--- title: Cognitive Model | pricing description: pricing | Machine learning - big data - artificial intelligence - data mining - analytics --- Posts about # pricing ## [Analytical Tools for Price Management: Pros and Cons](https://blog.sinnetic.com/en-us/cognitive_services/pricing_analytics) Posted by [Gabriel Contreras](https://blog.sinnetic.com/en-us/cognitive_services/author/gabriel-contreras) | Jun 1, 2021 5:54:16 PM We are currently experiencing moments in which brands are modifying their [marketing mix](https://www.sinnetic.com/visual-data-discovery?hsLang=en-us) to adapt to... [ CONTINUE READING ](https://blog.sinnetic.com/en-us/cognitive_services/pricing_analytics) ## [Analytics vs Research. How to drive towards Pricing Strategy?](https://blog.sinnetic.com/en-us/cognitive_services/analytics-o-research-for-my-pricing-strategy) Posted by [Gabriel Contreras](https://blog.sinnetic.com/en-us/cognitive_services/author/gabriel-contreras) | Jun 1, 2021 5:32:57 PM The boom in [analytics](https://blog.sinnetic.com/en-us/cognitive_services) leads us to think that exploring historical data can be a viable roadmap to... [ CONTINUE READING ](https://blog.sinnetic.com/en-us/cognitive_services/analytics-o-research-for-my-pricing-strategy) ### Grow your business more quickly with our solutions for inbound and ecommerce Discover a new digital growth model that attracts visitors, converts them into leads, transforms them into customers and then follows them in the after-sales phase, managed with traditional trade methods or with ecommerce. TAKE AN APPOINTMENT [![WhatsApp](https://upload.wikimedia.org/wikipedia/commons/6/6b/WhatsApp.svg) ](https://wa.me/573180242831) ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "articleBody" : "We are currently experiencing moments in which brands are modifying their marketing mix to adapt to modern market challenges. An important part of this restructuring is pricing. When analyzing prices from a market research perspective, we must check the consumer's reaction to price proposals; in this context we must define two major concepts: Optical price: The price that generates an attitudinal reaction from the consumer, e.g. prices that are expensive, luxurious, cheap, fair. Optimal price: The price that allows the product to increase market share, increase market size and differentiate itself from the competition. Depending on the type of study and the objectives, one or the other strategy will be better suited. Let us first define the scope of these two approaches to price analysis: Optical price Optimal price This is a psychological price profile rather than a price-based optimization of demand. It does not allow predicting the volume or market share that a specific pricing strategy would produce. It is a sound strategy when dealing with novel concepts or ideas that have not yet matured. Not efficient when making financial decisions regarding investment. It does not allow us to estimate the source of volume. We do not know which competitor we are taking market share from with a specific pricing strategy. It does not allow simulating market share or market size across different price settings. This is a competitive profile of the product's ability to challenge its competitors across different price scenarios in a variety of sales channels. It is a forecast of the ability of the product and its attributes to seize volume and market share. This is an efficient strategy for launching products, as well as for concepts or products already consolidated in the market. It is efficient for making financial decisions regarding investment because it involves demand forecasting. Allows us to break down the demand to ascertain whether it comes from customers already loyal to us or from customers from our competitors. Allows understanding the trade-offs that the consumer might make in order to buy a product at a given price. OPTICAL PRICE Next, we will show some efficient techniques for analyzing optimal price and how our methodologies integrate them: Methodology Tactical purpose Technique we use AdPpt: Concept Evaluation and pretest Concept evaluation Analysis of the psychological price profile of an idea that has not yet come to market. PVP: Perceived Value Pricing Sensory evaluation, product test and preference mapping How much is the customer willing to pay for a specific sensory experience? How much can we charge for a taste, smell or texture? BPTO: Brand Price Trade Off Brand health and equity If my brand is valuable, what are the price perception and the value perception for the consumer? The brand is analyzed here as just one element of the marketing mix. PSM Plus: Price Sensitivity Meter Plus Customer experience and satisfaction What role does price play in our customers' satisfaction and experience? Would customer satisfaction change if we raised the price? How would that be affected? Gabor Granger OPTIMAL PRICE Below, we show how our portfolio of market research and analytics methodologies addresses price optimization: Methodology Tactical purpose Technique we use Tap into internal information from CRM and transactional systems to see how customers tighten or push forward in the face of historical changes in pricing, distribution, promotion and investment. Analyzing historical volatility of price elasticity. Market data integration for competitor rivalry analysis in competitive settings. Marketing Mix Model Cross Media Research Price Package To optimize product line Price analysis at SKU level by channel. Analysis of demand advocating factors such as product attributes and distribution. Estimation of price elasticities, cross-elasticities. Volumetric and market share analysis Conjoint Full Profile Conjoint Analysis. Like HILCA: Hierarchical Individualized Limit Conjoint For concepts or launch strategies Analyze how a particular launch affects the demand for other products in the portfolio Volumetric estimates in the first year of launching. Conjoint CBC: Choice Base Conjoint. DCM: Discrete Choice Model For highly customized products or services Pricing for products or services to which extra functionalities, components or services may be added. Conjoint MBA: Menu Based Conjoint. For analysis of complex categories, or competitiveness between categories. For analysis of complex categories, or competitiveness between categories. How to turn carbonated soft drink consumers into beer consumers? How