In this article, we will explore mixed-use development and discuss the differences between retail and residential components and how to reflect these differences in the financial model. While this article will focus on these two types of real estate since they are my area of expertise, it’s worth noting that mixed-use developments can also include office, hospitality, and entertainment spaces, among others. 

Benefits The Mixed-Use Development 

Mixed-use development is an appealing option for developers and investors because it maximizes the potential of a property by utilizing it for multiple purposes. For example, a mixed-use building could have a retail store on the ground floor, offices on the middle floors, and residential units on the top floors. 

One of the most significant benefits of mixed-use development is that it creates a vibrant and dynamic community. With a blend of residential and commercial properties, residents have easy access to services and amenities, while local businesses have a built-in customer base. 

In addition, mixed-use developments can help reduce traffic congestion and carbon emissions. By living, working, and shopping in the same area, people are less likely to need to drive long distances, thereby reducing long car trips. 

From an investment perspective, mixed-use properties offer a more diverse, stable, and reliable source of income. With a varied range of tenants or essential services for residents, the risk of vacancy is reduced, and if one type of tenant leaves, there are still other sources of income to rely on. 

Challenges of the Mixed-Use Development 

While mixed-use developments offer many benefits, they also present challenges. One of the biggest obstacles is navigating zoning regulations, which can make it challenging to combine different types of properties in a single development. In addition, developers must ensure that each use is compatible with the others and that there are no conflicts for tenants and residents. 

Another challenge is designing a building that is flexible enough to accommodate different uses. Commercial properties may have different requirements than residential ones, particularly when it comes to heating, ventilation, and air conditioning. This can lead to additional design and material costs, which should be taken into account in the financial model. 

Despite these challenges, the popularity of the mixed-use model is on the rise as it benefits developers, investors, and communities. 

The Feasibility Model or Pro Forma   

The feasibility models for residential and retail development have many differences. The primary overall difference is that residential development focuses on creating units for people to reside, while retail development focuses on creating spaces for businesses to operate. This primary difference impacts the  

feasibility models for both types of development. Below, I will discuss the differences between the two types of models and how they may be reflected in the feasibility. However, I cover the differences in a more theoretical manner rather than covering the technical mechanics that you would see in an Excel file.     

Revenue  

Revenue streams differ for residential and retail developments. In a residential development, the primary revenue stream is rental income from the units. The feasibility analysis typically uses monthly rental amounts and considers factors such as unit mix and market rates to determine annual income. 

For retail developments, revenue comes from lease payments by businesses occupying the space. The revenue is typically calculated as annual rent per square foot and can be used to derive useful metrics such as rent by tenant type. Retail revenue is more volatile than residential revenue, as it depends on the success of businesses. Percentage rent deals can align the interests of landlords and tenants, with both benefiting from higher sales and foot traffic. 

Costs  

The costs associated with residential and retail development differ outside of land acquisition. Residential development costs primarily involve construction and marketing, whereas retail development costs include construction, marketing, and tenant improvement costs. Tenant improvement costs cover modifications to the space to meet the business’s requirements occupying it. Complexity in mixed-use developments usually leads to higher construction costs, costs from consultants for permitting, and design expenses. In retail, the more popular dining-related tenants tend to have higher tenant improvement costs.  

Financing 

Financing for residential and retail development differs as well. Residential development financing is easier to obtain and typically cheaper due to being perceived as less risky than retail development. Retail development financing is often more complex as lenders need to assess the creditworthiness of the businesses occupying the space and their ability to pay rent, leading to higher interest rates. The cost or interest rates for mixed-use developments usually sit between the cost of focused retail or multifamily residential developments.  

Demand Factors 

Population growth and demographic changes drive the demand for residential development. In contrast, consumer behavior, such as shopping habits and spending patterns, drives the demand for retail development. Competition from other retail spaces in the area and market/demographic factors like average household income in the area can also impact retail space demand. Successful mixed-use development and the tenant mix in a project can harness the demand for the area. Market research is crucial for mixed-use development projects in a given market.  (Article Continued Below)

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Risk Factors 

Residential development is generally less risky than retail development because there is a higher demand for housing than for retail space. The success of retail development is closely tied to the success of the businesses occupying the space, which can be impacted by economic conditions and competition. Attracting retail tenants that emphasize essential services can mitigate this risk. For example, many mixed-use developments attract retail tenants that provide services like food, salons/barbershops, dry cleaning, and healthcare.  

