As large enterprises seek greater business agility, having a complete and accurate view of each customer relationship is challenging. Progressive companies are looking at new ways to address this age-old problem. To give us insight into creating a comprehensive digital view of the customer, we invited Praful Saklani, founder and CEO of Pramata to join us for 20 Questions—an MGI Research Interview Series with leading technology industry executives, innovators, and investors.
Praful Saklani Profile
Prior to serving as CEO of Pramata, Mr. Saklani held the following positions:
- General Manager, Water Health International
- Head of Operations, Datalex PLC
- Co-founder/Head of Products and Operations, Yatra Corporation
Mr. Saklani holds patents in AI technology, practices meditation daily, and is a trained meditation instructor.
Mr. Saklani graduated with High Honors from Swarthmore College.
Key Issues
- How are the requirements for sales operations and contract management evolving?
- What areas of the business are ripe for Digital Transformation?
- Who are the winners and losers in the shift to Agile Monetization?
- Which emerging vendors are delivering true innovation to the enterprise?
- What are the hidden benefits of Digital Transformation projects?
The Interview
Andrew Dailey: Welcome to MGI Research’s 20 Questions—interviews with leading technology executives, innovators, and investors. Today’s guest is Praful Saklani, CEO of Pramata. Welcome Praful. Pramata is a privately held company, please give us a sense of the scale of your business?
Praful Saklani: Pramata is a global organization with more than 300 employees. We work with upper mid-market and large enterprises, including well-known brands like Century-Link, Hewlett Packard Enterprise (HPE), NCR, Comcast Business, and many others. The company has been around for a decade helping companies to operationalize their commercial relationships. Historically we’ve been cash flow positive. With the help of some growth equity investor partners, we’ve dramatically accelerated our growth.
Andrew Dailey: What is the founding story of Pramata?
Praful Saklani: Over the last 20 years, I’ve noticed many companies created by entrepreneurs who, as the result of direct personal experience with a specific business problem, created a business to address that problem. That’s the case with Pramata. In late 2000, I sold a company that I’d founded [Yatra Corporation, acquired by Datalex], and during the sale process we had armies of lawyers and finance professionals going through boxes of contracts. These documents held all of the key information regarding the nature of our relationship with our customers. These contracts held all of the commercial data—what customers bought, what they paid, what we were contractually obliged to deliver, etc. The lawyers and accountants were going through them by hand—jotting things down on yellow legal pads and spreadsheets. It was painfully manual and inefficient. I asked them if there wasn’t a way to automate the process. Their response was “no, this has to be done by hand—if you were a larger company, we’d simply bring more people in to go through all these contracts.” It really struck me that there was no better way to get to the key data needed other than to throw bodies, time, and spreadsheets at this challenge. Fast-forward to 2006, I was looking to get into the enterprise software space. I started researching the enterprise data collection problem to see if anyone had figured it out. As I delved into it, I realized that even multinational and large companies were flying blind when it came to having to lay their hands on the key details of their most complex (and important) commercial relationships. It just wasn’t happening.
Andrew Dailey: What is the specific business problem that you address?
Praful Saklani: If you look at large enterprises and particularly those in mature industries, they have very diverse and complex revenue bases, especially when it comes to their B2B relationships. They might have hundreds of products, operate in dozens of geographies, and have completed dozens of acquisitions. When it comes to understanding key milestones and events about each commercial relationship—e.g., what a customer bought, what price they paid and agreed to, what was purchased but not activated, what the key dates and triggers for price increases were, etc., I realized that these were common questions being asked throughout the organization. It’s amazing how limited the available information is on commercial relationships and how companies have little to no clean data to make good decisions about their existing revenue base. This is the problem we address: companies are in the dark when it comes to taking critical actions related to their revenue bases—that’s because it takes too long to get an accurate and up-to-date 360-degree view of a single customer relationship, and they often have thousands or more customers.
Andrew Dailey: Companies have invested substantial sums in various systems—CRM, contract management, ERP, and billing, to name just a few. And yet, it takes days or weeks to get a comprehensive and accurate view of what a customer has purchased, how much product or service the customer has consumed, and what the customer is committed to consuming and/or purchasing in the future. Customer relationships are organic and constantly in motion. Most software systems are static. They provide a very limited, point-in-time perspective that rarely, if ever, is comprehensive and up-to-date. Why hasn’t this problem been solved?
