Dynamic Pricing in the Private Education Business

Dynamic Pricing in the Private Education Business

Introduction 

The pricing process is a strategic business activity that requires continuous analysis and information sharing between departments in order to be assertive and financially beneficial. Over the years, it has been getting optimized through increasingly intelligent, dynamic processes powered by technology.

The intelligent dynamic pricing strategy is used to generate prices based on the analysis of information captured in the market (competition) and even on probabilistic values ​​generated by the use of Artificial Intelligence (AI). This innovative approach has gained prominence among organizations from different sectors, starting to be adopted by the main companies in the digital world or in the process of digital transformation, offering agile market adaptability, competitiveness, and profit maximization.

Based on that, today in our blog we are going to present dynamic pricing in private education, as it is one of the most important and challenging decisions in the sector.

How to value the brand and, at the same time, consider the investment possibilities of your students? How to price correctly in a market where scholarships are often offered? And yet, how to define the ideal tuition fee/scholarship for each student and at the same time ensure uniformity in the application of corporate pricing policies?

To answer these questions, we have prepared a material (which you can download for free), presenting our success story related to advanced pricing in the private education segment.

The case study addresses our client’s business problem, who had been immersed in a complex non-automated pricing system, with extensive rules to define the ideal tuition fee/scholarship for each student; the journey, marked by the structuring of the pricing process; and the results generated for the educational institution (what the client gained).

Dynamic Pricing in the Private Education Business: Conclusion

The dynamic pricing strategy has been gaining prominence in the education sector. However, its implementation process is complex, requiring artificial intelligence as well as qualified, market-recognized data analytics providers.

Also read – How to choose the best AI and data analytics provider?

Do you have any doubts about our success story of dynamic pricing in private education? Leave your comment.

Learn more about Smart Pricing, Aquarela Tactics module, and talk to our experts.

What is Aquarela Advanced Analytics?

Aquarela Analytics is Brazilian pioneering company and reference in the application of Artificial Intelligence in industry and large companies. With the Vortx platform and DCIM methodology, it serves important global customers such as Embraer (aerospace & defence), Scania and Randon Group (automotive), Solar Br Coca-Cola (beverages), Hospital das Clínicas (healthcare), NTS-Brasil (oil & gas), Votorantim Energia (energy), among others.

Stay tuned following Aquarela’s Linkedin!

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How can Big Data clustering strategy help business?

How can Big Data clustering strategy help business?

Hello folks,

To clarify the concept of clustering, which is a reoccurring theme in machine learning area (machine learning), we made a video tutorial that demonstrates a clustering problem that we can solve visually, then with finalize with a real case and some conclusions.  It is important to mention that other areas may benefit from this technique by targeting markets where you can meet different audiences according to their characteristics. We will use a video example

Below is the description of the video for those who like reading.

To facilitate the absorption of the concept, we will use a visual-based example. So, imagine that you have a textile factory and you want to produce as many flags as possible in the shortest time as with fewer materials as possible. Considering that there are around 200 national flags and each has different colors and shapes, we are interested to know which color patterns and shapes exist to optimize and organize the production line. That’s the idea, reduce costs and time while maintaining quality and volume.

All flags

Figure 1 – Representation of raw data without patterns detected

A good clustering algorithm should be able to identify patterns out of the raw data like we humans can visually identify looking at the Italian, Irish and Mexican flags like in the example below.  One factor that differentiates clustering algorithms from the classifying algorithms is that they have no hints about the patterns to study the model they must figure out automatically and this is a big challenge for practitioners.

bandeiras1

Figure 2: Cluster zero (0) composed of the Italian, Irish and the Mexican flags.

In this context, as important as to identify groups with similarities between each other and finding individuals who do not resemble any other element. The so-called outliers, which are the exceptions.

bandeiras2

Figure 3: Cluster six (6) composed of the flag of Nepal. An exception.

Finally, as the result of a good clustering process, we have the groups formed by the flags that have similar features and isolated individuals being the outliers.

bandeiras3

Figure 3: Clusters formed at the end of visual human-based processing.

One of the most important factors of clustering is the number of groups where the elements will be allocated. In many cases, we have observed very different results while applying the same data, and same parameterization in different algorithms. This is very important. See below what could be the result of an inaccurate clustering.

bandeiras4

Figure 4: Clusters result of a wrong clusterization

So, a practical question is:

Would you invest your money in this?

Probably not, and solving this problem is our challenge. A real application that we carried out was to identify the main characteristics of patients who don’t show up to their medical appointments, the well-known no-show problem that has deep implications in offices, clinics, and hospitals. The result was an amazing group with 50% of the analyzed data, which really deserves a specific policy. Doesn’t this give reason to the chief financial officers of these organizations?

