It can be challenging for businesses to prepare their internal systems for industry changes without accurate forecasts. This can significantly impact sales, revenue, profits, and customer retention.
However, with sales forecasting, companies can predict future sales so management can gain better control of their short and long-term performance.
Sales forecasting estimates future outcomes, such as sales, demand, and revenue, based on historical and real-time data. Sales forecasts can predict days, weeks, months, and even a year into the future. In its simplest form, forecasts estimate how the market will respond to a company's marketing efforts.
Forecasting software uses machine learning to detect trends from past sales data and project future revenue. When integrated with a point-of-sale (POS) system, forecasting solutions can incorporate real-time data from transactions to improve its accuracy, update sales patterns, and pinpoint anomalies.
With sales forecasting, businesses can anticipate fluctuating market trends to prepare internal operations. It also enables companies to detect threats to avoid risks and maintain profits. Organizations can use forecasts to improve their decision-making for any operation, including-
- Customer Relationship Management (CRM)
There are several internal and external factors that can affect sales forecasts, including-
Regardless if an employee leaves on their own terms or through termination, turnover can significantly limit revenue unless the business already has a pool of potential hires.
When businesses make changes to their policies, they must also adjust their forecasting software so it can update its algorithms. Otherwise, forecasts may be inaccurate.
For example, if the marketing team launches promotions at the beginning of each month, businesses should expect to see a spike in sales in the first couple of weeks. However, the sales will dip as the month closes as transaction volumes return to normal.
New representatives need time to acclimate to a business's territory, leading to a dip in the close rate. However, companies can expect an eventual spike when they become familiar with the process.
Depending on competitors' state of business, companies can see an increase or decrease in sales.
For example, if a nearby store runs a significant discount, surrounding companies may also need to mark down similar items to maintain sales. On the other hand, if a business shuts down, other stores may experience increased demand.
Poor economic conditions make it difficult for buyers to patronize their favorite businesses. However, when the economy is booming, consumers are able to invest in companies.
Businesses that run promotions with partners outside of their industry must monitor all relevant markets. For example, hotels that work with online booking services should track tourist trends and seasons to anticipate an influx of traffic.
Businesses also need to consider how the sales of particular items affect the sales of related products. For example, an increase in phone sales will probably lead to increased demand for phone accessories, chargers, and cases.
Regulations can boost or restrict business by either creating new demand or limiting production.
Product changes, such as additional features and new models, can help salespeople encourage impulse buys and cross-sales. This can positively impact inventory turnover, demand, and the average order value (AOV).
Every business has their own unique busy and slow seasons, making it easier to forecast demand fluctuations and prepare inventory levels.
Existing businesses have plenty of historical sales data for sales forecasting software to make predictions from. Companies can reference the monthly sales from the previous years to determine busy and slow seasons. They can also anticipate market trends based on consistent demand patterns.
Established businesses also already have a loyal customer base to collect data from, such as purchase histories, preferred sales channels, and product reviews. This enables companies to improve their staffing needs and product variances to meet the demand.
Sales forecasting is significantly more challenging for startups than established businesses because they do not have any prior sales data to reference. Therefore, new companies must conduct thorough research on their market and competitors to determine how to prepare their operations.
Through research, startups can discover their target demographics, biggest competitors, and high-demand products. This gives businesses an idea of what sales channels, products, and promotions to offer customers to boost income and collect valuable data for future forecasts.