How to use Predictive Analytics in your CRM for Truly Data-Driven Decisions?
Successfully implementing Predictive Analytics for data-driven decisions in your CRM requires more than just software.
Every successful sales team wants to become data-driven. Companies that successfully execute predictive analytics in their CRM are easy to identify.
They take care of their salespeople and successfully utilise their current data and processes. They employ the standard software and adapt them to their situation. Finally, they introduce Predictive Sales to CRM step by step and learn from their experience.
Better Customer Retention in B2B Wholesale Through Algorithms
Learn three ways to increase customer loyalty with algorithms and Predictive Sales Analytics in B2B wholesale.
Should computer programs be able to increase customer loyalty in wholesale? Yes. And no. We'll discuss what is exactly meant by this in this post.
According to a survey by Roland Berger, customer loyalty was already a top priority for wholesale companies in 2016. However, 1 in 5 wholesale companies also believed that their efforts in the area of digitalization were not yet sufficient to survive the digital competition.
Why a CRM System with Predictive Sales Analytics and AI?
CRM and Sales Analytics: In this article, you will learn what you can expect from a CRM system with artificial intelligence.
Using CRM systems have long been a common practice in large companies today, even if not always successfully implemented. Small and medium-sized enterprises (SMEs) often still have some catching up to do.
Meanwhile, the next expansion stage has long been underway. Predictive sales analytics/artificial intelligence (AI) is being added to the CRM systems.
Correlation does not equal causality - KPIs in Sales
Watch your step! Sales managers and managing directors in B2B confuse correlation and causality.
Data-based decisions in sales are not always ad-hoc better than intuition. The reason for this is the frequent confusion between the terms causality and correlation.
How nice it would be if managing directors or sales executives regularly knew why something happened. Why individual customers churn; why one product does not sell well or sells more than others; why in the end a promising sales lead does not become a customer, regardless of how good our salespeople are.
Why is internal data considered more reliable and easier to collect than external data?
Simply explained: Why internal data is better for predictive analytics in B2B.
Companies use sales forecast to make business decisions. They also employ them to predict future developments better than their competitors. However, reliable predictions are rare, and sales teams try to play a safe card by applying external forecasts. Companies are nevertheless better off using their in-house data - with predictive analytics.
"There are three types of lies: lies, damn lies, and statistics." This quote from Benjamin Disraeli, a British statesman and 19th-century novelist, fits the situation in companies very well.
Read more
AI Data Visualization for Advanced Sales Analytics
Understand your sales data and achieve a much higher business success through an intelligent visualization from Qymatix AI.
Sales Analytics Visualization
“A Picture is Worth a Thousand Words.” That is how Qymatix works. With only one sales data visualisation you can directly access all critical information available at any time: where are the Low-Hanging Fruits, Cross-Selling Quick Wins, Customers at Risk of Churning and Price Setting Potential. Sales managers are now able to implement and track the most appropriate sales KPI and to measure the overall status of existing customers, segmented turnover and profitability and new customer acquisition.
Market Basket Analysis in Excel - Example for Cross-Selling in B2B
One Useful Example of Predictive Sales Analytics Using Excel
Cross-selling is the practice of selling an additional product or service to an existing customer. Indeed, B2B companies define cross-selling in general and cross-selling analytics in particular, in many ways and with many names. One common naming used in retail or distribution is “market-basket-analytics”.
Most of us are familiar with cross-selling from our experience as online consumers. “Customers that bought X also bought Y” or “related products”. E-commerce websites make product suggestions based on a market basket analysis. The list of the possible suggestions is also known as “associating rules”. Marketing practitioners talk about “Buying Propensity”.
What is a Predictive Score Model in B2B Sales? How Can You Create Yours?
A predictive score model is a formula to calculate a probability.
There is a 70% chance that you will read this entire article. How do I know this? Because I used a predictive score model. The score is the probability of you reading to the end of the article (or one minus the likelihood that you will not – the exact opposite).
My example in the paragraph above is a well-known application of predictive analytics in marketing. The most common examples in business-to-business (B2B) sales are lead scoring, churn (or customer attrition), cross-selling, and pricing.
New Partnership between Qymatix Solutions GmbH and ORBIS AG
Karlsruhe, 10.02.2022. Qymatix Solutions GmbH cooperates with ORBIS AG. ORBIS supports medium-sized companies as well as international corporations in the digitalization of their business processes. The goal of the partnership is to accelerate the adoption of new AI technologies with the Qymatix Predictive Sales Software. Artificial intelligence "out-of-the-box", instead of long AI projects.
How Predictive Sales Analytics Works and Why It Matters
AI-based programs help your sales team to sell products are services more efficiently. The programs make predictions about your customers' behaviour: who will churn? Who might pay a different price or buy an additional product?
The technology behind this is called "predictive analytics" or, in sales terms, "predictive sales analytics".