Automating Marketing Data Analysis

Marketing Data Analysis

Executive summary

Implemented the first service- and on-demand based big data and data science infrastructure for the bank. Data pipelines are built and maintained leveraging two key infrastructure components: a custom-built aggregation tool and the marketing content & event platform. The aggregation tool builds the data lake for all analytics activities and enables the marketing platform to organically grow customer and campaign projects.

Problem statement

Relationship Managers spend too much valuable time researching talking points and themes that fit their different client profiles. A simple product recommender usually cannot grasp the complexity of private banking relationships and hence the product recommendations are usually without impact.

Target market / Industries

Private banking, wealth management, All relationship intense industries, i.e. insurance


Jointly with the client we developed a private banking marketing ontology (knowledge graph or rule book) that enabled various Machine Learning (ML) models to parse broad catalogue of unstructured data (financial research, company analysis, newsfeeds) to generate personalized investment themes and talking points.

The solution included:

  • Private banking marketing ontology
  • Thematic aggregator agents
  • Personalized clustering



  • Head of marketing and campaigns
  • Market heads
  • Relationship Manager
  • Chief Investment Officer

Data elements, Assets and Deliverables

As an Input from the client, the following items were used:

  • Access to CRM details
  • Client transaction history
  • Research details

Assets & Artefacts:

  • Financial Product Classification
  • Product Risk Classification
  • Event Lifecycle

The deliverables included:

  • Private banking marketing ontology
  • Thematic aggregator agents
  • Personalized clustering
  • End to end event cascade and workflow integration

Impact and benefits

Achieve a fully transparent Close the Loop on Campaigns and increased RoMI by 18%. Furthermore, this first mover program established the big data sandbox as a service capability to the entire bank. Also this project enabled marketing for the first time to close the loop between their digital client touchpoints and the events and campaigns run.

The use-case implementation resulted in:

  • +18% increase in RoMI (return on marketing investments)
  • -17% savings on campaign spend


“Using Ferris we were able to digest a massive amount of text and extract personalized investment themes which allows our RMs to increase their face time with the clients and surprise them with the meaningful content.” — Mr. R. Giger, Head of Marketing and Campaigns, Swiss Private Bank

Tags / Keywords

#marketdataanalysis #bigdata #bigdatainfrastructure #datascience #datascienceinfrastructure #financialservices #bank

Last modified November 13, 2023: init (cb2a58c)