From Planning to Launch: How to Implement Dynamic Pricing Strategy?

Dynamic pricing is a flexible and effective pricing strategy that adjusts prices in real time based on market demand and supply. Implementing a successful dynamic pricing strategy can be a complex process, but with the right guidance, it can provide significant benefits for your business.

In this article, we will guide you through the steps involved in implementing a dynamic pricing strategy, from planning to launch. We will cover everything from assessing your business’s readiness and defining your pricing goals, building a robust pricing model, setting up the necessary infrastructure, launching the dynamic pricing solution within your business, and measuring its effectiveness.

TL;DR

• Dynamic pricing is a flexible and effective pricing strategy that adjusts prices in real time based on market demand and supply.

• Implementing a dynamic pricing strategy is not an instant move. It is a long process that covers several crucial steps, from planning to launch and ongoing monitoring of a dynamic pricing model.

• Thorough planning and preparations are key, and they include assessing your business’s readiness, understanding your target market, and defining your commercial objective.

• Seeing production results and the true benefits of dynamic pricing models will be postponed as the process of data collection, model development, and solution integration is time-consuming.

• Dynamic pricing models need ongoing monitoring, optimization, and maintenance for continuous improvement.

• With the right guidance, businesses can implement a successful dynamic pricing system, shorten the path to launch and reap the benefits of this powerful pricing method.

• AI experts at nexocode can support you with custom dynamic pricing model implementation, ensuring your strategy is tailored to your specific needs and goals.

Dynamic Pricing Strategies - A Quick Recap

When implementing dynamic pricing into your business model, you can choose from a broad range of strategies. Each of them puts emphasis on different variables, from customer behavior to actual demand. Among the most popular dynamic pricing methods, we can mention:

• value-based pricing - in this case, your algorithm estimates the value your product or service provides to a particular customer and calculates the best price on that basis. Value-based pricing is a perfect solution for brands with a very specific niche or companies who want to take advantage of the demand peak.

• cost-plus pricing - this simple strategy calculates the price by adding a fixed percentage or amount to the direct costs of the product or service. If you have limited customer data processing capabilities, it might be a good idea to implement this strategy.

• time-based pricing - here, it’s all about taking advantage of time opportunities. The algorithm finds the price the customer is likely to pay at the moment, taking into account real-time variables like demand and accessibility. If these variables strongly fluctuate in your niche, time-based pricing is for you.

• competitor-based pricing - in the case of this strategy, the rule is simple - the algorithm takes the prices of competitors and either calculates an average, establishes a lower price than your top competitors, or equalizes it to theirs.

• inventory-based pricing - in the case of this strategy, dynamic pricing aims at stimulating sales in a way that will harmonize your stock levels, selling out the excess products.

  

If you want to know more about these strategies and read about their use cases, we have described them to detail in our previous article on dynamic pricing strategy. Picking your strategy is one of the first steps toward the implementation of dynamic pricing, but there are many others that await you. We will lead you through all the stages of this process, providing precious tips you can use on your own or in cooperation with an external partner.

Step 0: Planning the Implementation of Dynamic Pricing

Planning is the key, regardless of the project. In the case of dynamic pricing, at the initial stage, you will need to put a lot of energy into research and a thorough definition of your goals. You can do it internally, but an external company with expertise in dynamic pricing can help you make the most out of it. As a third party, they have a fresh, independent perspective and know how to organize the process in the most effective manner. Here’s what the planning phase usually looks like.

Assessing Your Business’s Readiness for Dynamic Pricing

In order to implement dynamic pricing, you need to secure access to particular data and be capable of processing it. Which data - that depends on the strategy you pick. However, in most cases, you will have to include internal and external data sources, for instance, marketplaces, search engines, stock markets, and so on. That means you should take into account the necessity of more API integrations and extensions.

Some companies will find it a piece of cake because they already operate on an infrastructure that enables smooth data flow and storage of customer information. The others, however, may need to start relying on cloud computing to a greater extent. At the same time, they likely will need to restructure their systems to extract relevant data and feed it to the algorithms in real-time.

At this point, the support of a dynamic pricing consultant will be particularly helpful, as they can help you plan the necessary improvements or system/infrastructure changes and estimate how much it will cost you. But worry not - if you don’t use outdated legacy systems, you may be ready to implement dynamic pricing right away!

Understanding Your Target Market and Customers

You already know whether your company is ready for dynamic pricing, but how about your customers? That’s what you should investigate in the next step. Here are some questions worth answering to understand your market and customers better:

1. Are my customers used to dynamic pricing? How may they approach it? Could they migrate to another company if I introduce this price calculation method?
2. What are the budget limits of my customers?
3. What are my target groups? Can I segment them to improve the accuracy of my pricing strategy?
4. Does the value of my product or service for different target groups fluctuate a lot?
5. What are their purchasing habits? Do they tend to wait for the bargains or do they buy right away?
6. Do my competitors use dynamic pricing, and if so, which strategies do they tend to rely on?
7. How many competitors do I have? Which of them are my benchmarks?