Other possible applications of the clustering strategy were presented in this post “14 sectors for application of Big Data and data necessary for analysis.”

Some conclusions

  • Our vision is very powerful clustering images as in the case of flags.
  • It is humanly impossible to do analysis and logical correlations of numbers from a large database, so the clustering algorithms were created.
  • The accuracy of the results of clustering is crucial for making investment decisions.
  • Several sectors can benefit from this management approach.

Thank you!

 

What is Aquarela Advanced Analytics?

Aquarela Analytics is Brazilian pioneering company and reference in the application of Artificial Intelligence in industry and large companies. With the Vortx platform and DCIM methodology, it serves important global customers such as Embraer (aerospace & defence), Scania and Randon Group (automotive), Solar Br Coca-Cola (beverages), Hospital das Clínicas (healthcare), NTS-Brasil (oil & gas), Votorantim Energia (energy), among others.

Stay tuned following Aquarela’s Linkedin!

What is Web 3.0 and why it is so important for business?

What is Web 3.0 and why it is so important for business?

Greetings to all!
Web 3.0 as concept and technology is important and here is why.

Day after day, the amount of data and information as we discussed in the last post on the internet grows exponentially. New sites, images, videos and all sort of digital materials are coming up every second. Thus, with this huge set of data, a major challenge is how to cost-effectively extract what is relevant to our day-to-day activities. Therefore:

In a complex ever-changing information-intensive context, Web 3.0 tools are valuable for users in organizing information and business processes at large scale.

The evolution of the Web

Firstly, since the emergence of the first Web version, created in the early 90s by Tim Berners-Lee in Switzerland, its technologies have undergone significant changes until we reach the surface of Web 3.0, this happened especially in terms of user’s interactivity and the massification of the internet usage.

In short, according to our research a Aquarela Analytics, the Web’s history presents three major stages:

The Static Web – Web 1.0

The Web 1.0 presented data and information in a predominantly static way, being characterised by low users’ interaction with the content. For instance: leaving comments, manipulating or creating content of a website.

Technologies and methods of Web 1.0 are still widely used for displaying static content such as laws and manuals like this example: http://copyright.gov/title17/92preface.html . Yet, this text was build on this paradigm.

That generation of the Web was marked by the centralisation of the content production – such as portals,  AOL and directories, Yahoo, and Craigslist.

On Web 1.0 the user is responsible for its own navigation and the identification of relevant content, having a predominantly passive role in the process.

Another important aspect is that just few produce information that is consumed for many. Likewise, the broadcasting model widely used in the media industry by TV, radio, newspapers and magazines.

Web 1.0’s greatest virtue was the democratisation of information access.

The Interactive Web – Web 2.0

Web 2.0 in contrast to Web 1.0 has its content predominantly generated by its users in a process where: many users produce content and many consume.

An example of this model is Wikipedia. Other examples of user-generated content platforms are in blogs, social networks and YouTube. In the Web 2.0 users are no longer just content consumers; they become producers or co-producers of contents.

In this version of the Web, search engines become more advanced and proliferate, since there is no more room for lists of links in directories, which has given a huge volume of content made by many.

Web 2.0’s great virtue is the democratisation of content production.

The Actionable Intelligent Web – Web 3.0

Web 3.0 or Semantic Web combines the virtues of Web 1.0 and 2.0 by adding machine intelligence.

Tim Berners-Lee (2001), who is the creator of the Web, has published an article in the Scientific American magazine setting up the foundation of the Semantic Web.

In his words, Berners-Lee explained how two brothers organised the logistics to support their mother health treatment, using intelligent agents, they do all the planning and execution of the process automatically interacting with clinical systems, among themselves and with their home devices.

In Web 3.0, the machines get along with users in content production and in decision-making, transforming traditional supportive role of the internet infrastructure to a protagonist entity in content/process generation.

Furthermore, Web 3.0 services can unite users and computers for problem-solving and intensive knowledge creation tasks. Therefore, with its large processing capacity, Web 3.0 is able to bring services and products to people and businesses with high added value because of their assertiveness and high customisation.

Web 3.0’s great virtue is the democratisation of the capacity of action and knowledge, which was previously only accessible to large businesses and governments.

Evolution of the Web summarized

Web 3.0 comparison among previous versions
Web 3.0 comparison among previous versions

Web 3.0 examples

Examples of Web 3.0 applications are Wolfram Alpha and Apple’s Siri, which can summarise large amounts of information into knowledge and useful actions for people. 

Wolfram Alpha

We can do a little comparison between Wolfram Alpha and Google, using both tools, typing the “Brazil vs. Argentina” phrase in both searching engines, and then we see big differences in the results:

Search results Google vs WolframAlpha

In the case of Google, the results turn out to be mostly about football games between Brazil and Argentina. Note that the word “football” or “games” were not mentioned in the search.  

