Dynamic Pricing: definition and benefits

Dynamic Pricing: definition and benefits

Dynamic pricing, also called smart pricing, price optimization, pricing customization, pricing recommendation, or strategic pricing, is a relevant point in strategic planning of companies which has been increasingly calling for special attention from CEOs, CIOs, sales management and revenue management.

According to Forbes, the main companies in the digital world who seek to stand out in a competitive market use dynamic or smart pricing. This kind of strategy can be applied in different sectors, helping companies achieve the quality and pragmatic marketing necessary for success.

In this article, we are going to show some benefits that dynamic pricing brings.

What is dynamic pricing ?

Dynamic pricing is a strategy used to establish flexible market prices for products and services offered by companies, taking into account some variables such as balance between supply and demand, seasonality and competitive strategy. This strategy allows companies to adapt to the market quickly and competitively.

Benefits

For a better understanding of the advantages of investing in dynamic pricing we show you some benefits that this strategy can provide for your business.

Profit Maximization

Using dynamic pricing, it is possible to track the value of one specific product more quickly, and simultaneously track competitor price fluctuations. This offers a vantage point that allows you to follow trends in order to increase your profit margin, without fearing the loss of market space. When a market trend results in the lowering of prices, it’s possible to prepare to increase the number of sale conversions then profit by volume as result.

Understanding the consumer behavior 

Another dynamic pricing benefit is the fact that it assists in understanding and addressing key consumer behavior points, for example, the types of promotions that work best and the best-selling products in a given period, among other things.

Benchmarking Automatization

In addition to allowing more competitive prices compared to the competition, dynamic pricing is a way to quickly keep up with the competition, allowing the company to prepare against threats or seize opportunities. It can also help in observing competitors to create attractive differentiators for your company.

Dynamic Pricing Implementation 

Based on sales indicators and pricing data, it is possible to create a tool to review prices at the desired frequency, based on the company’s rules. The recommendation is that the data be structured through a Data Lake. 

The usage of Advanced Analytics techniques and Artificial Intelligence (AI) algorithms foster improvements in pricing systems. By analyzing past data,  it’s possible to look for successful pricing patterns,  or to produce complex models for AIs. This allows for the development of tools driven by strategic goals ​​that can define prices based on present or future factors. In other words, AI can help organizations reach the point where they’d like to be.

Want to know more about how to implement dynamic pricing in your company? Click here.

Conclusions and recommendations

Dynamic pricing is a huge opportunity for your company’s growth, when you take advantage of all that it offers, it allows for superior planning while avoiding loss in conversions.

The decision to make prices dynamic is not simple, so it is important to choose a suitable data analytics supplier. Some aspects to be analyzed when looking for a supplier for the pricing solution can be found in this article: How to choose the best AI and Data Analytics provider?

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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), Randon Group (automotive), Solar Br Coca-Cola (food), Hospital das Clínicas (health), NTS- Brazil (oil and gas), Votorantim (energy), among others.

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Demand Forecasting: all you need to know about this subject

Demand Forecasting: all you need to know about this subject

In business or in our personal lives, we are used to making predictions and estimates about future events. For example, the estimated time taken to arrive at a friend’s house or the amount of food needed for a dinner for several people. Decisions made based on  a forecast can make us arrive at the right time for a meeting or buy the right amount of food or not. With that in mind, we at Aquarela have prepared this article on demand forecasting, covering its concepts, importance for business and evolution over the years.

What is Demand Forecasting?

Demand forecasting, as the name already tells, is the process for obtaining estimates of a future demand of products and services. This is done with existing data that’s been collected and stored. Forecasts can be generated through mathematical models, which use historical data; by qualitative methods, such as management experience or expert opinion; or even a combination of both.Some of the questions we seek to answer when making such predictions are “when”, “where”, and “how much”. It is important to note that predictions are not targets, but rather estimates of what will happen. Forecasting results aid businesses in planning their decision making. Therefore, a good demand forecasting process needs to produce good estimates.

How important is Demand Forecasting?

Demand Forecasting is an important activity that influences companies from different segments, such as: retail, consumer goods, pharmaceutical industry, automotive electronics, heavy machinery among others.

Demand forecasting is used for business planning, as every plan involves estimates about this type of forecast. Thus, the predictions are of importance, as they enable managers to plan more assertive activities toward the strategic goals of the business. It is also useful for the tactical and strategic process of companies. Managers and decision makers utilize demand forecasts on their daily activities. These predictions can also be used as inputs for the sales and marketing teams to create insights into demand generation and organize their actions.

