Tag: Client Experience

Client Experience Manager

Location: East Coast

Blooma is the leading AI-powered digital underwriting SaaS platform for commercial real estate. The Client Experience (Adoption) Manager is responsible for expediting time to value in a client’s first 90 days and driving ongoing adoption and engagement throughout the entire lifecycle for our users.

We are looking for a candidate with experience in commercial real estate (ideally with underwriting background), with strong communication skills, a history of helping clients achieve value by driving product adoption, and a passion for technology.

This role will require you to work cross-functionally to ensure Blooma delivers a best-in-class client experience that allows client’s to achieve their objectives by leveraging our industry leading SaaS solution. Success in the role will require a self-starter who organizes themselves for success with a passion for solving complex problems and using data / insights to drive meaningful client engagements.

Responsibilities 
  • Manage onboarding activities, including user training, across a portfolio of client onboarding projects, ensuring a successful and timely completion achieving key milestones to ensure an accelerated time to value
  • Drive product adoption and engagement to ensure users achieve business outcomes and value
  • Use critical problem solving-skills to build workable solutions in close collaboration with client-facing teams that include (Client Success, Sales, Professional services, and Support) to ensure the client remains on course
  • Utilize product adoption data and client insights to drive meaningful engagements 
  • Effectively communicate technical details and business outcomes to a diverse group of internal and external stakeholders
  • Identify and drive efficiency in the onboarding process by developing best practices and process improvements on an ongoing basis 
  • Become a Blooma product expert
Required Experience
  • 4+ years of experience in a client-facing adoption role in a SaaS technology company 
  • Background in at least one of the following: banking, financial services, underwriting, credit analysis, asset management, or commercial real estate
  • Bachelor’s degree or the equivalent 
  • Proven ability to effectively manage a group of users across a portfolio of accounts and/or projects
  • Self-starter with a solutions-oriented attitude
  • Excellent communicator 
  • History of leveraging product adoption data and/or client insights to drive meaningful engagements 
  • Ability to build client relationships
  • Adaptable and flexible

Apply using the form below or send your resume directly to jeffreyjavanbakht@blooma.ai

    Resume

    Cover Letter

    How to Build a Disruptive Product for a Conservative and Highly Regulated Industry

    Tal Almog, Chief Operating Officer

    Seven tips for success.

    I’ve been designing, building, and commercializing enterprise software products in several industries for the past 20 years. When Blooma was founded, I faced the new challenge of building a disruptive commercial lending platform for one of the most conservative and regulated industries – banks and financial institutions. Looking back at the last 18 months, it was an exciting and enlightening journey. We are getting great feedback on our product, particularly on our ability to utilize artificial intelligence to automate data collection, calculations, and valuation models, while enabling lending professionals to use their experience and talent in the art of their work.

    I have learned a lot along the way so wanted to share my 7 best practices for anyone planning to build and launch advanced technologies to conservative industries that may just be catching up on disruptive innovation.

    1. Build the product with customers for customers. We developed our product together with to-be-users, by partnering with future customers and hiring professionals from their specific industry. This is especially critical when the product team lacks experience in the industry that they are building for. The combination of views from both inside the targeted industry (for better product fit) and outside of that industry (for the “art of the possible”) typically leads to better innovations.

    2. Combine user-centered and vision-centered innovation principles. Start building your product by applying user-centered innovation methods. Strive to understand the needs of users through deep analysis of their behaviors and how they interact with existing products. Later, vision-centered innovation methods should be added. This is required in order to focus both on doing the same things better, and also on doing things differently. During our development process, I pushed the team to bring up ideas and concepts that customers did not specifically ask for or did not think were technically possible, and some of those ended up being key parts of our solution.

    I have learned a lot along the way so wanted to share my 7 best practices for anyone planning to build and launch advanced technologies to conservative industries that may just be catching up on disruptive innovation.”

    3. Build flexible workflows using multi-services architecture. Each customer has their own way of doing things and requires the technology to adapt to their own workflows. This is simple when you build a custom solution, but much harder with a SaaS platform that is used by many different customers, where typically “one size fits all.” Early on, we decided to build a flexible architecture, allowing users to configure their own scoring algorithms, workflows, and how they would like to consume the asset and borrower analysis (in our intuitive UI, a summary report, or even directly feeding their own Excel spreadsheet models).

    4. Build your human/machine interaction to be mutually beneficial. No matter the level of sophistication of the artificial intelligence and automation that you develop, it must be clear that the human users are always in control. AI can enable customers to make scientifically informed decisions, but it should not make decisions for them. Our product provides ways for users to validate AI-driven information gathering and to easily correct it where needed. For example, we use machine learning models to select the best sales and rent comparables for an accurate valuation, but users can still include or exclude comparables based on their specific experience and knowledge of the area. Not only do users end up getting better data that exactly fits their needs, but these interactions help train our AI models and make them even better.

