Maximizing Revenue Growth with Advanced CPQ Insights

On the surface, CPQ is a quoting tool that helps vendors streamline their sales process and improve quote-to-cash efficiency.

But it also helps them discover critical sales and product insights they can use to improve their customer experience, develop better products, and increase revenue.

And for that, they can thank analytics and transactional data.

Companies using CPQ average a 105% larger average deal size. That isn’t just from the improved efficiency or reduced errors. It’s equally due to sales teams having access to advanced insights that help you make more informed decisions.

As with any data, though, it’s about using it the right way. Let’s take a look at how CPQ insights can help you grow your sales revenue.

Today’s RevOps teams face an onslaught of challenges.

RevOps software vendor BoostUp surveyed hundreds of leaders across sales and revenue operations functions.

The three biggest roadblocks to revenue growth, according to participants:

• Poor processes
• A lack of alignment
• Data quality issues

A lack visibility into sales performance, territories and channels, and pricing/product strategies is literally killing revenue opportunities. And it’s making their lives more stressful in the process.

You could say CPQ is the central nervous system of your sales process. 83% of sales departments are using it, and yours is probably one of them.

It touches nearly every aspect of revenue generation, from the moment your rep configures a product to when they send out a contract or invoice. And the data it collects speaks to what your customers buy, why they end up choosing to, and how they prefer to transact.

That’s a goldmine of insights for your RevOps team. If it’s integrated with your business systems, you’ll have a 360-degree view of all your transactions, even those that didn’t result in a sale. If it isn’t, you’re guaranteed to run into those three problems above.

CPQ analytics to the rescue

Configure, price, quote (CPQ) software works exactly how it sounds: configure a product or service package, calculate a sale price based on its components, then generate a quote for the customer.

Here’s a basic rundown of how the process works internally:

1. The seller enters customer information from CRM.
2. They input the buyer’s product/service requirements.
3. Through guided selling, the platform presents a solution.
4. The seller and buyer negotiate pricing based on the quote.
5. Eventually, the quote becomes a contract and an invoice.
6. When a customer pays the invoice, the transaction is complete.

Since it’s involved in the transaction, CPQ also collects data on every step of the process. This includes product preferences, upsell opportunities, discounting strategies, engagement with the sales rep, and more.

For example, it can show you:

• Which types of customers are most likely to buy a particular product or service
• How certain sales territories perform compared to others
• Which pricing strategies result in the highest conversion rates and profit margins
• How long it takes for the average customer to move from quote to payment
• Where in the sales process you run into roadblocks and why
• The impact of a discount on margins vs. conversions
• How different sales reps perform in terms of efficiency and effectiveness
• The most effective ways to upsell or cross-sell existing customers

In other words, CPQ data helps you make the right decisions that result in more revenue.

How CPQ benefits RevOps teams

RevOps is tasked with end-to-end customer lifecycle management, from lead gen to retention. And its role and responsibilities have only grown more complex with digital transformation and the evolution of multichannel selling.

Yes, revenue operations software can help you align your sales, marketing, and CS teams. And you can use it to identify new potential revenue streams. But it isn’t connected to the sales process the way CPQ is.

Even if you’re using a RevOps platform, you need insights from CPQ to get the complete picture of your revenue-generating activities.

Your RevOps team can use CPQ data to:

• Understand sales territory and channel performance
• Forecast sales, accounting for product demand and market trends
• Make high-level revenue strategy decisions based on accurate data
• Validate prices and enhance pricing strategies
• Identify new cross-sell and upsell opportunities

Plus, they can align marketing with sales and customer success initiatives by giving them the most accurate and up-to-date data on products, pricing, customer preferences, and buying behavior.

Driving revenue growth with CPQ analytics

Let’s take a closer look at some of the ways CPQ analytics can take sales execution to the next level:

Enhanced operational efficiency

Products and client needs are both inherently complex, meaning you need an efficient system to handle their intricacy. CPQ eliminates errors from manual data entry, pricing miscalculations, and product misconfiguration.

By standardizing elements like pricing, inventory, and dependencies, you’re reducing the likelihood of costly mistakes​​. And, through automated bundling and guided selling, you speed up the process of configuring and quoting products.

Beyond this, KPIs like quote-to-cash cycle time and quote accuracy help you understand current efficiency and benchmark it against previous periods. Qualitative data points like where in the pipeline most leads drop out also show you exactly where your bottlenecks are, so you can fix them.

End-to-end sales workflow

The CPQ process makes up a huge percentage of the sales cycle. When you have one platform to manage all the steps of quoting, contracts, invoicing and reporting, you’re better equipped to sell effectively.

KPI measurement is one of the key ways analytics contribute to this. For instance, tracking the time it takes for reps to respond to customer quote requests and managers to respond to approval requests shows you any gaps in your pipeline. You can use this info to reduce friction when handing off leads and speed up the process for customers. 

You can also use CPQ data as the unifying element between all your business systems. Take Cloud Consultings’s Salesforce CPQ client case study as an example. After implementing Salesforce CPQ, Sales Cloud, Service Cloud, Community Cloud, and Salesforce Chatter, Cloud Consulting was able to fully integrate their entire quoting and ordering process from start to finish.

The result is an end-to-end sales workflow that is not only efficient and streamlined but also informed by data-driven insights at every stage​​.

Unified data across the customer lifecycle

Leveraging CPQ’s integration capabilities with other systems gives you a more complete picture, especially if your CPQ is part of a larger software ecosystem, like Salesforce, IBM, or SAP.

