We previously talked about price optimization and dynamic pricing. Today, we will explore the application of machine learning (ML) in dynamic pricing models.
With artificial intelligence (AI) technology now going mainstream, dynamic pricing is a tool that even small retailers and e-commerce players can utilize to compete in the retail market.
E-commerce activity generates an overwhelming amount of data for a team of humans to handle. ML solves this issue because it can process data faster and without stopping. A well-designed ML algorithm can even learn and make pricing suggestions in real-time.
This enables retailers to set product prices based on supply and demand, a practice known as dynamic pricing. To put it plainly, ML is valuable because it automates tasks that are almost impossible for humans to do manually.
Express Analytics puts the voice of the customer at the heart of the business. Want to learn more about the dynamic pricing model? Please complete this brief form, and our experts will be in touch with you.
But before that, the retailer needs to know not only their inventory but also what data is incoming. ML-powered software leverages data to generate dynamic pricing solutions.
ML works on a simple philosophy – the larger the data sets, the better the learning process, and the better the outcome. Over time, ML-based software continues to improve in performance.
Analysts can consider other factors when implementing dynamic pricing. For example, an analyst could consider factors such as weather, demand, the company's operating costs, competition, the minimum price, and the best price, among others.
Ultimately, for a competitive pricing strategy, ML solutions can repeatedly scrape the web to collect important information about prices set by competitors for similar products, as well as consumers' opinions about these products, and pricing history over the past few days/weeks.
So, when ML is used, what difference does it make in dynamic pricing? AI and ML enable more comprehensive data analysis, resulting in improved resolution functionality.
How to Develop a General Dynamic Pricing Model?
The most important aspect is the level of granularity you are aiming for. For example, are you looking at a single customer level or an entire segment?
Another crucial factor is defining strategic goals that align with the business's overall objectives. Profit maximizing is obvious, right? However, you could also choose goals for acquiring new customers or improving customer satisfaction metrics.
The ML-based dynamic pricing model can then be developed once the answers to the above points come in. The model will predict whether someone will make a purchase at a price best optimized at that moment in time.
The models can be used with either Generalized Linear Models (GLMs) or Deep Learning methods.
Transform Your Business using Express Analytics' Machine Learning Solutions >>>> Connect with Us
Benefits of the Dynamic Pricing Model
- Aiding product bundling and discount creation
- The clustering algorithm can quickly associate a new product/service with similar products to obtain a probable price segment.
- Predict demands for items that don't have transaction data
- To anticipate early trends
Businesses that deploy ML-based models for dynamic pricing tend to increase their margins by 10% or more, and sales also increase by up to 10%.
Adjusting prices in real-time through the pricing model does that for your business.
Dynamic Pricing Strategy: Common Mistakes to Avoid
A successful implementation of a dynamic pricing strategy needs careful planning to avoid common mistakes.
In this section, let's explore the most frequently seen challenges faced by organizations when using dynamic pricing strategies:
Neglecting user experience:
Marketing conditions and prices of competitors are crucial in determining product pricing adjustments.
Additionally, please don't ignore your customers, as they ultimately have the right to make informed decisions about purchasing products.
Also, ensure that your clients find price modifications fair and reasonable.
Frequently changing the price:
If customers perceive that you are changing prices frequently, they may become frustrated, which can lead to a decline in sales.
Sometimes, as market demands dictate, you may adjust prices to strike a balance between profit and cost. However, if this process repeats itself, it ultimately results in unhappy customers.
Lack of up-to-date data or reliable data:
Dynamic pricing is purely dependent on real-time data to create the perfect pricing strategy.
Without reliable or accurate data, your pricing strategy produces profitless results. Ensure that you have a precise system in place to gather and inspect data.
Improper segmentation of target audiences:
Suppose your business is providing different products or the same basic products with numerous variations. In that case, you're expected to attract a wide range of audiences, and each segment is drawn to various products for significantly different reasons.
It's not a systematic approach to offering customized experiences that every audience wants.
