The world of business is always changing. To keep up, companies must embrace new technologies and methods to streamline their processes. One area that can be greatly improved with automation is revenue forecasting. Automated revenue forecasting can reduce the amount of time spent on data gathering and analysis, leading to more accurate predictions and a better bottom line. However, automated forecasting with machine learning (ML) can take things one step further by incorporating past data, trends, and other factors that may not be easily observed or quantified. This leads to even more accurate predictions and a better understanding of the business landscape.
Topics: Predictive Revenue Forecasting
Forecasting is a discipline that reduces companies' exposure to risk. The process of forecasting alerts us to upcoming shortages in supply or demand, allowing us to take precautionary measures before we find ourselves in a negative cash-flow scenario. Forecasting is truly a critical business function within any organization. Automating your forecasting process can help you maximize efficiency while reducing costs and improving accuracy. This article will outline how you can use a variety of tools and approaches to automate various aspects of your forecasting process.
It is no secret that analytics and planning projects are based on data. Typically projects involve wrangling some historical datasets, each with their own peculiarities, and then adding in some projection or prediction. Because data is essential, it is also a common stumbling block as business users consider beginning a new analytics and planning project.
QueBIT routinely works with clients that have either selected a poor-performing planning software tool, or who are suffering from the poor implementation of a good planning software tool. Both of these scenarios-- bad software and a bad implementation-- can dramatically hinder the cross-functional planning process and make improvement efforts less impactful and responsive. The challenge for a given client often then comes in recognizing which scenario applies to the situation at hand. Is this client underperforming because their software isn't a good fit? Or is it really about how the software is set up inside that particular company?
Most organizations realized in 2020 that their traditional, manual planning processes were inadequate at best, and a liability at worst. QueBIT and its software partners specialize in helping clients move from disconnected, siloed planning into a collaborative and agile planning process turbocharged by automation and smart use of AI. This future state is Extended Planning & Analysis – or xP&A.
Partnerships are all about connecting the people and processes of multiple organizations to drive better results for the partner entities and their customers. We are pleased to announce the addition of Anaplan, the leader in “Connected Planning”, to QueBIT’s planning and analytics portfolio.
Today's digital economy has not only increased competition across geopolitical boundaries but has also disrupted traditional business models. To succeed in a world fraught with economic uncertainties that have only been accelerated due to the pandemic, businesses need to realign their operating models to become more agile, adaptable, and innovative.
Topics: supply chain planning
Today's customer has high expectations - they know exactly what they want and how they want it. Moreover, they're not scared to take it from whichever company offers the best deal. They're no longer attached loyally to a single brand. After all, the competition out there is so stiff, there'll always be a better product or a more generous deal available. This has put businesses under tremendous pressure to maintain their customer bases while keeping customer acquisition and retention costs as low as possible. One of the best ways to analyze this phenomenon is through the customer profitability metric.
You’ve been seeing people push AI and Machine Learning (ML) at you for years. Just about every magazine, research report, and unsolicited email you received in 2020 had an AI or ML angle. Maybe your organization has even talked about how ML can help solve your problems. But chances are, you haven’t implemented it. Most companies haven’t. But why is that? And why should you take the leap now?