In Wolfram Alpha, the tool considers that the search is a comparison between two countries and consequently brings organised statistics, historical, geographical (maps), demographic, linguistic and other useful aspects for comparison analysis.

Siri

The Apple’s Siri, in turn, uses techniques of speech recognition and artificial intelligence to bring results and perform actions such as:

“Where is the nearest pizzeria?” or

“How far am I from the nearest gas station” or “make an appointment at 9:00 am tomorrow.”

Above all, Traditional tools (Web 1.0 and 2.0) make search matching “word by word like” of the text in relation to what is published on the network. In other words, often it brings information bias of what is most abundant ending up not bringing what is most relevant to the user at that time.

Web 3.0 systems, however, seek contextualised knowledge to assist people in their jobs, pointing to series of analysis and potentially helpful information.

One of the distinctions of Web 3.0 search engine, is the time that user need to spend sailing in a sea of ​​information to find what he/she really wants to get solved.

Companies like Apple and IBM have been investing heavily in Web 3.0 technologies, for example, the Google Inc. over the past decade has made several acquisitions of companies in the Semantic Web area, such as Applied Semantics, and Metaweb Technologies, Inc, among others.

Conclusions em recommendations

We are living in an interesting time in history, where the Web begins to bring more knowledge and action capacity for its users, resulting in considerable changes in several aspects of daily life.

This new type of Web is moving fast towards a more dynamic and faster changing environment, where the democratisation of the capacity of action and knowledge can speed up business in almost all areas.

The areas impacted by Web 3.0 are ranging from: retail to applied molecular medicine; from micro-businesses to large corporations.

It is worth for innovative minds, whether business people, politicians, or researchers, to understand this new horizon of possibilities and be prepared for the new generation of businesses.

Some new business with the semantic web are already happening and, increasingly taking their momentum in the national and international markets. 

Web 3.0 is the progressive evolution of the Web. Hence by not getting along with its evolution, managers might bring organizational risks that suddenly might become obsolete or irrelevant at the time of paradigm shifts like the giants of the past such as Kodak, Nokia and Altavista.

In future posts, we will talk about Data Analytics and Big Data solutions that we developed and which we believe to be the way to materialize business faster (earlier) than Web 3.0 and Linked Open Data (LOD), although all of them are getting more and more intertwined.  It is important to understand the way Web 3.0 is getting through Big Data and LOD.

Several interesting challenges ahead!

Business update in 2022 – Link

What is Aquarela Advanced Analytics?

Aquarela Analytics is Brazilian pioneering company and reference in the application of Artificial Intelligence in industry and large companies. With the Vortx platform and DCIM methodology, it serves important global customers such as Embraer (aerospace & defence), Scania and Randon Group (automotive), Solar Br Coca-Cola (beverages), Hospital das Clínicas (healthcare), NTS-Brasil (oil & gas), Votorantim Energia (energy), among others.

Stay tuned following Aquarela’s Linkedin!

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AI in Retail: Applications in the Industry 4.0 Market

AI in Retail: Applications in the Industry 4.0 Market

Currently, we experience the technological advances present in the industries, directly impacting the processes of construction, development, and delivery of products to the consumer. Market competitiveness increasingly focuses on technological and digital pillars, thus making the automation and digitization of processes more recurrent in companies. The impact generated by Industry 4.0 has created a horizon of opportunities for the retail market to get ahead and compete at higher levels with competitors, seeking higher rankings in terms of delivery, quality, efficiency, and effectiveness in the processes until the product reaches the consumer.

AI in retail

In addition, advances in Artificial Intelligence, Machine Learning, and IoT (Internet of Things) provide new horizons for the various branches of the retail industry. The automation of storage processes, route monitoring, material storage strategies, demand forecasting, and customer satisfaction are examples of procedures adopted through these technologies to obtain better results in the market.

We are living in the data age. Being prepared for it, and orienting internal processes to data, will enable companies to immerse themselves in this ocean of opportunities, thus resulting in cost reductions based on analysis of losses and waste, and in more sustainability, competitiveness, and approval in the market.

Supply chain and S&OP

Among the different operational areas of the industry, artificial intelligence stands out strongly in the supply chain, promoting more automation in production processes. Tracing a procedural path of operations with AI, it is clear that the cycle ranges from the implementation of intelligent technologies in sales and operations (S&OP) processes, thus intensifying the analysis for better sales strategies with the help of the marketing area, to better formats to operate and the automation of exhausting and repetitive jobs.

Through the advancement of the Internet of Things, it becomes more efficient to capture data from different stages of production. Obtaining data from the first task to delivery to the final consumer is no longer a problem, with the possibility of extracting production data, for example, robots implemented for storage, applications for drivers, and products connected to the internet, among other ways of collecting data with IoT. Another important point is the bridge between stakeholders through advanced data analysis with Machine Learning and AI, aiming to filter raw material suppliers and final suppliers that are more aligned with the company’s interests and also to obtain a lower loss in the processes. procurement of materials and delivery to the consumer.

