Cloud Blog – Page 3

The Future of SaaS Finance: Machine Learning and AI

Posted on September 21, 2023November 21, 2023 by admin

Machine learning and AI will help SaaS CFOs tremendously. How and where? Let’s start with the FP&A function in SaaS organizations that relies on detailed data visualization and financial storytelling. CFO automation tools make those two tasks much simpler and more intuitive for finance teams, among many other benefits.

In this post, we’ll explore the latest trends in financial planning and analysis (FP&A), the benefits and risks of machine learning in FP&A, and how to build a SaaS financial model with machine learning and AI.

What’s next for FP&A?

Financial planning and analysis is the process of budgeting, forecasting, and analyzing financial performance. As SaaS companies continue to grow and mature, they must keep up with the latest FP&A trends to remain competitive.

Some of the top trends include:

Moving away from spreadsheets and towards dedicated FP&A software
Automating routine tasks to free up time for analysis and strategic planning
Focusing on key performance indicators (KPIs) that drive business outcomes
Using data visualization to communicate financial insights to stakeholders

The challenges facing the FP&A process

Despite these trends, FP&A professionals still face several challenges. Some of the most common include:

Data silos: Financial data is often stored in different systems and is difficult to access and integrate.
Manual processes: Many FP&A tasks are still done manually, leading to errors and inefficiencies.
Lack of automation: Even when software is used, many FP&A tasks are not yet fully automated, leaving room for error and inconsistency.

Automating processes at scale

One way to address these challenges is through automation at scale. Machine learning and AI can help automate routine FP&A tasks, freeing up time for more strategic work.

Machine learning in finance

Machine learning is a subset of AI that uses statistical algorithms to learn from data and make predictions or decisions without being explicitly programmed. In finance, machine learning can be used for tasks such as fraud detection, credit scoring, and forecasting.

Benefits of financial machine learning in FP&A

Financial machine learning enables companies to use algorithms to program software to perform tasks that require human cognition. This has been a game-changer in SaaS FP&A. Some of the top machine learning use cases for SaaS finance include:

Multiply efficiency: Since it uses algorithmic deep learning, machine learning offloads much of the burden of creating financial models. This increases finance teams’ effectiveness and overall freedom to maneuver.
Eliminate human error: Using CFO automation tools to create financial models, reports, and forecasts increases the likelihood of human error. This is due not only to errors in the deliverables themselves but from problems with the manual financial processes used to create them.
Enable rapid data scaling: machine learning enables finance departments to easily work with very large datasets. This makes scaling much smoother and faster by enabling back-end financial processes to keep pace with front-end growth.

When you rely on manual financial processes, you can expect to run into various internal and growth-related bottlenecks. CFO automation tools equipped with machine learning and AI go straight to the source of those problems–manual process reliance–and remove it from the equation.

Machine learning use cases in finance

From presentations to pricing rollouts, machine learning software has a broad range of potential use cases for FP&A teams. Some of the top use cases include:

Flexible and dynamic spending

Machine learning can help companies optimize spending by analyzing data in real-time and adjusting budgets accordingly.

Integrating across daily desktop tools

By integrating machine learning into everyday tools such as email and chat, finance teams can stay on top of financial data without switching between different systems.

Data available across the organization

Machine learning can help break down data silos and make financial data available across the organization.

Automation of processes

By automating routine tasks, machine learning can free up time for more strategic analysis.

Financial visualization in a board meeting

During board meetings and presentations, financial visualization with financial machine learning software can help your audience easily make sense of complex ideas and relationships.

Building flexibility into your campaigns

One of the major benefits of algorithmic FP&A is the ease and simplicity of altering your financial models. Compared to manual accounting methods, this leads to higher accuracy and lets teams quickly compare and contrast small adjustments.

Process unification across departments

Siloed data and disconnected processes scattered across departments can lead to serious operational liabilities over time. Financial machine learning software uses the cloud to sync data updates and operations across departments.

This has a positive impact on revenue recognition and many other financial processes.

As appealing as it is, however, machine learning for SaaS finance isn’t necessarily foolproof or without its limitations. Let’s examine a few of them.

