In view of rapid market dynamics and fierce competition, traditional competitor pricing is reaching its limits. AI solutions, however, are made for managing complexity. In this article, we’ll investigate how you can enhance your competitor pricing with AI and outperform once superior competitors.
Introduction to competitive pricing strategies
Competitor pricing is one of the most popular ways to optimize prices. It is effective because prices are a main decision factor for consumers. 87% of customers want to make a good deal when buying, according to a Google Retail study.
However, traditional competitor pricing is done manually or rule-based, and both methods are no longer able to keep up with current technology. Let’s take a closer look.
Manual price optimization: When price optimization is done manually, retailers keep an eye on their main competitors and try to underprice important products slightly to attract new customers while still making a good margin. At least, retailers try to price identical products on par with their competitors. However, especially when selling online, it’s impossible to monitor all relevant players without technological help.
Rule-based pricing: That’s where rule-based pricing tools offered a solution: Retailers add which retailers to monitor and set fixed rules on how to react to price changes, for example: “If retailer A discounts product B, then lower our price for product B and add another 2%.”
However, these tools are slow to react to market changes, which means discounts might run for an unnecessarily long time. The tools were developed for an era where price adjustments were made monthly, maybe weekly, but not for our time where prices on online marketplaces like Amazon change automatically every ten minutes. Also, the rate of price increase or decrease lacks a solid data foundation, which means unnecessary profit loss. With AI reaching solid maturity levels, retailers get a better option to execute competitor pricing.
Introduction to AI in retail pricing
Let’s quickly recap what artificial intelligence, or AI, actually is. AI software works with algorithms that mimic human decision-making. They can analyze huge amounts of data in near real-time which is impossible for humans, and spot correlations that humans miss. By doing so, AI solutions are able to optimize decisions and forecast how different decisions will affect the future.
In pricing, AI pricing tools are used to analyze all kinds of relevant internal and external data to determine the best prices that balance customers’ willingness to pay with a retailer’s interest to uplift profits. Predictive pricing algorithms calculate price elasticity and, based on historical data, are able to forecast how changes in prices will affect demand, inventory or sales. AI can even steer prices on autopilot, given a specific business goal.
Briefly, AI takes the guesswork out of pricing and allows retailers to reliably and efficiently maximize their profits. Watch our free webinar about AI in retail pricing, where our CEO explains the benefits of predictive pricing in more depth.
Key Roles of AI in competitive pricing
Given the growing complexity of markets and the speed of market changes, using AI in pricing is a mere necessity to stay competitive and integrating it into competitive pricing is the best way to keep the upper hand. Let’s take a closer look at where and how artificial intelligence enhances pricing success compared to traditional methods.
Data collection & analysis
Without AI, market analyses were limited by the amount of data that could be processed in a reasonable time and by the capacities of rule-based pricing tools. Pricing software could only consider a few key metrics to calculate optimal price points and wasn’t able to update its data input frequently, which meant big delays of price adjustments to market dynamics.
AI algorithms change the game: They are easily able to analyze hundreds and thousands of data points of various sources in seconds, i.e., competitors’ prices, market trends, and customer behavior. Retailers no longer need to know which changes in variables impact their demand or sales, but can rely on AI software to spot the correlations, and adjust prices to keep profits high. According to an Accenture report, companies that incorporate AI strategically in their organization can expect an increase in profitability of an average of 38 percent by 2035.
Dynamic pricing
Dynamic pricing is one of the most powerful changes in pricing enabled by AI. The machine learning algorithms continuously monitor the market and update their calculations of price elasticity in near-real time, which allows retailers to benefit from even small windows of opportunities that show up in the market.
Advanced AI pricing solutions not only update calculations fast, they can automatically adjust prices to meet pre-defined goals such as specific sales numbers, inventory levels or relations to competitor prices. Without AI-based automation, retailers are simply not as fast enough to take advantage of the situation. The results of dynamic pricing are impressive. According to our customer feedback and a pwc study, retailers can expect an average increase in revenue of 8-10 percent.
Predictive analytics
Predictive analytics is the next evolution of AI-based pricing. By analyzing historical data, AI algorithms are able to predict how price changes will impact major business metrics such as demand, revenue, inventory levels etc. While traditional forecasts were based on linear assumptions, AI can spot non-linear correlations and include many more variables in their calculations, which makes its forecasts highly accurate – in addition to its better cost-efficiency and working speed.
