A company gets hundreds or thousands of leads each month from various marketing channels but only a small fraction of these leads converts to sales. Advanced analytics can help the company to improve the conversion rate by identifying which of the leads are most likely to convert into paying customers. Narrowing down the list of leads to the high quality leads will allow the company to optimize its sales resources by focusing mainly on the leads with the highest probability of conversion. This will accelerate the sales and increase the return on investment in sales and marketing.
With Actable AI, the company can easily identify the high quality leads by using our state of the art AI algorithms and the company’s historical data. The first step will be to input the data for past and new leads into Actable AI’s platform. The dataset consists of all historical confirmed converted leads, unconverted leads, and new leads that you would like to predict the conversion likelihood along with all available lead parameters such as demographics, lead source, website engagement, marketing channel etc.
As a next step, the company can select Actable AI’s classification analytics then select the outcome column to predict whether the given lead would convert or not. All other variables that the company has can be used as predictors for the prediction. After running the analytics, Actable AI shows the results as a table with the predicted conversion for all current leads along with probabilities of how confident the model is about the results (Fig 1).
Under the Performance tab, the user can see various performance metrics to better understand the quality of the results (Fig 2). The Leaderboard tab lists all Machine Learning models that Actable AI trained before selecting the best one.
The dataset consists of around 9000 historical data points with various parameters such as Lead Source, Total Time Spent on Website, Total Visits, Last Activity, etc. The target variable is the “Converted_WithMissingValues” column which shows whether a lead converted or not (1 = converted; 0 = not converted). The variable descriptions are in the dataset.
