Predictive lead scoring is a powerful tool to help businesses identify and prioritise their most promising users for sales and marketing efforts. Companies can use a predictive model to predict which leads will most likely convert and take action. This enables them to focus their resources and efforts on the customers most likely to result in a sale.
Most sales and marketing teams have used traditional lead-scoring methods, which rely on manual input and static profiling of users. To build a more accurate scoring system, one must have a solid foundation in data science. Due to a lack of bandwidth, even companies with an in-house data science team may be unable to provide their growth team with sophisticated data-driven tools. Such tools, like predictive lead scoring, are needed to drive sales and improve performance.
Simplismart has been built to make it easy for software developers and marketing and sales professionals without coding experience to create and deploy such models in just a few minutes. The ingenious idea is that you get to control the entire lead-scoring process and build the exact predictive lead-scoring model that you want and need. This blog article will examine how you can create a predictive lead-scoring model using your data and Simplismart.
Data Exploration
In this tutorial, we will be working on Lead Scoring Dataset, which contains 37 columns comprising features like conversion status, Lead Source, Total Visits, Last activity, Country, and many more, with 9200 samples.
MLOps on Simplismart
In this section, we will go through different phases like uploading a dataset, training an image classification, and evaluating and predicting by deploying the model on the Inference Server. Before heading to the tutorial, we suggest you read Introduction to SimpliSmart Platform and sign up so you can follow us along in the tutorial.
Uploading Dataset
Let's start with uploading the dataset on SimpliSmart. Head over to Dataset Tab from the sidebar menu, click on Add Dataset, and fill in details like Name of Dataset and Description (Optional)
Click on Next, and choose the source of the data frame, whether to upload from local storage or import from cloud storage. We will upload the data frame from local storage for this blog.
Click on Upload a Dataframe; a dialogue box will open; choose the data frame, click finish, and wait until it is uploaded.
Once it gets uploaded, wait for a maximum of 5 minutes to get all details of the dataset, like the size on disk and the snapshot of the dataset like below.
Model Training
Click on Train Model, Add basic details like Name of Model and Description and click Next.
Select the dataset on which you want to train the model, select the output and input columns, and click next.
Select the Number of Experiments, Time Limit and Hardware to train your model and click Finish.
You would see the job as running in the card. Click the card and get more details like configuration, experiments and metrics. As we can see, we achieved an accuracy of 96% on the Train and 93% on Test Data.
Head to the Graphs section and turn some variables on to see the graph between different entities.
After it flags success, head to the model's section and click on deploy. Once deployed, it will provide a Live Flag and click on the card.
Click on Predict, and We have already filled with examples from training inputs. Scroll down and click on Predict.
Final Thoughts
Predictive lead scoring is an essential aspect of the lead generation process. Predictive lead scoring allows you to automate the lead qualification process and increase your sales team's efficiency.
Whether you are a Small business or a large enterprise, predictive lead scoring will help you be more productive, efficient and profitable. If you're trying to build a predictive model to predict your leads but want to save time and money, you're in luck. Simplismart allows you to create a predictive model in minutes and helps you determine which leads are ready to buy.