By considering this factor, you must set prices and identify natural segmentation to find your target audience when planning marketing and sales concepts.
Risks of Dynamic Pricing Strategy
Let's explore some potential algorithmic pricing risks:
Customer loyalty reduces:
The major problem with automated pricing is customers leaving your brand. This occurs in two ways:
- Market-oriented pricing
- Repeated and unforeseeable price changes
To make customers stay with the brands, they should stick to the brand principles by bringing price modifications in line:
- Give a valid reason for the hike in prices
- Reposition their marketing according to price changes
Unmanageable pricing:
Before allocating a price to any product, a team should be in place to approve pricing suggestions and test regularly to ensure that pricing does not influence their decisions.
However, once a large company implements dynamic pricing, it should have a team in place to monitor and control the algorithm.
One of the major causes is that computers can't consider the human psychology or mindset of pricing.
Once the prices are reduced to the 1s and 0s, the computer can discover the "best" price.
Computers should consider a wide range of factors to discover a suitable price, but in some cases, a human being declines the pricing suggestions by saying "no".
Transparency and Complexity:
Incorporating a personalized dynamic pricing strategy in business is not easy and requires complicated algorithms and data analysis.
Maintaining transparency in pricing concepts becomes important as customers start to doubt whether they can find clarity or identify the reasons behind price hikes.
Retail businesses must communicate their pricing approaches properly to maintain their customers' trust.
Audience privacy concerns:
The dynamic pricing algorithms rely on data gathering and analysis to understand audience behavior and determine prices.
Moreover, this raises privacy concerns, as audiences may not feel comfortable knowing that their confidential data is being used to determine prices.
Both e-commerce and retailers must adhere to data protection laws and maintain transparency to address issues related to privacy.
Dynamic Pricing in the Retail Industry: Dos
Focus more on the final price:
Along with the listed price, customers also consider the total cost, which includes shipping fees, taxes, and any additional charges.
Retail businesses must align their dynamic pricing approach with the selected value proposition, making informed decisions related to customized offers, shipping options, and promotions.
Track and enhance:
Dynamic pricing requires a test-and-learn concept. Inspecting the effect of price modifications and making adjustments based on real-time data is crucial for effective management.
Effective communication between merchants and pricing specialists can yield valuable insights.
Develop a strategy carefully:
Understand your current position in the competitive environment and the consumer's perspective on your brand.
Prepare a step-by-step approach to developing data, tools, talent, and infrastructure.
Looking to Improve Your Retail Operations using AI & ML? >>>> Let's Connect
Dynamic Pricing in the Retail Industry: Don'ts
Don't confuse customers:
Frequent price fluctuations can confuse customers, prompting them to seek alternatives.
Pricing offers should be consistent across channels and devices to prevent confusion among customers.
Don't alter prices unreasonably:
Price changes should have genuine triggers, such as seasonal fluctuations or the introduction of new products.
Stop altering prices when competitors' prices or costs are the same. Communicate price deductions to customers to increase their effect.
Don't depend on insufficient data:
Incorrect or generalized entries can result in inaccurate price recommendations, which can lead to margin losses.
Regularly review and update pricing entries to enhance accuracy in price attribution and ensure optimal price recommendations.
Transform Your Business using Express Analytics' Machine Learning Solutions >>>> Connect with Us
How Does Dynamic Pricing Use Machine Learning?
Dynamic pricing uses ML in numerous ways to boost pricing strategies.
Listed below are some use cases related to the same:
Demand prediction:
Machine learning can be used to analyze past data and various other factors, such as current market trends, promotions, and seasonality, to predict future demand for products.
Dynamic pricing automation:
ML can automate the process of dynamic pricing by regularly inspecting market data and fixing prices accordingly. This process can be completed relatively quickly compared to manually adjusting prices.
Inventory management:
By forecasting demand and fixing prices accordingly, businesses use ML to manage their inventory, minimizing holding costs and the possibility of overstocking or stockouts.