Artificial Intelligence (AI) in Sales - Use Cases for 2025 [Update]
- Lucas
- 22 hours ago
- 10 min read
Contents
How artificial intelligence is changing pricing in sales controlling
Algorithm-based price optimization with AI: Maximizing profitability
How do I build a machine-based learning/AI model for pricing?
How exactly do these predictive algorithms, which underlie the AI model, work?
Conclusion: The future of sales controlling and pricing with artificial intelligence
Sales controlling is a crucial task for any company that wants to achieve its sales targets and increase its market share. It involves planning, managing, and monitoring sales activities to improve sales effectiveness and efficiency. But how can sales controlling be optimized? How can the vast amounts of data generated by digitalization be leveraged? How can customer needs be better understood and addressed more effectively? The answer is: artificial intelligence (AI).
AI is a technology that enables machines to perform intelligent tasks that normally require human thought. AI can support and improve sales controlling in a variety of ways.
In this blog post, we'll show some examples of how AI is changing sales controlling. The use cases for artificial intelligence in sales are diverse. Some of these use cases include:
Pricing: AI can help determine the optimal price for a product or service by taking into account various factors such as demand, supply, competition, or customer value.
Lead generation: AI can help identify and prioritize the best leads by analyzing and evaluating data from various sources such as website visits, email campaigns, or social media.
Purchase readiness: AI can help predict customer purchase readiness by recognizing and interpreting their behavior, preferences, or emotions.
Sales strategy: AI can help recommend the most effective sales strategies by using and simulating historical data, market trends, or customer feedback.
Customer satisfaction: AI can help increase customer satisfaction and loyalty by providing and adapting personalized offers, communication, or services.
Sales costs: AI can help reduce sales costs and increase revenue by automating processes, optimizing resources, or reducing errors.
As you can see, artificial intelligence has the potential to fundamentally change sales controlling.
However, to fully leverage the potential of this technology, it is often necessary to draw on the expertise of AI strategy specialists . Targeted AI workshops can identify use cases that offer companies the greatest potential for process efficiency improvements.
In the following, we will discuss the first point in more detail and take a closer look at the use of artificial intelligence in pricing .
How artificial intelligence is changing pricing in sales controlling
Pricing is one of the most important factors for a company's success. An optimal pricing strategy can increase sales and profits while simultaneously improving customer satisfaction. But how do you find the optimal price for each product or service? And how do you adapt it to constantly changing market conditions?
This is where artificial intelligence comes into play. Using algorithms, it can analyze large amounts of data and recognize patterns to make predictions or decisions. It operates faster than manual human analyses and is able to detect patterns that were previously invisible to the human eye.
AI enables dynamic and flexible price adjustments based on supply, demand, competition, and customer behavior. Using historical data, it learns price elasticity and can therefore calculate the statistically optimal price based on multiple external factors. By employing an AI pricing model, companies can automate and dynamically adjust their prices, maximizing profitability.
Dynamic pricing through machine-based learning
AI offers many advantages for pricing. By using artificial intelligence to cleverly apply a pricing model, the following can be achieved:
Dynamically adjust prices and react to changing influencing factors such as competition, demand, inventory, economic situation or season.
Offer recommendations, personalized advertising, or dynamic pricing to your customers
Reduce human labor costs by automating customer service or other processes.
In the past, rule-based pricing models were typically used for this purpose. However, the use of machine learning (the approach to developing artificial intelligence) allows for the development of more precise pricing strategies.
The difference between rule-based and machine learning-based pricing lies in how prices are adjusted. Rule-based pricing uses static pricing rules or "if-then" formulas to dynamically adjust prices and react to changing influencing factors. For example: "Our own price is always 10% lower than that of our direct competitor."
Machine learning-based pricing uses algorithms that learn from historical data and measure the price elasticity of demand. They can also identify and optimize the impact of various marketing activities or external factors such as weather or events on demand.
Machine learning-based pricing is more flexible, accurate, and effective than rule-based pricing because it considers customer price elasticity and the impact of various factors on demand. It uses historical and current data to generate forecasts and calculate optimal prices for each product. By analyzing very large datasets and considering a multitude of variables, an AI-based pricing model is significantly more accurate than the conventional, rule-based pricing model.
