The ability to accurately predict future sales and customer demand, it's crucial for any business and can be the key to its success. Most of the companies are basing their forecast on sales pipelines and meetings, but those predictions are subjective and rarely precise. Instead, an accurate forecast should be based only on objective data such as historical sales, CRM activity, advertisement spending, etc...
Rely on a precise forecast is very important because it allows the company to properly plan and budget for the next period. With an accurate forecast is possible to set optimal stock levels, plan material supply, schedule production, manage the cash flow, etc...
A very useful Forecast method for Marketing is "Propensity Modeling". It allows ranking customers by the probability of performing an action. For instance, we can build a Propensity Model to predict the likelihood of a client to buy a product, so we can effectively personalize the marketing offers to each customer. The propensity model is often used to predict the probability of a customer to churn. Based on each customer's propensity to churn is possible to automatically send special offers before they unsubscribe or leave.
Forecast analysis is particularly effective if it is available a large dataset of historical data on the target variable to predict. Combining a large dataset with the most advanced machine learning algorithms, we can build accurate Forecast Models for our clients, to predict variables such as Revenue, new customer acquisition, churn rate, optimal stock levels, and many others.
We always start by identifying what are your main business challenges and by understanding what is your desired outcome. It is important to take enough time to deeply analyze the business and make sure we are on the same page. In this phase, we also define the variable that the model should predict and the minimum accuracy needed.
Once we have a good understanding of your business we can start planning, estimating the time and resources needed for the project. It is also important in this phase to define a clear target so we know when the project can be considered complete. In particular, we define in this phase the feasibility of the project based on the data available. If we consider the dataset available too small we would advise trying a different approach than a Statistical Forecast Model
When designing the appropriate solution we always privilege simplicity and usability. In this phase, we need your support to get access to all databases needed in order to deploy the project. We will submit also a draft of the model's prediction in an excel format, so we are sure that it meets your expectation. In this phase, we are also able to estimate the expected accuracy of the forecast model
Once we have your approval of the last draft, we deploy and deliver the Forecast Model in the format agreed. The format could be a dedicated web-based application or a dashboard built with the preferred BI tool of the client. We design the Model so that it is very intuitive and user-friendly, but also informative. It is in fact very important that the user can always see from which data and assumption the prediction was made.
Just delivering and deploying the dashboard is in most cases is not enough to solve our client's business problem. That's why we track the progress after the development of the project and we make sure that the solution we have design is used properly by our client organization.
29 Smithfield Market Block B
Dublin, Ireland
Email: info@datasensum.com
Phone: +353 857443036
Phone +39 3407500372
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