Ace the Pricing Game with Intelligent Pricing Analytics

Pricing analytics has become an indispensable tool for businesses on a quest for profitability. 

Even customers root for the use of advanced analytics as a way to get fair pricing — and high-performing businesses don’t think twice about leveraging pricing analytics techniques to achieve that. But does your business need it? And if so, what is the right framework for adopting smart price optimization? Let’s find out.

From optional to table stakes: what makes pricing analytics important for companies?

Back in the day, effective pricing strategies used to be hard to nail. Companies would have to drill down into the spreadsheets, crunch numbers, and, hopefully, locate a few patterns to inform their pricing models. But with data-driven pricing intelligence, it has become a one-click exercise — and that’s not the only thing that makes pricing analytics important.

Improved profit margins

To capture greater margins, companies have to go into the nitty-gritty of price metrics and effectiveness to spot the slightest tendencies. Without pricing analytics models, businesses can only get to the bottom of Average Selling Price by category, Gross Margins by segment, and Gross Margins by customer. But those pricing metrics hide too much discrepancy to be actionable.

Conversely, pricing analytics tools can identify patterns in customer retention rates, loss metrics, and other pricing data at an item level to spot pricing opportunities and margin leakage. According to Accenture, product pricing analysis is able to increase margins by two percentage points.

Reduced pricing-planning times

Usually, it takes a lot of effort to get to grips with financials, especially if the company is in the growth stage. Hundreds of product names sold at different price points, a few pricing tiers, and complex product bundles make companies go with the “sounds about right” pricing strategy as an easier alternative to spending hours manually calculating the medium.

With their drill-down abilities, pricing analysis tools reduce manual effort spent on price optimization and wrap unified data into pricing analytics dashboards that could be then sliced and diced for different departments. Also, the heavy lifting of price planning can be fully automated through smart approval workflows.

Higher pricing performance

To hit it big, a business’s pricing strategy should be based on customer behavior and customer expectations of pricing. In other words, your products should be sold at specific price points that align with the value of your product as seen by a customer. And the shortcut to this value-based pricing is hard data.

By analyzing product pricing data and the impact of past pricing actions, brands can sell items at optimal prices that match customers’ purchasing power. Being in alignment with your customer base also leads to higher customer loyalty and lower customer acquisition costs.

Informed price-setting decisions

No customer is ready to pay extra for your products if other brands retail the same exact items at a lower price. By taking into account your customer, competitor, and company data, predictive pricing analytics software can calculate the price elasticity of demand for your product, making sure you understand all the factors influencing consumer price sensitivity.

As a result, you can set fair and competitive prices, while keeping your customers coming back. Also, most AI-based tools allow you to simulate pricing impact and identify the optimal price points for the products.

Four ways pricing analytics can improve your top and bottom lines

Price testing, competitor analysis, focus groups — one can adopt various approaches to optimize pricing and arrive at maximum profit. But there’s no more efficient and faster way to come across high-value pricing decisions than to wield the sword of predictive analytics.

Finance, marketing, and sales put under one roof

A pricing structure doesn’t exist in a vacuum. Instead, the adoption of new pricing structures should be done across marketing, sales, and product development.

But you know how it goes in business: sales are chasing volume, marketers are after discount-based promotion, and product development is grappling with increasing supplier prices. The result? Your teams cannot make their mind as one, each having a different understanding of optimal pricing options.

Through price analytics, companies can coordinate pricing and promotions, estimate the outcome of promotions at a product level, and apply customer segmentation to offer tailored promotions.

Also, a unified implementation across teams will help you achieve brand consistency in your discount strategy. Your marketers can implement strategic discounts without making your company lose a high-end brand perception.

Allowing for timely markdowns

Markdowns are the go-to strategy for brands to shed excess inventory. By offering goods at discounted prices, retailers can reel in price-sensitive consumers, boost sales, and bring new customers into their stores. However, the challenge is to mark products down in a way that benefits the business without harming brand perception or profitability.

To make sure all products are bargained for a fair price, retailers need to have a 360-degree view of the following data:

• Average revenue generated by item to put up the right items for clearance: here, companies need to stack up historical data of each item’s performance during a specific period against the sales plan.
• Optimal sales channels for a given item: retailers run the item-level analysis for each store to make sure the item is put up for sale in the right place — and adjust discount prices to each channel/store.
• The timing and frequency of markdowns, which should be based on the merchandise life cycle, seasonality, and customer demand.
• Clearance price optimized for gross margin and sell-through: drawing on historical consumer pricing behavior, companies should apply differential discounting across items.

If calculated precisely, each markdown factor can contribute to markdown optimization and increase profitability.

Enabling dynamic pricing

The goal of dynamic pricing is to allow a retailer to adjust prices on the fly to account for changing demand. Companies that implement this technique can change their prices in real-time based on the fluctuations in supply and demand, racking up greater revenues and increasing sales.

