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Predictive Sales Analytics: The Other Side of Forecasting
Foresight is a cornerstone of business. Having the inside scoop on tomorrow can make or break a company. In fact, the process of discovering better and more effective ways of modeling the future has become a $168 billion industry.
Today, most of the insight about the future that companies use to make decisions is generated by data analytics software. Data pertaining to everything from sales to demand to opex to win rate and more is compiled and crunched to see what different aspects of your business and the market itself will look like as time goes on.
But how is this collection and analysis really achieved? In the world of data analytics, there are two main methods for modeling the future: forecasting and prediction. In regular parlance, and often within the business world itself, these terms are used interchangeably. However, they refer to two distinct methods which deal with different data and generate different conclusions.
Forecasting methods of data analysis deal with the big picture–large groups of people or sectors of the market and how they behave as single units. Forecasting also operates on a longer timeline, looking at how trends will be behaving months, quarters, or years from now. Predictive analytics techniques, however, are focused on individual prospects or clients and what they’ll do in the immediate future.
Using forecasting and predictive analytics for sales has become indispensable for the success and competitiveness of businesses. In this article, we’re going to focus specifically on predictive sales analytics. We’ll run through some examples to demonstrate how companies can use data analytics to increase sales; we’ll go over how this modelling actually works and is achieved; we’ll examine how AI and machine learning are important to these processes; we’ll show how all this can be beneficial for the smooth functioning of your pipeline and, finally, explain how the whole process can significantly impact and improve the way you think about and do sales.
Predictive Sales Analytics Examples
Knowing how to do predictive analytics well will depend on a firm grasp of the way it works. Some predictive sales analytics examples will be helpful in understanding the types of data considered and the types of models generated by such software.
Imagine that there is a prospect that has been determined to be part of a target demographic potentially in need of the products or services your company provides. Utilizing data taken from visitors to your website and how they engage with various lead magnets, your predictive software can determine with great accuracy whether and to what extent that prospect will be funneled. Which regions of your webpage the prospect accesses, how long they spend on them, and when and from what search engine they reach your page will all be analyzed to place the prospect into a predictable behavioral category. Knowing what previous visitors who behaved the same ways did next allows the next actions of the prospect to be modeled with incredible accuracy. This can be useful for implementing ad and CTA personalization which enables you to cater to the preferences of a wide variety of prospects and drive funneling.
The same principles can be applied to individuals already within your sales pipeline. Using data taken from your business’s CRM, software can define certain categories of buyers based on their background, pipeline timeline, and deal size. By observing and analyzing the initial behaviors of new buyers, they can then each be placed into one of these categories and their next moves modeled. With this, you’ll be able to determine what to do next–when to reach out, what to say, and how to structure your offer. This is a great way to make prospects feel comfortable and confident and drive sales.
These two predictive analytics examples just scratch the surface of the capabilities of this type of software, and sales analytics use cases are practically infinite. This should, however, give a good idea of the basic function and applications of these tools.
Predictive Sales Model
A predictive sales model should be specially constructed from and suited to the kind of data it works with. In other words, all predictive models will look different and offer different types of insight. Generally, however, sales analytics models work in the following way.
In order to generate predictions about the imminent behaviors of prospects and clients, analytic software will need to draw from a large well of data. The accuracy and precision of a piece of software is directly tied to the amount of data it has at its disposal, so information collection is a core part of modelling.
Your business’s CRM will often play a central role in this aspect of sales prediction, and indeed all types of sales forecasting. The CRM contains intricate data regarding the background, history, and behavioral trends of prospects and past clients. These are the exact types of data predictive software needs to function.
Analyzing all this data, your software will then generate internal categories based on this information. How many of these there are and what they contain will depend on the software and the situation, but these serve as potential blueprints for the behavior of new prospects. As soon as any information about a potential buyer is fed into your predictive software, it will start trying to place them into one of these constructed categories.
When an individual’s actions sufficiently mirror one of the preset categories, the software begins generating predictions about their next move based on the average behavioral trajectory of that category. Everytime the prospect deviates from this path, however, they are reclassified into a new group more closely matching their action set. This process continues until their behavior patterns settle into one of the established patterns, or, if they continue to be deviant, they are placed into a brand new category which can subsequently be used to classify new potential buyers as well.
Predictive Sales AI
This kind of rapid sorting, intelligent category construction, and error correction can only be achieved with AI technology. AI learns as it works, so the longer a program operates, the more effective and accurate it will become. Different algorithms learn in different ways and at different rates, however. As such, the efficiency of your sales modelling will be directly dependent on how advanced your predictive sales AI is.
This means that choosing the right online sales forecasting tool provider is crucial to the success of your company. Canopy is one of the foremost purveyors of sales prediction software on the market and offers a whole suite of tools specially built to help businesses construct a powerful and versatile sales forecasting system.
Canopy’s state-of-the-art predictive and forecasting solutions are built by analysts with years of experience in data management and integrate frontrunning machine learning and AI technology to ensure unparalleled performance and dexterity. Canopy’s augmented revenue analysis engine is the pinnacle of this technology, able to capture and synthesize enormous quantities of information and provide users with unrivaled modeling capabilities which blur the lines between forecasting and clairvoyance.
Consider Canopy when entering into the world of predictive analysis, because the quality of your models will depend on the quality of your tools.
Importance of Data Analytics in Sales
The importance of data analytics in sales cannot be overstressed. While sales forecasting software might help companies make structural changes to follow and meet demands over time, individual sales are helped little by this macro-level data.
Predictive analysis helps companies and sales reps interact with individual buyers. Every prospect and client is different, and determining and catering to their needs and preferences is what makes or breaks a deal. For websites or other prospect-facing content, predictive analysis helps generate leads by targeting potential buyers with the right ads at the right time. It can also help tailor the UX and UI of online platforms to make them more appealing to individual tastes. The same process applies for the different stages of a deal, helping reps determine how to tailor and customize the process to build confidence and loyalty, increasing the likelihood of a win.
Without predictive analysis, companies would have to guess how to appeal to prospects based on general averages. Only a small number of individuals would feel that their needs are being met, and a vast majority would feel ignored or unimportant and flock to competitors. The benefits of sales analytics software lies in its ability to make each buyer feel prioritized. Predictive software helps optimize the face-to-face side of business, which, in the end, is where all sales are made.
Sales Pipeline Predictive Analytics
Even more than with lead generation and prospecting, predictive software can help optimize your pipeline. The copious amounts of data stored in your CRM are really only handy for modelling the behavior of individuals once they’ve entered this process. Because of this, the accuracy of prediction increases substantially once a prospect has actually engaged with your company in some way.
Predictive analysis can help sales representatives determine when and by what means a prospect wants to be contacted, how they want to be talked to, and how long they are likely to take while moving through the stages of a deal. Personalization has always been a hallmark of an effective sales strategy, but with predictive analysis, this process can be nearly perfected, helping your business drive sales like never before.
Finally, predictive analysis can also help with sales pipeline inspection, helping you probe for any weaknesses or opportunities for improvement in your pipeline. This can help improve the operational efficiency of your team and, thus, increase revenue.
To learn more about the capabilities and business benefits of predictive analytics, visit our website and watch a demo of Canopy’s software suite in action.