Orders and invoices have been digitized and tracked in ERP systems for more than half a century. Sales prospects and deals have been captured in CRM systems for decades. But across every industry, the contracts on which all of those transactions rest still sit in filing cabinets and document systems. Capturing the mass of unstructured data hidden away in those contracts has been the lifetime mission of quote-to-cash vendor Conga since it was founded almost twenty years ago. But the advent of generative AI has brought a new energy to that mission, with its ability to find meaning and extract data from any form of written or spoken language.
Noel Goggin, Conga's CEO, sees a new opportunity to capture data and analyze it, not only from sales contracts but also across the procurement realm, to help businesses grow revenues and manage costs. He says:
Getting everybody anchored on the same data model is hard, but the value of doing that is really, really big.
Many enterprises have lost track of the terms on which they originally signed contracts with customers and suppliers, especially as M&A activity over time brings different histories together. And yet the largest contributor to revenue comes from existing customers. Even the very basic issue of when and by how much a vendor can increase prices may be buried away -- if it's mentioned at all, are increases tied to CPI, or is it CPI plus some percentage, and does it compound each year or do you only have one bite at the cherry?
Then there are regulatory, compliance and risk issues. Requirements may have changed since a contract was originally signed, and it's important to make sure that these new factors are built in and put forward well in advance when it's due for renewal. In addition, many businesses are now exploring new revenue models, in particular recurring revenue opportunities. For all these reasons, gaining an understanding of what's in those existing contracts is crucial. Goggin explains:
The ability to understand what's actually in the contracts for existing accounts is really important to constructing and architecting these new contracts, or addendums to these contracts, that allow them to bring these new products and bundles and pricing models, where it's usage or consumption, to market -- but also not cannibalizing existing revenue streams, and that's the hard part.
I think most contracts, the commercial data is buried in tables in contracts, and the ability to extract those tables, understand those tables, do computational logic against those tables -- so you can do deal modeling, deal analysis, deal construction, to be able to, as I said, architect the best deals for people that are on board -- I think is really important.
Originally focused on helping salespeople move away from handcrafted emails and proposals, Conga's mission has expanded over time to encompass automated contract creation and digital documentation, particularly since its merger with Apttus in May 2020. Goggin became CEO shortly afterwards. Customers cut across a broad spectrum of industries and include, for example, ABB, Bosch, Michelin, Nasdaq, Prudential, Roche and Starbucks. Goggin sees its role as bringing together people across the enterprise to collaborate on contracts:
If I just take, for a second, quote-to-cash, as a process, there's two dimensions. There's the initial customer acquisition, and then there's the ongoing expansion within that customer. Take those two distinct process flows.
If you look at the personas that have to come together on that -- the chief product officer and their team, what are the products, services, pricing, terms like? What is it I'm deploying, building, differentiating on, etc? If you look at the sales team, then how do I sell that, the value proposition, the commercialization of it, the contracting part of it, the managing and fulfilling part of that? CFO, if it's hardware, software, supply chain. But then I look at all the renew and expand portions of that, and it's obviously very innovative with the customer success organization. And then the CIO has got to put all this stuff together.
There's further complexity when it comes to evaluating what was originally contracted and matching that with what was delivered. He goes on:
In most larger companies, they may have one contract that governs their commercial relationship with their customer, but the fulfillment of those contracts may span a multitude of ERPs in different regions, different product sets, different revenue types. So the ability to be able to do the analysis and the intelligence around what was contracted versus what was built, there's huge revenue leakage opportunities for customers. People don't know, because there you need the commercial team, the legal team, the finance team, the supply chain team, all to interact together on the same data set. That doesn't happen organically in large companies.
This is how the phenomenon of revenue leakage arises, where the complexity of contracts and the difficulty of finding out what's in them means that enterprises often simply miss the opportunity to bill for what they've delivered. He says Conga clients often discover this:
'We're not billing customers for products and services that we're providing' -- because people didn't understand the contracts. That's all 100% margin. That all goes straight to bottom line.