Risks and limitations of financial machine learning in FP&A

Despite the above benefits, there are also risks and limitations to using machine learning in FP&A. Some potential hurdles to be mindful of include:

Lack of data: If you’re a brand new company or you’ve only recently launched, you might not have sufficient data to justify an investment in machine learning. Remember, machine learning models must be trained on your specific datasets before they’re of any use to you personally.
Bias in the data: Biased data refers to data that’s unbalanced and therefore difficult for machine learning to accurately work with. You might get “workable” models from biased data, but they won’t be accurate. If your company only recently launched, your dataset might be overbalanced to reflect an initial adoption rush of one type of customer.
Inability to blend with legacy tech: Unlike cloud-enabled accounting tools, machine learning applications are incapable of “lifting and shifting” onto legacy accounting tech. This requires a firmer commitment to moving away from older tools before you make the machine learning leap.
Model continuity problems: Often, only one or two people on a finance team fully understand how to craft and manipulate machine learning models. That can be a considerable liability if they leave the company.

With all this in mind, how would you ideally get started with AI and machine learning in your SaaS finance department?

Getting started with machine learning and AI

Now that we’ve covered the benefits and risks of machine learning in FP&A, let’s dive into how to build a SaaS financial model with machine learning and AI. When you’re first getting started with AI and machine learning, keep these four important best practices in mind:

1. Make sure everyone in your department–as well as your other stakeholders–understands why the shift matters and what will be gained.

2. Take a second to double-check that you won’t be stuck relying on outdated tech that will complicate or slow down implementation.

3. Talk with the other leaders at your firm and get ideas and thoughts on clear FP&A goals and results that people would like to see. What’s their mental image of your company’s FP&A before and after implementing machine learning and AI?

4. Continue to check back with stakeholders and department members as the rollout unfolds, and periodically afterward as well. How are they feeling about the recent change? Have any of their stated goals around machine learning and AI been met?

Let’s dive back into specific benefits and use cases of FP&A advanced analytics machine learning for SaaS accounting teams.

Financial machine learning for SaaS FP&A

Financial analysis relies on quickly and accurately working with large datasets that are split into many different categories. CFO automation tools make it considerably simpler to scale, manage, and use your data.

Finance pros use FP&A advanced analytics machine learning to help companies chart their present and future cash flow. This helps organizations:

Make effective hiring calls: Because effective hiring often involves multiple layers of “if-then” forecasting, FP&A advanced analytics machine learning can be very helpful. Built-in model flexibility makes it easier to forecast potential scenarios, and the elimination of manual forecast assembly reduces variance significantly.
Successfully navigate recessions: One of the biggest benefits of AI and machine learning is the clarity they bring to recession FP&A. Accounting software equipped with AI and machine learning allows finance teams to manage their cash flow in a much more granular and effective way than legacy systems are capable of. When the markets head south, this is invaluable.

Cut operational costs with automation: Financial process automation enables finance teams to maximize their budgets by relegating repetitive but essential tasks to software instead of humans. Rather than eliminating the need for employees, this frees them up to make more valuable contributions.

Let’s look at machine learning as it relates to financial models.

Building models with financial machine learning?

As the name implies, machine learning enables software applications to autonomously learn to create predictive models. This is the backbone of FP&A advanced analytics machine learning.

Let’s compare legacy financial modeling to financial modeling done with CFO automation tools. With legacy accounting systems, financial modeling has two primary steps–speaking very broadly of course.

Step one is to gather and organize large sums of financial data, customer data, and other types of info relevant to FP&A. (Machine learning and AI simplify and streamline this otherwise complex and lengthy task.)

Step two is to manually organize this data, and then use tedious spreadsheet formulas or a similar method to assemble reports and forecasts.

When accounting teams invest in FP&A advanced analytics machine learning, step two is taken care of automatically. CFO automation tools equipped with machine learning can autonomously use existing data pools to create forecast models, budget models, and pricing models, among other varieties.

What is the use of machine learning in forecasting?