Example  

The company I was at owned an aging shopping center with a lot of foot traffic due to its favorable location. We decided to pivot to a mixed-use redevelopment with a residential component due to increasing rental rates, a lack of residential spaces in the market, and a growing population in the area. 

We saw potential tailwinds for permitting costs because the local government’s interests aligned with ours and our organization had a good relationship with them. Demand factors looked favorable, and risk factors were mitigated when looking at the local market outlook and our regulatory body relationship. 

To keep transparency on the revenue side, I built separate tabs for each revenue model portion to explain different revenue streams. The costs were difficult to separate, but we noted that the per sq ft cost was higher than our usual benchmark. Overall, the yield for the project settled at around 7.5%, which made sense given the benchmarked returns we saw for each product type. 

Finally, we looked at potential outside financing and found that the quoted rates were noticeably less than quotes for a project that only built additional retail space. 

Conclusion

The feasibility models for residential and retail development differ in several ways, such as revenue streams, costs, financing, demand, and risk. While this article is not an exhaustive list, it aims to highlight the main differences to provide the reader with an overview. Developers and investors must carefully analyze these factors when assessing a project’s feasibility and determining its potential return on investment. Understanding these differences is crucial to developing a successful project and maximizing the chances of success for a mixed-use development. 

Forecasting stands as a pivotal function that allows businesses to maintain competitiveness, expand their market share and execute long-term strategies. Regardless of a company’s size, one of management’s central responsibilities involves evaluating the overall health of the business and addressing crucial inquiries: Is the company profitable or incurring losses? Can we manage our debts as they become due? Is our growth aligned with industry and market conditions?

Answering these questions relies on well-crafted forecasts developed by the internal management team, requiring a process that analyzes both internal and external data, converts it into meaningful projections and makes decisions that steer the company’s trajectory. However, the value of forecasts diminishes without accuracy. Precise short-term forecasts are essential for informed decisions, while inaccuracies can lead to severe consequences like misallocated resources, missed opportunities, financial instability and compromised strategic planning.

Drawing from more than two decades of experience in corporate finance, much of which was devoted to refining processes for generating precise forecasts, I’ve distilled key insights that have not only elevated forecasting accuracy, but also saved substantial time and effort along the way.

1. Start with Evaluating Data Quality

“Garbage in, garbage out” underscores the significance of accurate and clean financial records as the bedrock of reliable forecasts. Ensuring all revenue, cost, capital expenditure and related transactions are promptly recorded in accordance with GAAP standards is crucial. Special attention is needed, particularly for revenue transactions, often the most substantial figures on the income statement. Situations involving non-intuitive revenue recognition rules—common in software, SaaS, contract-based, real estate and certain manufacturing industries—call for scrutiny to ensure accurate recording. Robust internal controls and monthly reconciliations are essential for maintaining precise financial reporting and accounting for all system transactions.

2. Understand Key Business Drivers

Much like accurate financial records rely on competent accounting following clear processes, insightful forecasting demands Finance to grasp financial reporting and the underlying metrics. This entails understanding revenue generation, primary sales components and seasonal trends. For major expenses like labor and production, sensitivity to input prices, shifts, hours and automation is crucial. Coupled with a strong grasp of company drivers, industry insights provide context for a comprehensive understanding.

These insights, combined with the core principles of accurate data, effective data management and a robust forecasting model, form the foundation of precise forecasting practices, empowering businesses to navigate the market effectively.

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3. Effectively Manage and Utilize Data

Maintaining clean and accurate business data is at the core of this step. The accuracy of forecasts directly depends on the quality of the data that drives them. This requires a meticulous commitment to ensuring data integrity. Leveraging key data sources like ERP systems, operational data, industry benchmarks and competitor analyses will form a robust foundation. These sources not only inform forecasts, but also provide vital insights for navigating the competitive landscape. By prioritizing data integrity and effectively utilizing available information, businesses can enhance their forecasting capabilities and make well-informed decisions.

4. Construct a Robust Forecasting Model

With a foundation established through accurate historical reporting and an understanding of key business drivers, the next step involves constructing a forecasting model. While an Excel-based model is a conventional choice, alternative approaches that offer greater efficiency could also be considered. Regardless of the chosen software or method, the model should hinge on essential assumptions (or drivers) that underpin financial projections. For instance, sales revenue could be contingent on units sold, subscriptions, contracts, customers or other relevant metrics that shape revenue generation. Additional pertinent assumptions might involve average order values, product/service mix, and incorporating price and cost adjustments based on the latest consensus (pertaining to cost-of-living adjustments and merit-based increases for internal labor costs).

