www.feedbrics.ai
FEEDBACK DATA HUB
The fix for the trillion-dollar blind spot.
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PROBLEM
ENTERPRISE P&L RIDES ON CUSTOMER BEHAVIORS
Someone said yes.
Someone said no.
Someone walked away.
Someone spent more
Someone spent less
Someone didn’t pay the bill.
Someone called support.
142Billion
Spent to understand and predict
customer behaviors
Trillions
Spent to influence them
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THE OPPORTUNITY
Sales Interactions
Email Exchanges
Surveys
Chatbots
Social Media
Feedback Forms
Customer Service
Maps & Guides
Online Reviews
Messengers
App Reviews
27 days later…
Paula churned.
WHY CUSTOMERS STAY,
LEAVE, OR SWITCH
IS SPOKEN
Long before the P&L reflects it!
99% of these "spoken"
signals are wasted
issue solved
"HI, I NEED TO CHANGE MY CREDIT CARD DUE DATE FROM THE 15TH TO THE 19TH.”
“Also, the QR scanner on your app isn't working on my Samsung, it's so frustrating.
And honestly, my credit limit is ridiculous.
I had two transactions declined at the grocery store yesterday with a full cart of groceries and people behind me in line. I was mortified! Look, I've been with you guys for five years, and now ACME is offering me twice the credit limit and a lower interest rate if I switch to their card.”
APP ISSUES
CREDIT LIMIT ISSUE
FRUSTRATED TRANSACTION
COMPETITOR OFFER
CHURN CONSIDERATION
WASTED DATA ASSET
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Per $1B in revenue.A cada $1B de receita.
More than $300M in losses.Mais de $300M em perdas.
Per bank. Per year.Por banco. Por ano.
Feedbrics analyzes the millions of conversations and interactions with your bank and pinpoints, customer by customer, who is at risk, how much is at stake, and the action most likely to change the outcome. A Feedbrics analisa milhões de conversas e interações com o seu banco e aponta, cliente a cliente: quem está em risco, quanto está em jogo e a ação com maior chance de mudar o resultado.
MILLIONS OF CUSTOMER CONVERSATIONS AND FEEDBACKMILHÕES
DE CONVERSAS COM CLIENTES
CUSTOMER
SERVICE
SALES
INTERACTIONS
CUSTOMER
SURVEYS
EMPLOYEES & PARTNERS
APP
STORES
SOCIAL
MEDIA
MAPS
& GUIDES
OTHER
REVIEWS
1.PREDICTED P&L AND LTV IMPACTPREDIÇÃO DE IMPACTO NO P&L E LTVThe customers most likely to generate a loss.Os clientes mais propensos a gerar perda.
CLIENTCLIENTE
BEHAVIORCOMPORTAMENTO
CONFIDENCECONFIANÇA
PREDICTIONPREDIÇÃO
IMPACTIMPACTO
PaulaPaula
DEFAULTINADIMPLÊNCIA
91%
T-67 daysT-67 dias
-$240,800-$240.800
Move the due date to payday and enable partial payment in the app.Mover o vencimento para o dia do salário; habilitar pagamento parcial no app.
APP · SMS · E-MAILAPP · SMS · E-MAIL
AVOIDEDEVITADO✓
LucasLucas
LAWSUITLITÍGIO JUDICIAL
88%
T-84 daysT-84 dias
-$188,200-$188.200
Reverse the disputed charge now. Crediting the charge costs less than a lawsuit.Estornar a cobrança contestada agora. O estorno custa menos que o processo.
CALL CENTER · BRANCHCALL CENTER · AGÊNCIA
AVOIDEDEVITADO✓
PeterPedro
CHURNCHURN
86%
T-38 daysT-38 dias
-$148,700-$148.700
Match the competitor rate he was already quoted. Manager outreach this week.Cobrir a taxa do concorrente que ele já citou. Contato do gerente nesta semana.
WHATSAPP · APPWHATSAPP · APP
AVOIDEDEVITADO✓
JohnJoão
COMPLAINTRECLAMAÇÃO BACEN
84%
T-62 daysT-62 dias
-$121,900-$121.900
Follow up on the limit cut before the complaint reaches the regulator.Retornar sobre o corte de limite antes que a queixa chegue ao regulador.
PHONE · E-MAILTELEFONE · E-MAIL
AVOIDEDEVITADO✓
+ millions of customers like these. Reranked daily.milhões de clientes como estes.
Lista repriorizada diariamente.
PREDICTED LOSSPERDA PREVISTA
$300M
2.INDIVIDUAL PRESCRIPTIONPRESCRIÇÃO INDIVIDUALThe action most likely to prevent the loss.A ação com maior chance de evitar a perda.
PRESCRIPTIONPRESCRIÇÃO
3.ONGOING CAMPAIGNCAMPANHA CONTÍNUARapid loss reversal.A reversão rápida das perdas.
CHANNELSCANAIS
OUTCOMERESULTADO
$10M a $40M = COST AVOIDANCE + LTV
É o ganho anual do banco com a cada redução de 1 p.p. por comportamento negativo.
LOSS AVOIDED BY FEEDBRICSPERDA EVITADA PELA FEEDBRICS
~$66M$66M
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THE SOLUTION
IMPACT P&L IN SECONDS.
Understand why. Predict behaviors. Influence customers.
CUSTOMER
SERVICE
SALES
INTERACTIONS
CUSTOMER
SURVEYS
EMPLOYEES & PARTNERS
APP
STORES
SOCIAL
MEDIA
MAPS
& GUIDES
OTHER
REVIEWS
AI AGENTS &
PROMPT FEEDING
BI & DATA
VISUALIZATION
CAMPAIGN
ENGINES
CRM &
CDP
DATA
LAKES
APPLICATIONS &
AUTOMATIONS

