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!

Authors

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!

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!

5 real data challenges in the food sector

5 real data challenges in the food sector

The food sector and food security are a global concern and Brazil is one of the main countries responsible for the world demand for food (Estadão). In this sense, what are the main challenges related to data management to optimize Brazil’s operational efficiency in the food/agribusiness sector, which today represents 21% of Brazil’s GDP?

This article addresses the issue with the bias of Aquarela’s experience in Advanced Analytics and Artificial Intelligence projects carried out in large operations in Brazil. The risk of a lack of information is as relevant as its excess and lack of analysis, which can impact the efficiency of the sector’s logistics chain as a whole.

Below, we have elaborated on some of these main risks.

Characterization of the food sector

The food sector is quite varied due to the large extension of the production chain, which ranges from agricultural inputs, industrialization, transport logistics to commercialization in consumer markets and finally the final consumer.

As fundamental characteristics, the food sector is directly linked to factors that can have great variability and little control, such as: 

  • Climate (temperature, water volume, luminosity and others);
  • Economic factors such as currency fluctuations;
  • Infrastructure;
  • Domestic/external market demand.

In addition to these factors, below we list some related to data management. We also show how they, if well organized, can help mitigate the effects of uncontrollable variables in the food supply chain.

01 – Incompleteness of information

The supply chain is quite large. This makes the data complex and difficult to interpret due to the different phases of each process, culture and region. In addition, it causes many important planning decisions to take place with very limited information and high risk. In other words, decisions are made without a vision of the complete scenario of the chain, largely following the manager’s intuition.

The lack of quality information is a big risk. If data is lacking today, imagine what the scenario was like 10 or 20 years ago.

In recent years, the industry and retail have shown great advances in their computerization processes with various traceability solutions. With the evolution of Industry 4.0 technologies (IOT and 5G) in the coming years, it is likely that the food market, from the agricultural and industrial sector to the commercial sector, will hold more complete information for decision making than what is currently available today.

02 – Data from multiple sources

If data is becoming more and more present with the development of informatization and communication, then the next problem is trying to analyze data from multiple and disconnected sources.

Different data is often stored on different systems, thus leading to incomplete or inaccurate analyses. Combining data manually to form datasets (what are datasets?) for analysis is quite heavy and time-consuming work and can limit insights into the reality of operations.

What is sought is the construction of Data Lakes adherent to the type of management to democratize access to data by market professionals, thus optimizing their activities with increasingly powerful analytics solutions. This not only frees up time spent accessing multiple sources, it also allows for cross-comparisons and ensures that the data is complete.

03 – Low quality data

Having incorrect data can be just as or more harmful than not having it. Nothing is more harmful to data analysis than inaccurate data, especially if the idea is to use data science and machine learning practices. Without a good input, the output will be unreliable.

One of the main causes of inaccurate data is manual errors made during data entry, especially when information is collected manually. Another problem is asymmetric data: when information from one system does not reflect changes made to another system and thus makes it out of date.

Analytics strategic planning projects seek to mitigate and/or eliminate these problems. This happens from systematic processes of data dictionarization, survey of processes, functions, and so on.

04 – Lack of data talents

Some organizations and companies, in general, are not able to achieve better levels of efficiency in operations, as they suffer from a lack of talent in the area of ​​data analysis. In other words, even if the company has consistent technologies and data, the manpower to execute the analysis and action plans still counts a lot at the end of the day.

This challenge can be mitigated in three ways:

  • Develop an analytical technology stack that is always up-to-date and adherent to the business and with up-to-date training materials.
  • Add analytical skills to the hiring process. In addition, invest in the constant training of the team on new data technologies related to the technological stack of the operation.
  • Use analytics outsourcing to accelerate the process. In this article, for example, we list the main aspects to be considered when choosing a good supplier.

05 – Customization of values ​​and product characteristics in the food sector

Although, according to Embrapa, about 75% of the entire world food sector is based on just 12 types of plants and 5 types of animals, there are thousands of different products, marketed in multiple ways, prices and deadlines in the final consumer market.

Just as an example, in the area of ​​animal protein, the process of marketing cattle meat requires investments, infrastructure, deadlines and processes that are quite different from what would be for the production of pork or even chicken.

Since the processes are different, the data generated by the production chain also becomes different, requiring customizations in information systems and databases. As a consequence, there are changes in models of:

The recommendation is to parameterize the systems based on the most common classifications in the market and focus on the most important products from a strategic point of view (contribution margin, volume or sales price).

5 real data challenges in the food sector – Final thoughts

In this article, we have collected some relevant points about the real challenges of data in the area of ​​food, a sector in which  Brazil stands out as one of the main global players.

It is a complex area with several risk factors and great opportunities for optimization with the increasingly intensive use of data. Previously, we wrote an article related to data strategies for energy trading and which in part has the same challenges related to decision making in the food sector.

We, at Aquarela Analytics, constantly work with these challenges of making the complex  things simple and with good risk mitigation. So if you have any questions, get in touch with us!

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!

Author

AI and Analytics strategic planning: concepts and impacts

AI and Analytics strategic planning: concepts and impacts

The benefits and positive impacts of the use of data and, above all, artificial intelligence are already a reality in the Brazilian Industry. These benefits are most evident in areas ranging from dynamic pricing in education, forecasting missed medical appointments, predicting equipment breakdowns, and even monitoring the auto parts replacement market. However, to achieve these benefits, organizations need to reach a level of analytical maturity that is adequate for every challenge they face.

