Shweta Gupta is a growth marketing specialist working with 2xSaS. She creates content that converts website visitors into paying customers for SaaS companies.
Accurate sales forecasting is key to the success of any business. Companies have been using traditional sales forecasting techniques for years, but are they as effective as they need to be?
The truth is accurate sales forecasting is difficult to predict and achieve under the most favorable of circumstances. A report by Korn Ferry revealed that only one-fourth of sales teams are able to attain 75% accuracy in their sales forecasting.
Thankfully, times have changed and sales forecasting has seen a great improvement with the introduction of CRM analytics tools. These tools provide you with reliable predictions that are highly accurate, making it easier to get the desired outcome for your business.
This article provides insight into how CRM analytics can be leveraged to make accurate sales forecasts. It aims to help you by outlining a winning strategy for your business, through executing CRM analytics in sales forecasting.
Firstly, let us define sales forecast accuracy. This is the accuracy of your projection tools to predict forecasted sales for a given period of time. Accurate sales projections are essential to ensure adequate resource allocation, production forecasts, insights into customer behavior and a better understanding of market trends not only for the coming quarter, but ideally for several quarters ahead. Such data is vital for businesses to make well-informed decisions.
Here are some common outcomes if a sales forecast goes wrong.
A reliable way to measure sales forecast accuracy is by looking back at the percentage difference between actual and forecasted sales performance. A smaller gap indicates that the prediction was more accurate, while a larger gap would mean the projection was not accurate. Businesses can use this information to refine their sales forecasting models, improve their forecasting processes, and make better decisions.
In the final two decades of the 20th century, sales and marketing achieved some significant milestones. Way back in 1987, the first automated CRM - Automated Contact Tracking (ACT) - was released. Over time, sales forecasting has been incorporated as a feature within this software and is used by many businesses today.
Let's dive into some of the key roles of CRM analytics in sales forecasting.
By segregating customers based on common characteristics or behaviors, your business can tailor their sales forecasting strategies to each group. This can lead to higher customer satisfaction and increased sales.
E.g., your business can create segments based on demographic data, including gender, age, and location. The segments might also include purchase behavior, such as frequency or value of purchases.
CRM analytics use data and statistical models to predict future customer behavior, like plausibility to purchase or churn. This way, your business can anticipate customer needs and proactively address issues.
Using CRM analytics, you can analyze historical data and identify which campaigns and promotions have successfully driven sales. This can help your business focus the resources on the most effective strategies, improving your return on investment and overall profitability.
Using CRM analytics, now you can identify overlooked customer segments and new markets by the data acquired. These are budding opportunities to reach out to the needs of your customers by launching new products and services, which in turn boost sales revenue and scalability.
Using CRM analytics, you can monitor sales performance in real time. As a result, you can always be updated with the correct implementation of the current strategy, identify areas of improvement, and adjust your sales strategies accordingly.
CRM analytics can help you predict the potential lifetime value of customers. So next time, you can prioritize and invest in the customers adding more value to your organization.
Forecasting accuracy is achieved by identifying the singular opportunities that add to increased revenue. Cross-selling and upselling are some critical points for potential revenue you wouldn’t want to miss without having to drastically increase lead volume. CRM analytics can analyze customer data and provide business-driven insights to pinpoint the places your business could be better optimizing your cross-sell and upsell strategies.
Although we have covered CRM sales forecasting as a successful method for getting accurate results, there are several significant reasons why accurate sales forecasting is critical for businesses. Here are a few:
Sales forecasts are used to allocate resources such as personnel, equipment, and inventory. If a company overestimates sales, it may invest too much in these resources, resulting in overproduction, waste, and unnecessary costs. Conversely, if a company underestimates sales, it may not allocate enough resources, leading to stockouts and lost sales.
Accurate sales forecasts are essential for financial planning, budgeting, and cash flow management. By accurately forecasting sales, businesses can plan their expenses and investments to optimize their financial performance including overhead and new hires.
Sales forecasts play a critical role in production planning, as they help businesses determine how much product to produce and when to produce it. Accurate forecasts can help businesses avoid stockouts and excess inventory–issues that often lead to reduced profits and increased costs.
Sales forecasts give businesses insights into consumer behavior and market trends, which can inform marketing tactics. By accurately forecasting sales, businesses can adjust their marketing efforts to maximize their return on investment.
Investors rely on accurate sales forecasts to assess a company's performance and growth potential. Investors may view a company that consistently misses its sales targets negatively, which can impact its ability to secure funding and grow.
In a nutshell, accurate sales forecasting is essential for businesses to make informed decisions, optimize resources, and improve their financial performance. By investing in accurate forecasting methods and tools, businesses can gain a competitive advantage and position themselves for long-term success.
CRM analytics refers to the process of analyzing data from a company's customer relationship management system (CRM) to gain insights into customer behavior, preferences, and needs. This data can then be used to increase sales, improve customer engagement, and boost customer satisfaction.
Let’s dive a bit deeper and understand what CRM does and how its analytics benefit you.
A CRM is a powerful database system that stores information on:
Companies can unleash patterns and trends in their customer data by using tools like data visualization, predictive modeling, and machine learning algorithms.
For example, they might discover that customers who purchase certain products are more likely to make repeat purchases or that customers who interact with customer service through social media are more likely to give high satisfaction ratings.
Likewise, companies can make informed and data-driven decisions on how to engage with their customers. Another way to utilize customer data would be to send personalized recommendations to customers based on their purchase history. This creates a sense of customized experiences contributing to customer trust and retention.
Depicted above is how CRM and sales forecasting are linked. Let's walk through four easy steps to execute accurate sales forecasting using CRM analytics :
The first step to implementing CRM sales forecasting is identifying the data sources. Here are some data points of which you ought to be mindful:
The data from these sources can help your business understand customer behavior and preferences comprehensively.
The next step to successful sales forecasting is to develop a data model.
A data model will help you structure the facts and figures to pave the way for a successful analysis. This step may involve creating segments on customer characteristics or purchase behavior. You will also be able to develop a predictive model to forecast the customers’ future behavior.
Now that you have a well-versed data model, you can now identify patterns and trends through the data model analysis. E.g., using statistical models to identify correlations and patterns or using machine learning algorithms to predict future behavior.
The final step in implementing CRM analytics for sales forecasting is to generate reports and insights. With this final step, you will be ready to make informed business-winning decisions.
You can see your insights in KPI dashboards and monitor real-time data on sales performance. You can also get a hold of reports providing insights into customer behavior and preferences.
Summing up how CRM analytics affects sales forecasts, here are a few key takeaways:
Initially implementing CRM analytics requires a significant investment in data infrastructure and expertise. But this investment paves a long road to greater ROI.
To learn how to optimize your sales process end-to-end, get in touch with the professionals at Marin Software. We're ready to help you make the most of your advertising investment leading to a more consistent CRM lead funnel.
Shweta is a guest contributor to the Marin Software blog.