Customization is a critical component of effective dynamic pricing, that’s why dynamic pricing tools make predictions based on a large number of variables, including:

• Profitability analysis
• Price/trend forecasting
• Competitive pricing analysis
• Customer analysis for personalized pricing

To execute on differentiated price points, companies should also have price management automation in place that allows for quick, at-scale product price setting. Also, pricing should be dictated by accurate, centralized data — otherwise, price reduction can result in big margin losses.

Empowering your sales with personalized pricing

Personalized pricing allows businesses to adjust prices based on customer demographics, location, purchase, and other customer data. Unlike dynamic pricing, this pricing strategy is specific to each customer, instead of relying on external market factors.

By fine-tuning your prices to each client, you can maximize revenue, improve profitability, and identify the maximum value of a product as perceived by the customer. To make the most out of customized pricing, you need to follow a systematic process that involves:

• Customer segmentation: grouping customers into cohorts based on similar characteristics.
• Data collection: collating relevant data about customers, including purchase history, geography, and other customer insights.
• Competitor analysis: monitoring competitors’ prices to strike the right balance between price customization and profitability.
• Automation and customization: managing price changes on the go and at scale.

Custom data analytics enables businesses to master all four components at once, fuelling real-time price alterations grounded on unified and complete data.

Pricing analytics tools aren’t all-mighty, you need data excellence

Although pricing software can ramp up your operational efficiency and increase revenue, it’s a blunt instrument if its data management capabilities are scanty and half-hearted. Let’s look at the building blocks of a pricing analytics tool that can actually move the needle.

Data collection

The first step towards a data-driven pricing strategy is to collect reliable data from multiple sources. Therefore, your pricing analytics tool should be able to collect and vacuum up data related to transaction data, CRM systems, market research, competitor pricing, and supply chain costs.

Data quality and cleansing

No good can come out of incomplete and erroneous data. That’s why your pricing analytics software should ensure data accuracy and reliability by filtering out anomalies, weeding out errors, and handling missing data points.

Integration capabilities

High-performing pricing software integrates with CRMs, sales platforms, ERPs, and inventory management platforms to pave the way for informed decisions based on a holistic view of business operations.

Data analysis

This is when magic happens. Data analytics pricing models churn through your data and pick up the low-hanging fruit that can create extra revenue. To offer on-point pricing optimization strategies, tools should be able to run multiple types of pricing analyses.

• Segmentation analysis:

Splitting customers into customer segments based on common characteristics such as purchasing behaviors, preferences, and others, to tailor pricing strategies accordingly.

• Competitive analysis:

Analyzing the pricing and promotional strategies of your competitors to gain market share.

• Elasticity analysis:

Determining the optimal price for a product by understanding how price changes impact demand.

• Cost analysis:

Understanding the rationale and economics of each cost component, from production to distribution, to ensure pricing doesn’t affect profitability.

• Scenario analysis:

Simulating various pricing strategies to assess the future performance of price changes and their potential impacts on revenue.

• Promotion and discount analysis:

Identifying the impact of promotional campaigns and discounts on overall profitability and assessing their effectiveness.

Pricing analytics dashboards

Visualizing data through interactive dashboards will help your team make sense of pricing tendencies, performance, and potential issues or opportunities. From average selling price to discounting strategy, dashboards reflect outliers and trends in your company’s pricing optimization efforts.

Also, when business models change, current pricing model, product bundling, and channel-specific strategies are rendered irrelevant. Dashboards with iterative forecasts (like the one you can see below) help you keep up with changes without manually updating data.

Turning pricing into smart pricing with AI

Through the use of advanced analytics and artificial intelligence, companies can amplify the capabilities of their pricing analysis tools. Levi’s, for example, has elevated its average unit retail by 10% without negatively impacting demand — all thanks to AI.

Analyzing different data sources, pricing recommendation engines, what-if simulations, and other next-level AI-enabled features give an upper hand to pricing tools, allowing them to create more effective pricing strategies, run more accurate customer segmentation, and recommend optimal price points.

Security and compliance

Last, but not least, your pricing analysis tool should keep your customer data under lock and key to maintain customer privacy. The data processing flow must also meet industry regulations, ensuring compliance excellence at all levels of analysis.

What does it take to brew a successful pricing strategy?

Keeping tabs on customer buying habits, competitor prices, and market conditions can be quite dizzying unless it’s automated. Pricing analytics tools take this burden off your shoulders, revealing a real-time view of every data point that can affect your pricing decisions.

Paired with AI, smart pricing analysis turns from descriptive into predictive, identifying tangible opportunities for revenue growth. But to capitalize on the potential of pricing analytics, your company should first shore up current capabilities, assess data readiness, and make necessary connections in the existing IT ecosystem.

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