Praful Saklani: This is an issue of transaction automation versus transaction optimization. In the last 20 years, IT has largely focused on how to automate any given transaction so it could happen quickly, at scale, and at the lowest possible cost. That’s very powerful to the extent that the transactions are simple and straightforward—e.g., there is a discrete price with limited negotiation, a quote fits within a set of manageable rules, there is one billing system (rather than multiple billing systems). In that case, transaction automation goes a long way.
However, when you have complexity in your commercial relationships, automation ceases to work. For instance, maybe your competition is fierce and your company wins by creating unique deals for each customer; that makes the deals difficult to track as they progress. Or, maybe you have global customers, but your systems are set up to handle regions or products. More recently we’ve been seeing hardware companies that now want to extend their solutions by adding software and services—but their transactional systems are geared towards processing one-time charges (for hardware systems with SKUs) and they’re not equipped to track or manage continuous services. The reality is that the more complexity you bring into your business, the harder it is to get a complete view of any customer.
Andrew Dailey: What is the perfect customer for Pramata?
Praful Saklani: It’s companies with highly complex offerings and environments that are looking to improve their monetization capabilities, increase retention, optimize pricing, improve cross-sell/up-sell results, and derive greater value from their existing relationships.
We consider companies complex if they have multiple product offerings, price structures, business models, customers, and/or industries—these are all good candidates for the solution we offer. Also, industries that are being transformed by technological disruption or by a change in competitive dynamics are good candidates.
We are seeing strong demand across three industries in particular—high tech (hardware, software, cloud), telecommunications service providers, and financial services (especially on the asset management side of the house). These industries tend to have very complex commercial relationships and a strong desire to improve results and drive growth.
Andrew Dailey: What are the typical benefits to Pramata customers?
Praful Saklani: Depending on the organization, and the part of the organization that is the primary user of Pramata, typical benefits include reduced revenue leakage (often this is 1-2% of revenue, a material boost to results), 15-20% increase in active selling time, reduction in customer churn combined with an increase in renewal rates (typically 4-5% increase in renewals), and 1-4% margin increase with existing customers.
Two key parts of the organization that we work with are: 1) the sales organization, and 2) the finance team. For sales operations and account execs with deeper and more accurate insights into exactly what a customer has purchased, what the customer has bought or used but is not being billed or collected, we can illuminate contract renewals/sales opportunities.
It sounds simple, but when you have commercial agreements that span many years, multiple acquisitions, and that are often 100+ pages in length, it’s actually very difficult to know exactly what a customer has purchased. Often there is revenue that is going unbilled—that’s what we refer to as revenue leakage. Other times, the sales teams don’t have any vision into the exact products that are being used, and as a result are unable to upgrade the customer to a higher margin solution. It’s not unusual for a company to realize a 1-2% revenue gain just by starting to bill for what is contractually due.
Andrew Dailey: What are the missing gaps across a contract management system, a CRM system, and the billing system?
Praful Saklani: In our experience, the key data that’s needed to both identify these revenue moments, or revenue opportunities—as well as to take action on them—doesn’t exist in any one system.
The data needs to be extracted from the contract in a very specific way. It’s not as simple as just saying “this is the clause” because even if you just say “this is the clause” somebody still needs to read it, and actually extract the actionable information, and connect that key data with one or more other contracts that have come before it because they may actually govern a part of that clause. This ability to synthesize information from the contract into an actionable piece of intelligence on the commercial relationship is a big leap that needs to take place to make any headway.
Contract management systems are not designed to do this. They’re designed to help with redlining, document construction, and archival storage—all of which is useful but doesn’t connect the key data with the billing system, nor do they link with the CRM system. This leaves companies with virtually no way to act on the data.
Equally, the billing system won’t tell companies what’s in the contracts that’s not being enforced or acted on, like contractual price changes. The CRM system won’t tell the company either.
To create a 360-degree view of a customer, you have to select and synthesize data from multiple systems. This is the challenge (and what we do). We bring this to light with companies who are scratching their heads that, despite people and investment in their systems, they can’t get to the information they need when they need it, so they’re beginning to look for a single source of truth that reflects their customer. We teach them about the crucial interplay of data across these systems and how to utilize it for their gain.
Andrew Dailey: How is this different from a data-cleansing tool that tries to just standardize core objects and data elements?
Praful Saklani: Almost every company that we are working with has made multiple attempts at a single version of the truth, a customer 360 view, an MDM (master data management) project, and many other initiatives. In our experience, it’s not so much an issue of creating master data objects, nor is it an application level problem. It’s a fundamental data curation and operational problem.
Creating a massive data lake that is fed by every single piece of data from contract management, billing, and other systems doesn’t do much to address the problem. As one CFO told me, it feels like I’m looking for a needle in a haystack, and the data lake proponents want to add more hay, on the mistaken belief that the needle is in the hay, and if you have more hay there is a higher probability of finding a needle.
