Customer Acquisition

Background

Customer acquisition is the process of acquiring new customers, or persuading customer to purchase your products or services. It is a technique for moving customers from brand awareness to purchase consideration through the marketing funnel.Customer acquisition cost is the price involved in obtaining a new customer (or CAC in short). When the process of acquiring new customers is finished, it shifts over to customer retention and re-acquisition.There are many different marketing techniques, internet platforms, and strategies you may use to attract customers (both on or offline). A variety of cross-channel marketing tactics are frequently used.Any customer acquisition plan must adapt to the shifting preferences and behaviors of consumers in order to be successful.Gaining new customers is crucial since it boosts recommendations, brand recognition, sales, and business endeavors. There is a good chance that a new customer will look at your company's other goods or services. If they keep enjoying what they discover, they will tell their friends and family about your company, bringing in even more customers. As your customer base grows, word of your company will get out and your brand awareness will rise. Sales for your company will consequently increase as a result. Your ability to add additional services or products or to grow into other areas will increase as your business makes more sales. Customer acquisition is eventually what will increase the financial health of your company.

Companies all over the world use big data to develop prospect-centric marketing strategies and increase conversion rates in order to increase customer acquisition. With the help of machine learning and artificial intelligence, marketing teams can better target their initiatives and boost brand recognition while also fostering customer loyalty.Customer segmentation is simply one application that marketers make use of machine learning to focus their efforts.Further, businesses may provide faster and more customized experiences when they are aware of their consumers' behaviors and preferences. Moreover, by assisting companies in predicting the likely next move a customer will make, machine learning solutions might boost sales.The key factors behind removing non-buyers from the equation are higher response rates at reduced acquisition costs.

Benefits of using machine learning for customer acquisition

• The key to increasing your customer acquisition is using machine learning for customer relationship management (CRM) product.
• Accessing predictive analytics, automating email marketing campaigns, and facilitating consumer transactions are all made possible by a CRM that incorporates ML.
• Businesses can generate personas, or sample customers, with the use of analytics from machine learning. These personas can then be used to segment consumers and develop marketing strategies tailored to each persona.
• You may produce more focused leads by utilizing the marketing automation feature of machine learning.
• Automation of specific operations, such order confirmations, offering FAQs, seeking product surveys, and other extensive communications campaigns, can improve customer retention.
• Businesses can utilize ML to assist them understand the customer experience and then use that information to support customer retention for subsequent purchases.

Customer acquisition models build up the best ways to turn these prospects into active customers by automatically identifying the best possible leads. The use of automated email marketing, social media posts, customized customer reminders, and customized offer creation are just a few of the strategies.

Objective

This usecase aids in the analysis of consumer demographic data, the development of unsupervised learning techniques for customer segmentation, and the identification of the demographic groups that most closely match the company's core clientele.

Relevance of Xceed Analytics

Xceed Analytics provides a single integrated data and AI platform that reduces friction in bring data and building machine models rapidly. It further empowers everyone including Citizen Data Engineers/Scientist to bring data together and build and delivery data and ml usecases rapidly. It's Low code/No code visual designer and model builder can be leveraged to bridge the gap and expand the availability of key data science and engineering skills.

This usecase showcases how to create, train/test, and deploy a customer acquisition clustering model. The dataset is obtained from UCI Machine Learning Repository. Bank Marketing dataset is used for this purpose .Xceed will provide a NO-CODE environment for the end-to-end implementation of this project, starting with the uploading of datasets from numerous sources to the deployment of the model at the end point. All of these steps are built using Visual Workflow Designer, from analyzing the data to constructing a model and deploying it.

Data Requirements

Columns of interest in the dataset

Model Objective

Steps followed to develop and deploy the model

1. Upload the data to Xceed Analytics and create a dataset
2. Create the Workflow for the experiment
3. Perform initial exploration of data columns.
4. Perform Cleanup and Tranform operations
5. Build/Train a clustering model
6. Review the model output and Evaluate the model
7. Improve on the metrics which will be useful for the productionizing
8. Deploy/Publish the model

Upload the data to Xceed Analytics and Create the dataset

• From the Data Connections Page, upload the the dataset to Xceed Analytics. For more information on Data Connections refer to Data Connections

• Create a dataset for each dataset from the uploaded datasource in the data catalogue. Refer to Data Catalogue for more information on how to generate a dataset.

Create the Workflow for the experiment

• Create a Workflow by going to the Workflows Tab in the Navigation.Refer Create Workflow for more information.

You will see entry on the workflow's page listing our workflow once it's been created.

To navigate to the workflow Details Page, double-click on the Workflow List Item and then click Design Workflow. Visit the Workflow Designer Main Page for additional information.

• By clicking on + icon you can add the Input Dataset to the step view. The input step will be added to the Step View.

Perform initial exploration of data columns.

• Examine the output view with Header Profile, paying special attention to the column datatypes. for more information refer to output window

• Column Statistics Tab (Refer to Column Statistics for more details on individual KPI)

Perform Cleanup and Transform Operations

1. Clean Age Column.

2. Drop Unecessary Columns.

3. Rename Target Column.

4. Update datatype of Column.

Build/Train a clustering Model