Businesses, in general, need to make forecasts about their products to prepare an effective plan in both short and long term. With that in mind, demand forecasts are crucial for companies, as they affect inventory planning, logistical planning, production planning, cash flow planning, hiring decisions, purchasing decisions, among others. Poor or absent forecasting can lead to bad decisions. So, without good demand forecasting, businesses  would be poorly preparing themselves for the future events.

Benefits

Better forecasts can result in better service levels, customer retention, cost savings, waste reduction (excess inventory and unsold products). In addition, the need for emergency production to meet unexpected demand is relieved, because, with predictions, companies can plan and make decisions that allow a better response. 

Not being prepared for the demand can cause incalculable losses, such as the reduction in market share. For example, when a customer needs a product, but can not find the desired brand, he can often find a replacement with a competing brand. Due to this, companies can no longer be reactive to meet demands for their products. Furthermore, the exponential growth of supply chains belonging to certain companies was only possible due to demand forecasting improvements. They invest so that they can plan better, because they know that it generates a financial return. It is important to know what is happening at points of sales to obtain a more assertive demand forecast.

How are forecasts horizons classified?

The demand forecasts can be sorted by what are called “future horizon” in the time it applies. The three classification categories in relation to the time horizon are:

  • Short-Range Forecast: usually less than three months, but can be up to one year. Used for purchase plans, production scheduling, predictions of labor and production levels.
  • Medium-Range Forecast: Usually three months to one year, however can be from one year to three years. Useful for sales planning, operation plan analysis, production planning and budget planning.
  • Long-Range Forecast: Three years or more. This kind of forecasting can be applied to planning new products, business expansion, as well as research and development.

Short-range forecasts tend to be more assertive than long-range forecasts.

What are the main categories of demand forecasting methods?

The methods of demand forecasting fall into three basic categories, which are: 

  1. Qualitatives: methods that work based on opinions data of managers, experts, sales staff, and customers questionnaires to estimate the quantitative value of demand.
  2. Time series: statistical approach that relies on historical data to forecast future demand. Through these methods, there is a recognition of seasonal trends and patterns.
  3. Causal models: quantitative method of demand forecasting that uses historical data together with independent variables, such as economic conditions, competitors’ actions and promotional campaigns.

Regardless of the category of demand, a prerequisite is that there is a pattern or relationship that can be identified and modelled.

Regardless of the category of demand, a prerequisite is that there is a pattern or relationship that can be identified and modelled.

Evolution of demand forecast throughout history

Organizations began creating demand forecasting departments and functions in the late 1980s. However, this subject was already explored in previous decades in academia and by segments of products that need different parts to be assembled. The beginning of demand forecasting in companies, for the most cases, consisted of simple statistical models , such as moving averages, simple exponential smoothing and judgment by instinct (“gut feelings”).

Technological advances in data storage and processing (Big Data) have positioned demand forecasting as one of the main actors of value generation for Supply Chain. As a result of these advances, demand forecasting is getting better and better.

Conclusions and Recommendations 

Forecasting future demands is a challenge that companies have to face in order to be able to make decisions that allow them to compete by generating better supply chain results. Demand forecasting is an essential activity for business planning, as it results in several benefits, such as: reduced waste, better allocation of resources, increased sales and revenue. This way, it helps organizations to be in the right place, at the right time, with the right product.

Also read: AI for demand forecasting in the food industry

Keep improving your companies’ demand forecasting process and technologies to keep pace with this ever-changing world. Aquarela Tactics has a demand forecasting module that can help you with this journey to know in advance what to expect from the consumer market and optimize your decision making.

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), Randon Group (automotive), Solar Br Coca-Cola (food), Hospital das Clínicas (health), NTS- Brazil (oil and gas), Votorantim (energy), among others.

Stay tuned following Aquarela’s Linkedin!

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AI for demand forecasting in the food industry

AI for demand forecasting in the food industry

The concept of a balance point between supply and demand is used to explain various situations in our daily lives, from bread in the neighborhood bakery, which can be sold at the equilibrium price, which equals the quantities desired by buyers and sellers, to the negotiation of securities of companies in the stock market.

On the supply side, a definition of the correct price to be practiced and mainly the quantity are common issues in the planning and execution of the strategy of several companies.

In this context, how are technological innovations in the data area establishing themselves in the food sector?

The construction of the demand forecast

The projection of demand is often built through historical sales data, growth prospects for the sector or even targets set to engage sales of a certain product.

When considering only these means of forecasting, without considering the specific growth of each SKU (Stock Keeping Unit), companies can fall into the traps of subjectivity or generalism.