Based on the answers, you can start adjusting your strategy to improve the probability of purchase at the maximum price possible.

Defining Your Pricing Goals and Objectives

Once you have a good understanding of your market and customers, time to delve deeper into the effects you want to achieve. Is your primary goal:

• maximizing your profit margins?
• improving stock levels management by stimulating purchases of excess items?
• balancing the accessibility of your services or products and distributing the demand?
• providing your customers with more bargaining opportunities?
• adjusting your prices to the dynamically changing market conditions (fuel costs, etc.)?

You can have a few goals, but then organize them hierarchically to have a good sense of the direction of your project. Based on that, establish the key KPIs that will later help you measure the effectiveness of your pricing rules and indicate the changes you should implement.

Step 1: Data Collection and Initial Exploratory Data Analysis

Congratulations! You have to build a solid foundation for your future dynamic pricing implementation. Now, here’s where the fun begins! It’s where we’ll start working on data, without which the dynamic pricing systems cannot do their estimations. These steps are essential to ensuring the right data flow.

Identifying Relevant Data Sources

Not all the data sources will be relevant for you. Try to narrow down your selection to the most essential ones to avoid lowering the price accuracy. You will need the training datasets for your ML algorithms and sources that will provide you with real-time data for the model to calculate the most relevant prices at the moment.

Where to take your data from? Start from your own backyard! There are various company data assets that can provide your dynamic pricing tools with valuable insights. It might be the revenue/sales/trend reports, competition data, ERP/SAP data, customer surveys, and reviews as well as customer behavior tracking data, stock levels, etc. Any XLS files with numbers in them may turn out precious! You will also need historical pricing records. If you lack these resources, you can also acquire training datasets (free or paid) from online depositories.

If your pricing methods are customer-oriented, you will need external data sources that track customer behavior and collect their demographic data, purchase preferences, etc. For those whose strategies strongly rely on environmental variables (fuel prices, weather, etc.), integrations with the external entities that register these data - from comparison engines to national data systems - are essential.

Cleaning and Preprocessing the Data

The right amount of data is crucial to train your algorithm properly. However, remember - quality over quantity. Whatever size your dataset will be, the most important is to prepare it well. That means you will need to clean it, removing duplicate records and irrelevant information from the files. If your dataset is too small and you struggle to find quality sources for training purposes, try the data augmentation technique.

Exploring the Data to Identify Trends and Patterns

With the help of the Data Scientists and Exploratory Data Analysis (EDA) step, you will make sense out of all the data you fed it with, finding the correlations between the variables and learning to find the optimal price points on this basis. The insights you draw at this point will be crucial for building an initial dynamic pricing model, which will later be developed.

Step 2: Building a Robust Dynamic Pricing Model

Now that you understand the mechanisms behind your pricing, you can build a model that will make your profit skyrocket! From decision trees, through bayesian, to reinforcement learning - you can pick different models for this purpose. Here are the fundamental steps of the dynamic pricing-based system development process.

Choosing the Right Pricing Model and Developing Models or Solutions That Provide Additional Insights

The more insights your dynamic pricing models provide, the better it is for the accuracy of your prices. You can combine a few different solutions to make your price fully reflect market conditions and the expectations of your clients. A robust dynamic pricing system is a network of dependencies, and here are some of the modules it may involve.

Market Demand Prediction

Demand is one of the most relevant variables of dynamic pricing. You can follow it in real-time, based on the number of requests, searches, or purchases. In the case of time-based strategies, commonly used among e-commerce retailers or ridesharing service providers, real-time demand forecasting is essential.

Sometimes, on the other hand, more approximate predictions should be just enough. To foresee demand fluctuation, you can employ forecasting models which extract insights from historical data, learning how the multiple variables impacted demand in particular periods. Time-series forecasting models serve perfectly for that purpose.

Cost Analysis

If you rely on a cost-plus strategy, this will be the essential aspect of your dynamic pricing system. However, in any case, your dynamic pricing model has to know the direct and indirect costs of delivering your product or service to the market. The direct costs include manufacturing supplies, materials, and labor, while indirect usually refer to rental expenses, utilities, back office, etc.

Some costs (like salaries of the employees involved in the process) tend to be fixed, while others fluctuate intensely (for instance, fuel or raw material prices). In order to feed your model with cost-related data, you may reach out for internal financial documents and reports. Others - like fuel costs - are acquired from external sources. Having put them all together, your dynamic pricing model will be capable of increasing the relevance of the estimated price.

