Navigating the Future of Finance with AI-Powered CPQ

As far as financial forecasting and modeling are concerned, two things are happening at once:

• Rapidly advancing technology is making it easier to process large amounts of financial data and analyze them in new ways.
• An increasing number of variables are influencing the financial landscape, making it more complex and unpredictable.

It’s a bit of a paradox: as technology continues to evolve, it creates huge opportunities for businesses while simultaneously making everything more complicated.

But the benefits far outweigh the challenges. In fact, PwC’s Global Artificial Intelligence Study projects AI will have contributed $15.7 trillion to the global economy by 2030.

While CPQ is, by definition, a sales tool, it plays a critical role in accounting, financial planning, and high-level decision-making.

And with AI, it’s perhaps the most useful source of financial data your business can leverage.

The rise of AI in CPQ

At its core, configure, price, quote (CPQ) software is designed to help businesses automate the process of generating quotes for orders.

Let’s look more closely at the three functions of the CPQ process:

• Configure — Selecting product options and customizations to meet customer requirements.
• Price — Calculating accurate pricing for each product configuration and the total price, accounting for factors like volume discounts, customizations, and geographical pricing variations.
• Quote — Generating a detailed quote for the customer, which includes the configured product or service, pricing, and relevant terms and conditions.

Of course, a lot more than that goes into the sales process. Plus, what you choose to sell, how you sell it, and for what price ultimately determine whether or not you can run a sustainable and profitable business.

In the past, guesswork and intuition were critical components of pricing decisions, but that’s changing. With AI, much of the CPQ process either happens automatically or is guided by intelligent algorithms.

Dynamic pricing

Dynamic pricing is a pricing strategy where businesses set flexible prices for products or services based on real-time market demand. It uses AI to adjust prices in response to changing market conditions, such as supply and demand fluctuations or competitor pricing changes.

With dynamic pricing, your CPQ can take into account a myriad of factors when determining your price:

• Customer history and behavior
• Competitor pricing
• Seasonality and current trends
• Product availability and inventory levels
• Economic indicators and market conditions

Airlines and ride-sharing companies are two examples of businesses that have successfully implemented this model. Without AI, these types of companies simply wouldn’t be able to exist, let alone thrive, in the way they do today.

Price optimization

The price your customers see is just a number. But the factors that go into it are anything but arbitrary. This is where AI-powered CPQ really shines.

Price optimization is the process of using data and algorithms to determine a price point that maximizes profits while still maintaining customer satisfaction and demand. Done right, it lands you in that sweet spot between too high and too low, where you’re not leaving money on the table or turning away customers with sticker shock.

Although the responsibility lies on your sales, marketing, and finance leaders to set the price and structure, AI makes it a lot easier to make an informed decision.

For example, Oracle CPQ uses AI and machine learning to analyze a variety of factors and recommend the best pricing structure for each deal.

Personalized recommendations

Guided selling is a standard feature in practically every CPQ system. AI and machine learning take this concept to the next level.

Machine learning algorithms can quickly sort through countless configuration options to identify the most viable solutions for each customer (while applying advanced rules). This significantly reduces the time and complexity involved in this process​​.

With AI, your CPQ can even analyze customer data and previous interactions to provide personalized product recommendations and configurations. If you’re selling complex products, like a multi-tiered SaaS solution or custom-manufactured goods, this is how you get closer to quoting with precision.

And, with natural language processing (NLP), cusotmers can describe their requirements in their own words, and the tool will still understand. It can use this to generate recommendations and personalized quotes.

CPQ revolutionizes financial forecasting.

Sales automation is the usual selling point for CPQ software. But the real value lies in the wealth of historical sales data that your sales department generates by using it.

Your CPQ knows knows your whole sales process start to finish — how long it takes, who’s involved, and the steps required to close the deal. It also knows which types of customers are more likely to buy and which configurations tend to generate the most revenue.

These ultra-granular insights into revenue performance don’t produce the average forecasts you can only use in the boardroom They set the stage for predictive analytics across your entire financial landscape. 

Here’s a look at a few ways CPQ’s AI can help your finance team generate more dynamic and actionable financial forecasts:

Propensity modeling

Propensity models forecast a customer’s likelihood of customers responding to a particular pricing or marketing strategy — for example, bundling, special pricing offers, and other prompts that encourage purchases, upgrades, or cross-selling.

Leveraging predictive analytics and machine learning from CPQ and CRM data, propensity models evaluate potential future actions of your customers and prospects. Based on their preferences and historical behavior, it can predict which of the options you offer will lead to a successful sale.

Moreover, you can tailor these models to specific customer personas. And you can test different strategies and approaches to determine which ones perform best for each audience.

White space analysis

‘White space’ is refers to potential opportunities for growth, upsells, and cross-sells among your current or past customers. Machine learning techniques analyze your current customer base to discover relationships between customers, their industry, and their existing products.

From there, it can forecast the probability of those customers purchasing new products. Your CS team can use this info target high-probability opportunities to win more deals.

