Personalized Offers

Use Case: Personalized Offers

Problem

72% of customers view a personalized banking experience as integral to their relationship with their bank. Customers want offers that are relevant to them, and financial institutions want to market to the right audience – but getting that match is difficult. 

Creating personalized offers for users requires sophisticated cashflow analysis and modeling  efforts to:

  • Detect retail payments, bills, rent, subscriptions, and more in transaction data
  • Define user segments and personas

How we help:

Cashflow Analysis

Given that a consumer’s cashflow is complicated, doing extensive analysis for personalized offers is necessary to accurately predict the expected balance by analyzing the user’s predicted income deposits, bill payments, transfer activity, and other risk signals.

Pave allows you to:

  • Receive a unified view of a user’s cashflow patterns  
  • Monitor fluctuations in spending behavior
  • Bypass much of the effort to enrich data from scratch, and allow you to focus on deliver the best possible user experience  

Segmentation Analytics

It can be useful to segment users into different categories based on their spending behavior in order to drive a highly personalized customer experience.

Pave allows you to:

  • Segment users across earning and spend profiles (eg. Health conscious, Pet Owner, Homeowner)
  • Build models to match users with corresponding product offerings
  • Improve targeting based on a highly enriched user spending profile

Personalize and Cross-sell Offers

Pave is partnering with companies like Otomo who are helping fintechs and banks embed personalized solutions for their users. This includes insights such as spending recommendations and curated offers for financial services and products.