For retailers, predictive pricing is an invaluable advantage. Strategically used, it helps companies stay one step ahead of the competition and face market challenges well- prepared. With knowledge about future trends, it also becomes a lot easier to prepare for changes in customer preferences and always deliver what’s in demand. An additional boost in profits included.
Price optimization
AI-driven price optimization determines customer willingness to pay for a certain product based on historical data. Machine learning algorithms calculate price elasticity, the optimal balance between demand and supply considerations. In contrast to traditional methods, the data foundation of AI-based pricing is much broader and gives retailers a more holistic view.
Depending on the AI solution, retailers might also be able to optimize across functions, for example optimize marketing and pricing together to make sure discounts don’t sabotage profitability. They can choose between long term and short term forecasts and adjust prices with more foresight.
Benefits of AI-enhanced pricing strategies
Compared to competitor pricing, AI-based pricing offers more options to adjust prices depending on your individual business situation and goals. As BCG has mentioned, pricing is one of the best places to start AI integration in any organization. Let’s recap the benefits of AI-based pricing.
Increased accuracy & efficiency
AI significantly reduces manual errors and speeds up market analyzes, as it eliminates 80 percent of the manual work that was necessary until recently to define optimal prices. Also, AI algorithms ensure reliable forecasts that not only work short term but also long term allowing for price optimization that considers the whole lifecycle of a product.
Enhanced competitiveness
AI is not standard in pricing, yet. For now, retailers using advanced AI pricing optimization have an unbeatable advantage over their AI-less competition – and will see their success probably in a double digit uplift in profits.
Maximized profit margins
Quicker price adjustments, more accurate forecasts and near-real time marketing monitoring – there are plenty of ways AI enables retailers to uplift their profits. With our customers, we have seen revenue and profits increase up to 10 percent in a matter of weeks.
What are the challenges to tackle when enhancing competitor pricing with AI?
The best AI pricing solution can meet expectations when working with a solid data foundation. Retailers need to invest in increasing their data management, especially the accessibility and quality of their data. Inaccurate data will lead to flawed AI calculations and, as a result, decisions that may negatively impact customer satisfaction and revenue.
Therefore, retailers should take a close look at individual providers: There are major differences in requirements that AI solutions place on data to achieve reliably good results. 7Learnings, for example, can work with a comparatively small database and supports customers in cleaning and aggregating their data.
Another challenge, when implementing AI supported competitive pricing, is integrating the new pricing solution with existing systems. Ideally, you find a solution that doesn’t force you to invest in API programming but offers multiple connectors so that you can build seamless data pipelines with just a few clicks.
Real-world results of AI-enhanced competitor pricing
The rules of successful pricing have changed
We’ve talked a lot about the potential of AI in pricing. Let’s look at two of our customers to see what companies actually did achieve with 7Learnings.
Westwing is one of the leading premium e-commerce retailers in the home & living sector. The company operates in a highly competitive market. Low prices are necessary to keep market share, but there is always the risk to lower prices unnecessarily. With 7Learnings Westwing could optimize their prices to greatly boost profits while not losing market share.
With AI analyzes and automations, Westwing was also able to successfully adapt their pricing for regional variations, sales channels, and product attributes – while halving their operational time.
Sports fashion retailer INTERSPORT is one of Germany’s best-known companies in the industry, and sells both online and offline. The extensive assortment of highly seasonal products makes price optimization challenging. With 7Learnings, INTERSPORT connected their multiple different data sources and made them accessible for analyzes. AI sped up and automated the process of price adjustments, allowing for an accuracy in optimization that wasn’t possible with the manual means used before. The results are stunning: After several weeks, INTERSPORT achieved an increase in revenue of 53 percent and an uplift in profits of even 115 percent.
With artificial intelligence, price optimization has entered a new era. Retailers can adjust prices quickly with minimal manual effort and maximal foresight, and easily uplift their profits in a way that was previously impossible. In view of the high market dynamics, tough competition and an ambiguous economic outlook, AI is an effective safeguard for business success and growth.
As impressive as it may be, we have only seen a faint foretaste of the impact AI solutions will achieve for businesses in the coming years. Retailers should familiarize themselves with the new technologies and workflows now as quickly as possible to stay adaptable to future technological developments. Humans cannot outperform skillfully used AI, so the only way to stay competitive in the future is to embrace AI and master it. In pricing and every other department of your organization.