Rather than attempting to curate thousands of pieces of data, we believe it is easier to identify and curate the six to ten pieces of key data that are relevant to a specific business need. So, for instance, if your goal is to manage renewals more consistently and effectively, you have to ask, “What are the eight key pieces of information that I need with 99% accuracy to be available across my organization to help drive renewals?” It might be just a couple of key data points procured from CRM, CPQ, billing, and contract management systems.
At Pramata, after years of helping huge B2B companies do this, we excel at identifying the key data elements needed to derive greater value from commercial relationships. For example, for a renewals manager, it might be ten key data items that inform volume contracts based on volume or consumption thresholds. We provide a very specific data approach, applications, and curation skills to ensure you have the right information in the right hands, so that you can take action with the confidence of knowing it’s accurate, timely, and relevant. This is a very different approach to data management, MDM, and data analytics/data lake strategies.
Andrew Dailey: How do you address the issue of new data coming into the environment? It’s one thing to look across existing systems, but customer relationships are organic—they’re changing every day, and the data is in-flight. I buy something today, I call you up tomorrow and say I want to buy more product A, less of product B, and add on service C—and then two weeks later I cross the consumption threshold and earn a retroactive discount for everything I use. And now I want to add a new business unit to our contract. How do you handle the constantly changing nature of the data and agreements?
Praful Saklani: When you consider what it takes to operationalize the information contained in your commercial relationships, there are four key requirements to accomplish this.
First, the data needs to be accurate, and that means that if I’m actually taking out a piece of information, can I go all the way back to the source of that information and validate that yes, this information was taken out with a 99%-plus level of accuracy? If it’s not accurate (and auditable), then any follow-on action is kind of meaningless.
Second, is it complete? How do I make sure that if I’m looking at my customer base, I have all of the sources of information for given problems? How do I avoid having 2000 contracts in another office that nobody knows about?
Third, is the data up to date? In our case, we integrate with various e-signature platforms or whatever internal distribution mechanisms are in place to make sure that as soon as a contract is signed, it gets brought into Pramata. Then that same transaction will connect with any master data system to ensure accuracy and consistency with the CRM record, the billing record, and/or any other core system. We unify the vital information, so once the transaction occurs, the data is updated.
The fourth piece is, even if you have all three of those things, then how do you make it actionable? How can someone take a specific action—whether it is making a price change, a renewal action, a compliance step, et al?
Those are the four things we focus on, and any time a deal is signed, changed, or amended, the record is brought into the Pramata platform and synthesized to drive action on the commercial relationship.
Andrew Dailey: How do customers license your product, and what is the rationale for your pricing and licensing model?
Praful Saklani: We have the standard aspects of a SaaS pricing model, including the number of users, transactions (i.e., number of deals signed each year), and some metrics tied to each customer environment. Customers like NCR and CenturyLink have deployed across thousands of end users, both within the Pramata cloud and bringing Pramata into applications like CRM and ERP.
Another option is customer and application specific. For example, we can accommodate organizations who want to do customer-specific quoting inside their CPQ platform before they take it enterprise-wide. Pramata is designed to meet the customer wherever they are in their transformation or maturity level. The pricing supports going after low-hanging fruit in a walk-crawl-run type of approach, and it easily scales to an enterprise-wide model as well.
Andrew Dailey: What is the typical ROI that customers achieve, how do they quantify the business benefit?
Praful Saklani: We work with each customer and identify the benefits of our solution in the context of their business.
During the sales process, we run a conference room pilot and ingest some of the customer’s contract and transactional data, and then detail the ROI that is within reach in days and weeks. At the end of the pilot, there is a clear business case and timeline associated with adopting Pramata.
Our implementation methodology incorporates specific business goals and quantified financial and sales targets. We push our customers to go live within 90 days, and to ensure there is a concrete business benefit delivered upon go-live. ROI is customer-specific, but we’re seeing a three to five percent increase in renewal rates in the first six to twelve months. That’s enough to deliver an upside earnings surprise for many companies, and enough to change the overall valuation of the business.
For companies with large—e.g., 5-10 thousand person sales forces, we have seen 10-15% increases in active selling time. We also have some customers who realize 3-4% revenue uplift with their Pramata system. However, it’s very customer-specific, which is why we build a business case at the outset.
Andrew Dailey: Who is the primary buyer of Pramata, and who is the primary beneficiary of the system?
