Customer Experience

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Model Data for Servicing Data Models

Your data is precious. But is it being leveraged correctly to tell the whole story of your homeowner’s experiences? Better yet, is your data accessible enough to be leveraged?

Data driven companies win the day.

According to McKinsey, data-driven companies are 23 times more likely to attract more business, 6 times more likely to retain customers, and 19 times more likely to be profitable than companies that aren’t.

Most companies think their data culture is better than it is.

 

On the flip side, according to Alation’s (named by Forrester as a Wave Leader in data governance solutions) State of Data Culture Report (2020), 54% of data leaders say their c-suite ignores data for gut feelings. The report also states that 66% of companies self-access their data to be better than it is. Only 33% of the 66% of companies who self-accessed their data culture grade to be an ‘A’ actually received that score—showing that many companies possess a Dunning-Kruger bias for their company data. 

7 Steps to Data Modeling 

The fact is data management is very complex. It’s an industry in and of itself, and people spend up to half their lives getting PhDs in data analytics. 

At a very high level, there’s a standardized process to prepare (or model) the data an organization interacts with that ultimately provides valuable insights. In general, data modeling can be broken down into the following seven steps.  

  1.  Identify the Data: Identify the source, frequency, and format of the data.
  2. Define a Data Lifecycle: Each company must define its data lifecycle. A data lifecycle must set a termination date to prevent data pileup—this is critical for data privacy.
  3. Clean-up the Data: There has to be a process in place to review data for quality and fitness for purpose.
  4. Store the Data; For privacy, security, and access controls, data could be stored in a data lake (or repository). Here the data is cataloged/indexed, so the next person knows how to use it. Data lakes are a complex topic; Upsolver has an informative article on the subject.
  5. Monitor the Data: Look at the state of the machinery/mechanics and get notified of abnormalities.
  6. Surface the Data: Two use cases for data are exploratory and operational. Today, many businesses have multiple digital tools to help with their data goals.
    1. Exploratory: With exploratory data, companies use management tools such as Tableau to gather and analyze data from various sources.
    2. Operational: Operational data are constant feeds of information presented to an end user. Ecommerce and marketing companies use tools such as WakeupData to manage large amounts of operational data. 
  7. Act on the Data: Business transformation occurs based on accurate data and its signals. 

10 Complaints About Data Analysis Tools 

As mentioned above, effective data solutions are challenging to grasp. Even the experts who work at the companies creating the tools companies utilize to analyze the data don’t always get it right. That’s because they’re building a generalized tool for their target market. 

But, as we all know, dozens of industry-specific use cases for data make it challenging for software developers to cover all of them. So, unless a company builds a data analytics tool internally, they have to pick a tool that either has features it doesn’t need or find workarounds for missing features. 

Cumul.io, a leader in embedded SaaS analytics, composed a top-ten list of complaints about the feature sets of commonly used data SaaS analytics tools.

10. Limited Sharing Options

Many data analytic tools aren’t built with integrated sharing options, leaving companies having to share screenshots through email and chat. 

9. Disconnected From Normal Workflows

Spreadsheets not importing or exporting correctly are prime examples of this, but they can also manifest in other ways.

8. No Collaboration or Alerts

Collaboration and alerts are features many users don’t know they’re missing out on unless they’ve experienced them in other tools. The ability to comment/suggest in tasks directly (instead of sharing images or discussing in chat) or receive direct alerts on critical matters are essential to efficient workflows.

7. Dated or Poor Design

Of those surveyed, 20% referenced poor design choices, giving further credence to the importance of UX/UI in both B2B and B2C software. Overcrowded dashboards, primitive design, and wrong chart choices were among the top complaints about poor design choices. 

6. Slow Performance

According to HubSpot, website conversion rates drop by an average of 4.42% per second of load time. These expectations bleed through to software people pay good money to use, including analytical tools. 

5. Lack of Interactivity 

Like collaboration and alert capabilities, interactivity is another “if you know, you know” feature. Platforms with interlinking charts that can dive into the granular details of data without leaving the app provide a premier user experience.

4. Problems Pulling Data and Reports

Pulling data and reports is an issue that Servicers are too familiar with. Most analytic tools fall short in reporting features because of the generalized approach most take. Tools that can perform data modeling specific to the needs of their users offer a next-level experience. 

3. Lack of Customization Options

The Cumul.io report shows that 46% complain about the lack of customization in their tools, which can be connected to most other complaints. Customization complaints run the gamut from language and numbering to an inability to white-label.

2. Lack of Relevant Insights

Again, an analytics tool not providing relevant insights can be traced back to the one-size-fits-all approach to software. Top complaints in this category range from an overwhelming amount of information resulting in less confident decisions to critical user-specific insights missing altogether. 

1. Poor User Experience 

The catch-all for almost everything that is terrible, poor user experience comes in at number one, resulting in low adoption of data usage. From too many clicks, to a steep learning curve, to being unable to find what you need, these complaints should be the basis for continuous improvement of digital products—because a quality user experience provides higher-quality data. 

What if Homeowners Could Choose Their Servicer?

By now, you’re likely wondering how all this relates to mortgage servicers. The answer comes in the form of a question. If homeowners could choose their servicer, would all Servicers go to greater lengths to provide a better customer experience? Undoubtedly, they would. But, before that’s even an option, they would need to implement a digital infrastructure where their organization-specific data models could be enforced.  

Yet Servicers know (although most homeowners don’t) that providing a better digital experience is easier said than done. What most consumers don’t understand is that mortgage servicing isn’t built on technology but on compliance. Building technology that adheres to strict regulatory guidelines while also delivering an excellent experience to consumers and is a useful operational tool for Servicers is an arduous journey that most software companies don’t dare embark on.   

Fortunately, before we built our product, Brace committed to years of discovery to understand every aspect of the mortgage servicing industry.  By doing so, we know there is no one-size-fits-all for Servicers. Instead, Brace partners with each client to create a white-labeled tool that provides homeowners and Servicers next-level experiences.

Effective data models allow Servicers to easily access and leverage data while driving operational efficiency and elevating the user experience. Contact us to learn more about how Brace can help your organization.