Revenue forecasting is an area of interest to every business leader because every department provides input to revenue forecasting models and manages budgets. However, accurately predicting sales is very difficult. Sales performance management company Xactly’s 2024 Revenue Forecast Benchmarking Report43% of respondents said sales forecasts were typically off by more than 10%, 38% said they had data quality issues, and 35% said the forecasting process took too long.
“Forecasting is essential to the financial success of any business, but it often presents significant challenges,” said Jacktley CEO Arnab Mishra. “Sales and finance teams do not have access to historical CRM or performance data when making forecasts. “The most successful companies face common obstacles, including uncertainty about reporting systems and the source of pipeline data, and have sales and finance executives who integrate innovative forecasting technology solutions and prioritize accurate forecasts.”
Companies typically deploy financial planning and analysis (FP&A) professionals who develop revenue forecasting models, dashboards, reports, and recommended actions. Listed companies SEC Financial Reporting GuidelinesThey must follow financial and regulatory requirements, utilize specialized financial reporting tools, utilize machine learning models, and often generate multiple forecasts. Small businesses may prefer to develop forecasts using rules-based approaches and self-service business intelligence tools.
Data professionals must develop business insights and collaborate with FP&A professionals as customers. Understanding and collaborating on data, modeling, analytics, and forecasting objectives will help your data team deliver business value to the enterprise.
Steps for Profit Forecasting
Earnings forecasts are made at a macro level, where companies seek guidance and public companies issue forecasts for the next quarter and year. Many companies also forecast revenue for each business unit, product line, and region.
For profit forecasts, next stepsis included. This is a step that FP&A professionals typically perform during the budgeting process, at strategic decision points, or when major business changes or external factors occur.
- Data is collected from internal sources such as ERP, CRM, marketing automation platforms, and customer service tools, as well as external sources such as economic factors, customer demand, regulatory changes, climate forecasts, and political factors.
- The analysis period is chosen to take into account the data sets and segments needed for analysis.
- Review external factors, constraints, and other risks that could accelerate or hinder growth, including your business’ supply chain factors, strategic decisions, labor conditions, and other global and local events.
- Tools and tools to predict revenue from existing customers, revenue streams, and new customer orders prediction modelSelect . Revenue ModelingA top-down approach that predicts changes in existing revenue and a bottom-up approach that considers the sales pipeline can be used. Multiple models are often created to account for different planning scenarios and benchmarked against external forecasts.
- The completed model is presented to management and connected to ERP, CRM and other planning tools to coordinate resources and monitor accuracy.
Although it is fundamentally simple, it is difficult to make accurate predictions. There are several ways data and analytics teams can support this process.
Having clean and centralized data is a prerequisite
Centralizing, cleaning, and consolidating data and resolving data debt are key responsibilities of dataops and data governance professionals. Without these disciplines, FP&A professionals lose trust in their company’s data sources, spend more time wrangling data, and can’t focus on developing accurate models.
Document automation and contract management company conga“As AI rises, most companies are struggling to understand how to create more value from their systems and data,” said Grant Peterson, Chief Product Officer at (Conga). “The key to getting the most value is “The scope and organization of the data, the ability to integrate additional data sources, and the ability to integrate tools and systems are what companies lack in terms of comprehensive solutions to collect data to support decision-making and accurately drive revenue.”
Data professionals should consider the following strategies to provide the highest quality data that FP&A professionals can easily leverage.
- Large enterprises may want to invest in a data fabric to centralize access to multiple enterprise and SaaS platforms.
- DataOps teams must develop robust real-time data pipelines and select a subset of data quality metrics to optimize.
- Data governance leaders must ensure that data catalogs are up-to-date and meet the needs of FP&A professionals.
- Data and analytics leaders should establish governance for citizen data science programs and consider FP&A professionals as key participants.
- data scientist Agile data practicesdesign thinking in data science, and model ops should be established to establish guidelines and standards to collaborate with FP&A experts in modeling efforts.
Challenges in Forecasting Growth
Data professionals should also learn about some of the challenges FP&A professionals face, especially when predicting growth. These forecasts require data on sales pipelines, supply chains, and economic factors to be joined and modeled in a transparent manner to produce reliable results.
Because forecasting often requires additional data quality considerations and data lineage practices, DataOps and data governance leaders should consider FP&A key stakeholders when identifying data quality issues. for example, Use a spreadsheetSolving data problems by doing so is error-prone, delays predictions, limits collaboration, and creates transparency issues. Forecasts that rely on sales data must examine timeliness, accuracy, and other data quality issues that arise due to how and when sales professionals work in CRM.
Document automation and order management software company Esker“Data quality plays a huge role in revenue forecasting, especially growth forecasting,” said Steve Smith, Global Director of Strategic Projects at . While forecasting existing sales is simple, relying on past sales forecasts for future growth can be problematic due to potential bias or incomplete data. “In addition, complex sales cycles requiring multiple payments and market volatility further hinder the timing and accuracy of order forecasting.”
Forecasts should also consider factors external to the company and leverage third-party data sources on the economy, customers, and other trends. To enable growth forecasting, it is important to evaluate, profile, and integrate new data sources, including unstructured data sources such as news sources.
AI data analysis solution company LatentView“Predictive models traditionally rely heavily on internal data sources such as marketing spend and customer counts,” said Krishnan Venkata, Chief Customer Officer at ). “These internal metrics are very important, but they often fail to incorporate external data inputs that can have a significant impact on forecasts.”
Venkata recommended incorporating external trend data for changes in user interests, social media for real-time sentiment analysis, and relevant news or events for market impact as data sources. Additionally, with the right data sources, “companies can gain a more comprehensive view of potential disruptions and trends, improving the effectiveness of forecasting as well as strategic decision-making and market positioning.”
Tools for revenue lifecycle management
Data scientists may be enthusiastic about creating machine learning models to predict revenue, but they may be making mistakes. Many revenue forecasting tools include collaboration, annotation, and advisory features to support FP&A workflows. Optimizing SAP Revenue Growth, Workday adaptive planning, Microsoft Dynamic Sales, Netsuite The same predictive capabilities connect to enterprise ERP and sales platforms. Revenue Lifecycle Management Capabilities provide input data for revenue forecasts and improve the accuracy of forecasts through workflows such as quote-to-cash and contract management.
“Our AI-powered open source revenue lifecycle management platform and open, integrated data solution can transform enterprises through AI-driven revenue insights, improve cross-functional collaboration across the revenue lifecycle, and provide a single source of truth,” said Conga’s Peterson. “We provide support to facilitate overall decision-making.”
Data professionals need to learn more about how companies forecast revenue and use predictive analytics to make strategic decisions. A good way to start is to review who is responsible for forecasting, along with the following:
- Types of predictions they provide
- The data sets they leverage
- Frequency and schedule for providing forecasts
- Data quality issues that need to be addressed
- Data model used
- Skills that matter to your workflow
Data scientists, engineers, and governance experts will deliver significant value by collaborating on forecasting, centralizing data, improving data quality, and sharing expertise in modeling and visualization.
editor@itworld.co.kr
Source: www.itworld.co.kr