Praful Saklani: The two key buyers and beneficiaries are sales and finance. Within sales, often it is the head of sales operations, and within finance, it is the CFO’s team. Sales ops benefits from having instant access to reliable information and insights into pricing. Deal desks and inside sales organizations know what a customer has bought, what the customer has used, and how the terms of a relationship have changed over time. Within finance, revenue recognition is improved and it’s easier for the finance organization to help the sales team offer the optimal pricing and terms. Ultimately the business overall benefits from higher selling prices, a better customer experience, and less internal confusion and friction.
Andrew Dailey: Which competitors do you see in every deal and which competitors do you respect the most?
Praful Saklani: Believe it or not, there is no direct competitor for Pramata at this time. There are, however, two issues we encounter in the sales process. One is organizational inertia, and the other asks the question “why do something now?” Often (invariably) there are multiple initiatives floating around the organization, and the head of sales or the CFOs asks us why this should be pushed to the top of the priority stack. We love this question, because we can share the data that quantify the value of the problem that we address. Often we quickly pinpoint the millions of dollars that are being lost because sales teams are flying blind when it comes to knowing exactly what are the most important, complex commercial relationships. Some clients see Pramata as a tool that helps extract or accelerate the value that they hope to derive from a CPQ implementation.
Andrew Dailey: What are the pieces that need to be in place to ensure a successful adoption and operation of the solution?
Praful Saklani: The prerequisites are simple—you need either a CFO or a head of sales ops who recognizes complexity in the revenue base, that they don’t have full control or understanding over that complexity, and as a result their competitiveness and/or performance is significantly constrained. It takes someone high enough in the organization who sees the value in having an accurate, up-to-date, 360-degree view of the customer.
Andrew Dailey: If you had to pick a single point of differentiation versus all the other options, what would it be?
Praful Saklani: Pramata’s biggest differentiator is that we provide an end-to-end solution that encompasses all the key data needed to take action on a company’s most valuable commercial relationships. We also provide the applications they need to operationalize information in the revenue lifecycle. We don’t give companies an empty shell and say “now it’s your job to populate it.” We extract key data by using AI, machine learning, highly skilled lawyers, and financial professionals to QA data in a very specific way. Because the data we provide is 99 percent accurate, sales, legal, or finance teams can take action on it right away. Our end-to-end approach is something you don’t see in CRM or a CPQ. In fact, CRM data integrity is notoriously poor. So providing accurate, actionable information to the front lines of sales, legal, and finance is both a first, and a big differentiator.
There are very few solutions that you can bring to an enterprise that have an accelerating effect on the ROI for existing systems. When you implement Pramata, you actually make your CRM and CPQ better because in both systems we provide visibility into existing customer pricing that’s in line with the current price book. Armed with this vision, you can automatically create quotes that would otherwise be handled manually. Plus, your financial analysis and BI tools will perform better because you’re now getting clean, accurate data so your financial analysts can identify patterns or make decisions—all without having to manually curate all the necessary information on a spreadsheet. Our customer experiences tell us that companies should implement (a solution like Pramata) as early as possible in order to make future investments more valuable.
Andrew Dailey: Simplistically, is this a turbo boost to your existing quote to cash efforts?
Praful Saklani: Yes, it’s a turbo boost. Also, if you don’t operationalize your commercial relationship data, you’ll end up with gaping holes in your data leaving quote-to-cash and revenue management strategies hindered, in the best case, and completely ineffective, in the worst case.
Andrew Dailey: What does the typical implementation cycle look like?
Praful Saklani: Customers typically go live with their first milestone, usually a significant, value-generating milestone, within 90 days of signature. That’s been the case in 100% of our implementations. We strive to find high value-add—maybe it’s within a particular customer or revenue segment of the business, where the customer can realize value and ROI in 90 days or less. From there, our adoption model is designed to continue delivering value in 90-day increments (or less).
The guiding principle of our implementation cycles is to divide the project into discrete, attainable, and measurable business results. This keeps us all focused. It might start with providing greater customer insights in CRM, move quickly to price optimization, and then go into improving renewal management. Sometimes these can all be done in parallel, but it’s designed to deliver value rapidly. That’s been the experience of our customers so far.
Andrew Dailey: What’s on your product roadmap?
Praful Saklani: There are some core elements on the product roadmap that we’re very excited about. The first one is achieving applications portability throughout the enterprise. As a SaaS provider, we have a cloud application in which you can interact with your commercial relationship data and take very specific actions within our cloud environment.