AI, ML, and IoT technologies also influence revenue generation, increasing profits and better results with customer and supplier relationship management. An example of this is intelligent dynamic pricing, which uses artificial intelligence and adopts market and consumer-based strategies to determine the best price (not necessarily the highest, but the most appropriate price to compete in the market), aiming at increasing revenue.

Demand Forecasting 

Regarding demand forecasting processes, the implementation of AI and ML produce an assertiveness of around 90%, generating impact and improvements in demand forecasts based on advanced analysis of different data, such as weather conditions, the economic situation of the market, available quantities, consumer desire, and consumption predictability. In addition, advanced analytics and intelligent models that have continuous learning through greater data collection and time provide predictive actions in real-time, helping decisions in a way assisted by professionals. This reduces the failures and risks in operations with decision-making and can change them in case of negative predictions that can generate several impacts.

Furthermore, in the area of ​​dairy and perishable products, AI has great strength, as strategies for goods with short dates and more fragile logistics need to be much sharper. This contribution is supported by collecting data, and information and creating predictive demand models that deliver better strategies for storing products, defining the best routes, reducing fuel waste, and forecasting geolocation in the case of products with greater demand. so as not to keep them in distant stocks, thus facilitating the preservation of the products until their final delivery.

Big Data

This is a term that has been gaining great proportion and space in the context of industry 4.0, representing the large mass of data, intensive collection, and importance of artificial intelligence and machine learning to handle this information that can add a lot of value to companies. Represented by the thousands of data produced by the different stages and experiences of the market, big data includes purchase data, online browsing of consumers, media and marketing data, and customer satisfaction with the service and/or product, among other diverse information.

The process of collecting and storing data is complex and analyzing thousands of data becomes an impossible human task. Thus, AI and intelligent models based on machine learning go hand in hand with big data to integrate external market and internal company data in a way that makes forecasting and planning of demand, greater revenue, profit, reduction of waste, and sustainability.

Logistics 4.0 

It is clear the advances that industry 4.0 has been allocating. For example various automation in production processes, digitalization of products for testing improvements, speed of information, and implementation of results.

With industry 4.0 comes logistics 4.0, aimed at optimizing the loading and unloading processes of goods. Automation and use of AI in various stages of logistics, such as the organization of products in warehouses made by robots that, through AI, strategically and hierarchically organize products to facilitate and increase the speed of cargo operations.

In addition, it is possible to generate forecasts of events on highways, such as works that interrupt routes, using AI and real-time data analysis. This allows the adoption of a better route in the present time, without relying on the historical past and wasting resources, also resulting in customer satisfaction and speed of delivery. Taking advantage of routes, inappropriate and unnecessary use of vehicles, higher gas emissions, and high fuel and maintenance costs are problems interrupted by logistics 4.0 directives, aiming at more assertiveness, intelligence, sustainability, greater revenue, and consumer and supplier satisfaction.

AI in Retail – Final Thoughts

Implementing AI and machine learning through intelligent models is not an easy and instantaneous task. However, the result of all the preparation and construction of these technologies directed to the specifics of the business will result in several benefits.

The power of AI provides intelligent market insight, demand forecasting with higher hit rates, reduced product loss due to expiration or warehouse saturation, and precision in price adjustments supported by different variables that can influence revenue variation. In addition, through advanced data analysis, it is possible to filter suppliers looking for those that deliver the most results and are more aligned with the company’s values.

These positive points are in line with the use of AI to obtain better sustainable results, aiming at the use of routes, continuous delivery, analysis of better routes, reduction in costs, and gas emissions.

The access generated by advanced analytics and AI of the entire supply chain and operations of the company results in great predictability of risks or failures in the initial stages, preparation, and delivery to the end customer. This power of predictability and intelligent strategies consolidates the idea of ​​risk management in a real-time, drastic reduction of failures and waste, and unified control of the stages of sales, operations, production, and delivery of goods. In short, smarter and more sustainable companies have never been so close to being consolidated. The way forward only depends on preparation and organization for greater intelligence and predictability.

What is Aquarela Advanced Analytics?

Aquarela Analytics is Brazilian pioneering company and reference in the application of Artificial Intelligence in industry and large companies. With the Vortx platform and DCIM methodology, it serves important global customers such as Embraer (aerospace & defence), Scania and Randon Group (automotive), Solar Br Coca-Cola (beverages), Hospital das Clínicas (healthcare), NTS-Brasil (oil & gas), Votorantim Energia (energy), among others.

Stay tuned following Aquarela’s Linkedin!

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