Unify

All customer feedback and conversations.

Activate

From raw signals to customer intelligence, live.

Feed

Systems and teams that drive action and P&L results.

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MARKET FIT - THE MISSING LAYER
Mature in most enterprises…
TRANSACTIONAL AND RELATIONAL DATA
What & Who
What customers do, and Who they are
CDP/Lakes CRM FDHFeedbackData Hub FULLCUSTOMERSTORY
Data differential for the AI-era
FEEDBACK DATA SIGNALS
Why
Why they do, What they’ll do next, How to influence them
As data analytics commoditize, advantage shifts to the company that owns the Why, and embeds it everywhere it can influence customers and impact P&L
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WHY NOW
AI IS MAKING EVERYONE FASTER
EXCEPT THIS TRILLION DOLLAR GAP
Churn increaseLitigation costsDelinquency growthPublic complaintsLTV variationsmeetingsagenciessurveysdata gatheringreportspresentationsdashboardsunderstandingdata modelingmore meetingsmodelingprescriptionsplanningpredictingimplementationsdecisionsinfluencingP&LRESULTS
CUSTOMER’S
SIGNALS
+6 MONTHS OF P&L BLEEDING
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USE CASE
Understand
Know why customers behave as they do.
A live, continuous picture of the behavioral reality behind your P&L. The foundation.
PERFORMANCE
‘Why customers value us’
RISKS & TRENDS
‘What are they talking about’
BENCHMARK
‘Who they see as better than us’
KEEP DOING
Fraud Detection
Cashback Programs
Travel benefits
Statement Clarity
Payment Features
DO BETTER
Customer Service
Dispute Resolution
Chargeback Experience
Declined Transactions
Replacement Processes
COMPETITIVE BENCHMARK ON TOP ATTRIBUTE
Player A
Player B
Player C
Player D
Player E
REGIONAL PERFORMANCE
HISTORICAL PERFORMANCE
INDUSTRY TRENDS
GLOBAL INDUSTRY PERFORMANCE
Data delivered to leading visualization tools, including:
MS Power BI
Tableau
Qlik
And many others…
Confidential. Unauthorized use, disclosure, or distribution is strictly prohibited.
USE CASE
Predict
Every customer scored for churn risk, sales propensity, delinquency risk, LTV trajectory, and more…
→Probability (how likely)
→Prediction window (how soon)
→P&L at risk (how much)
→How to Influence (what to do)
The output is a prioritized action list with a dollar value.
NAMESCOREPROBABILITYPREDICTION$$$ IMPACTKEEP DOINGDO BETTER
John Peterson2476%32 days$54,247Loan ApprovalCard Benefits
Maria Sanchez3171%64 days$62,323Credit LimitMobile Aoo
Angela Moore4268%72 days$38,624Service SpeedCashback
David Chen5644%84 days$29,288FlexibilityRewards
Carlos Oliveira8218%89 days$72,765AttentionRules Clarity
Sarah Patel9208%90 days$69,284Online BankingCard Delivery
P&L IMPACT
$657M
Q3-Q4
SEGMENTCOUNTAVG SCOREPREDICTIONTOTAL AT RISK
CRITICAL24,32376%32 days$230M
HIGH56,06571%64 days$464M
MEDIUM132,72768%72 days$1,2B
LOW564,82344%84 days$8,6B
How to
influence
the predicted
behavior
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USE CASE
Influence
Automated personalization driving CRMs, service platforms, campaign engines, and AI agents, live.
Changes in…
→ Customer risk
→ Performance
→ Competitors
It triggers automation
& messaging tools
PERSONALIZATION OF AGENTS OR BOTS‘What to say’ and
‘What not to say’
PRODUCT ROADMAPExpectations associated with behaviors
MARKETING CAMPAIGNSCreative content, and ads personalization
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USE CASE EXAMPLE

FEEDBACK CHAOS: THE P&L TAKES THE HIT

A customer calls, frustrated with an issue. During the call, she mentions a competitor’s new offer. The agent fixes the issue, closes the ticket, and moves on. The transcript sits unused, alongside millions of other conversations across the entire omnichannel. Months later, an agency is hired for millions. A survey is designed. A report is written. A deck is built. A meeting is scheduled. Two VPs can’t make it. The insight gets summarized, then summarized again. They finally see the competitor offer. By then, she and thousands of other customers are already gone.
meetingsagenciessurveysreportspresentationsunderstandingmore meetingsImport/exportmodelingdashboardsplanningpredictingimplementationsdecisionsinfluencingCUSTOMER’SSIGNALSP&LRESULTS
+6 MONTHS

