21 Mar Retail Pricing Intelligence

Pricing has always been the most critical aspect of retail business. Set prices a bit too high, and you’ll lose customers. Set them a bit low, and your profit margins will be taking a sharp dive.

So, what’s the solution? A concept formally known as retail pricing intelligence. A price strategy that uses data and analysis to help you price retail items at the best price points.

The following article breaks down everything you need to know about the concept of retail pricing intelligence  –  what it is, how it works and why it’s essential for retailers looking to stay competitive while maintaining profits in today’s markets.

What is Retail Pricing Intelligence?

Retail pricing intelligence, in its simplest form, is the process of using data-driven insights to make smarter pricing decisions.

Traditionally, retail pricing was largely based on guesswork or simple cost-plus formulas. Now, retailers use real-time market data, AI and predictive analytics to determine the most profitable prices for their products. That’s what retail pricing intelligence is all about.

Retail pricing involves…

• Market Trends  –  Analyzing demand fluctuations based on seasons, events, and other external factors.
• Competitors Pricing  –  Analyzing how competitors are pricing similar items across different channels.
• Consumer Behavior  –  Understanding how price changes affect consumers’ buying habits.
• Profit Margins  –  Ensuring price remains competitive without sacrificing profitability.

Overall, retail pricing intelligence is about moving pricing strategies from reactive to proactive. This allows retailers to make real-time adjustments and keep the overall business as profitable as possible.

Why Traditional Pricing Strategies No Longer Work

Many retailers still use the traditional pricing models like cost-plus pricing. This involves adding a fixed profit percentage to the cost of goods. For instance, if the cost of something is $50 and retailers have a fixed profit margin of 5%, the retail price would be – $50 + $2.5 = $52.5.

While such models are fairly simple and consistent, they don’t account for real-time insights such as the following.

• Market trends  –  demand and supply
• Competitors pricing
• Customers’ willingness to pay

Simply put, these static pricing models don’t work in today’s dynamic, omnichannel retail environment, where pricing changes on a moment’s notice and customers expect competitive rates.

The Role of AI & Predictive Analytics in Today’s Pricing Models

AI-based pricing intelligence models are changing how retailers approach pricing. With advanced AI models processing massive amounts of live data points, these pricing models can detect patterns that humans might miss. Predictive models then take these patterns to predict retail prices with precision.

Here’s how it works…

• AI gathers market data including competitors’ prices, customer behavior, market trends, seasonal variations, and other variables.
• Machine learning algorithms then identify patterns hidden in the collected data. These patterns may include supply demand trends, peak sales period, and pricing sensitivity.
• AI then recommends optimal price points that perfectly balance demand and profitability. These prices are then displayed live using advanced ESLs such as ours at Display Data.
• Predictive analytics then comes in, taking the whole process a step further by forecasting future demand and helping retailers to anticipate price shifts months before they happen.

To see AI-based pricing intelligence in action, we can take a look at Amazon retail prices. The company updates prices every 10 minutes based on demand, competitor pricing, and inventory levels.

Demand Forecasting  –  Predicting What Customers Will Pay

The concept of demand forecasting deals with predicting retail prices that customers are willing to pay while maintaining profits. It allows retailers to price their products at the sweet spot without any guesses or gut feelings.

Here’s how demand forecasting works…

1. The process starts with analyzing vast amounts of market data.
2. The data highlights which products are seeing a spike in demand. For example, if a storm is coming, umbrella demand is likely going to rise. So, the prices are raised.
3. The data also shows which products are going out of demand and require price cuts to sell before it’s too late.

A great example of price forecasting is airline pricing. Airlines use demand forecasting to adjust ticket prices based on booking patterns, holidays, and fuel costs.

Competitive Pricing Strategies

Retail intelligence is not just about analyzing markets and predicting prices, it actually provides a complete framework of pricing strategies for retailers to price their products effectively. Here are some of the most used strategies based on pricing intelligence.

Dynamic Pricing

Under this strategy, prices are adjusted in real time based on demand, competitor prices and other market factors. Dynamic pricing strategy is mostly used in e-commerce and is powered by AI pricing engines.

Price Matching

This strategy is based on retailers tracking competitor pricing and matching lower prices to stay competitive. AI simplifies this by automatically adjusting pricing based on competitors’ prices.

Psychological Pricing

Psychological pricing tactics like charm pricing (displaying £9.99 instead of £10) or anchor pricing (showing a higher price next to the discounted price) to influence buying decisions.

Personalized Pricing

Leveraging customer data to offer individual discounts based on shopping habits. Loyalty programs and AI-driven recommendations make this more effective.

By combining these strategies, you can attract price-sensitive customers while protecting your margins.

Profit Margin Optimization

One of the biggest challenges for retailers is to find the right balance between competitive pricing and healthy margins.

Here’s how AI can help you optimize profit margins to near perfection.

• You can use predictive analytics to set prices based on expected prices instead of gut feeling and fear of losing customers.
• You can implement personalized pricing to maximize revenue per customer. A regular customer might be willing to pay slightly higher prices than a new customer.
• You can easily reduce markdowns by forecasting slow moving inventory and adjusting pricing proactively.

For example, Zara uses AI-driven demand forecasting to predict which items will sell out and adjusts pricing accordingly. This reduces waste while ensuring maximum profitability.

Enabling Intelligent Pricing at the Shelf

For retailers to truly capitalize on pricing intelligence, the final piece of the puzzle is execution – getting price changes onto the shelf quickly and accurately. Digital labels (ESLs) remove the delays and costs associated with printing, distributing, and manually updating paper tags.

With ESLs, price updates happen instantly across an entire store, ensuring accuracy while eliminating the labor costs of re-tagging. This speed is critical for dynamic pricing strategies, letting you adjust quickly based on demand, stock levels, and/or competitor activity.

More than just digital price tags, ESLs act as a bridge between data-driven pricing models and the in-store experience, ensuring that the right price is always displayed at the right time.

The Next Step in Smarter Pricing

ESLs aren’t just a replacement for paper tags – they’re a direct link between pricing strategy and the store floor. With automatic updates, retailers can react instantly to demand, competition, and inventory shifts, without the delays and costs of manual changes.

But that’s only part of the story. In an upcoming article, we’ll explore how ESLs do more than just display prices – they enable smarter, more flexible pricing that keeps retailers ahead in an unpredictable market.

Final Thoughts

Retail market is more competitive than it has ever been and pricing intelligence is the only way through for you as a retailer. Businesses still relying on traditional pricing methods will struggle over the coming years, while those who adapt to innovative concepts such as pricing intelligence will thrive.