Integrating assumptions into your forecast actually results in more accurate projections, as comparisons between what transpired versus what was expected to occur can be reviewed and any deviations between the two can be reduced through refining the assumptions. This flexibility to rapidly reflect changes in assumptions and quantify the impact will help to significantly increase the accuracy of the modeling over time.

Conclusion

In summary, honing accurate forecasts isn’t just about getting around in the market; it’s about setting a clear path forward. Our dive into these details underscores the importance of having precise data, understanding what really drives the business, managing data effectively and building a strong forecasting model. By embracing these principles, businesses give their management sharper tools to make smart decisions, strengthen their base, fuel growth and significantly improve the accuracy of their forecasts.

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The process of selecting and implementing any system can be difficult at best and a complete failure at worst. We know from experience that many companies make avoidable mistakes in regards to new systems, such as delegating the implementation process to internal employees, avoiding formality or not planning for post go-live support. If you’d like to explore the support we offer, then visit our financial systems consulting page and start a conversation. Our free guide below also offers insight into the process of system implementations:

Let’s say you’ve recognized your current processes are slow and can’t possibly scale alongside the company’s massive growth, so you’ve decided to explore FP&A systems. You know a couple household names (e.g., Hyperion, TM1), are aware of recent additions (e.g., Planful, Adaptive), and have heard tell of new options (e.g., DataRails, Cube) popping up within the last few years.

Given the range of options, narrowing the field and selecting a system can seem like a daunting task. But many finance professionals use very common criteria when selecting a system, including reporting functionality, forecasting capabilities, collaboration workflows and more. Let’s focus on a couple of important, but less common, key factors you should consider when evaluating your next FP&A system.  

Growth Profile: How will the company grow over the next few years?

Selecting a system with the flexibility to scale alongside your company is a key factor to consider. When the company grows, acquires companies or creates new revenue streams, the needs will change, and the system will need to adapt accordingly. 

It’d be a mistake to implement a new system today that doesn’t align with company growth, only to start a new system selection and implementation process in two to three years’ time. To avoid this, think of scenarios that have resulted in significant changes to your existing models and processes and determine how a system would deal with those items.

For example, here are some scenarios to consider:  

• If your company will likely hit $1B, $10B or $50B in revenue in the next several years, does your vendor have customers in those ranges? Understanding the customers they service is a good proxy to understand their ability to service your requirements.
• If you added a brand-new revenue stream or changed an existing one, can the system handle that?
• Are you expanding into additional countries with new reporting requirements/FX translation?  

Don’t Boil the Ocean: What are our key deliverables, and how can we keep the focus on them?

When exploring a new FP&A system, many companies fall prey to feature frenzy. Demos and dialog can widen to cover the 100 items on the to-do or would-be-nice lists, when the primary focus should be on how well the system accomplishes the three to five highest-priority items. Most FP&A systems have similar functionality, so it becomes imperative that your selected system meets your most-critical needs. For example, if your company is still figuring out its revenue streams and KPIs, understanding the system’s flexibility becomes paramount. If your company is more stable, then generating reporting/dashboards quickly might be the focus. Whatever your priorities are, focus on them and how well the respective systems address those items.  
 
Here are some ideas of items that might be key priorities: 

• Expense Forecasting – Is there zero-based budgeting, per HC forecasting, applying growth rates to prior year, etc.?  
• Scenario Planning – How easy is it to spin up different scenarios or produce what-if analysis?  
• Revenue Planning – What is the functionality around sensitivity analysis, seasonality, assumption changes, etc.  
• Dashboards – How easy are they to create, update, distribute? Are dashboards a huge requirement? How self-service are they? What functionality is there? How do dashboards show data that doesn’t have a common key between datasets?  
• Collaboration/Workflow Capabilities – How well does the system handle tracking workflows, document sharing, commenting, etc.?  
• Company Industry – Does the vendor have functionality focused on a certain industry (e.g., SaaS)? This may be an advantage of one vendor if they have built in functionality for that industry, but it may be a disadvantage if you’re not in that industry. If the vendor has numerous of your competitors as customers, then it may be a good sign that they have the functionality you require. 

Ease of Use: How easy is it to learn the system, and is there ample availability of resources with hands-on experience? 