to win from other markets? Conjoint ACBC: Adaptative Choice Based Conjoint ACA: Adaptative Conjoint Analysis. In separate blog posts in the near future, we will be elaborating on the scope of each methodology and technique. In the meantime, we would like to get in touch with you to discuss your pricing challenges and needs.", "author" : { "@type" : "Person", "name" : "Gabriel Contreras", "sameAs" : "", "url" : "https://blog.sinnetic.com/en-us/cognitive_services/author/gabriel-contreras" }, "dateModified" : "01/06/2021", "datePublished" : "01/06/2021", "headline" : "Analytical Tools for Price Management: Pros and Cons", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://f.hubspotusercontent00.net/hubfs/8862358/web/BLOG/sinnetic_pricing_blog.jpg", "width" : 750 }, "mainEntityOfPage" : "https://blog.sinnetic.com/en-us/cognitive_services/pricing_analytics", "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://f.hubspotusercontent00.net/hubfs/8862358/web/logo-sinnetic--slogan-web.png" }, "name" : "SINNETIC LATAM", "url" : "blog.sinnetic.com" } } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "articleBody" : "The boom in analytics leads us to think that exploring historical data can be a viable roadmap to structuring an efficient pricing strategy. On the other hand, market research suggests that, given the changes taking place, it is necessary to study the pricing strategy from the consumer's standpoint, since in historical data we do not have the same competitors, SKUs or settings in the present as in the future. Which path to choose? In this blog, we want to share some experiences, pros and cons of both approaches and to do so the table below may help: Analytics Research What’s the approach? Exploit historical data to define future pricing strategies Study the consumer's response to different price scenarios, understanding the trade-offs the consumer must make to purchase a product at a given price. Conjoint methods play a key role in price optimization Pros You monetize the historical investment of data purchase, such as Nielsen, Kantar, etc. You take advantage of transactional data of your relationships with channels and retailers This represents a clear overview of price and volume of one's portfolio The business is in control of the analytical processes that revolve around pricing Simulate prices without the need to carry out studies each time a re-analysis is required Allows for testing pricing strategies in current contexts Helps analyze the adoption of pricing strategies for products that do not yet exist in the market Use of statistical and econometric models that are highly accepted and understood by financial and product management Simulation based on empirical methods, centered on the data collected in the study Enables analysis of the trade-offs that consumers are willing to make in order to purchase a specific product at a specific price Contra To gain insight into the competitor, you will need to purchase exogenous data If you only have information from your own portfolio, you will only be able to estimate point-slope elasticities but not migrations Use of machine learning methods, whose results are difficult to understand by the end business user Monte Carlo simulation, based on data created based on variables that explain the demand Trained teams are required to prevent errors, false positives or erratic predictions Does not allow analysis of the consumer's trade-off Typically, the process of design, data collection and analysis can take a minimum of 5 weeks The number of SKUs, product attributes, etc., are often limited The main challenge is to simulate purchase situations in the same way as the human brain processes information in payment scenarios. These situations are difficult to achieve in an experimental setting Representative samples are required to achieve market coverage What method to implement? Data integration: Creation of data models to integrate information that revolves around the business Automation of data ingestion and ETL processes to build efficient data models Data quality techniques Analytics: Supervised analysis: Methods for predicting sales volume in different price scenarios Unsupervised analysis: Segmentation of SKUs and channels for higher accuracy Visualization: Dashboards showing the analytical process Automate the process so that dashboards always show the analysis in a timely manner Optical price study PSM Price Sensitivity Meter Garbor Granger BPTO Brand Price Trade Off PVP: Perceived Value Price Optimal price study Full Profile Conjoint (FPC) Choice Based Conjoint (CBC) Descrete Choice Model (DCM) Adaptative Conjoint (ACA) Adaptative Choice Based (ACBC) HILCA Menu Based Conjoint (MBC) Conjoint + Analytics = Pricing It is essential to recognize the virtues of each approach: Conjoint: It allows us to analyze the consumer's response to the pricing strategy and to explore the consumer's trade off. This makes it easier to analyze the impact of our movements on the competition and vice versa. It also allows us to issue short-term data that can be used to simulate the strategy in the future. Analytics: It allows us to analyze the volatility of price indicators, including the point-slope elasticity. With this historical volatility, the conjoint data can be fed into the future, providing a 360° view of the price. Our consultants will be ready to listen to your needs. Let's make an appointment to explore together the benefits of this methodological approach for your research goals. Find below the location that is most convenient for you: South America Mexico and Central America", "author" : { "@type" : "Person", "name" : "Gabriel Contreras", "sameAs" : "", "url" : "https://blog.sinnetic.com/en-us/cognitive_services/author/gabriel-contreras" }, "dateModified" : "01/06/2021", "datePublished" : "01/06/2021", "headline" : "Analytics vs Research. How to drive towards Pricing Strategy?", "image" : { "@type" : "ImageObject", "height" : 400, "url" : "https://fs.hubspotusercontent00.net/hubfs/8862358/analytics%20vs%20research%20ingles%20715x374.jpg", "width" : 750 }, "mainEntityOfPage" : "https://blog.sinnetic.com/en-us/cognitive_services/analytics-o-research-for-my-pricing-strategy", "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://f.hubspotusercontent00.net/hubfs/8862358/web/logo-sinnetic--slogan-web.png" }, "name" : "SINNETIC LATAM", "url" : "blog.sinnetic.com" } } ```