The expansion of a sector does not result in a growth of the same magnitude for the entire product mix. For example, does a projected annual growth of 6% for the food sector necessarily imply equivalent growth for the noble meat segment?

Possibly not, as this market niche may be more resilient or sensitive than the food sector, or it may even suffer from recent changes in consumer habits.

Impacts of Demand Forecasting Errors

For companies, mainly large ones with economies of scale and geographic capillarity, an error in the forecast of demand can cause several consequences, such as:

  • Stock break;
  • Perishable waste (What is FIFO?);
  • Drop in production;
  • Idle stock (slow moving)
  • Pricing errors

Adversities like these directly impact the companies’ final results, as they result in loss of market share, increase in costs or low optimization in the dilution of fixed costs, growth in the loss of perishable products, frustration of employees in relation to the goals and mainly break in the confidence of recurring customers who depend on supply for their operations.

The demand forecast in the food sector

The food industry is situated in a context of highly perishable products with the following characteristics:

  • High inventory turnover;
  • Parallel supply in different locations;
  • Large number of Skus, points of production and points of sale;
  • Verticalized supply chain;
  • Non-linearity in data patterns;
  • Seasonality.

These characteristics make the sector a business niche that is more sensitive to deviations in demand forecast and adjacent planning.

Supply chain opportunity

As an alternative to the traditional demand forecast format, there are opportunities to use market and AI data to assist managers in the S&OP (Sales & Operations Planning) process, as well as in the S&OE (Sales and Operations Execution) process.

During the S&OP process, demand forecasting supported by AI facilitates the work of the marketing and sales areas, as well as reducing uncertainty and increasing predictability for the supply chain areas.

In the S&OE process, AI can be used to identify new opportunities and to correct deviations from what was planned.

In addition to the technical attributes that AI can add to the process, the data base reduces points of conflict between teams, reduces historical disputes between preferences for SKUs and makes the process more transparent between areas.

Previously, in our blog, we addressed the challenges of forecasting demand in our view (pt. 1 in portuguese). In the articles, we cite the differentials of the predictive approach in relation to demand, taking into account factors such as seasonality, geographic / regional preferences and changes in consumer behavior.

We understand that the need for a predictive approach through data, mainly external to the company, is increasingly latent.

The role of machine learning in the food sector

The use of AI through machine learning techniques associated with a coherent technological stack of analytics (What is a technological stack?) Provides greater information speed, data organization with different granularities (region, state, city and neighborhood), adjustments seasonality, exploration of opportunities and decision making in real time.

In the case of the food sector, the greatest accuracy in forecasting demand means:

  • Inventory optimization among Distribution Centers (CDs);
  • Reduction of idle stocks;
  • Decrease in disruptions that cause loss of market share due to substitute products;
  • Direct reduction in losses with perishability (FIFO).

The great technical and conceptual challenge faced by data scientists (The profile of data scientists in the view of Aquarela), however, is the modeling of analysis datasets (what are datasets?) That will serve for the proper training of machines.

Please note that:

“Performing machine training with data from the past alone will cause the machines to replicate the same mistakes and successes of the past, especially in terms of pricing, so the goal should be to create hybrid models that help AI replicate with more intensity and emphasis the desired behaviors of the management strategy “.

In the case of Aquarela Analytics, the demand forecast module of Aquarela Tactics makes it possible to obtain forecasts integrated into corporate systems and management strategies. It was created based on real national-wide retail data and algorithms designed to meet specific demands in the areas of marketing, sales, supply chain, operations and planning (S&OP and S&OE).

Conclusions and recommendations

In this article, we present some key characteristics of the operation of demand forecasting in the food sector. We also comment, based on our experiences, on the role of structuring analytics and AI in forecasting demand. Both are prominent and challenging themes for managers, mathematicians and data scientists.

Technological innovations in forecasting, especially with the use of Artificial Intelligence algorithms, are increasingly present in the operation of companies and their benefits are increasingly evident in industry publications.

In addition to avoiding negative points of underestimating demand, the predictive approach, when done well, makes it possible to gain market share in current products and a great competitive advantage in forecasting opportunities in other niches before competitors.

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), Randon Group (automotive), Solar Br Coca-Cola (food), Hospital das Clínicas (health), NTS- Brazil (oil and gas), Votorantim (energy), among others.

Stay tuned following Aquarela’s Linkedin!