Internal Network Analysis

Dynamic pricing can boost your sales, but also optimize your inventory and resource management. If you want your algorithm to do that, you should integrate it with your internal systems (from ERP to warehouse management software). It will draw insights into the stocks, free and unused resources, warehousing capacity, and supply chain. Based on that, the model will know, for instance, that it’s a good time to lower the prices of the excess product to free up the warehouse space or put a higher price tag on another product which consumes a lot of resources which are scarce in your company at the moment.

Customer Segmentation and Churn Prediction

In order to gain better accuracy, you may also segment your customers based on their demographic data and other relevant variables. Each of these groups may have different expectations and place value on different things, so they will react differently to the same price. This element helps you find an optimal individual price point based on what you know about your customer.

Testing and Validating the Pricing Model

Once you have constructed your pricing model, time to check whether it needs some polishing or it’s ready for implementation. Through the series of manual and automated tests carried out with tools depending on your tech stack, you can identify errors and issues that could affect the effectiveness of your price estimation and correct them before they can affect your sales.

It may turn out, for example, that your model returns worse results than during the testing phase. That performance mismatch might lie in so-called overfitting or inappropriate data selection or preparation, so consider taking a closer look at your datasets again. It’s the last moment to refine your model based on performance results - later, you will be preparing for its implementation.

Remember that it’s not all on you - your machine learning algorithm will draw the lessons from the results of its estimations and gradually improve the accuracy based on the collected insights.

Step 3: Setting up the Infrastructure for a Dynamic Pricing

You made it! You have your model, and now all you need to do this preparing for its implementation. We’ve already mentioned you will need to integrate the dynamic pricing model with your existing systems and processes. This stage may take various turns depending on the company’s specifics, so we will not delve into its details.

It is crucial to establish automated data pipelines, so that information can smoothly flow between your model, your systems, and the external platforms where the prices are often updated. For that, the support of DataOps and MLOps specialists will be priceless. You may want to automate the pricing process fully or to a certain extent. Some companies leave it entirely for the dynamic pricing system to estimate and update prices, while others prefer to control this process manually.

Step 4: Launching the Dynamic Pricing Strategy

We’re almost at the finish line! At this point, your strategy debuts for your target groups. What does it usually look like?

Running Tests on Staging Environments or Doing Partial Rollouts

Before you go all in, try out your new dynamic pricing strategy in small groups to check how your customers react to the estimated prices. It may turn out that their behavior is different than expected. Then you’ll have to link it to a particular element of your pricing strategy. Maybe, for instance, you should segment customers into smaller groups or shift an emphasis on a different variable?

A partial rollout protects you from potential financial loss if your strategy turns out to be mismatched. Alternatively, you can test it in a staged environment - but this requires a little more preparation.

Clear Communication of the New Pricing Strategy to Customers, Stakeholders, and Employees

Everyone should be on the same page about your new pricing strategy, as it impacts them on various levels. Customers have to reevaluate their shopping habits, and stakeholders need to rearrange their budgeting approach. Employees have to get used to a new system in their daily work. So, take it slow and start sneaking the idea to them in advance, not when it’s already implemented. Be transparent and explain in detail how this system works to show them there is nothing to worry about. This switch to dynamic pricing should be clearly communicated in your channels.

Full Rollout

And…action! Your dynamic pricing is already working on multiplying your margins. But that’s not the end of the journey - you will continue to improve your strategy through ongoing testing and refinement. Here’s what you should remember.

Step 5: Measuring the Effectiveness of Dynamic Pricing Strategy

In the first phase, you have established the key metrics for measuring the success of your strategy. You will likely reevaluate it with time, but the main KPIs will likely remain the same. Gross margin, operating margin per product, average margin per lifecycle, and sales volume are some of the metrics you should monitor to measure the effectiveness of your pricing.

Analyzing Customer Behavior and Market Trends

Your customers change, and so does the market, and because of that, no dynamic pricing strategy will work forever. By observing the key metrics closely, you may notice early hints of that changes. Stay in touch with the market reports, linking particular customer behavior to trends that could potentially impact the effectiveness of your strategy. If necessary, adjust to it - constant improvement is a clou of a successful dynamic pricing strategy.

Evaluating the Impact on Revenue and Profit Margins

Besides the market changes, follow your finances. The key metrics listed above will help you estimate the impact of your strategy on revenue and profit margins. Ideally, it should improve with time, as it often takes a while for customers to switch to a new model. Also, your machine learning model should improve at finding optimal price points with practice. If you are interested in this topic, read more about the pros and cons of dynamic pricing here.

Sounds complicated? We can lead you through all this process, using experience and insights gained throughout similar projects. Contact us if you could use such help!