In the next part of our series exploring the benefits of implementing a Configure, Price, and Quote (CPQ) system, we’re going to look at quote analytics, reporting, and the related topic of Artificial Intelligence (AI).
In the context of CPQ, we’ll see what they're really all about.
When we consider the amount of data that passes through a CPQ system, it’s clear why many CPQ vendors are keen to stake a claim to the creation and automation of intelligence around all that information. Typically a CPQ system will hold or touch data on customers, selling prices, costs, product and part quantities, approvals and many others.
When analyzed correctly, this data can yield great insights into many metrics. For example, win-rates and achieved prices could be inspected to help influence future pricing decisions. If you regularly win deals in a sector at or above a given price, then it may make sense to set discount limits to that price.
You can also use the data inside a CPQ system to look internally and make your processes more efficient. For example, by tracking the amount of time a deal spends with different approvers or in different stages of its lifecycle, you can highlight bottlenecks.
These may be due to inefficiencies or perhaps it’s just that your Finance Director is too busy to approve all the deals that he’s sitting on. One solution is to empower someone more junior to make the decisions. One of the biggest complaints a salesperson has is when their deals take longer to approve internally than by customers! It makes sense to look at the facts, see if they have a point, then take appropriate action to get your business flowing faster.
Within most CPQ systems there are a range of analytical tools that can help you extract insights. You can look to simple reporting where any attributes stored can be reported in a tabular manner and perhaps exported to a spreadsheet for deeper analysis and cross-fertilization with other data sets. This is fast, simple and gives most organizations the ability to get information out with ease.
Beyond that, there are now many tools that can give you a real-time understanding of your data during the deal construction process. Using the example above regarding price optimization, you could present a scatter graph to your salespeople of ‘won’ prices in their sector or territory by deal size to let them make their own estimate of how much discount to give. Or, you could even generate a simple regression curve to show the ‘best-fit’ of prices. These are useful insights that can help a salesperson make an informed decision.
You can go further still with data-cube export functionality to allow the full CPQ data-set to be taken into a specialist Business Intelligence or data warehouse application. While this is incredibly powerful, it requires a specialist team to work with the tools and the data to get the best out of it.
Personally I’m a little wary of vendors that claim to give full AI at the click of a button. Machine learning can undoubtedly bring huge benefits (although if, like me you watch too much sci-fi, you’ll know it can bring huge risks too!), and I am sure this area will develop. However, if you truly want to get deep and automated analysis of all your data, you need to be prepared to invest further in the specialist teams to set it up.
If you too are wondering what the hype about AI in sales and marketing really means, I also suggest you read the wise words of Alex Low who articulates this so much more eloquently than I could ever manage.
In conclusion, CPQ tools give a wonderful opportunity to gain insights into your customers, sales and internal processes.
* Originally published on walpolepartnership.com.