FP&A advanced analytics machine learning gives CFOs more confidence in the various forecast models they use.

Machine learning plays an important role in SaaS FP&A forecasting by:

Considerably reducing forecast variance.
Enabling more complicated multifactor forecasts.
Significantly extending forecasts’ effective timeframe.
Allowing teams to alter financial projections with a single click.

FP&A advanced analytics machine learning simplifies and streamlines SaaS FP&A for modern accounting teams. CFO automation tools make it easier for SaaS finance professionals to get an accurate read on a company’s financial future.

See what the future holds

SaaS finance is changing at a more rapid pace than ever before. AI and ML are causing teams to think about workflows and employee roles in a new light. Advances in accounting technology are allowing finance leaders to work much more effectively, but are also making the landscape much more competitive.

That’s why we created the Modern SaaS Finance Academy, a collection of online courses taught by industry leaders and experts designed to help SaaS finance pros level up their FP&A results, cut down on forecast variance, and much more. Each curated lesson helps finance and accounting leaders learn the skills and perspectives to scale the business to IPO and beyond.

Pump-up your sales by getting high-quality leads

Posted on April 10, 2023September 26, 2023 by admin

Kick your sales into a higher gear
Increase productivity
Polish your leads
Focus on quality opportunities
Close deals faster

Kick your sales into higher gear, increase the productivity of your sales team, polish your leads, land on high-quality opportunities, and close deals faster. Fine-tune your CRM and accelerate your sales process.

In this highly competitive marketplace, it’s vital to keep your sales productivity ahead of the competition. It is very important to have fast and easy access to better sales force automation tools, streamlined sales processes, visibility to critical data from back-end systems, and a well-trained sales team.

Here are 5 things you can do to increase your sales activities and grow your customer base and revenues:

1. Revamp your customer portal

Understanding your customer’s needs, after you have identified who your target customers are, and finding out what they have and what they want to buy are the first steps to a successful sale of your products and services.

Double your effort on enhancing your customer portal, website, community forums, and blog sites. Look at ways to seek customers’ feedback, and provide them with fresh information in the form of white papers and articles.

2. Rank and score your Leads & Opportunity

With limited resources, it is impossible to go after every lead you get. Identifying whom to go after is very vital. Look at all the customer parameters and the data you have collected through your portal and front line. Overlay external data available and then rank and stack your leads. There are algorithms we can implement in your CRM to help with your lead ranking.

3. Increase targeted campaigns

Once you have identified the Who, the What, and the Why of your customers, it’s time to launch targeted campaigns. It is impossible to manually track and measure your campaigns and you are losing time with traditional methods. Modern CRM products provide many efficient ways to run your campaigns. Leveraging your CRM’s functionality to do this is the key to automating the tracking and measurement of your campaigns.

4. Get your back-end team involved

During the active period of your sales cycle, provide customers with demos and product evaluation opportunities. In this world of try-and-buy, during the trial period, it is very important to support your potential customers like your current customers. The support experience they get during the trial period will have a significant influence on your customer’s buying decisions. Show them how you care and even involve your back-end team and engineers to address their concerns. Look at Salesforce CRM Customer 360 to engage your back-end team efficiently and effectively.

5. Fine-tune and renew your messages

Last, but not least, fine-tune your messages. Through automated tracking and measurement of your campaigns, you will soon discover which incentives and messages are working for you. Discard the poor-performing methods and increase your focus and effort on the better-performing messages.

NetSuite Training On-demand Annual Pass

Posted on January 23, 2023September 26, 2023 by admin

With the Self-Study Single User Annual Pass, you have access to the most comprehensive NetSuite learning offering available.

The Self-Study center contains unlimited online access to the equivalent of all of NetSuite’s publicly offered courses for one annual fee. As it is a cloud solution you will have access to learning content anytime, anywhere.

Benefits

An easy way to learn NetSuite that fits into your schedule 24×7
On-demand access to expert-led learning
Apply and practice new skills with hands-on exercises1
One cost-effective annual training fee

Content

Content is current with each NetSuite release
Self-study versions of NetSuite’s publicly offered courses
Courses include:
Recorded presentations
Student workbooks
One demo account per course with 30 days access2
Instructor email support within 1 business day

*Pricing will vary depending on country and region.