Algorithm-based price optimization with AI: Maximizing profitability
Algorithm-based price optimization with AI means that prices for products or services are automatically set by AI. Artificial intelligence adjusts prices in real time by analyzing extensive data sets – such as market changes, demand, weather, competitor activity, and other relevant factors. In contrast, sales often involve setting a fixed price. However, the problem with fixing a price lies in the lack of consideration for price elasticity – meaning profitability is not maximized.
You may remember the term "price elasticity" from your business or economics studies. The Federal Agency for Civic Education defines price elasticity as follows:
[Price elasticity is the] percentage change in the quantity demanded of a good when the price of that good changes by one percent, either upwards or downwards. It is therefore a measure of how quickly demand responds to price changes. (Source: bpb )
Price elasticity is calculated by dividing the percentage change in the quantity demanded of a product or service by the percentage change in its price. Demand is considered elastic if the calculated value of the price elasticity is greater than 1, meaning the change in the quantity demanded is greater than the change in price.
To measure price elasticity, companies need large amounts of data on historical prices and purchase quantities for each product. Once the price elasticity is determined, the following general scenarios apply:
Price elasticity < 1 (inelastic demand): Customers buy a similar amount despite a higher price, e.g. for essential or unique products - here prices can be increased to maximize return.
Price elasticity > 1 (elastic demand): A price reduction leads to significantly more purchases, e.g., for products with many alternatives. A price reduction can lead to a disproportionate increase in demand and thus boost sales.
For AI, price elasticity is a crucial factor in testing pricing strategies. It's therefore beneficial to incorporate costs into the model. Simply put, a price reduction that leads to negative margins offers relatively little benefit to a company. If the AI knows the cost per product, it can suggest price adjustments and calculate how these adjustments affect revenue (price x predicted sales volume) and profit (revenue - costs). The goal is to increase profit by identifying the optimal price point.
Of course, price alone isn't the deciding factor. High inflation or the weather (especially in retail) affects certain products and services differently. Therefore, the AI's training dataset is fed a range of factors needed to calculate the perfect price point. Since every product and service is different, there's no one-size-fits-all AI that can be applied to your business.
In our projects, we therefore usually proceed as follows to develop a customized, algorithm-based pricing AI:
Analysis of available (historical) price and market information
Data cleansing and enhancement
Identifying the most important factors that influence the price
Adding these factors to historical price data
Training an AI based on machine learning
In the next section, we will explain how to build such a machine-based learning model so that algorithm-based pricing is possible.
How do I build a machine-based learning/AI model for pricing?
To build a pricing model based on machine learning, you first need data about your products, your customers, and your market. You also need to define your business objectives, such as increasing revenue, maximizing profit, or gaining market share. Then you can use or have developed a software solution ( e.g., by VISUS Advisory ) that uses machine learning algorithms to measure price elasticity, forecast demand, and determine the optimal prices for each product. You can also create custom pricing rules to tailor your optimization, for example, by setting maximum discounts or linking prices to competitors.
To illustrate the construction of a pricing model based on machine learning, an example from e-commerce is helpful. Imagine you sell various products online and want to find the optimal prices for each one. You have data on your historical sales, your competitors' prices, customer behavior, and the weather. By using or developing an artificial neural network, internal and external data can be analyzed to determine the price elasticity for each product. This measures how the demand for a product changes when its price changes. This process can also generate forecasts about future demand and revenue for each product. Based on this information and your business objectives, such an AI model can then calculate and adjust the optimal prices for each product. This is known as dynamic pricing .
But how exactly do these predictive algorithms, which underlie the AI model, work?
Forecasting algorithms for pricing models based on machine learning are methods that use data and statistical models to estimate future demand and revenue for each product. These algorithms can consider various factors such as competitor pricing, seasonality, customer preferences, and external events. They can also incorporate uncertainties and risks into the forecasts. These forecasting algorithms help determine the optimal prices for each product that align with the company's objectives. They also enable dynamic price adjustments to reflect changing market conditions.
A forecasting algorithm for dynamic pricing can use various forms and examples of dynamic pricing, depending on the sales concept and customer analysis. The differences are:
Individual pricing: Based on customer analysis, individual prices are offered to different people. Those with a higher willingness to pay are shown higher prices for the products than others. This requires high data quality and personalized pricing.
