Introducing Forecast Models from Akkio

Akkio, a company building predictive AI for analysts, is thrilled to announce that forecasting is now generally available for Enterprise customers. The new autoML model provides analysts with an effortless, intelligent, and seamless way to make forecasts from your data. 

Analysts traditionally face a multitude of complex challenges when forecasting. Constant macro and micro fluctuations, interruptions from outliers and noise, irregularities, and overlapping seasonal shifts all make accurate forecasting a daunting task. These forecasts are often a manual process, built from a series of assumptions laden with bias and guesswork—and they require frequent updates and maintenance. 

We understand these pain points and have designed our AI platform to make forecasting simple, accurate, and fast.

Akkio’s time-series modeling allows you to understand patterns, analyze influential factors, and create a model to forecast the future. And, if you integrate your forecast with your data warehouse, you can see reports update live on your predictions. 

Let's dig deeper into three frequent use cases: supply chain optimization, sales revenue, and contact center staffing.

1. Supply Chain Optimization

Analysts in supply chain management are constantly grappling with a plethora of variables. Factors such as fluctuations in demand, raw material availability, labor shortages, and geopolitical events can significantly impact the supply chain. These challenges are further compounded by seasonal shifts and unexpected disruptions such as natural disasters. Akkio can effectively ingest all of your complex data from various sources, analyze its patterns, and provide accurate forecasts.

For instance, by feeding the tool historical data on demand variations, you can optimize inventory management and avoid both overstocking and stockouts. Akkio also lets you integrate with your data warehouse, so the tool can automatically update these forecasts as new data comes in, ensuring you always have the most up-to-date information for efficient logistics planning.

2. Sales Revenue

Predicting sales revenue is a complex exercise that requires taking into account numerous influencing factors—such as market trends, consumer behavior, promotional activities, and competitive dynamics. 

As with supply chain management, you can use Akkio to identify revenue patterns and trends that can help forecast future sales. Similarly, by inputting data on promotional activities, you can understand their impact on sales and plan future campaigns accordingly. The tool also enables collaboration with your team, making it easy to white-label the platform and share reports and insights so your team can collaborate and prepare to react to projected sales targets.

3. Contact Center Staffing

In the customer service industry, accurate forecasting of call volumes and staffing needs can significantly impact customer satisfaction. An overstaffed contact center leads to wasted resources, while an understaffed one can result in long wait times and frustrated customers.

Building forecasts based on past call volumes and staffing reports can help you best prepare for changing demand. For instance, if data shows a surge in call volumes during holiday seasons, the tool can help forecast this surge and enable you to staff your contact centers appropriately. Similarly, by analyzing data on staff performance, the tool can help optimize staff schedules and improve overall customer satisfaction.

All of this shows how Akkio is focused on empowering you with fast, accurate predictions for confident decision-making. It's about letting you explore your data, understand patterns, collaborate with your team, and gain a deeper understanding of what’s impacting your business. With Akkio, you'll always know what's going to happen, why it’s going to happen, and what you can do about it.

Learn more in our webinar

Interested in learning more? Register for our webinar "Mastering Sales Forecasting with Predictive AI: From Guesswork to Science'' on Thursday, May 25, 2023, at 2 pm ET. Two machine learning leaders will dive deeper into the science of sales forecasting with predictive AI. They’ll explore how you can easily and quickly predict sales revenue and get insights into the key factors that will impact the accuracy of your forecast.