Every system will have some sort of learning curve, but issues can arise when staff inevitably turn over, lowering internal knowledge to support the system. Focusing on how easy it is for people to learn the system can help avoid those issues. While some systems can be figured out by digging around, others may require the help of external consultants to help troubleshoot. Here’s a list of ideas to test the ease of use:  

• Can a typical finance professional poke around and figure out most of the functionality?  
• How easy is it to create reports? 
• How easy is it to input data into the various templates/models?
• How easy is it to create/modify the Revenue and Operating Expense models?
• Does the syntax used in the system mirror Excel’s? (Because all finance professionals are Excel pros, the more similar the syntax, the better.)

For each of these items, think of the curveballs that you’ve experienced in your current process, how you’ve had to modify existing files/processes and ask how the system would deal with it. You’re looking for relative ease in making similar adjustments.  

If the system isn’t particularly easy to use, then the other consideration is whether the system is widely used enough that ample finance professionals with hands-on experience are easy to find. While not as critical in a system that is easy to use, a large user base becomes more of a consideration for systems that are more complicated. That allows you to find someone who can slot in and manage the system without much issue.

Additionally, if you’re planning to roll out the system to BU partners, then you’ll want to understand their use cases and see whether it is easy for them to use. Can they easily navigate through reporting/dashboards? Can they create their own easily?  

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Data Cleanliness: How clean is your data?  

In order to unlock the full power of FP&A systems, you need to be able to intake data from a variety of sources across your organization. You’ll want to take stock of all the data sources (e.g., GL system, CRM, BI tools, databases) you want to bring into your FP&A system and understand whether the system has native integrations or would require custom integrations. Then you must understand your underlying data structure to determine what it looks like, if it has all the fields you care about and if data cleanup is required. If you’re constantly undergoing data cleanups, changing KPIs, modifying hierarchies, etc., how will the system accommodate that? Some systems require that your data be clean at the source while other systems will allow you to use logic to modify the data as you see fit.

Once you’re done with that, you’ll likely need to do some data cleaning, but if you’re constantly undergoing data cleanups, changing KPIs, modifying hierarchies, etc., then you’ll want to seek out a system that is flexible enough to deal with those situations. 

Some FP&A systems work best when the data is stable and changes are relatively minor, but not all can deal with messy data. 

Where You Work: Do you want users to work primarily in Excel/Google Sheets, or do you want to work in a system?

There are two schools of thought around this question. One school is centered around the belief that you will be the most efficient when you transition work from Excel to a centralized system. The other school believes nothing will ever replace the flexibility that Excel/Google sheets provides.

While many systems offer plugins to also do work in Excel, only a handful of systems are built with Excel/Google Sheets as the primary UI (e.g., DataRails, Cube, Vena). Of course, you should expect natural differences in systems that were built with Excel/Google Sheets in mind and those that added that functionality over the top.  

It is up to you to determine what you think makes the most sense and how you want your team to operate. When it comes time to decide which approach makes sense, go back to your pain points and see which can handle them better. For example, if you have a ton of data, can these Excel/Google Sheets-based tools handle it quickly and efficiently?

Online Community: How can you ask questions, find answers and suggest improvements? 

The more robust the system’s community is, the more resources that will be available to quickly troubleshoot issues and collaborate on functionality you want to explore and implement. These communities become places where other finance professionals share their struggles, and many members share their solutions. Additionally, the communities often function as a way for the vendor to gather ideas and eventually implement them into the system. It’s quite common for a given community to vote on desired functionality and for software companies to roll out that functionality over time.

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The process of selecting and implementing any system can be difficult at best and a complete failure at worst. We know from experience that many companies make avoidable mistakes in regards to new systems, such as delegating the implementation process to internal employees, avoiding formality or not planning for post go-live support. If you’d like to learn more about the support we offer, then visit our financial systems consulting page and start a conversation. If you’d like to learn more about systems implementations, then download our free guide below:

Power BI is an extremely powerful data transformation and reporting tool with solid modeling capabilities via tools such as DAX and Python/Panadas. Besides connecting a litany of Microsoft products (e.g., SQL Server, Azure, SharePoint, Excel), it can also connect to many other data sources including Salesforce, Anaplan, Postgres, Oracle and SAP. This in-house ability to access diverse data sources puts a strong tool in the analyst’s tool kit. The ability to access, transform and visualize large amounts of data does present some important best practices for long-term success. In this article, let’s review a few Power BI tips that can help you leverage the tool effectively.

Optimize Your Data Warehousing

The first of our Power BI tips is the actual condition of the “data warehouse,” whether it be a relational, multi-dimensional or other data structure (e.g., Big Data). To optimize Power BI, the data at the source should be cleansed, transformed and consolidated as much as possible in the source system. This doesn’t necessarily mean every table or cube should be perfect, but they should be in the best shape possible.