FEEDBACK DATA HUB: THE P&L IS PROTECTED

The same customer calls. Same issue addressed. Same competitor offer mentioned. But this time, seconds later, the agent already has context, because she wasn’t the first customer to mention it that day. The CRM has a risk score. The campaign engine has a next-best-action. The product team knows how to react. No analyst. No deck. No meeting. No delay. She gets a call back that afternoon with a personalized offer. Every other customer reaching out that day receives a competitive counter-offer as they speak. And every customer who hasn't reached out, but shares the same risk profile, gets a proactive intervention before they ever pick up the phone.
CUSTOMER’S
SIGNALS
P&L
RESULTS
SECONDS
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RESEARCH – PREDICTION + PRESCRIPTION (insight to influence)
PROVEN ROI
banking, retail, and telco
When prediction and influence operate at scale,
every $1 invested
generates $3-5
in incremental P&L.
SOURCES
S1 McKinsey, Next‑Best‑Experience: +5–8% revenue; −20–30% cost: https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/next-best-experience-how-ai-can-power-every-customer-interaction
S2 BCG, Personalization leaders grow ~10 pp faster YoY: https://www.bcg.com/capabilities/marketing-sales/personalization
S3 National Australia Bank (Pega CDH), +40% engagement; +50% mortgage conv.; 3× opps: https://www.pega.com/customers/national-australia-bank-customer-decision-hub
S4 Bradesco, hyper‑personalized credit conversions (30%+): https://www.fico.com/en/latest-thinking/white-paper/bradesco-drives-conversions-real-time-hyper-personalized-credit-spanish
S5 Retail/press outlier ~4× engagement with Gen‑AI: https://www.theaustralian.com.au/business/financial-services/nab-says-customer-engagement-rose-with-ai-as-bupa-joins-tests/news-story/ea107dae38d958c38868d20d9be56b3c
S6 McKinsey, Telecom churn: up to −15% via advanced analytics: https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/reducing-churn-in-telecom-through-advanced-analytics
S7 McKinsey, Digital‑first collections: +12% early; ~+30% late: https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/going-digital-in-collections-to-improve-resilience-against-credit-losses
S8 McKinsey, Contact center: −20% billing call volume; −60s auth (energy utility case): https://www.mckinsey.com/capabilities/operations/our-insights/the-contact-center-crossroads-finding-the-right-mix-of-humans-and-ai
S9 McKinsey, Utilities IVR: 10–30% call‑center cost cut in 3–6 months: https://www.mckinsey.com/industries/electric-power-and-natural-gas/our-insights/transforming-interactive-voice-response-systems-in-utilities
S10 Accenture, Global Banking Consumer Study 2025: advocacy/primacy 1.7× global; 2.6× NA; more products & wallet: https://www.accenture.com/content/dam/accenture/final/industry/banking/document/Accenture-Global-Banking-Consumer-Study-2025-Report.pdf
*Predict Only" ranges are conservative industry baselines for comparison, not from the cited sources.
**Ranges are cross-industry benchmarks (banking, retail, telco). Dollar impacts are normalized per unit ($1B revenue; $100M cost/balance) so they plug into either banks or retailers. Delinquency and advocacy/primacy are bank-centric; retailers can map to store-card/financing or share-of-wallet analogs, or omit if not applicable.
CLASSIC PREDICTION MODELS vs. PREDICTION _ PRESCRIPTION (influence)
BEHAVIOR**UNITCLASSIC PREDICTION MODEL(LIFT)*PREDICTION + INFLUENCE (LIFT)PREDICT ONLY → PREDICT + INFLUENCE($ PER UNIT, REVENUE OR COST SAVED)REF
new sales (revenue uplift)per $1B influenced revenue+3–7%+10–15%(up to 25%)$30–70M → $100–150MS1
S2
cross‑sell (take rate)per $1B cross‑sell opp.+5–15%+20–50%$50–150M → $200–500MS3
S4
up‑sell (take‑rate/ARPU)per $1B up‑sell opp.+5–15%+10–40%(up to 50%)$50–150M → $100–400MS3
Engagement (qualified)your $ per qualified session+10–20%+30–50%(to 3–4×)use lift × '$' per qualified sessionS3
S5
churn (attrition)per $1B at‑risk revenue−3% to −8%−10% to −20%$30–80M saved → $100–200M savedS6
delinquency (payments)per $100M delinquent balance+5–10%+12–30%$5–10M → $12–30MS7
service cost (deflection)per $100M contact‑
center cost		−5–10%−20–30%$5–10M saved → $20–30M savedS8
S9
competitors’ consideration (primacy)per $1B revenuesmall+5–30% wallet‑share delta$10–30M → $50–300M (directional)S10
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