Predictive B2B customer acquisition

Client Background

Our client, a fortune 100 financial services firm, is a leading issuer of credit card, charge card & travelers cheque. It’s a leader in both the consumer & corporate cards. A whopping 30% of all credit card transactions in the US are attributable to their credit card business.

Business Objective

Client desires to build a B2B business intelligence platform to have

1. Customer acquisition model to find out potential buyers of the corporate credit card by exploring data of corporate clients from historical company data, social media trends, news/RSS feeds. The model should also predict estimated revenues.
2. Interactive dashboards to show graphical reports of different matrix-like Product, Revenue, Potential sales, Customers etc.
3. Database of companies consisting of their details like Name, address, contact information, revenue, no. Of employees etc
4. Target companies list for marketing purpose. These companies can be their potential customers.

Current process of accomplishing the above is completely manual and time consuming with the sales team collecting data in different spreadsheets from third-party websites. Information in the spreadsheet is manually cleaned and enriched before feeding into a common database. The platform to be developed is proposed to do away with this manual process.

Solution

Our team proposed to create a cloud-based application with modular architecture using open source technologies i.e.  Angularjs, Django, MySQL, Python. Microsoft Azure was chosen as the cloud platform for application deployment. Application comprised of the following modules

1. Data Collection: Web Crawlers using python & selenium
2. Data Transformation & Aggregation: Cleaning & enriching the data like standardizing of text fields, creating derived fields. Finally aggregating different company datasets to create a single view of the company.
3. Lookalike models: Set of models to identify potential companies from new datasets that looked similar to the existing customer base. These companies, showing a high degree of similarity, would be then targeted by the sales team for corporate cards
4. Product Recommendation Model: Ranked list of products to be pitched to potential targets
5. User Interface: web-based application for sales team & management to
   1. View and segment list of prospects using similarity score and other attributes.
   2. Allocate a list of prospects to individual sales team members based on hierarchy and other business rules.
   3. Export the list to internal sales CRM with excel export.
   4. Import the list of existing customers and retraining the model for finding similar leads.

Outcome

1. Application went live for Switzerland market in 3 months time with the manual effort of the sales team being reduced to 50% within 1st month of implementation and down to nearly zero by the third month.
2. Sales effort focused on a narrowed list of companies (based on similarity scores and recommended products) produced 75% more leads compared to existing process in 6 months of testing.
3. The tool was further expanded to Middle east and other European markets