Contact NetSuite to purchase your Self-Study Annual Pass today!

SuiteTraining Self-Study access is purchased as an individual license. The confirmed user is the only person who may use the course and/or materials. Sharing the training with others in any way is expressly prohibited. Pricing will vary depending on country and taxation requirements. Self-Study does not include NetSuite Consultant Bootcamp or Certification vouchers.

NetSuite Demo Accounts are included when they form part of the standard course curriculum.

Prepare for the self-study experience by first viewing the system requirements.

Sales & Use Tax Automation using Vertex SMB Built for NetSuite SuiteApp

Posted on January 23, 2023September 26, 2023 by admin

Vertex helps automate sales and use tax compliance for businesses of all sizes. By enabling calculations and returns, Vertex meets the sales and use tax automation needs of growing businesses.

Simply connect NetSuite to Vertex through a certified integration for more accurate sales and use tax calculations.

Vertex centralizes the rates and rules required for product taxability and calculation of tax in your system. No need to manage tax rate changes manually. Let Vertex’s research team stay on top of the latest tax changes, while you grow your business.

Key Benefits

Vertex SMB for NetSuite offers full support for your order entry to invoicing processes, including:

Real-time tax calculations for sales and procurement transactions
Quotes/estimates
Cash sales
Sales orders
Invoices
Supports Value Added Tax (VAT)
Simplified NetSuite tax setup Consumers Use Tax on the SuiteTax integration
Address validation: validate address information for accurate tax calculation
Estimates: get accurate tax rates for the estimates you prepare
Sales orders: ensure the accuracy of taxes to be collected or paid
Invoices: update your tax journal with real-time tax information
Credit memos: calculate the correct tax amount for credit memos
Cash sales: enter correct tax information for in-person purchases
Cash sales refunds: calculate the correct tax amount for cash returns
Subsidiaries: separate taxation amount subsidiaries in NetSuite OneWorld

Available for use in three deployment models

Vertex for SuiteTax is available for use in three deployment models – on-premise, on-demand and cloud to integrate directly with mid-market ERPs, procurement solutions, and eCommerce platforms. From returns-only processing, tax calculations, and signature-ready PDF returns to outsourcing services that include returns filing and payment processing, Vertex provides a proven and reliable solution for businesses looking to save time, effort, and risk associated with sales and use tax calculation, returns, remittance, and compliance.

Features

Rate Files

Download rate files by location or upload addresses for your specific business needs and access tax rate files. Jurisdiction tax amounts are continuously researched and updated, maintaining our tax content to keep you current.

Fully Automated for Sales and Purchasing

Vertex enables companies of all sizes to realize the full strategic potential of the tax function by automating and integrating tax processes while leveraging advanced and predictive analytics of tax data. Vertex provides cloud-based and on-premise solutions that can be tailored to specific industries for every major line of tax, including income, sales, and consumer use, value-added, and payroll.

Flex Fields

This new functionality applies additional attributes to transactions for specialized data needed to provide accurate taxability and tax rates. This gives you more flexibility and alleviates manual updates in your process. With Vertex, you don’t need to spend extra time reorganizing your product catalog!

Simplified Processing

Vertex provides accurate and complete tax calculations regardless of your NetSuite configuration. Since you have many configuration choices with NetSuite (Advanced Taxes, Advanced Shipping, Line Item Shipping, etc.) Vertex handles each scenario to provide the taxation that you need at any level.

Engineered to Scale with Businesses as they Grow

Vertex enables customers to continue focusing on their core business and react to new market opportunities quickly and confidently. The enhanced technology provides companies with the tools needed to accelerate growth and drive efficiency.

Reference: Vertex SMB

APEX code nuggets – How to avoid executing SOQL or DML in loops

Posted on January 23, 2023September 26, 2023 by admin

This is a common mistake where queries or DML statements are placed inside a for loop to lookup a record once per iteration. By doing this you will very quickly hit the governor limit of SOQL queries and/or DML statements (insert, update, delete, undelete).

