Implementation Support Enables Banking Data Solutions Provider to Adopt Agile Delivery Model

Results

• 8 weeks from data collection to ingestion
• 4 white-labeled team members
• 100% on budget
• 8 active customer implementations 

Praxent partnered with Neocova, fully integrating with its implementation team, to roll out an Agile Execution Model to its customers in just two months. The model is fully optimized, with metrics and quality stage gates, as well as prescriptive onboarding and offboarding processes. In addition, security is baked into Neocova’s entire product development and infrastructure processes, from solutions to delivery to customer success. 

Thanks to implementing the new delivery model, which their internal team and customers “love,” Praxent has helped Neocova to responsibly grow from having only three customers to 13 in a span of just six months. Neocova is confident it can continue this rate of growth with help from the Praxent team to execute on its goals for the new model: continuing to practice and operationalize it while working directly with customers to fine-tune it.

Key Features/Deliverables

• Coordinate with Financial Institutions (FIs) to Extract Data 
  • Primary point of contact for FIs
  • Lead working sessions 
  • Walk FIs through data gathering from various banking systems and the configuration of data extraction processes to execute on a recurring basis
• Source Data Analysis  
  • Evaluate the scope of each data set 

• Document Data
  • Document end-to-end implementation process to streamline and accelerate future integrations

• Define Data Mapping Requirements  
  • Identify and document data mapping requirements for the extract, transform, and load (ETL) parsers that will be developed to translate source data formats into Neocova Data Format (NDF)

• Implementat Financial Institutions 
  • Utilized Fiserv Precision and Premier and CSI NuPoint 
  • Enabled FIs to ETL data from their cores

Client Testimonial

Background

“Who is Neocova? First and foremost, we service small community banks and regional banks. These banks today struggle with getting a universal view of their commercial and retail customers. Customers struggle with determining who their customers are and what products and services to provide, based on our research and based on the implementations of our products, we know they need help in this arena to identify what their customers are doing and what are good products and services to provide these customers—with the ultimate outcome of providing value to their customers, increasing revenue or fine-tuning product or service line(s) and offerings within the bank to align with changing customer needs.

“What are [our customers] struggling with? One major struggle is the lack of cost-effective business intelligence and analysis for a bank’s retail and commercial customers, and the reason why they struggle is because there are a ton of data points to collect in order to come up with a 360-degree view of their customers. In order to do that, you need modern technology and sophisticated data technologies to bring to the table. From that perspective, they struggle with the technology itself, because they typically have legacy environments and systems such as their banking core. And then they struggle with staffing a full-blown data team for the bank so they can collect all these data points and perform advanced and predictive analysis. And they typically go into a DIY or do-it-yourself approach, which on average requires them to invest around an estimated $1.2 million a year. And they’re quickly finding out that after that $1.2 million on average of investment they’re really not hitting the mark. They’re missing that, 360-degree view of their commercial or retail customer. And that’s creating massive competitive pressure against larger financial institutions. Smaller community and regional banks struggle to compete from a digital experience and having that deep knowledge of how a customer leverages their banking services and which products to refine or create to generate growth is crucial. That’s one major aspect of what we solve. That’s the higher-level problem statement. 

“The other area is our ability to not only modernize the banking infrastructure from a data perspective, but once they get a hold of these data points, then we make use of artificial intelligence and machine learning to help them predict certain parameters moving forward. What that means is, once you understand [the customer]—let’s say I understand who [my customer] Christina is—then I can predict what she’s going to need. For example, if I see transactions that she’s shopping at Home Goods or at Home Depot for two or three weeks, and the transactions together come up to $6,000 or $7,000, I may want to offer her a home line of credit based on the equity of her home. That’s just one example. There are other examples, like if Christina’s direct deposit goes away after a couple of pay periods, then maybe that’s a red flag that she’s going to leave the bank, and maybe I want to get ahead of that and through my CRM such as Salesforce … reach out to her proactively, and say, ‘I noticed you may be modernizing your home, can we help with a low interest loan? We’re here to help.’

“Our model for delivery involves a very finite set of [user] stories that are very business focused. We don’t go in there and say, ‘Okay, I’m just going to drop in some software and then connect to your database.’ We actually interview the business and we develop user stories, which our delivery team puts into our model for delivery. Praxent is a big part of that. They helped set up that whole model: We deliver against business stories; we don’t deliver against technical feature requirements. Things are changing very rapidly in the FinTech ecosystem It is just amazing to see the speed of innovation, speed of delivery, and speed of modernization. That’s where we’re headed from a product and solutions and delivery perspective.” 

The Problem and Solution

“The ‘before’ is, there wasn’t a delivery execution model. The ‘after’ is the starting point of making the delivery arm of a software company scalable to tens, hundreds, maybe even thousands of customers. This model allows for a startup like us to reduce the pressure cooker approach on product, engineering, and delivery. 

