With data coming from nearly every area of your business, sometimes it’s hard to know what to do with it all. But one area where data’s influence shines brightest—and can have the biggest impact—is in sales. Your sales data isn’t just a record of past transactions; it’s a treasure trove of insights waiting to be discovered and capitalized upon. When properly harnessed using a data-driven sales strategy, data can drive sales by providing insights into customer behavior, predicting future sales trends, and identifying the most effective strategies to close deals. But how do you navigate this sea of data to find the insights that matter?
Enter advanced analytics and metrics techniques. Going beyond the basic analysis, these methods use more sophisticated tools and algorithms to analyze data and extract deeper insights. These techniques help you not only understand what is happening, but also why it’s happening, so you can better predict what might happen next. This way, you can adjust your sales strategies to maximize effectiveness.
Today, we’re delving into the nitty-gritty of a data-driven sales strategy that uses advanced analytics and metrics techniques to help turn your data into a powerful sales-boosting tool.
The Evolution of Sales Data Analytics
As the data businesses collect has become more sophisticated and plentiful, the way that modern sales teams use that data has evolved markedly. Let’s look at the history of traditional sales analytics and how things have changed over time.
A couple decades ago, B2B sales data analysis was relatively simple. Sales teams primarily relied on basic spreadsheets to track metrics like:
- Total sales
- Sales per representative
- Sales per region
Example: A B2B company selling industrial machinery might have tracked monthly sales volumes by each sales representative and used this data to identify the top performers. These basic metrics only provided a limited view, however, with a focus mainly on the results without much information related to customer behavior or demographics.
As technology evolved, the complexity and power of sales analytics evolved along with it. Advanced analytics introduced the ability to handle large data sets, draw insights from unstructured data, and make predictions for the future. For example, a B2B software-as-a-service (SaaS) company might use advanced analytics to identify patterns in the buying behavior of their customers and track information like:
- Usage data
- Customer feedback
- Sales trends
Businesses can use this more granular data to predict which customers are most likely to upgrade their software package or need additional services.
Today’s Sales Data Analytics in a Data-Driven Sales Strategy
Today, businesses are harnessing advanced sales analytics technology involving machine learning, AI, and big data to make real-time decisions and automate processes.
Example: A B2B digital marketing firm can apply machine learning algorithms to historical sales and marketing data to predict the likelihood of closing a deal with a prospective client based on specific factors, including company size, industry, and previous engagement with marketing content.
As we look to the future, the potential for sales data analytics and data-driven sales is expansive. Predictive analytics are likely to become even more precise as algorithms become smarter and data sets grow larger. And as more tools integrate AI technology, there’s more opportunity for developing highly personalized sales strategies as systems will be able to analyze individual customer preferences and behaviors to offer tailored solutions.
Let’s take a closer look at what a data-driven sales strategy entails and why it’s crucial for your sales decisions.
What is a Data-Driven Sales Strategy?
A data-driven sales strategy is an approach that uses data to inform all sales-related decisions and processes. This process involves making choices based on hard data, numbers, and facts, from sales trends to customer demographics and more.
Example: A B2B company that manufactures and sells industrial equipment can use a data-driven approach that involves analyzing purchasing data from their CRM, tracking industry trends, and using customer feedback surveys to understand what customers truly value. With these insights, the company can then shape its sales tactics, offerings, and even pricing strategy more effectively.
The Role of Data in Sales Decision Making
Needless to say, data plays a critical role in informing sales decisions in a data-driven strategy. It serves as the compass that directs your sales team towards successful outcomes. With accurate, up-to-date data at your disposal, your sales team can make informed decisions that maximize results and reduce risks.
Example: A B2B software company wants to increase its market share in a particular industry. Using a data-driven approach, they can analyze historical sales data, industry growth projections, and competitive landscape to identify potential growth opportunities and obstacles. The company could also use data from customer interactions to understand the features and services most valued by clients in this industry. Based on these insights, the company could develop targeted sales strategies for prospects in this industry, helping increase the likelihood of success.
Overall, a data-driven sales strategy provides a more scientific approach to selling, reducing uncertainty, and increasing the probability of success. Next, let’s explore some common data-driven sales metrics.
Key Metrics in Data-Driven Sales
An important aspect of a data-driven sales strategy is knowing which metrics to track. Sales metrics tell the story of your sales processes, customer behaviors, and overall performance.
5 Essential Sales Metrics
Here are five essential sales metrics to monitor:
- Sales Revenue. The total income from sales before expenses are subtracted. It gives a clear picture of your sales performance in terms of money generated.
- Conversion Rate. The percentage of prospects who make a purchase, out of everyone who expresses interest in your offering. Monitoring this metric can help identify how effective your sales process is at turning potential customers into buyers.
- Sales Cycle Length. The average amount of time from when a lead is generated to when a sale is closed. Understanding this metric can help pinpoint bottlenecks in your sales process.
- Customer Acquisition Cost (CAC). The total cost of acquiring a new customer, including marketing expenses, sales expenses, and overhead.
- Customer Lifetime Value (CLTV). This metric predicts the net profit from the entire future relationship with a customer. A high CLTV compared to CAC indicates a healthy return on investment.
These metrics offer invaluable insights into various aspects of your sales process.
Example: If a B2B cybersecurity company notices a higher than industry-average CAC, they might need to revisit their sales processes or marketing strategies. If their CLTV is also low, they may need to consider ways to improve customer retention or increase the value of each sale.
Advanced Analytics Techniques for Data-Driven Sales
Gather and act upon more sophisticated data points through advanced analytics. Let’s look at how these methods, when integrated into your sales strategy, can drive impressive results.
- Predictive Analytics. This process involves using historical data, statistical algorithms, and machine learning to predict future outcomes. For example, an agency could use predictive analytics to anticipate which clients are most likely to churn, allowing them to proactively address issues and improve customer retention. Predictive analytics can help you forecast: Sales trends, customer behaviors, and market changes.
- Prescriptive Analytics. This method goes a step further than predictive analytics, not only forecasting what will happen but also suggesting actions to benefit from those predictions. For instance, a software provider could use prescriptive analytics to identify the optimal price for their product, maximizing both sales volume and profit margins.
- Machine Learning. This process involves algorithms that can learn from and make decisions based on data. Machine learning can help automate and optimize many sales processes, from lead scoring to personalized marketing. A manufacturing company, for instance, could use machine learning to analyze vast amounts of data on customer behavior and segment their customers for personalized marketing campaigns.
Case Study: IBM
Let’s look at software company, IBM. They used prescriptive analytics to optimize their sales process. Their advanced analytics solution, called Sales Operational Effectiveness (SOE), analyzes sales data and recommends actions to sales representatives. This solution helped IBM increase their lead conversion rates and improve sales efficiency.
Next Steps for Introducing a Data-Driven Sales Strategy
As we’ve covered today, a data-driven sales strategy is a systematic approach to sales that involves using data to inform decisions, drive actions, and measure success. This strategy moves sales leaders from hunches and guesswork to a realm of actionable insights and evidence-based decisions.
With key sales metrics, you can monitor sales processes, understand customer behaviors, and measure sales performance. Plus, advanced analytics methods like predictive analytics, prescriptive analytics, and machine learning help take your sales analytics a step further.
If you’re interested in implementing a data-driven sales strategy in your business, stay tuned for our upcoming article with a step-by-step guide on how to get started. And if you’re looking to expand your prospecting data as part of your data-driven sales, try out LeadLander’s website visitor identification platform free for 14 days, no credit card needed.