In this article, we are going to discuss the concepts of AI and Analytics Strategic Planning and also look at which characteristics of the scenarios demand this type of project within the Digital Transformation journey of companies towards Industry 4.0.

What is AI and Analytics strategic planning?

The AI ​​and Data Analytics Strategic Planning is a structuring project that combines a set of elaborate consultative activities (preferably by teams with an external view of the organization) for the survey of scenarios, mapping of analytical processes, elaboration of digital assets (systems, databases, and others) to assess the different levels of analytical maturity of teams, departments and the organization as a whole.

As a result, shared definitions of the vision, mission, values, policies, strategies, action plans, and good data governance practices are accomplished to leverage the organization’s analytical maturity level in the least possible time and cost.

Symptoms of low analytic maturity scenarios

Although there are many types of businesses, products, and services on the market, here we present emerging patterns that help to characterize the problem of companies analytical maturity and can generate interesting reflections:

  1. Is it currently possible to know which analytics initiatives (data analytics) have already taken place and are taking place? Who is responsible? And what were the results?
  2. In analytics initiatives, is it possible to know what data was used and even reproduce the same analysis?
  3. Does data analysis happen randomly, spontaneously, and isolated in departments?
  4. Is it possible to view all data assets or datasets available to generate analytics?
  5. Are there situations in which the same indicator appears with different values ​​depending on the department in which the analysis is carried out?
  6. Are there defined analytic data dictionaries?
  7. What is the analytical technology stack?
  8. Are data analytics structuring projects being considered in strategic planning?

Other common problems

Organizational identity

Scenarios with low analytic maturity do not have data quality problems in isolation. There are usually systemic problems that involve the complexity of business processes, the level of training of teams, knowledge management processes, and finally, the choice of technologies for operating ERP, CRM, SCM and how these transactional systems are related.

Security Issues

Companies are living organisms that constantly evolve with people working in different areas. Thus, over time, control of the access levels of each employee is lost, causing unauthorized people to have access to sensitive information and also the opposite when people cannot access the data they need for their work.

Excessive use of spreadsheets and duplicates

Spreadsheets are one of the most useful and important management tools and for that reason, they are always helping in various processes. The big side effect of excessive use of spreadsheets is the maintenance of knowledge of each process. When there are two or more people and the volume of information and updates starts to grow, it becomes difficult to manage the knowledge that travels in blocks with spreadsheets. Additionally, many duplications occur and make it virtually impossible to securely consolidate data in large volumes.

What are the benefits of AI and Analytics strategic planning?

Data-driven management is expected to provide not just drawings and sketches of operations or market conditions, but a high-resolution photograph of present and future reality. Thus, it provides subsidies for corporate strategic planning in the short, medium, and long term with the following gains:

  • Procedural and technological readiness for data lakes projects and Advanced Analytics and AI labs.
  • Increased intensity of application of scientific techniques to businesses, such as comparative analysis, scenario simulations, identification of behavior patterns, demand forecasting, and others.
  • Increased accuracy of information.
  • Security of access to information at different levels.
  • Acceleration of the onboarding processes (entry of new team members) who in turn learn more quickly the work scenario and also begin to communicate more efficiently.
  • Greater data enrichment from increased interaction of teams from different sectors for analytical challenges.
  • Increased visibility into analytics operations, Organization for localizability, accessibility, interoperability, and reuse of digital assets.
  • Optimized plan of change for data-driven Corporate Governance.
  • Incorporation of Analytical and AI mindset in different sectors.
  • Homogenization of data policies and controls.

AI and Analytics strategic planning – Conclusions and recommendations 

The preparation of strategic AI and Analytics planning is an important step to reach the level of data governance that allows the intensive use of analytics and artificial intelligence in operations since the high failure rate of analytical projects is linked to low quality of data, processes, and even the correct use of technologies (training).

Structuring projects, such as AI strategic planning and Analytics are, or at least should be, the first step in the journey of digital transformation of traditional companies. Therefore, we are convinced that in the future every successful company will have a clear and shared idea (vision, mission, and values) of what data means to them and their business model, in contrast to investments in data technology purely and simply because of the competition.

We believe that the focus on orchestrated (tidy and synchronized) data will be reflected in almost every area, for example: in the range of services, in revenue models, in key resources, processes, cost structures, in your corporate culture, in your focus on clients and networks, and in its corporate strategy.

Last but not least, it is worth pointing out that, for a successful structuring to happen, a long-term holistic approach must be taken. This means investments in optimized technology, people, and processes to enable continued business growth.

How Aquarela has been acting

Developing new technologies and new data-driven business models in a vision that the amount and availability of more data will continue to grow, taking the business to new heights of optimization.

What we do specifically for companies:

  • We analyze data-generating enterprise ecosystems.
  • We determine analytic maturity and derive action fields for data-driven organizations and services.
  • We develop and evaluate data-based services.
  • We identify and estimate the data’s potential for future business models.
  • We design science-based digital transformation processes and guide their organizational integration.

For more information – Click here.

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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!

Authors

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