“There’s a stage of a startup where you’re at a certain size, and you’re accelerating in your customer adoption, but for many reasons, the company may not be linearly increasing the capital that it takes to sustain that growth. If we didn’t have this agile execution model, it would have been a very difficult situation, because it would have applied this massive pressure on our product and engineering team to deliver based on old-school principles that require a significant amount of capital to deliver tangible results in a timely basis. As a fast growing startup, you may run into a situation where It’s ‘We bought the subscription, we bought the service, I need it in two weeks,’ versus ‘We bought the subscription, but we understand this as an agile approach and solution to a complex data and analytics problem. Data is everywhere; it’s a mess. We’re going to iteratively get there together.’ The latter relieves a lot of the pressure, so it has benefits for the startup. But it also has benefits for the customer. Because they get to see what’s being delivered every two weeks and adjust to hit the mark. Customers focus a spotlight, for lack of a better term, on the roadmap or product, items that they would like to see prioritized more, as well as pre-existing data cards from a catalog. Customers can also benchmark themselves and have a huge advantage around ‘What is my peer bank doing around data, data analytics, or predictive analytics? How do I compete?’ That’s the type of information we can provide to them much faster because of the way we operate versus waiting for a six- to eight-month product cycle … because it’s all about speed. It’s all about speed and how fast you can deliver new innovative banking solutions to customers.

“Another metric we track is time to insights as well as time to data ingestion. How fast can you ingest the data? How fast can you make sense of it, and how fast can I drag insights out of the data? The ‘before’ for our customers is, there is manual or spreadsheet based analytics or there wasn’t a model at all. Neocova solves this big problem. From a Neocova perspective, there wasn’t a robust delivery model in place. And now it’s a completely different, super-optimized agile model with metrics and quality stage gates, with bookends—there’s an onboarding process; there’s also an offboarding process. There’s security throughout the entire process, both … from a product development perspective and an infrastructure perspective—our security team has engaged [Praxent] in all areas of our solutions and delivery process and customer success as well. … It’s night and day. I was floored we were able to go from nothing to something in a matter of two months. That was impossible without Praxent. And we worked together to put this model together, which I think can be replicated quite easily.

“The internal team, they love it, because now they have that subject matter expertise and a very controlled delivery process. Our product managers, our VP of engineering, and our CTO, they’re loving this model completely. They’re extremely happy with this model and the way Praxent operates in and from a customer perspective. They also love it because they feel like they’re part of the process of getting this—they understand this is a new product sector in their industry. 

“A lot of our customers have attempted to do this on their own, and they’ve hit a brick wall, saying ‘We need the expertise to actually put something in place,’ and that’s where they rely on us. They already have some level of understanding saying, ‘This is difficult stuff,’ and in collaboration with us as well as with Praxent, they get to actually participate in delivering this type of solution to their bank and, in the end, improving the whole journey for their commercial and retail customers. And they love it. They’re really engaged, as opposed to an old-school model that’s a waterfall in nature, with much less engagement and just milestone checkpoints. At Neocova, we have what we call the iteration review and the customer delivery all built into the delivery process, and it gives immediate feedback directly to product and engineering, which is great.”

Why Praxent

“Even before joining Neocova, I was in contact with Matt Beecher, the CEO. We discussed what a good partner would look like moving forward. And a couple of things popped up. One is subject matter expertise in banking, which is critical, absolutely critical, and the other is coachability. Because the model that we put into place is a new model; it’s not typical in startup environments. 

“The first four weeks of ‘Hey, let’s work together,’ everybody was scratching their heads like ‘What is this thing,’ this agile execution model that puts together product and engineering all the way through delivery using metrics and key stage gates and so forth. The way the model works is, a customer basically is prescriptive. onboarded very specifically onto this engine. This engine basically takes their user stories or business requirements and puts them into a cycle that is two weeks in length. That then goes right back out as a product into the customer’s hands. Part of that process is actually doing the data discovery, data ingest, data manipulation, and data transformation. It’s interesting because it’s not your typical project lifecycle. In typical organizations, the product and engineering teams are not directly aligned with the solutions and delivery and customer success teams. At Neocova, this model is actually aligned 100 percent. As a matter of fact, product and engineering is part of our solutions and delivery team. 

“[Praxent’s] coachability and ability to say, ‘Let’s step back and listen to the customer,’ was instrumental in our relationship. So those are the two key things: subject matter expertise in banking [and] stepping back and really listening to the customer and saying, ‘Well, this thing is new. Like we’re not 100% sure, but let us work together to implement it, find the kinks, fine-tune it, and then measure success, which has been phenomenal, and how we took Neocova from three customers in June to 13 in October. 

“All this was built in conjunction with Praxent, and that’s why it was so critical to work together and have that collaborative nature versus the ‘We’ve done this for a thousand years. Let me tell you how to do this type of approach,’ which I don’t think would have worked because of the way we were setting up the business and where the business was at that stage.”

Results

“With the existing resources we have and with the existing team, we could easily run a portfolio of 30 customers at once. And it’s a very small team [between Praxent and us]. Our little crew is handling, right now, 13. We can easily scale up to 30. And the onboarding process is so prescriptive that if we wanted to, we could onboard four customers a day. If there was a situation where ‘Whoa, we’re completely killing it in the market,’ and we’re scaling, then I have a model for scaling the team as well. So if we go from X number to X number of customers, we go from four Praxent team members to eight to 12, etc. And that gives you that huge scale factor: from 30 to 60 to 120 [customers]. It’s very calculated. 

“I think that was a huge eye-opener for us as leaders in the business, to be able to operate a business like this that has a complex product portfolio in such a way that’s just awesome. I can’t express the level of calmness and the level of assurance that this model brings into both Neocova’s operations as well as customer operations. There’s no guessing. Everything’s so transparent because of the way we operate. It’s great, and the scale is there. Everything gets staggered across different iterations for different customers and the scrum masters know exactly where each customer is, and each scrum master can handle 5 to 10 implementations.