For instance, SAP CPQ seamlessly integrates with SAP ERP and SAP S/4HANA, so the benefits of analytics beyond the CPQ system itself. This integration allows for a unified data flow and a comprehensive view of the sales process, from quote creation to order fulfillment and post-sales support.

For subscription companies, another example is DealHub CPQ. Using DealHub’s CPQ, CLM, subscription management, and digital sales room tools together means real-time customer engagement insights throughout the sales cycle as well as ongoing customer data post-sale.

Improved customer experience

When you know where you need to fix the sales process, you aren’t just saving your team members from the stress and frustration of manual tasks. You’re also improving the experience buyers have with your company.

More than three-quarters of B2B buyers say their last purchase was “very complex or difficult.” That helps to explain why 75% of buyers say they don’t want to talk to sales reps at all.

CPQ analytics allow you to spot the places where deal velocity is slow, which steps take the most time, and where you’re making mistakes that are souring your customer experience.

Accelerated sales cycles

CPQ reduces sales cycle times by 28% on average. Part of the reason for this is you’re using data to make the sales process easier and more efficient. Another factor is that you can reduce time spent on non-selling activities, freeing up reps to sell more.

By reducing the number of manual tasks, you can focus on more value-add activities like identifying upsell and cross-sell opportunities, engaging with customers to deepen relationships, and crafting personalized quotes that are tailor-made for each buyer’s unique needs.

Increased win rates

Companies using CPQ software have 17% higher lead conversion rates than those that aren’t. Your sales team can use analytics in CPQ to target the right products, pricing tactics, and discounts that align with your target buyers’ needs, preferences, and price sensitivity.

In addition to this, CPQ can also help you identify opportunities for cross-selling and upselling by analyzing customer buying behavior and suggesting complementary products or services.

So, every step of the way, you’re able to add more value to your customers and increase your chances of closing each deal.

CPQ Implementation Best Practices

To get the most out of analytics and reporting in your CPQ system, you need to approach CPQ implementation with extreme attention to detail.

Integrate data seamlessly across platforms.

Although CPQ has a wealth of information on its own, its true power lies in its ability to integrate with other systems.

• CRM data adds context to your sales insights.
• ERP data tells you more about your pricing and margins.
• Ecommerce data helps you understand your customer preferences and buying patterns through self-service portals.
• Billing data keeps your system up to date on changes in your customer’s payment information, who your most valuable customers are, and how often they purchase.
• Subscription management insights show you which customer segments are most engaged with different elements of your product, which can help your sales team personalize their offers to each buyer.

Assemble a cross-departmental implementation team.

Your implementation team should include front-line users (i.e., sales reps), middle managers from different departments (sales, IT, customer service), and executive leadership.

Having representatives from all departments helps ensure everyone’s needs are accounted for, and you can avoid common pitfalls, like over-customization, missing product rules, or lack of buy-in.

Continuously monitor CPQ performance and data accuracy.

After CPQ implementation, you need to continuously monitor its adoption and effectiveness. This involves tracking key performance indicators (KPIs) to ensure the system meets its goals, such as reducing time spent on sales processes, improving quote accuracy, and increasing win rates.

It’s also crucial to establish data governance protocols, like regular data audits, and have a process in place for handling discrepancies. Over time, pay attention to how decisions you’ve made based on CPQ data have affected KPIs, and use that information to determine how accurate your CPQ analytics actually are.

Future trends in CPQ analytics

AI and machine learning

CPQ systems already use AI to score leads, predict customer behavior (e.g., likelihood of churning), and recommend products and pricing strategies based on dozens of factors.

Looking into the future, generative AI, a subset of AI, holds promising prospects for CPQ. Generative AI can manage complex enterprise systems involving global operations, supply chains, and various configurations. It can adapt to changes and updates, learning over time to provide sellers with comprehensive information to aid their sales process.

Additionally, with advancements in natural language processing, future CPQ systems could incorporate natural language capabilities, allowing sellers and customers to interact with the CPQ system more naturally and conversationally, making the sales process even more intuitive and efficient​​.

Integration with IoT and big data

IoT devices collect vast amounts of real-time data through sensors and connectivity. When integrated with CPQ and ERP systems, this data can be used to gain deeper insights into customer behavior, equipment performance, and market trends.

For instance, IoT sensors in manufacturing equipment can provide real-time data on the machine’s condition. This helps manufacturing CPQ users understand production capacity, timelines, product availability, delivery status, and upcoming maintenance more accurately when quoting customers.

The combination of IoT and big data is what allows businesses to understand customer preferences better, anticipate market trends, and improve decision-making processes. 

And integration with CPQ systems means that these insights can directly influence sales strategies, pricing models, and customer engagement tactics​​.

Focus on predictive analytics and forecasting

For sales, specifically, predictive analytics can be highly beneficial in areas like lead scoring. By tapping into CPQ CRM, marketing automation, product analytics, and social media data, businesses can build predictive analytics models that offer a comprehensive view of leads. In turn, sales reps know which deals in their pipeline are most likely to close and can prioritize their time accordingly.

Forecasting is also a critical part of CPQ analytics, providing businesses with valuable insights into future sales trends. By analyzing past data and current market conditions, businesses can more accurately predict future sales, resulting in better strategic planning and decision-making.

CPQ analytics employers RevOps teams.

By leveraging CPQ analytics, sales and RevOps teams can make more informed decisions, improve processes, and streamline their operations.

From sales reps to executives, everyone can benefit from having access to real-time data and insights that CPQ analytics provide. That’s why sales leaders should prioritize implementing CPQ systems and empower their sales and RevOps teams to leverage the full potential of CPQ analytics in driving business success.