Many small and medium-sized studios cannot splurge on sky-high budgets to promote their movies.
  2. Even big production houses sometimes go overboard with the promotions, and the movies earn less than expected.

Efficient promotions are the only way to go forward irrespective of the might of the production houses.

Commercial Forecasting System

Hollywood is no stranger to big-budget titles bombing at the box office while total underdogs clinch big victories. Sometimes there have been instances of a movie bombing locally but performing exceptionally well at international box offices like China.

This AI (Artificial Intelligence) based project management system from Affine helps production companies execute smart, efficient insight-filled decisions across the film’s production processes.

With this AI solution, production companies can predict the performance of their movies on local and international markets and across various demographics and populace at respective production stages of the film.

Production industries can stand to gain benefits as mentioned below by leveraging the Commercial Forecasting System:

  • Ascertain key foresight into film performances well in advance
  • Make necessary changes in the preliminary stages of production
  • Project realistic output numbers
  • Carry out efficient and data-driven marketing/promotional activities in tune with the film’s predicted performance across demographics and media types

Script Analysis

Time and time again, it has been proven that a good script is a foundation for a successful movie. With the diversity of content today, it is challenging to design a script that will assure superior performance at the box office.Script Analysis is an AI and ML (Machine Learning) solution that learns from the plethora of data fed into it and analyzes the storyline to determine its success in respective release regions, even at a pre-production phase. Historic film data helps the solution analyze similar script performances and predict the outcome with near-perfect accuracy over the micro level of demographics and age groups.

With the Script Analysis solution, production companies can leverage the benefits mentioned below:

  • Predict the near-accurate outcome of a script if it’s shot into a movie
  • Ascertain valuable insights that help make data-driven business decisions well before the production stage
  • Green-light scripts that are assured of performing well while making necessary changes to scripts that are not as optimal for business

Talent and Casting Analytics

Many great movies have had surprise castings that worked for them and changed fortunes for both – the filmmakers and the talent. But there have been cases of miscasts that have ruined good movies as well. Leaving casting to gut feeling is not feasible anymore and must be treated like any other business process.

Many production businesses have already adopted AI-based casting methods to choose the right talent optimally. Affine’s Talent and Casting Analytics leverages data to generate insights on the impact of key talent on a movie’s box office performance.

Production companies can indeed gain advantages from the Talent and Casting Analytics solution in the following ways:

  • Provides casting suggestions based on historical roles and in the actor’s portfolio
  • Use the cast as a variable to determine the film’s performance at the box office
  • Rank and simulate talent options based on their economic impact across the film industry like media type, genre, and key territories

AI-powered box office predictor system

The sheer number of filmmakers has grown over the years, and many are challenging each other at the box office, which may be a treat for the viewers, but as a business, production houses can end up with losses.

At the end of the day, the commercial success of a film is just as crucial, if not more than its critical acclaim. If all the above solutions are the factors of the success equation of a movie, then an AI-powered box office predictor system is the main act.

With this solution, production houses, independent filmmakers, and distributors can predict the movie’s box office performance up to 6 months in advance. The plethora of business opportunities this solution provides is immensely insightful and can help film businesses make valuable decisions.

With the Affine’s solution, you can leverage the following:

  • Predict film revenue at the box office well in advance with the highest accuracy rate
  • Decision makers take steps for ROI (Return on Investment) improvement
  • Forecast the promotional/marketing effort required per box office performance across regions, genres, and many other factors

The film industry will sustain AI behind the scenes

Films are not going anywhere, irrespective of the competitors. But the post-pandemic era comes with many changes due to multiple factors, ranging from content consumption behavior to global inflation.

People worldwide are in a price-sensitive phase, which brings the need for film production companies to improvise the game-plan. With the Film industry-specific AI practices, they stand to benefit from box office success and an efficient production, casting, and marketing process, contributing to the overall ROI.

What does Affine bring to the table?

Affine is a pioneer and a veteran in the data analytics industry and has worked with giants like Warner Bros Theatricals, Zee 5, Disney Studios, Sony, Epic, and many other marquee organizations. From game analytics to media and entertainment, Affine has been instrumental in the success stories of many Fortune 500 global organizations; and is an expert in personalization science with its prowess in AI & ML.

Learn more about how Affine can revamp your film production business!

Manas Agrawal

CEO & Co-Founder

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