14 sectors for applying Big Data and their input datasets

14 sectors for applying Big Data and their input datasets

Hello folks, 

In the vast majority of talks with clients and prospects about Big Data, we soon realized an astonishing gap between the business itself and the expectations of Data Analytics projects. Therefore, we carried out a research to respond the following questions: 

  • What are the main business sectors that already use Big Data?
  • What are the most common Big Data results per sector?
  • What is the minimum dataset to reach the results per sector

The summary is organized in the table below.

,Business type / sector,Raw data examples,Business Opportunities,, ,"1 - Bank, Credit and Insurance ","Transaction history. Registration forms. External references such as the Credit Protection Service. Micro and macro economic indices. Geographic and demographic data.","Credit approval. Interest rates changes. Market analysis. Prediction of default . Fraud detection. Identifying new niches. Credit risk analysis.",, ,2 - Security,"Access history. Registration form. Texts of news and WEB content.",Pattern detection of physical or digital behaviours that offer any type of risk.,, ,3 - Health,"Medical records. Geographic and demographic data. Sequencing genomes.","Predictive diagnosis (forecast). Analysis of genetic data. Detection of diseases and treatments. Map of health based on historical data. Adverse effects of medications / treatments.",, ,"4 - Oil, gas and electricity",Distributed sensor data.,"Optimization of production resources. Prediction / fault and found detection.",, ,5 - Retail,"Transaction history. Registration form. Purchase path in physical and/or virtual stores. Geographic and demographic data. Advertising data. Customer complaints.","Increasing sales by product mix optimization based on behaviour patterns during purchase. Billing analysis (as-is, trends), the high volume of customers and transactions, credit profile by regions. Increasing satisfaction / loyalty.",, ,6 - Production,"Data management system / ERP production. Market Data.","Optimization of production over sales. Decreased time / amount of storage. Quality control.",, ,7 - Representative organizations,"Customer's registration form. Event data. Business process management and CRM systems.","Suggestion of optimal combinations of company profiles, customers, business leverage to suppliers. Synergy opportunities identification.",, ,8 - Marketing,"Micro and macroeconomic indices. Market research. Geographic and demographic data. Content generated by users. Data from competitors. ","Market segmentation. Optimizing the allocation of advertising resources. Finding niche markets. Performance brand / product. Identifying trends.",, ,9 - Education,"Transcripts and frequencies. Geographic and demographic data. ","Personalization of education. Predictive analytics for school evasion.",, ,10 - Financial / Economic,"List of assets and their values. Transaction history. Micro and macroeconomics indexes.","Identify the optimal value of buying complex assets with many analysis variables (vehicles, real estate, stocks, etc.). Determining trends in asset values. Discovery of opportunities.",, ,11 - Logistic,"Data products. Routes and delivery points.","Optimization of goods flows. Inventory optimization.",, ,12 - E-commerce,"Customer registration. Transaction history. Users' generated content.","Increased sales through automatic product recommendations. Increased satisfaction / loyalty.",, ,"13 - Games, social networks and platforms (freemium)","Access history. Registration of users. Geographic and demographic data.",Increase free users conversion rate for paying users by detecting the behaviour and preferences of users. ,, ,14 - Recruitment,"Registration of prospects employees. Professional history, CV. Conections on social networks.","The person's profile evaluation for a specific job role. Criteria for hiring, promotions and dismissal. Better allocation of human resources.",,

Conclusions

  • The table presents a summary for easy understanding of the subject. However, for each business there are many more variables, opportunities and of course, risks. It is highly recommended to use multivariate analysis algorithms to help you prioritize the data and reduce project’s cost and complexity.
  • There are many more sectors in which excellent results have been derived from Big Data and data science methodology initiatives. However we believe that these can serve as examples for the many other types of similar businesses willing to use Big Data.
  • Common to all sectors, Big Data projects need to have relevant and clear input data; therefore it is important to have a good understanding of these datasets and the business model itself. We’ve noticed that currently many businesses haven’t been yet collecting the right data in their systems, which suggests the need pre-Big Data projects. (We will write about this soon). 
  • One obstacle for Big Data projects is the great effort to collect, organize, and clean the input data. This can surely cause overall frustration on stakeholders.
  • At least as far as we are concerned, plug & play Big Data solutions that automatically get the data and bring the analysis immediately still don’t exist. In 100% of the cases, all team members (technical and business) need to cooperate, creating hypothesis, selecting data samples, calibrating parameters, validating results and then drawing conclusions. In this way, an advanced scientific based methodology must be used to take into account business as well as technical aspects of the problem.

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), Randon Group (automotive), Solar Br Coca-Cola (food), Hospital das Clínicas (health), NTS- Brazil (oil and gas), Votorantim (energy), among others.

Stay tuned following Aquarela’s Linkedin!

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