Idea to Trade / Next Best Product - Financial Services

Idea to Trade / Next Best Product

Executive summary

To support advisors and clients with a “next best product” recommendation, a closed loop flow has been established from Research / Chief Investment Officer to Relationship Managers and eventually to the client. Evaluating which recommendations worked for RMs and Clients allowed for a learning loop informing Research & CIO to improve selection & tailoring of investment themes.

Problem statement

The information flow from research or strategic asset allocation (CIO) to client advisors and eventually to clients does rarely follow a structured path. Instead the bank‘s “house view” is communicated broadly to all front-office staff and portfolio managers. They then use their direct client relationship to assess risk appetite and extract specific investment themes or ideas from their client interaction. If these match, the resulting research / advice is forwarded to the client. It seems like a lucky punch if product information leads to a trade / product sale.

Target market / Industries

The challenge of customizing the offering to the customer profile is a common challenge across the industries. Financial services industry is benefiting most from this use case that can be efficiently applied e.g. in Industries, that are benefitting most of this use-case are:

  • Banks
  • Investment and finance firms
  • Real estate brokers
  • Tax and accounting firms
  • Insurance companies


Starting with investment themes / product and occasion specific sales / investment opportunities, the existing client‘s portfolios and client to bank communication is screened for possible gap / fit. Research or asset allocation can then focus their efforts on topics suggested by front-office staff and / or clients themselves. Observing and identifying trade success the best practices are understood and can be multiplied across other (similar) client scenarios. Asset allocation and advisors work collaboratively as they both evaluate which information / proposals / investment ideas are forwarded to clients (and then accepted or not) and which ones are kept back by advisors (and for what reasons).

The solution included:

  • Clustering & topic mapping of existing marketing material & client portfolio structures
  • Optionally inclusion of CRM notes and written advisor to client communication
  • Sales Ontology setup & learning loop inclusion & topic matching
  • Identification of individual investment themes / topics of interest
  • Aggregation of findings, reporting, alerting & action recommendation


  • Chief Investment Officers (CIO)
  • Client Advisors / Relationship Managers
  • Product Managers

Data elements, Assets and Deliverables

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

  • Sales organization setup (desks / books)
  • Client to Client / Client to Company graphs

Capabilities utilized:

  • Unstructured Data
  • Semantic Harmonization
  • Natural Language Processing
  • Personalization

Assets & Artefacts:

  • Financial Product Ontology
  • Analytical CRM Models

The deliverables included:

  • Sales & Onboarding Ontology
  • Use case specific orchestration flow
  • Integration with many info sources

Impact and benefits

Proposal / offer conversion rates were increased by 42% after an initial learning curve & algorithm calibration phase of 6 months resulting in additional Asset under Management growth of 8% from targeted clients.

The use-case implementation resulted in:

+18% increased targeted product sales

+8% share of wallet

Tags / Keywords

#ideatotrade #nextbestproduct #salesadvice #financialservices #bank #insurance #investment

Last modified November 13, 2023: init (cb2a58c)