This optimization may translate into building special tables, objects or views in the source that result in cleaned, consolidated and transformed data. The importance of this cannot be understated: cleaning and transforming large data sets can slow and even stop Power BI processing.

Build Common Data Tables and Structures

The second of our Power BI tips would be moving Power BI calculations that occur in multiple dashboards, reports or datasets into a common data table or structure. These calculations should then be accessed in one location by all Power BI resources—versus each resource running the calculation(s) locally.

This effort will free up Power BI computing resources, making page loads faster and, more importantly, ensuring that all Power BI resources are using the same formula. Often when the same formula is used across multiple Power BI resources, changes in one are not always carried over to all resources using the formulas. Also, considerable time can be spent managing formulas across resources. Moving the formulas to a common data structure eliminates these issues.

Identify Necessary Data Granularity

With the ability to work with large amounts of data, one challenge that occurs is pulling only on the amount of data required versus everything available. Power BI resources can be overcome with too much data which results in slow performance and even the resource “freezing.”

Which brings up our third Power BI tip: One way to manage the amount of data pulled into a resource is to determine the correct amount of granularity required to support a visual. For example, if a visual requires analysis on a low level of granularity (e.g., daily), can that analysis be performed in the data warehouse and the Power BI resource pull only required elements? Another option is to create aggregated data tables/Objects in the source by omitting columns not required by the Power BI resource.

Implement an Oversight Process

Last tip: When a company has many Power BI resources (e.g., Dashboards, Reports, Data sets) in production, it is important to have an oversight process. This ensures consistency across Dashboards and Reports—both in terms of data and presentation when needed. For example, the process would ensure similar formulas are properly managed across resources.

An Oversight Process also helps manage the resource landscape by minimizing the occurrence of duplicate resources, supporting future development, and simplifying general management of the Power BI landscape (e.g., Security, Development Flow, etc.). Setting up a process can be implemented by having periodic meetings which include Power BI resource developers, project managers and data warehouse engineers and cover the current and future reporting landscape and how the data warehouse/sources play a role.

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Employing these Power BI tips will result in more streamlined resources that run faster, are easier to manage and can provide better insight into the data analyzed. That noted, many companies don’t have the internal bandwidth to implement these best practices. If you’d like support on your data transformation and reporting efforts, then consider contacting us. Our Accounting and Finance consulting team can deploy to support or drive key projects or serve in an interim management capacity if needed.

If you’d like to learn more about generating internal momentum for operational objectives, then you might be interested in our guide:

In part one, we explored best practices for empowering cost-center transparency in your QuickBooks to NetSuite migration. Those recommendations and the exploration below come from my experience in supporting a company through that process. As an FP&A professional, I found the migration exciting due to the advanced reporting and analysis that was available in the new platform.

The QuickBooks to NetSuite migration commonly occurs when a company seeks aggressive growth and advanced efficiency in its finance and accounting functions. In our case, it allowed for enhanced reporting, budgeting and forecasting.

SG&A – Allocations & Headcount

In QuickBooks, the allocations split in terms of projects SOWs (i.e., revenue) and expenses (i.e., resource cost) was done manually. In terms of headcount, various employees were allocated to multiple cost centers. This was inefficient and created a lot of room for potential errors.

Due to the approach to allocations and headcount in QuickBooks, our Scorecard was not always accurate. The cost center owners frequently disputed the numbers from finance, as these numbers affected the perceived performance of their departments. But often, the disputes were centered on inaccurate or imperfect data. Because various people worked on projects across more than one cost center, in some cases, the true expense didn’t match the revenue. As a result, finance had to frequently make manual changes to make the Scorecard more accurate.

We worked with our NetSuite implementation team to ensure we would correctly allocate employees working across cost centers. We leveraged the time entry function to empower everyone to track their time in a way that accurately reflected cost center expenses. After the QuickBooks to NetSuite migration, the allocations split directly mirrored an employee’s timecard for that week, down to the project level.

For example, people working in both the R&D and Technology cost centers had their time split in NetSuite according to their respective, current projects. This level of granularity generated a truly accurate Scorecard—and for the first time, cost center profitability was completely transparent and accurate. The biggest rewards from this time entry function in NetSuite were removing unnecessary disputes and giving the cost center owners more confidence in the finance team and the integrity of our work.

