Chapter 8: Building a Pricing Ecosystem
Banks spend as much as 70% of their total technology budget on maintaining their core systems, but that is not where value is created.
In Chapter 7, we told the story of Ryan, a client who was reluctant to share information with his lenders. Ryan did eventually come around, and the bank’s newfound transparency helped them become one of the top performing banks in the United States.
But it wasn’t as simple as saying, “Let there be light!” A change of that magnitude in both culture and process is a daunting task to undertake. Sharing may be a lesson taught in kindergarten, but it’s a skill that’s often in short supply at large banks, leading to substantial roadblocks when attempting real transformation.
To solve this problem, Ryan’s bank knew they needed to overhaul much of their organization, including people, processes, and much of their existing infrastructure. They began by hiring Dean to function as their Chief Pricing Officer.
Dean didn’t jump right into the math of Price Setting. Instead, he led the systems overhaul that would enable the Price Getting. (See Chapter 2 and Chapter 3 for more on Price Setting and Price Getting.) Dean knew that establishing an efficient and transparent pricing process would require much more than just an update to the bank’s dated pricing model. It meant building an entire pricing ecosystem.
What does a pricing ecosystem look like? To start, let’s back up and take a look at the typical building blocks banks have in place.
The Heart vs. The Brain
How do banks really add value? How do they make money? Those questions should not only have easy answers, they should also be the driving force behind the bank’s most important strategy decisions. So why aren’t more banks pointing resources to the systems that help them get better at those two things?
The answer, unfortunately, might just lie with the marketing genius who decided to name the bank’s accounting software the “core system.” Every bank we’ve ever talked to has a love/hate relationship with their “core” and the vendor that supports it.
The issue is that we are asking these systems to do things they were never designed to do. Core systems were built for debits and credits, making sure that everything balances and that we don’t lose any pennies. They are the epitome of a commodity in the banking business in that there is really no way to differentiate based on them. Keeping track of transactions and balances is the absolute minimum requirement, and everyone ends up delivering the same end product to the customer (an accurate accounting of transactions).
So, yes, the core is vital, which is why we call it the “heart” of the bank. You have to have it, and its job is to circulate basic but essential elements to the other parts of the bank. But, it was NOT designed to be a central data warehouse from which we can manage the bank.
The brain is the collection of systems and processes where banks make decisions that generate real value and returns. Here’s what it looks like when those tools are put to work closing a valuable deal.
Generally, the sales process is tracked in some sort of CRM system, you negotiate through a pricing and profitability tool, and then the deal runs through various workflow systems (underwriting, doc prep, etc.) to get to closing. After it is on the books, the same CRM and underwriting tools will be used to service and manage the loan, and you will use other portfolio level resources to measure the profits and risk so that the bank can adjust strategy where necessary. Altogether, the systems will look something like this:
The brain is where banks build relationships with prospects and customers, it’s where they analyze and price the risks they are taking, and it’s the means by which banks can attentively service and support their customers once they are on the books. Those three functions are the ONLY ways that banks truly differentiate themselves and generate value for all stakeholders.
When we walk through this “heart versus brain” layout with banks, they almost universally agree with the principals, including the argument that the brain is where they add value. And yet, if you compare the amount of resources allocated to each, including vendors and dedicated personnel, spending on the heart systems still dwarfs the spending on the brain systems. This disconnect between cost and value is one of the roots of the industry’s vulnerability to new competition and customer apathy.
So, how can bankers solve this problem? The answer is twofold.
First, they need to continue pushing back on their core vendors. The basic strategy employed by the vendors has been to make it so painful to switch that you will put up with the continued price increases, the forced bundling, the slow updates, and the inferior service. There are plenty of consulting groups that can help you renegotiate, and it is generally worth the time and effort to do so. Pushing may not always result in lower costs, but it should result in better products to justify the price.
Second, when it comes to technology spending, bankers need to shift from “cost-cutting mode” to “value-adding mode.” There is still plenty of growth and revenue to be had in banking, and plenty of opportunity to differentiate. To do that, though, bankers will need to start investing heavily in brain systems and in personnel with the expertise to integrate those systems into the bank’s practices and culture. Doing so will build a better customer experience, and bankers know that this has always had a lucrative ROI that far outweighs the impact of simply cutting costs.
The “Big Four”
To get a better understanding of just how powerful and effective a banking ecosystem can be, let’s take a closer look at the most important systems – i.e. “The Big Four.” We’ll explain what each system does and how it fits into the overall ecosystem.
Customer Relationship Management
Customer Relationship Management (CRM) systems seem to have a fairly nasty reputation in the banking industry. Blame it on the fact that banks are the ideal candidate for CRM, and therefore many tried to adopt those systems when they first became available. Unfortunately, early CRM tools were big, bulky, and expensive, and they didn’t really integrate with anything the banks already had in place.
On top of that, early CRMs were incredibly tedious to use. Management teams wrote big checks, and then spent the next several years trying to threaten and bludgeon their employees into actually using them. Management wanted access to data and insights on their sales process, but getting that data required consistent manual input from the staff. The hill simply proved to be too steep in most cases, and the projects were abandoned more often than not. CRM projects started to be viewed as career killers, and many bank executives still carry those battle scars.
However, the tide finally seems to be turning. The CRM technology of today has transformed to be nearly unrecognizable from the earliest versions. These are not your father’s CRM tools, and the allure of a more efficient and transparent sales process is finally coaxing bankers back to take another look. They know that in order to succeed in today’s competitive market, the stack of yellow legal pads in the cabinet can no longer serve as the method for tracking and measuring sales.
Modern CRM systems are usually cloud based, and have become much more than just a way to track sales calls. Many organizations are using them as the foundation for their entire sales, approval, and origination process. Unlike your core, they have been built from the ground up to handle data coming and going in multiple directions. Lots of data.
A CRM should serve as your central repository for all institutional knowledge about each of your customers. It will tell you who that customer is, what business you do with them, how profitable they are, and what opportunities there are for earning future business.
In addition, CRMs are excellent stage tracking and task management tools. They can track the history of all activity with each customer, and can be used to schedule future tasks and activities. Think of the CRM as the conveyor belt in your loan assembly line. Once a loan is created on the system (as what is usually called an “object”) the status can be moved from one process to the next, with an employee completing the necessary work and then handing it off to the next person in line.
Once this is in place, you can actually see the production process in motion. We know bankers who have dashboards in place measuring sales velocity, how many calls it takes to generate a deal, and how many days it takes at each step in the process. Bottlenecks are found and fixed, and the entire organization gets smarter about selling and servicing their customers.
All of that functionality is why the CRM is not only listed as one of our “big 4” systems in the brain of the bank, but is at the beginning of the loan origination process. It is where your conversations with customers start, and where you should start your work. Then it can act to keep moving the deal forward until you have met your customer’s needs as quickly (and profitably) as possible.
Pricing & Profitability Management
Pricing and Profitability Management is the easiest one for us; it’s the box we fill at PrecisionLender, and the basis for much of this book. As we discussed in Chapter 1, pricing is the most powerful performance lever that a bank has, and yet, many banks treat it as an afterthought.
We see the full spectrum of solutions in this space, ranging from no solution at all (“The market dictates pricing, so why bother?”) to elaborate homemade models. The issue with nearly all of them is that they are entirely disconnected from the rest of the sales process.
The reality in most banks is that pricing is a seat-of-the-pants decision from a lender, based on competitive offers that are determined in a similar fashion. Then, when it’s already too late, that deal is plugged into a cumbersome pricing model that is nothing more than a glorified calculator. Even worse, it’s a calculator with a binary outcome; the deal either passes or fails the hurdle rate. With this setup, it is actually impossible for a pricing system to create value. At best, it will cause you to say no to some bad deals that are already well into your process.
The solution, as we discussed in Chapter 4, is to move the pricing decision as close to the customer as you can possibly get it. In terms of systems, that means it should be directly connected to your CRM. This strikes some bankers as an odd pairing. Why would we attach a pricing model to our CRM? Isn’t pairing the warm and fuzzy relationship stuff with the cold hard reality of the math a little like mixing oil and water? That might be true in a traditional business, where you are attaching a price to a separate, tangible product. The price for an iPhone, for example, doesn’t need much CRM interaction.
