Businesses can benefit greatly by replacing the manual forecasting process with a new set of technology-driven data science approaches based on predictive analysis and a combination of statistics, machine learning, data mining, and modeling. For example, sales teams can use predictive analytics to forecast revenue for the coming period, predict the probability that monthly studies and subscriptions will convert users and take appropriate steps to improve results. Prediction techniques can be used for sales forecasts, but they can also be applied to forecast margins, capital expenditure, and other important factors.     

The good news is that Zedvue predictive sales analysis programs are easy to set up and can dramatically increase profits, improve decision-making, increase efficiency, and expand your capacity to do more business. To improve the accuracy of your revenue forecasts and efficiency, forecasting methodologies depend on several factors, including strong organizational coordination, automation and reliable data and analysis-based processes. A predictive analytics program is great for multivariable forecasting, which means that your forecasts can be based on a broad range of factors, such as the performance of your sales team last year.   

ZedVue sales analytics, a powerful analytics tool that mines historical data to make future predictions. Predictive sales analytics uses sales data to identify patterns of customer and lead behavior and to generate future sales forecasts. By adopting a networked planning approach that brings people, data and processes together across the company, companies can generate accurate revenue forecasts and network enterprise-wide teams to prepare for the future.    

Our models extract patterns from historical transaction data to identify risks and opportunities. ZedVue measures demographic and behavioral data using data sets that already exist to help customers find the best fit. Predictive analytics enable you to use models that analyze customers’ data, including past sales, customer interactions with your email and website, demographics, location and other information to predict the likelihood that potential customers will buy your product or service.  

ZedVue gives you the ability to identify the most important factors causing customer losses so you can identify and approach high-risk accounts quickly to address their concerns and ensure they are satisfied. Your sales data also enables you to identify and eliminate vulnerabilities and bottlenecks that can cause leads to get stuck in the sales process or fall out of your pipeline. ZedVue can help increase the average value of a customer’s life so that you can find new ways to sell them to you with additional products. 

 Actionable  business insights not only enable your company to better understand what your customers want, but also provide predictive analytics and other machine learning technologies to discover hidden patterns, tell data-driven stories, and give customers recommendations for products and services they are likely to be interested in. Sales support tools with predictive analysis provide a detailed picture of what lies ahead, as they analyze not only previous sales data, but also your processes, stores in the pipeline, customers, and more. Advanced analytics can help companies to produce actionable customer insights to predict future actions of consumers as outlined above. 

This can help your sales team to reach out to prospective buyers who are likely to buy from the start without having to make contact or find out that a prospective buyer is not interested because there is no time, energy or resources to invest. ZedVue also provides a powerful way to create personas for current customers based on past behavior. When ZedVue analyzes data from closed sales, you can use that data to create “personas” for your buyers or add potential personas based on demographic and other information.

If your business is B2B or B2C, ZedVue forward-looking predictive analysis can be used to examine past and current data to determine whether a discount level leads to more sales than another discount level. Customer data includes past sales, customers interactions with your email and website, demographics, location and other information that prospective customers can categorize and tag based on their likelihood of purchasing your product or service. In short, you can identify profitable customers.

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