    5. Pay attention to the unique needs of the sector you’re serving. If you’re working with AI, the type of models that you develop must fit the needs of the specific industry that you’re building for. In more conservative and highly regulated industries there will always be a need to know exactly how the machine came to the results that it did. You should avoid deep learning models which typically don’t provide reasons or explanations. Think of the machine learning-based movie recommendations in Netflix. Based on a list of movies that you’ve watched, they tell you that you’ll probably like a certain recommended movie. If Netflix recommended a movie for you without explaining why, you might be less likely to pay attention. And in a heavily regulated industry, a conclusion like that without an explanation would not be acceptable at all.

    6. Keep it simple and avoid developing complex technology where it’s not needed. Technologists many times are so focused on the art of the possible that they build features that will never be used just because they’re cutting edge or the latest trend. I say, where advanced technology doesn’t make sense, don’t use it! For example, you can spend months building complicated screens and models that are expensive to build and hard to maintain, when what users really want is a simple download to an Excel spreadsheet. When I review each proposed new feature, I always consider usability, simplicity, and maintainability in equal measures.

    7. Build the “leap” disruptive innovation in parallel. Sometimes customers (or industries) are just not ready for truly disruptive innovation. So, while adhering to the previous recommendations, I always maintain a product innovation track where we research and develop truly disruptive innovation that might not be accepted today but could be eased into in the future. For example, today users would probably not accept making loan decisions based on an AI model, but we can build this model in the background while still allowing users to analyze risk the way they are used to. Then sometime in the future when we can show them how accurate the AI model would have been in predicting a loan default, they might be ready to adopt it.

    Hopefully you’ll find something helpful in these 7 tips. Introducing something new to a sector that has been satisfied with doing things the old way for a long time can be daunting. But it can also be fun, especially when you know that you’re truly making a difference.

    The Life and Times of a Former CRE Underwriter

    Geoffrey Eng, Director of Customer Experience

    How the traditional approach can lead to more distractions than deals.

    As Blooma’s Director of Customer Experience, I work a lot with lenders and brokers, especially their commercial real estate underwriting teams. I’d like to think I’m familiar with the motivations and struggles of our clients, since in a former life I used to be a CRE underwriter as well. The truth is, CRE underwriting and analysis processes are very fragmented, which leads to inefficiency.

    Much of my job as an underwriter involved the management of many disparate data sources. For as large and mature as the CRE lending and investment space is, underwriting processes and innovation have not evolved or kept pace.

    Got a new deal coming in? You’ll start by pulling up a model you used on a prior like-kind deal, and then overwriting the data. You then begin searching for sales and lease comparables on multiple sites, speaking with brokers about transactions and trends in the area, reviewing maps and street views to analyze the area surrounding the property, rounding up and reviewing title data, creating proforma cashflow models, the list goes on. Not to mention requesting and analyzing borrower financials, all in different mediums. You become a data aggregator, pulling in files, documents, and data from numerous sources and saving them all into a shared folder, drive, or system where they come together and start to make some sense. All of this work ultimately culminates in a final Excel model and a credit report/memo drafted in Microsoft Word. This is a painful process, and no matter how good you are at it, by its nature it is prone to errors.

    “You become a data aggregator, pulling in files, documents, and data from numerous sources and saving them all into a shared folder, drive, or system where they come together and start to make some sense.”

    An underwriter’s true job is to come to a binary decision as accurately as possible. Are we funding or passing on this? That’s it. But don’t get me wrong. Underwriters do not have a simple job even if their end goal is to make a decision one way or the other. And the “noise” of the process described above doesn’t help to get there – in fact I think it can mostly distract from this goal.

    I think technologically we’re at a bit of a crossroads. Advancements in artificial intelligence have made it possible to take the more monotonous and time-consuming parts of the job of a CRE underwriter and hand a lot of this to computers. Tasks like parsing through borrower, financial spreading, searching for comps, etc. This allows underwriters to be much more efficient, and frankly frees them up to review each deal more analytically. To use their vast experience to engage in a strategic conversation about a deal and whether it should happen, which is what they do best.

    If we hadn’t developed this approach at Blooma, someone else would have because its time has come. The only question now is how quickly lenders and brokers will embrace it. I’m excited working with all of the lenders and brokers that already have. It feels good to make the jobs of CRE professionals a little more enjoyable, by helping transition them to a newer way of doing things. It wasn’t that long ago that I was one of them.

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