Instead, move any database operations outside of for loops. Form a single query such that you get all the required records at once. You can then iterate over the results. If you need to modify the data, batch update into a list and invoke your DML once for that list of data.

Here is an example showing both a query and a DML statement inside a for loop:

				
					trigger accountTestTrggr on Account (before insert, before update) {
   //For loop to iterate through all the incoming Account records
   for(Account a: Trigger.new) {         
      //THE FOLLOWING QUERY IS INEFFICIENT AND DOESN'T SCALE
      //Since the SOQL Query for related Contacts is within the FOR
      //loop, if this trigger is initiated with more than 100 records,
      //the trigger will exceed the trigger governor limit of maximum
      //100 SOQL Queries.
      List<Contact> contacts = [select id, salutation, firstname,
         lastname, email from Contact where accountId = :a.Id];       
      for(Contact c: contacts) {
         System.debug('Contact Id[' + c.Id + '], FirstName[' 
            + c.firstname + '], LastName[' + c.lastname +']');
         c.Description = c.salutation + ' ' + c.firstName + ' ' 
            + c.lastname;          
         //THIS FOLLOWING DML STATEMENT IS INEFFICIENT AND DOESN'T
         //SCALE Since the UPDATE dml operation is within the FOR
         //loop, if this trigger is initiated with more than 150
         //records, the trigger will exceed the trigger governor limit
         //of 150 DML Operations maximum.                                   
         update c;
      }      
   }
}

Since there is a SOQL query within the for loop that iterates across all the Account objects that initiated this trigger, a query will be executed for each Account. An individual Apex request gets a maximum of 100 SOQL queries before exceeding that governor limit. So if this trigger is invoked by a batch of more than 100 Account records, the governor limit will throw a runtime exception.

In this example, because there is a limit of 150 DML operations per request, a governor limit will be exceeded after the 150th contact is updated.

Here is the optimal way to efficiently query the contacts in a single query and only perform a single update DML operation:

				
					trigger accountTestTrggr on Account (before insert, before update) {
  //In this case we are using the child relationships to filter down
  //and form a single query to get the required records.
  List<Account> accountsWithContacts = [select id, name, (select id,
    salutation, description, firstname, lastname, email from Contacts)
    from Account where Id IN :Trigger.newMap.keySet()];
    
  List<Contact> contactsToUpdate = new List<Contact>{};
  // For loop to iterate through all the queried Account records
  for(Account a: accountsWithContacts){
     // Use the child relationships to access the related Contacts
     for(Contact c: a.Contacts){
      System.debug('Contact Id[' + c.Id + '], FirstName[' + c.firstname 
        + '], LastName[' + c.lastname +']');
      c.Description=c.salutation + ' ' + c.firstName + ' ' 
        + c.lastname;
      contactsToUpdate.add(c);
     }       
   }
   //Now outside the FOR Loop, perform a single Update DML statement.
   update contactsToUpdate;
}

Now if this trigger is invoked with a single account record or up to 200 account records, only one SOQL query and one update statement is executed.

Digital Transformation

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Salesforce CRM

Workday

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What’s next for FP&A?

The challenges facing the FP&A process

Automating processes at scale

Machine learning in finance

Benefits of financial machine learning in FP&A

Machine learning use cases in finance

Risks and limitations of financial machine learning in FP&A

Getting started with machine learning and AI

Financial machine learning for SaaS FP&A

Building models with financial machine learning?

What is the use of machine learning in forecasting?

See what the future holds

1. Revamp your customer portal

2. Rank and score your Leads & Opportunity

3. Increase targeted campaigns

4. Get your back-end team involved

5. Fine-tune and renew your messages

Key Benefits

Available for use in three deployment models

Features

Rate Files

Fully Automated for Sales and Purchasing

Flex Fields

Simplified Processing

Engineered to Scale with Businesses as they Grow

Here is an example showing both a query and a DML statement inside a for loop:

Here is the optimal way to efficiently query the contacts in a single query and only perform a single update DML operation:

Useful Links

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