In banking, pricing is the product. The structure and terms attached to the funds you lend are the solution for your customer, and to get the right fit, you absolutely need all of the context provided by the CRM. In addition, it is the next logical step in the process. Once all of your business development efforts managed in the CRM result in a live opportunity, you will move into formal discussions about the right structure and price for that funding need. For maximum efficiency, lenders should be able to jump directly from their opportunity to a pricing discussion, and have the full benefit of knowing the history, relationship status, and profitability of that customer.
All of that context should be combined with a pricing tool that is designed to enable lenders, not block them. Most pricing systems are designed with one goal in mind: keep lenders from doing bad deals. A successful system, though, should approach it in the exact opposite way: help lenders do great deals.
This may seem like a subtle difference, but to the lender, it changes everything. Instead of a pricing model being one more thing that can trip up a deal, and keep them from serving their customer, it becomes a sales and negotiation tool that helps them find ways to help. What does this really look like? Most pricing tools communicate red lights and warning labels to the lender with these sort of terms:
- “The maximum fixed term is 7 years.”
- “Non-recourse loans are strictly prohibited.”
- “No loans with LTV >75% will be approved.”
We have seen huge improvements in performance by altering this approach to using green lights that highlight suggestions. Instead of a list of things to avoid, use a list of options that work. It becomes a “menu” of solutions that will benefit both the bank and the borrower, and helps the lender work through these options with the customer.
Changing the perspective of the tool in turn changes the perspective of the users (which should be the lenders, since they’re the ones actually negotiating the deal), enabling them to influence outcomes in a big way. Lenders using this approach win more deals, they win better deals, and they earn loyalty from their customers. Strong relationships aren’t built on the golf course; they’re built at the negotiating table.
The real magic of integrating pricing and CRM, though, happens after the deal is priced. The pricing of a deal creates a wealth of usable data, as we now have deal terms, scenario details, and profitability results. Those profitability results cover not just the new opportunity, but also the profitability of all of the existing business you have with that customer. You now have profitability metrics on individual accounts, bundles of business, and customers that can be rolled up to measure products, lenders, markets, risk grades, collateral types, and dozens of other categories. All of this can be pushed back into the CRM, where it creates context for all future discussions with that customer. And, you get it without having to manually enter more data into the CRM.
Banks that have figured this out are the ones that are taking market share from their competition. They often come into deals with unique offers that cause other banks to scratch their heads and wonder: “How can they possibly offer a deal like that?” The answer isn’t that they are simply more aggressive. Their advantage is that they have an insight the competition doesn’t, because they have a complete picture of that customer and how winning this next deal impacts their portfolio.
After the deal is priced, it then moves on to the next systems in the value chain – origination workflow management and portfolio management.
Origination Workflow Management
Origination Workflow Management (we’ll call it workflow for short) covers several functions, but the big picture of this phase is that it’s where we underwrite and document the transaction. While that sounds simple, the reality is that many of these processes get complicated and messy. This is the first place bankers think of when working on efficiency, as there are sometimes dozens of disparate systems involved that don’t communicate with each other. Employees end up keying in the same information multiple times, leading not only to slow turnaround times, but also rampant errors and discrepancies. Banks also end up with data spread across these siloed systems, making it nearly impossible to report on and evaluate risk. Risk grades, collateral, and guarantor data may all be stored in different systems, and none of them link back to the core.
We see banks taking a couple of approaches to solve this problem. There are a few vendors that have started offering a comprehensive “origination system” or “operating system” for commercial loans, and they have gotten significant traction. The upside is that everything comes in one package, is seamlessly integrated from day one and the origination process is significantly streamlined. The downside is that, as you can imagine, implementation can be a beast. The system touches EVERYTHING in the bank, and requires an overhaul of just about every process in the loan function.
The second option we see banks taking is to use the CRM as a tracking tool to coordinate all of these systems. Remember, these platforms do a great job of tracking stages (originally designed for sales stages) and task management. With that functionality, banks are able to create stages, with approval authority, that encompass not only the sale, but also the origination.
For example, a stage can be created called “underwriting,” and only certain employees have the ability to move it from “underwriting” to the next stage. When it is marked for underwriting, a series of tasks can be triggered and then assigned, like having an analyst evaluate financial statements and order appraisals. All of the progress is tracked in the CRM, even if other tools are used to actually complete the work. Then, the results can be stored in a document repository with links in the CRM. It simply acts as a central hub so that anyone involved can see where a deal is in the process, who owns which tasks, and find the documents and data they need.