What we have learned is that a large percentage of the users that gain value from Pramata have environments where they spend 80-100% of their day. Over the last six months, we’ve introduced a core feature set that we call digital snapshots, allowing us to very quickly publish a piece of relationship data and then tie it back to the actual fragment of a paragraph or text. For example, if we show you a renewal date, we can actually show you the paragraphs in the master agreement and statement of work that accurately reflect how the renewal date is calculated. The cool part is this type of data resides not just inside Pramata’s cloud environment, but also in a CRM or CPQ system or other existing applications. Right now, we have two Fortune 500 companies where Pramata applications are enabled inside their CPQ systems. But it’s not just the data being enabled; the Pramata application also exists inside the CPQ flow so that the user can get see the pricing information for any existing customer, so they can efficiently produce a quote. This unique capability, on our roadmap, of bringing key data to you (via existing systems or in the Pramata platform), where you are, whenever you need it, is how you derive the most value from it. We think this is pretty revolutionary.
The second big item on our roadmap is being able to leverage predictive analytics. We have the cleanest customer and commercial relationship intelligence in the enterprise, and we’re working with our strategic customers to understand their predictive strategies and align our applications to their requirements. We want to give our customers the ability to zero in on the key customer relationships that will have the highest impact on the business based on a combination of factors. It’s not just about seeing which large customers are up for renewals, it’s about understanding the risk profile and a variety of indicators that can suggest the most effective sales activities (e.g., cross-sell, upsell, etc.). There are these revenue moments, as we call them, and the data within Pramata is easily translated into actionable insights for the commercial teams.
Andrew Dailey: Where do you find your talent, and how do you get the right mix of skills in the organization?
Praful Saklani: The first thing you have to do when you’re building your talent base is to have a good understanding of what you’re trying to do and what the right mix of people is to accomplish your goals. Early on, we recognized that we would need to be very good at synthesizing data from a wide range of sources. Having people with legal, finance, billing, and commercial sales experience obviously was going to be an important requirement. I have a background in machine learning and artificial intelligence and recognized that we would need very strong business analysts who could decompose business problems and parse data. The more you are able to narrowly define and refine your view of the data, the easier it is to apply technology like AI and ML. It all starts with defining your very specific needs and then attracting the right mix of people. We have hubs in Silicon Valley, Kansas City, and in India, and we’ve been able to successfully recruit and train folks in all three locations.
Andrew Dailey: As CEO, where are you allocating your investment dollars?
Praful Saklani: We’re investing in two areas–increasing market visibility and product innovations. The sales team is growing, as is the pipeline, and this means we need to continuously be investing in greater market awareness.
The second area of investment is around curated insights. What I mean by that is, we are continuously learning about industry variations and idiosyncrasies. We’re constantly learning about differences in deal structures—different pricing models, variations in volume commitments, etc. As we delve deeper into each one, we see a massive opportunity to add/extract incremental value and deliver new feature sets and capabilities.
Andrew Dailey: How many of the insights that you’ve been able to uncover come from going deeper within a specific industry versus insights gained by learning something in one industry and applying them across other industries? How much of it is going deep in an industry versus kind of cross-fertilizing across industries?
Praful Saklani: It’s very use case driven. In a use case such as renewals, for example, we’re seeing a lot of parallels between what you see in retention in any kind of a recurring revenue business regardless of industry. Retention in a SaaS business is similar in some ways to retention in a telecom business, which is similar to retention in a services business. The same is true with propagating price changes across the enterprise—companies in all industries want enterprise-wide price change visibility.
In some of the more heavily regulated industries such as telecoms and financial services, it’s really important to understand the details in order to extract the most value. Within financial services, there are some specific dynamics relating to fee schedules and agreements and ensuring they can meet compliance requirements with their schedules when it comes to billing. In telecom, for example, there is a U.S. federal government program that gives schools a price advantage. Ensuring that your sales teams are aware of this is obviously an important issue.
Andrew Dailey: A common refrain among CFO’s, CEO’s, CIO’s, and business leaders is that cloud applications today have a very narrow, siloed view of the problem they address. They excel at solving a very specific problem, but they have no view (or even interest) in their role in an enterprise-wide process. How are you unique or different than the other cloud applications, or are you just another stovepipe supplier of a single application?
Praful Saklani: The answer to this comes down to the transaction automation vs. optimization topic we discussed earlier. Pramata is uniquely focused on the core commercial relationship information that is necessary to drive a whole host of different events. We know through experience that this core commercial relationship information is necessary to effectively drive a whole variety of downstream processes. We’re not trying to automate any of those processes alone. Pramata aims to give you value across a range of processes. That’s why our value prop is so much greater than most cloud solutions.
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