Lead scoring

Although lead scoring is an AI-driven CRM feature, its accuracy relies heavily on whether its algorithms get trained on adequate data from CPQ.

Integrating CPQ and CRM connects the dots between sales cycle data, product engagement, and customer lifetime value. When you can map out each customer’s journey from A to Z, the forecasted values and conversion probabilities from your lead scoring system will be much more accurate.

Churn forecasting

Some CPQ platforms analyze revenue and customer churn rates for you, so you can identify patterns, predict per-product and per-segment turnover, then use that data to inform your retention strategy.

PROS Smart CPQ is a perfect example. Through machine learning algorithms, the platform provides visibility into all the factors that influence churn. It notifies you when declining patterns like price sensitivity emerge. You can use these to make proactive efforts to retain your at-risk customers.

High-level forecasting

Of course, the high-level forecasts you do use in the boardroom and executive meetings are longer-term projections. But they’re based on data that’s often outdated or too general to make accurate predictions.

By leveraging AI, companies can create adaptable, accurate forecasts that consider a wider range of variables than traditional methods.

For instance, AI forecasting models aren’t limited by the amount, type, or quality of data they can analyze. This allows them to consider both internal performance variables and external factors like macroeconomic conditions, market trends, and even weather impacts on business​​.

Pricing leaders rely on AI-powered CPQ for data-driven decision-making.

With CPQ’s data visibility and analytics, sales, marketing, and RevOps leaders can make tactical decisions based on what they know to be the complete picture.

By combining all the sales data points that impact your pricing strategy — like customer segmentation, competitive information, product costs, market trends — CPQ provides a holistic view of your business.

Let’s look at some advanced ways pricing managers can use AI-powered CPQ to guide their decision-making process:

Profitability analysis and optimization

Pricing teams can use CPQ to accurately determine a customer’s willingness to pay before setting prices. But they can also use it for gain/loss analysis and ris​k​​-adjusted pricing.

For example, if a customer is willing to pay x% more for an additional product or feature, how will that impact your margin? And what’s the likelihood of winning the deal if you offer it?

AI-powered CPQ can identify which products have the most potential for upselling and which ones carry the highest risk of reducing margin. Based on this insight, you can make informed decisions regarding pricing strategy to achieve maximum profitability.

Dynamic discounting

Traditional methods like spreadsheets fall short in uncovering hidden patterns and inconsistencies in pricing, discounts, and deal sizes for different customers and products. AI and machine learning come into play by enabling pricing managers to critically evaluate the effectiveness of existing discount strategies.

It achieves this by analyzing the correlation between the size of deals and the discounts offered, and by spotting anomalies where discounts may have been disproportionately allocated due to savvy negotiation tactics from customers.

A standout in this innovative field is Salesforce Einstein, known for its ability to predict optimal purchase prices, based on the exact location.

Price segmentation

Price segmentation (also called price discrimination) is a microeconomic pricing strategy where different custoemrs or segments have distinct price points for products or services. Normally, they arrive at these prices by analyzing historical transaction data.

AI and machine learning technologies are adept at identifying price sensitivities by segment and providing valuable recommendations to sales and revenue managers. But their accuracy in predicting customers’ willingness to pay improves significantly with access to high-quality sales and transactional data.

So, integrating automated, segment-specific pricing suggestions into CRM and CPQ is crucial for successful price segmentation strategies.

Pricing, volume, and product mix analysis

Using AI and machine learning to uncover insights in transactional data, like pricing, sales, and product trends, is perhaps the most critical function of advanced CPQ analytics tools.

Deciphering price, volume, and product mix fluctuations within transactional data is the challenge here. Merging data for analysis and understanding fluctuations in a user-friendly manner has proven complex for many.

One system that stands out for successfully integrating transactional and product mix data with AI is Vendavo. It addresses the usability issues that have hampered other price optimization solutions.

This innovative approach allows for dynamic price optimization, tailored to local market dynamics, competitive landscapes, and international considerations. For example, Corning Optical Communications leveraged Vendavo’s AI-powered Profit Analyzer to pinpoint opportunities for price, margin, and profit enhancements, leading to a significant $10 million boost in their first year.

What the future holds for AI-powered CPQ software

AI and machine learning will continue to evolve, and the CPQ industry will be among its main beneficiaries. Everything from business risk alerts to dynamic pricing, deal optimization strategies, volume and product mix insights will integrate with advanced AI-powered CPQ analytics tools.

And, as AI becomes more accessible and scalable, organizations of all sizes can leverage it to gain a competitive advantage in the market.

That said, there are a few challenges for financial departments:

• Data quality and accuracy
• Integration with existing systems
• Change management and learning curve

Beyond these implementation challenges, there are practical considerations like data security and managing expectations about what AI can achieve within CPQ systems. Businesses need to be cautious and strategic, ensuring technology adoption addresses specific business challenges​​.

That said, it’s 100% worth the investment for financial leaders looking to increase their bottom line while reducing profit margin risk. With AI-powered CPQ, businesses can make data-backed decisions that improve sales and profitability, strengthen customer relationships, and drive sustainable growth.