The key, of course, is the system has to be used consistently across the organization. This only works if the next steps get kicked off by moving the stages in the CRM – in other words, it must be properly documented within the CRM before the next steps begin. Analysts can complete their tasks, and then move it to the next stage for official approvals, and then it must be marked as approved to start preparing closing documents.
Whatever the mechanism, though, the workflow systems will need to receive the deal terms from the pricing solution so that they can be underwritten (Is this deal really a grade 3?) and documented (loan agreement matches the economics of the pricing and structure) accurately. This can either be handled as a direct connection to the pricing system, or those deal terms can be pulled from where they are stored in the CRM. Once all of the workflow processes are completed, and the deal closes, it gets uploaded to the core system, and from there it will show up in the data feeds to the pricing system to measure its profitability going forward.
Last, but not least, are the systems we refer to as Portfolio Management. In this section, the bank is evaluating all the business that has been booked at an aggregate level. Here, the management team can monitor levels of interest rate and liquidity risk, analyze profit trends, allocate capital, and determine if the tradeoffs for credit risk and return are appropriate. The hard – but critical – part of this step is linking it back to the tactical business decisions that are made every day, instead of just creating stacks of reports and vanity metrics that don’t ever lead to action.
That struggle is the reason that portfolio management should be closely connected with pricing. The bank’s portfolio metrics should determine the appetite for specific types of deals and risk going forward, and deals can be priced accordingly. Pricing then becomes the steering mechanism by which management can allocate the balance sheet according to high level strategy.
Of course, all of this requires data to serve as a feedback mechanism. How is the pricing translating to production? What types of deals are we winning, and at what profit levels? Where do we have opportunities to achieve growth without relaxing our risk standards?
This is data that is available in your pricing system. Once you have a grip on how current pricing targets are driving production, you can close the loop by tweaking those targets in the pricing system to get production to align with the goals for the balance sheet.
For example, if you are nearing concentration limits in commercial real estate, you can increase the targets for those, and use that limited shelf space for only the best, most profitable, deals. If you have an exposure to rising interest rates, you can adjust targets to win more variable rate deals and core deposits.
Dean’s vision was impressive, but also daunting. It meant gutting the bank’s entire operational platform and rebuilding it. However, a few key philosophies helped guide the way.
First, Dean’s team didn’t try to do a complete overhaul all at once. They broke the project into manageable pieces, focusing their early efforts on where they would get the biggest impact. There was a “master plan,” but it was executed in bite size chunks.
They decided to start with a pricing system, knowing it would have the biggest revenue impact and thus would help pay for the rest of the overhaul. Following closely behind (and as a part of the selection criteria for the pricing system) was a CRM that would act as the “conveyor belt” for the entire process.
Second, they chose systems that would “play nice” with all of the surrounding systems. This meant looking at options outside the list of typical bank vendors, who tend to build closed systems and charge dearly for clunky integrations. Dean chose vendors that embraced APIs, software that makes connecting all the systems much simpler and more efficient.
And third, Dean’s team added resources and capabilities so that if something wasn’t available out of the box, they could build it in house. Yes, that meant writing some code. But, it wasn’t code to build complex and hard to maintain proprietary systems; it was code to connect and automate various parts of the ecosystem. Having already selected the right vendors and systems, Dean’s coders were often just connecting already available end points, or requesting that new end points be surfaced by the vendors.
In less than two years, Dean’s bank had overhauled the process from end to end. It took a lot of time and resources, but they now have an infrastructure in place that is tangibly different from the competition. Not just in terms of efficiency – though it certainly is that – but in terms of customer experience.
Their commercial customers now get fast, personal, and customized responses from the bank, and employees don’t have to spend their time and energy focused on internal procedures and policies. Instead, they have very customer-focused conversations that help them best match the bank’s products to the customer’s needs.
The results speak for themselves; Dean’s bank has generated organic growth of nearly 40% per year since putting the ecosystem in place, and has done so with higher levels of profit and less risk than they had while the portfolio was stagnant. The ROI on the ecosystem is breathtaking, and they are able to use those profits to continue evolving and refining the system.