Nexa Lab Blog – Advanced sales analytics is an approach to understanding and optimising sales performance using complex data analysis techniques, predictive modeling, and artificial intelligence.
For Managed Service Provider (MSP) business owners, advanced sales analytics can help you identify high-value opportunities, optimize pricing, predict customer behaviour, and tailor services to meet market demands.
Today, we are going to deep dive into the topics of advanced sales analytics.
We’ll explore more about the definition, techniques, and best practices to implement it into your business.
So, without further ado, let’s get to it.
What is Advanced Sales Analytics?
Imagine having a crystal ball that could not only show you what’s happened in your sales history but also predict future trends and suggest the best course of action.
That’s essentially what advanced sales analytics is.
It’s an approach to understanding and optimising sales performance that goes far beyond traditional reporting methods.
To illustrate the difference between basic and advanced sales analytics, let’s consider an example.
Basic sales analytics might involve tracking monthly sales figures, comparing them to previous periods, and creating simple charts to visualise trends.
For example, a basic report might show that your MSP business sold 50 service contracts last month, up from 40 the previous month, with a bar chart displaying this growth.
Advanced sales analytics, on the other hand, takes this much further.
It might use machine learning algorithms to analyse not just the number of contracts sold, but also factors like customer demographics, service usage patterns, and market conditions.
This could reveal that your most profitable customers share certain characteristics, such as being in the healthcare sector and having between 50-100 employees.
The advanced system might then predict which prospects are most likely to become high-value customers and suggest personalised marketing strategies for each.
Furthermore, advanced analytics could use predictive modelling to forecast future sales with greater accuracy, factoring in seasonal trends, economic indicators, and even social media sentiment.
It might also employ prescriptive analytics to recommend optimal pricing strategies or the best times to approach different types of customers.
In essence, while basic sales analytics tells you what happened, advanced sales analytics helps you understand why it happened, what’s likely to happen next, and how you can influence future outcomes.
More on Nexalab’s blog: Digital Sales Analytics, Definition, Metrics, and How to Implement It
Components of Advanced Sales Analytics
To harness the full potential of advanced sales analytics, it’s crucial to understand its key components. Think of these components as the building blocks of a powerful analytics engine that can drive your business forward.
Data Integration and Management
At the heart of any advanced analytics system is data—lots of it.
This component is like the foundation of a house; without a solid base, everything else falls apart.
Data integration and management involve collecting, integrating, and managing data from various sources – your CRM systems, social media platforms, customer feedback channels, market trend reports, and more.
But it’s not just about gathering data; it’s about ensuring its quality, consistency, and accessibility. Think of it as curating a vast library of information.
You need to make sure all the books are in the right place, up-to-date, and easy to find when you need them. Effective data integration and management lay the groundwork for all subsequent analytical processes.
Predictive and Prescriptive Analysis
If data integration is the foundation, predictive and prescriptive analyses are the architects designing the future of your business.
Predictive analysis uses historical data to forecast future outcomes. It’s like having a weather forecaster for your sales, helping you anticipate trends, customer behaviour, and market changes.
Prescriptive analysis takes this a step further.
Not only does it predict what might happen, but it also recommends specific actions to achieve desired outcomes. It’s like having a strategic advisor who not only tells you what’s likely to happen but also suggests the best way to respond. Together, these analyses empower us to make proactive decisions and stay ahead of the curve.
Machine Learning and AI
Machine learning and AI are the rocket boosters of advanced sales analytics.
These technologies can process vast amounts of data, identify complex patterns, and continuously learn and improve their models. From lead scoring to customer segmentation, ML and AI can automate and enhance various aspects of our sales processes.
Imagine having an assistant that never sleeps, constantly analysing your sales data to uncover new insights and opportunities.
That’s what machine learning and AI bring to the table. They can spot trends and patterns that might be invisible to the human eye, helping us make more informed decisions and predict outcomes with greater accuracy.
Visualisation and Reporting
Even the most insightful data is useless if it can’t be understood and acted upon.
This is where visualisation and reporting come in. Think of this component as the storyteller of your data.
Visualisation tools transform complex data into easy-to-understand charts, graphs, and dashboards. They take the raw numbers and turn them into a compelling narrative that anyone in your organisation can understand.
Effective reporting ensures that these insights reach the right people at the right time, enabling quick and informed decision-making.