Additionally, every employee in the company needed to have a home cost center, even if their allocation for the month was split. HR housed everyone’s designated cost center and the allocations in NetSuite aligned with the direction from HR.

Reporting, Finance Model Flow & Forecasting Accuracy After the QuickBooks to NetSuite Migration

QuickBooks is great for a smaller-sized business. However, for a medium-sized business looking to grow, NetSuite supports scale by creating more automation and efficiencies in data quality, report creation and day-to-day transactions such as JEs, AP, AR and PO workflow approvals.

In terms of financial analysis and forecasting, we pursued the QuickBooks to NetSuite migration to better analyze and monitor issues that affected profitability, growth and productivity. We achieved this by creating cost center hierarchies as well as other various dimensions in the data in terms of revenue and expenses. We also wanted to clearly identify trends and developments in sales and costs to improve forecast accuracy.

Feeding NetSuite Data Into Our Forecast Model

For organizations that have many subsidiaries as mine did, NetSuite allows a consolidated view of the financials at any given point in time, and it allows you to drill down to specific subsidiaries or other dimensions as needed. This helped us better align our Sales and Finance forecasting models to identify key drivers and identifying root causes of previous discrepancies.

For our forecast model, the data out of NetSuite was much more refined than when it was in QuickBooks. Using a NetSuite financial report, we cleansed the data and made sure it contained all the dimensions (fields) and measures we wanted to see. We used Excel for our forecast, so the way we updated our financial forecast model was by taking an extract of that NetSuite report. It was easy to copy and paste this data into our Excel model in the data tab inputs, and the output pages by cost center would automatically refresh.

As we actualized months, our forecast became an extrapolation of the actuals depending on run-rate or trend, and it became easier to create the remainder of the year forecast. We could then layer on any one-time events or uptick in revenues as needed. We could do this with expenses as well—build a run-rate forecast then layer in any expected additional expenses.

We were also able to actualize our live forecast based on where we were in the year in terms of actuals. So, if we had six months of actuals and six months of forecast, we then had a true, live, actualized 12-month forecast. This enabled us to see where the hits and misses were and also better informed us if we needed to update the forecast.

I have also previously worked at companies who fed NetSuite actuals directly into cloud-based systems such as Hyperion Essbase or Workday Adaptive Planning. In that case, if you had a live 12-month forecast in Hyperion Essbase and six months of actuals (e.g., January – June) in NetSuite, these actuals and six months of forecast (e.g., July – December) would reflect “6+6,” or six months of actuals and six months of forecast. The actuals serve to inform the direction of the forecast, so utilizing NetSuite with a cloud-based system is very useful. The actuals give you insight into trends occurring through historical data, which will give you the baseline assumptions for your forecast. You can then layer in larger, one-time events into your forecast as long as you’re clear on what your baseline is.

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Custom Reports and Month-End Efficiency

NetSuite also allows users to create unique reports by adding various columns and rows of relevant data. For the Income Statement at month-end close, FP&A was able to pull together a detailed report that gave us a good preliminary snapshot of what month-end close would look like at the cost-center level. From this, we were able to determine if there were any erroneous or missing JEs.

When the income statement was final at the end of the month, the raw data was then copied into our finance model (which was actualized by month). We would consider the model refreshed, and then the numbers would flow into our month-end, Excel-based financials, which also served as the basis for our final month-end decks.

The finance model contained all of the MTD and YTD budget and forecast information. Once finalized actuals were populated into the model, it was easy to see where the variances were.

I also created a financial scorecard by cost center, and we held a monthly meeting with the C-level audience. Thanks to the data quality in NetSuite, we were able to feed this newly refined data into our Excel-based Scorecard and track MTD & YTD results accurately. This Scorecard tracked revenue and gross margin dollars so that profitability by cost center could be determined—and annual bonuses could be paid to GMs based on actual results.

Get Support for Your QuickBooks to NetSuite Migration

While I’ve focused on the specifics I experienced while supporting a company through a QuickBooks to NetSuite migration, the fact is that transitioning between financial systems and optimizing their effectiveness are dependent on your organization’s goals and your approach to them. If you suspect you need supplemental support or would like a dedicated project manager to spur and maintain progress, we invite you to contact us to start a conversation.

And if you missed the first part of this series, make sure to check it out to explore how to increase cost center transparency when moving from QuickBooks to NetSuite. You can also keep up with our team of finance and accounting consultants by subscribing to our CFO insights blog. For help with your financial systems, visit our service page to learn how we work with our clients: