Many companies struggle to forecast sales accurately: four out of five sales organizations get their forecast wrong by more than 10%. This may lead to mistaken investment decisions, overload or lack of salespeople, or stock issues. Alternatively, sales forecasting using AI allows business leaders and sales reps to make smarter decisions when defining goals, budgeting, and hiring.
A Look at Sales Forecasting from the Inside
According to Salesforce, one fourth of all US companies use predictive analytics. These forecasting techniques help businesses set a budget, allocate resources, estimate potential revenue, and plan future growth.
Sales forecasts usually contain:
- Individual and team sales quotas, which determine targets and identify the progress of sales campaigns daily, weekly, monthly, or quarterly
- Documented sales processes for team members to follow
- A customer relationship management (CRM) database that includes the interaction of sales reps with prospects, leads, and customers
Many factors impact sales forecasts. Internal factors include changes in product lines or personnel. External aspects are competitive changes, economic conditions, seasonal changes in demand, and force majeure circumstances like the pandemic.
Accurate sales forecasting is impossible without proper tools. This may include CRMs, spreadsheets, sales analytics platforms, accounting software, and other solutions containing sales reports. Sales data from previous periods helps companies predict future sales for a week, month, or year. Sales reps and business managers use such predictions to estimate revenue and create a sales strategy.
Tools are classified by the number of possibilities they bring. While simple spreadsheets can be segmental, CRM systems combine instruments that analyze customer behavior, track leads, differentiate funnels, and manage calls at one dashboard. Smart CRMs can incorporate artificial intelligence algorithms that add accuracy to forecasting based on current and historical data.
There are several approaches to forecasting. A historical approach is based on data collected. An intuitive method works best if the company has no historical sales data. Pipeline forecasting relies on both data and multiple unique factors.
AI in Sales Forecasting: What Value Is Added
The global predictive analytics market size is expected to hit $21.5 billion by 2025, growing 25% year by year between 2020 and 2025. Greater adoption of AI and ML techniques are among the factors driving this market. Others include:
- Growing focus on digital transformation
- Increasing adoption of big data
- Rising need for remote monitoring in support of the COVID-19 pandemic
The smart combo of data, analytics, and AI helps businesses to improve forecasts. Artificial intelligence in sales allows for creating predictive models that examine datasets and reveal factors that impact a profit. Machine learning algorithms enable the software to train on data and improve over time. Natural language processing is capable of adding context. Moreover, AI-powered software can equip the sales forecast with related data such as weather and traffic.
AI-powered tools can make forecasts by market segments, regions, and product types, track historical data, and provide real-time updates. Also, machine learning solutions gather information about user behavior, purchases, preferences, and dislikes in many ways. These solutions can utilize CRMs, social media, and emails. The software can also track how often a sales rep contacted a particular customer and guide with the next steps for closing deals.
An in-depth data analysis helps analyze missed opportunities, successes, and win rates to create a forecast. Managers can convert this data into actionable insights, improve user experience, and suggest products according to the user’s possible needs.
Moreover, the AI component of the software helps interpret data without bias and anticipation more quickly than humans. According to Aberdeen Group research, accurate sales forecasts increase year-over-year company revenue by 10% and attain quotas with 7% more efficiency.
Here’s How AI Helps Optimize Business Processes across Multiple Industries
CRMs and accounting software equipped with AI predictive analytics bring insights to company leaders, sales reps, and business partners across many industries, including manufacturing, finance, and e-commerce.
E-commerce and retail
There are many ways that AI applications help e-commerce companies forecast precisely and plan future growth. These include relevant product recommendations, a smart supply chain to improve production and logistics, and chatbots to enhance customer service. All these aspects help drive sales, introduce customer-centric search, add personalization, and localize customer experience.
McDonald’s, the world’s biggest fast-food chain, uses AI to optimize its supply chain and balance customer demand and stock levels. For example, when the restaurant has a surplus of chicken sandwiches but lacks beef burgers, the menu shows greater visibility for chicken offerings to prevent outages.
Also, a dynamically changing menu helps the company drive sales more intuitively. For example, the menu may suggest a sauce to couple with french fries and offer a water bottle when a customer orders a healthy salad. The system relies on the time of the day and weather conditions. AI-powered software allows McDonald’s to monitor supply and outages across the restaurant network and use data to suggest other items that bring more profit and customer satisfaction.
Banking and financial institutions
McKinsey estimates the potential value of AI and analytics for global banking could deliver up to $1 trillion annually. According to their Global AI Survey report, banks use AI technologies to improve customer experience and back-office operations, such as the following:
- Automate operational tasks using biometrics to authorize and facial scanning to initiate transactions
- Support customers with the help of conversational bots and humanoid robots
- Use machine learning techniques to detect fraud and cybersecurity attacks as well as perform risk management
In the meantime, artificial intelligence forecasts allow banks and other financial institutions to reduce risks ”in particular, estimate the chance that applicants won’t pay off the mortgage. The decision relies on customer financial information like income, employment, and credit score. This data may also help decide how much money to borrow. Overall, AI in banking leads to higher automation, speed, and accuracy. It‘s no surprise that outcomes result in more accurate sales forecasts.
Manufacturing
Smart, predictive software allows manufacturing companies to track many factors that affect future sales. It may control product quality, track equipment performance, predict asset failure or downtime, reduce gaps in production and extra expenses, and identify potential issues on the factory floor. It’s possible to gather lots of data from sensors attached to the equipment. Here are just a few parameters that predictive manufacturing systems can monitor:
- Production quality (vendor quality, data accuracy, cost of quality)
- Delivery reliability (schedule adherence, lost sales)
- Costs (inventory turns, waste rates, overhead efficiency)
- Lead times (setup time, material availability, machine uptime, customer service time)
Many large manufacturers, including leaders of the Supply Chain Top 25, deploy AI and ML techniques in their supply processes. Colgate-Palmolive monitors the “health” of their machinery 24/7 through wireless sensors combined with ML-powered analytical software to facilitate the smooth supply. The platform collects the data and compares it to extracted information from over 80,000 other machines operating worldwide. These insights help the famous company optimize performance.
In industrial manufacturing, machine learning helps reveal patterns in the data produced by connected equipment and disparate IT systems in a factory. As a result, predictive maintenance solutions enable manufacturers to maximize equipment uptime, avoid costly repairs, boost production output, and reduce maintenance costs by up to 95%.
What’s the Bottom Line?
Artificial intelligence techniques improve forecasting in many aspects that affect sales. Here are just a few benefits that smart sales prediction brings:
- Ensure faster planning and decision-making. In particular, AI-enhanced software interprets information faster than humans
- Add accuracy to forecasting based on current and historical data
- Helps extract actionable and valuable insights
- Improve processes like hiring and budgeting
- Contribute to solving out-of-stock and overstock issues
Simultaneously, smart software doesn’t replace human sales spirit, well-coordinated teamwork, and intuition based on years of experience. A combo of proactive team members and software that guides sales reps and helps them avoid errors is the smart way to ensure year-over-year growth.
