Cross-Sell Lead Scoring and Personalised Outreach
Score existing customers for products they are likely to need from consented banking data, and draft compliant, personalised outreach that relationship managers review before sending.
- Capability:
- AI Sales & Marketing Automation
- Industry:
- Banking
Who it is for
- Head of Retail Liabilities
- Head of Customer Analytics
- Branch Relationship Manager
- Head of Marketing
The problem
Banks hold clear signals about customer needs, such as salary credits, maturing deposits, rent payments and foreign card spends, yet cross-sell often runs on broad campaigns and product targets. Customers receive offers that do not fit them, relationship managers chase cold lists, and every untargeted message adds consent and mis-selling risk.
Lists built on product targets
Campaigns pick customers by simple filters, so a recent home loan borrower receives another home loan offer.
Relationship managers without context
RMs call customers with no clear reason why a product fits, and results depend on individual skill.
Consent and DND checked too late
Opt-outs, DND preferences and the purpose of consent are checked at sending time, if at all, rather than when the list is built.
Suitability of third-party products
Insurance and investment products sold through the bank carry suitability and mis-selling risk that generic scripts ignore.
What we build
We build a lead scoring and outreach service on your existing data warehouse. Propensity models score customers for selected products using consented transaction and relationship data, and every score comes with plain-language reasons, such as a fixed deposit maturing next month or regular rent debits. Eligibility, consent, DND and exclusion rules are applied before a customer enters any list. A language model drafts personalised messages for WhatsApp, email or an RM call script in the customer's preferred language, using approved product facts only. RMs see prioritised leads with reasons and drafts, approve or edit each message, and record outcomes that feed model retraining.
- Product propensity scoring with plain-language reasons per customer
- Eligibility, consent, DND and exclusion rules applied before any outreach list is built
- Drafts for WhatsApp, email and RM call scripts in English, Hindi and regional languages
- Content restricted to approved product facts, rates and disclosures
- RM worklist with priority, reasons, drafts and one-click outcome capture
- Campaign and outcome analytics feeding scheduled model retraining
How it works
- 01Prepare signalsConsented transaction, product holding and relationship data from the warehouse become customer-level signals.
- 02Score and explainPropensity models score each customer for each product, and reason codes explain every score.
- 03Apply eligibility and consentCustomers failing eligibility, consent, DND or exclusion rules are removed before any list is built.
- 04Draft outreachThe language model drafts channel-specific messages from approved product content in the customer's preferred language.
- 05RM review and sendRMs approve, edit or drop each lead and message, and sending goes through approved channels and templates.
- 06Learn from outcomesResponses, conversions, opt-outs and complaints feed back into scoring and content reviews.
Architecture
- Data
- Data warehouse feature pipelines
- Consent and preference store
- Product eligibility rules
- Scoring
- Product propensity models
- Reason-code generator
- Drift and fairness monitoring
- Content
- Approved product content library
- LLM message drafter per channel and language
- Content compliance checks
- Engagement
- RM worklist in the CRM
- WhatsApp, email and SMS sending through approved templates
- Feedback
- Outcome capture
- Campaign analytics and retraining pipeline
Inputs and outputs
Inputs
- Consented transaction and product holding data from the warehouse
- Customer consent, DND and channel preferences
- Product eligibility rules and exclusion lists
- Approved product content, rates and disclosures
- Historical campaign outcomes and RM feedback
Outputs
- Prioritised lead lists with reason codes
- Personalised message drafts per channel and language
- RM worklists with outcome capture
- Exclusion log showing why customers were removed
- Campaign performance and complaint reports
Where people stay in control
Relationship managers approve or edit every message before it goes out, and product and compliance teams approve the content library. Leads for third-party products such as insurance and mutual funds go only to staff qualified to discuss them.
Integrations
- Enterprise data warehouse or lakehouse
- CRM such as Salesforce, Microsoft Dynamics or an in-house RM tool
- WhatsApp Business API through an approved provider
- Email and SMS gateways with DLT-registered templates
- Consent management platform
Guardrails
- Consent, DND and exclusion rules applied before results reach any list
- Messages use approved content only, with no invented rates, offers or eligibility
- Frequency caps per customer across all channels
- Sensitive attributes excluded from models, with fairness checks each cycle
- No promise of loan approval or investment returns in any draft
Compliance notes
- Designed to support DPDP Act 2023 consent and purpose limitation, and TRAI commercial communication rules on DLT registration and customer preferences.
- Supports RBI fair-practice expectations on suitability and mis-selling; insurance and investment products also follow their IRDAI and SEBI distribution rules.
A typical pilot
Duration: 4 weeks
One product, such as fixed deposit renewal or a pre-approved credit card, for customers of a group of branches, with RM-approved outreach compared against the current campaign approach.
Deliverables
- 01Propensity model with reason codes for the pilot product
- 02Consent and eligibility rule set
- 03RM worklist with message drafts
- 04Pilot results compared with a held-out control group
- 05Plan for further products and channels
What we measure
- Response and conversion against control
- Outcomes for scored customers compared with a randomly held-out control group over the same period.
- RM acceptance of leads and drafts
- Leads RMs pursued, and drafts sent as written, edited or rejected.
- Opt-outs and complaints
- Opt-outs and complaints per message, compared with the current campaign baseline.
- Score calibration
- Predicted propensity bands compared with observed responses.
FAQs
01What data do the models use?
Only data covered by the customer's consent and your privacy notice, typically product holdings, transaction categories and relationship history. Sensitive attributes such as religion or caste are excluded, and we review likely proxies during model development.
02Can the AI send messages on its own?
Not in our default design. RMs approve each message, or for routine service reminders marketing approves a template that the system fills. All sending goes through your existing DLT-registered and WhatsApp-approved templates.
03Which languages can drafts be written in?
English, Hindi and regional languages, based on the customer's stated preference. Product facts and disclosures come from the approved library in each language, reviewed by your team.
04How do we know the scoring works?
The pilot holds back a random control group, so results compare scored outreach with your current approach on the same population and period.
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Founded by Akhilesh Mittal. 8 years building a security platform for a New Zealand company. IIT ISM Dhanbad and IMT Ghaziabad. Builds production agentic AI.
Pick one banking process and test it with evidence
Book a discovery call to choose a narrow pilot, from KYC file checks to voice-agent red-teaming, and see measured results on your own documents before any wider roll-out.