Time-based pricing: Here, prices are adjusted depending on the time of day, day of the week, or season. This is particularly useful for products or services with high demand fluctuations, such as airline tickets, hotel rooms, or electricity. This requires accurate demand forecasting and flexible pricing.
Segmented pricing: Here, prices are adjusted according to customer segment or market segment. This is particularly useful for products or services that target different customer groups, such as magazines, software, or insurance. This requires effective customer segmentation and optimized pricing.
Challenges in implementing AI in sales controlling
Introducing AI into sales controlling presents a challenge, as it often requires a deeper understanding of AI technologies and their applications. Companies should therefore familiarize their teams with the fundamentals and application possibilities of AI early on to ensure successful implementation.
Our AI workshops They offer valuable support by providing practical guidance on how AI can be used effectively and how challenges can be actively addressed. At the same time, other challenges include providing high-quality data, the necessary technical infrastructure, and compliance with data protection regulations.
Technical infrastructure and potential solutions
A key hurdle in implementing AI in sales controlling is building a high-performance and scalable technical infrastructure. Companies must provide sufficient hardware resources and cloud computing capacity to efficiently run sophisticated AI algorithms.
It is important to seamlessly integrate AI solutions into existing sales and CRM systems to ensure optimal workflows across the entire company. For this, companies should utilize AI platforms and tools specifically tailored to the requirements of sales controlling.
One such solution is Obviously.AI , a tool for the structured analysis of sales data and the development of sales forecasts. To facilitate the implementation process and overcome technical challenges, it can be helpful to collaborate with specialized AI providers and consultants.
Employee training
Raising awareness of the importance and benefits of AI in sales controlling
Providing basic knowledge about AI technologies and how they work
Training in the application of AI-supported tools and platforms to fully exploit the potential of the technology.
Promoting interdisciplinary teams where sales and technology experts collaborate to successfully drive AI implementation.
Ongoing training and development to keep pace with the rapid developments in the field of artificial intelligence.
Data protection and GDPR compliance for AI development
To avoid legal problems, especially regarding GDPR violations, companies should also take the following steps:
Ensure that the use of AI-powered systems complies with the requirements of the EU General Data Protection Regulation (GDPR).
Implementation of data protection and data security measures to ensure the confidentiality, integrity and availability of personal data.
Adherence to data protection principles such as data minimization, purpose limitation and transparency in the processing of personal data
Involvement of data protection officers and experts
Implementation of AI solutions to minimize compliance risks
Regular review and adaptation of data protection and security measures to meet current standards and regulations.
In summary, implementing AI in sales controlling and addressing the associated challenges is a crucial step towards a successful and future-proof sales strategy. By integrating tools like Obviously.AI, companies can create more efficient forecasts and analyses to make data-driven decisions and achieve their sales targets. At the same time, however, they must consider data protection and compliance requirements such as the GDPR and train their employees accordingly to fully leverage the potential of AI technologies and ensure a smooth implementation. Ultimately, the successful use of AI in sales controlling can help optimize sales processes, unlock new business opportunities, and increase revenue.
Conclusion: The future of sales controlling and pricing with artificial intelligence
A forecasting algorithm for dynamic pricing is an automated algorithm that incorporates factors such as competitors' pricing, supply and demand, and other external factors to calculate the optimal prices for various products or services.
The revolution in sales controlling and pricing through artificial intelligence is unstoppable. In a world where innovation and disruption have become the norm, companies that use AI and machine learning in their sales processes are at the forefront of the next wave of progress.
As visionary thinkers, sales leaders, and entrepreneurs, our goal should be to continually push the boundaries of what's possible. In the areas of sales controlling and pricing, artificial intelligence offers the opportunity to broaden our horizons and reach new heights. With precise, flexible, and data-driven strategies, we can respond to constantly changing market conditions and create a customer-centric experience that is second to none.
The future belongs to companies that invest in AI technologies and train their teams accordingly. It is our duty as leaders to drive these changes and inspire the next generation of innovators.
Ultimately, the integration of artificial intelligence into sales controlling and pricing will change the way we do business – and just as the introduction of the iPhone changed the world, these technologies will usher in a new era of business.
Are you looking for AI consultants? Then VISUS Advisory is the right place for you. We offer a comprehensive range of services, from use case development and process analysis to the selection and in-house development of AI models. Visit our AI page and book a free introductory meeting today, where we will analyze your AI challenges.
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