Business Intelligence for Customer Lifecycle Management

In this blog, we will talk about

  • Campaign design and measurement: Reaching out to prospects requires understanding their demographics, advertising the right product and choosing the right channel for advertisements. Machine-learning models can be used to identify suitable prospects. Once the campaign is launched and then concluded, measuring its success is important. For more details on campaign design and campaign measurement, refer to the blogs hyperlinked.
  • Sales funnel: Prospects are generated through the campaign. A prospect moves through the sales funnel and becomes a customer. Understanding the sales funnel process leads to better prospect-conversion ratios.
  • Customer onboarding: Once a prospect is converted, it is important to make them understand factors such as how hassle-free the account-opening process is, and for the business to understand which type of customer buys which type of product and how much money the customer brought in.
  • Customer life stage — Understanding customer demographics and financial status helps to align with their transactional behaviour and suggest suitable products and services
  • Transactional behaviour — Understanding how often the customer transacts, the frequency of transactions and the value of transactions helps calculate profitability by customer
  • Touchpoints — A single customer may buy multiple products and use multiple services. Looking at a holistic picture helps understand the customer better
  • Customer value — This is closely related to the customer life stage. Identifying high-value customer vs low-value customer helps a bank utilise its resources efficiently
  • Centralised source of information — Data for different stages of the customer lifecycle is obtained from different sources. BI tools help aggregate this in one place for data visualisation and analysis
  • Real-time reporting — BI tools are able to refresh the data in real time and, thus, make sure the latest data is always available for analysis
  • Self-service and actionable insights — Users of BI can interact with dashboards using a number of slicers and dicers to generate actionable insights
  • Integration with machine-learning (ML) models — ML models used for prospect scoring, attrition risk modelling, etc. can be integrated with BI to understand model performance



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Acuity Knowledge Partners

Acuity Knowledge Partners


We write about financial industry trends, the impact of regulatory changes and opinions on industry inflection points.