In the case of highly complex industrial machinery, contracts often extend to include implementation, maintenance and consumables as well as the equipment itself, so tracking it all can become a major project. He explains:
You look at the decomposition of the SKUs, the actual discrete products, the composition of the revenue recognition that goes along with those products. Some are milestone based. Some are time-and-materials based. Some are subscription based. You've every permutation and combination of complexity there. And it takes months, if not years, to get these contracts negotiated, but once they're negotiated, then ... what we would term as obligation management through the fulfillment cycle has a big implication then on revenue forecasting.
Interestingly, Conga is now also applying its contract analysis capabilities in the procurement realm. He explains:
We just very naturally started with sell-side contracts with customers, because we were very natively integrated in with Sales Cloud and Salesforce. But then once people started understanding the capabilities of what the technology can do, then it very naturally expanded into buy-side contracts.
Just as sell-side contracts can suffer from revenue leakage, so too buy-side contracts can have similar instances where cost savings are being missed. He explains:
I may have negotiated these really interesting rebate programs in my contracts, particularly in hardware companies and medtech devices and stuff like that, that if I buy this number of units, and I get this, and I get that, I get a rebate here. But how you operationalize that data in the contract so you can actually administer and manage the reclaim process on rebates is very immature in most companies as well.
He outlines three phases of maturity for Conga adoption. The first is simply to standardize routine contracts -- NDAs are a good starting point -- and then automate the process of putting them in place. Some organizations have started to put in place 'legal tech ops' functions that operationalize the legal aspect of contract creation and completion. Rather than having a lawyer manually look at every contract, standardization and self-service automation allows many of them to become a touchless process.
Adding this self-service process is the next phase. This can often extend to very complex or high value contracts, where the parameters are well established. Legal teams can be brought in where minor variations are required. This level of process standardization is particularly useful for sales contracts, where salespeople traditionally have been very creative to land deals, but the result is a myriad of contracts with no commonality. It's important to set guiderails that they can work within, while AI can help make the process easier to navigate. So for example:
We would like to maximize the term of a contract. But there's trade-offs -- we might give a little bit on price increase for a longer term. But there's a fine art to that as well. I don't want to give you a five year deal if I'm giving you all this pricing, if I lower my price increase, because I'm giving away future revenue, so I'll do a three-year deal.
It's having people understand the levers on deal design and deal construct in context of a current relationship that they have. Typically, the customer is better versed in the contract than the salespeople are. So part of it is again, equipping the salespeople, in language that they understand, in tools that they understand, not in legal mumbo jumbo, which is where generative AI is hugely valuable.
With existing customers, sometimes the entire sales process can be automated, saving on the cost of involving salespeople. He says:
I may have a contract, but now I can add on products. I can do it on my own. I can add extra users. I can remove users. [It's] like a proper product-led growth motion. You don't need a lot of expensive sales people to go do that, something that can be done with your customer success managers, as an example.
The third level is when an enterprise can understand how to structure deals and contracts to maximize revenue based on a range of factors, and then build that into the guidance provided to salespeople, so that they can make deals that deliver the best outcomes for the business. He explains:
Recurring revenue is more valuable than one-time revenue. Deal composition and deal bundling allows you to maximize the contribution margin of a particular deal with a particular customer. But the salespeople, depending on your incentive structures, will maybe not be sophisticated enough to know how to do that, whereas the finance people will have a point of view on it.
So the question to me is, how do you bring this end-to-end revenue process together? Obviously, contracts are at the core of it. But how do you build more tooling to give the visibility, give the optionality, give the science behind closing deals faster? -- increasing win rates, increasing contribution margin of deals, maximizing the things that matter in your business [such as] term, price increases, tiering, so on so forth.
It's crazy that most enterprises have so much technology tracking orders and invoices, and yet the contracts on which those commercial relationships rest are still largely created and recorded manually. In the quest for end-to-end digital business processes, automating the contract process is a major barrier to overcome -- maybe the time to achieve this goal has come.