It’s like having a news service for your sales data, delivering tailored reports to different departments and stakeholders, ensuring everyone has the information they need to perform at their best.
5 Important Techniques in Advanced Sales Analytics
Now that we’ve laid the groundwork, let’s explore some cutting-edge techniques that can take our sales analytics to the next level.
These techniques are like different lenses through which we can view our sales data, each offering a unique perspective and valuable insights.
According to Prolifics, there are at least 5 techniques that are used in advanced sales analytics.
Data Mining
Imagine being able to sift through mountains of data to find hidden gems of insight. That’s what data mining does. This technique involves analysing large datasets to discover patterns and relationships. It can help us uncover hidden insights about customer preferences, sales trends, and market dynamics that we might otherwise miss.
Cohort Analysis
This technique is like grouping your customers into different classes, each with its own unique characteristics.
Cohort means group. So, by donig cohort analysis, it means grouping customers into “cohorts” based on shared characteristics or experiences. The goal is so we can better understand how different groups behave over time.
This is particularly useful for analysing customer retention and lifetime value, helping us tailor our strategies to different customer segments.
Cluster Analysis
Think of cluster analysis as creating a map of your customer landscape. This technique groups similar data points together, helping us identify distinct customer segments or product categories. It’s invaluable for targeted marketing and personalised sales strategies, allowing us to approach different customer groups in the most effective way.
Retention Analysis
Understanding why customers or clients stay or leave can be important, especially for managed services providerd business.
Retention analysis is the way to get that insight.
It’s like conducting an exit interview with every customer, even those who stay. It helps us identify factors influencing customer loyalty and develop strategies to improve retention rates. Understanding what keeps customers coming back allows us to fine-tune our offerings and services to increase customer lifetime value.
Complex Event Processing
Complex event processing analyses multiple data streams in real-time to identify significant patterns or anomalies.
It’s particularly useful for detecting fraud, identifying sales opportunities, or responding to market changes quickly. Think of it as having a vigilant guardian watching over your sales processes, ready to alert you to threats or opportunities as they arise.
Best Practices for Advanced Sales Analytics
To make the most of advanced sales analytics, consider these best practices. Think of these as your guiding principles, helping you navigate the complex world of data analytics:
- Start with Clear Objectives: Define what you want to achieve with your analytics before diving in. Clear goals will guide your data collection and analysis efforts.
- Monitor Data Quality: Invest in data cleaning and validation processes to ensure your insights are based on reliable data. You can gain more clarity into your insights when you use clean data.
- Establish a Data-Driven Culture: Encourage your team to base decisions on data rather than intuition alone. Provide training and support to help them understand and use analytics effectively.
- Combine Analytics with Domain Expertise: While data is powerful, it shouldn’t replace human judgement entirely. The best insights often come from combining analytical findings with industry knowledge and experience.
- Continuously Iterate and Improve: Regularly review and update your analytics processes to stay current with the latest techniques and technologies.
- Prioritise Data Security and Privacy: Make sure your analytics practices comply with data protection regulations and maintain customer trust. Treating customer data with respect and care is not just a legal requirement – it’s part of maintaining strong customer relationships.
We understand that every business might have different conditions. However, these best practices can give you general guides to start doing sales analytics.
Though this advanced process might look more insightful, it’s important to start small. So, if you are not used to data driven decision making, you can always start with the basic sales analytics first.
Starting with basic sales analytics allows your teams to gradually build up skills and the logic behind drive decision-making in a business.
As you become more comfortable with the process, you can then begin to incorporate more advanced analytics techniques to create more advanced sales strategies.
More on Nexalab’s blog: 5 Best Free Sales Analytics Software To Help You Make Sense of Your Sales Data
Conclusion
Advanced sales analytics is not just a tool; it’s a strategic asset that can transform our approach to sales and business growth. We can gain insights into our sales processes and customer behaviours by using sophisticated data integration, predictive analysis, machine learning, and visualisation techniques.
Did you know that if you are using professional service automation (PSA) software, you can create your own sales analytics software if you combine it with the BI platform?
Introducing Nexalab’s MSP Sales Analytics, an integration solution for your professional services automation software and business intelligence platform.
It creates custom sales analytics dashboards that provide real-time insights into your sales performance, allowing you to make data-driven decisions and optimise your sales processes. Contact Nexalab today to learn more.
