Turn customer, transactional and operational data into recurring revenue streams — predictive propensity, per-cluster mix and prescriptive activation via i6 Signal.
Transformation · Data Monetization
Companies accumulate data from purchases, navigation, inventory, pricing, CRM, channel, store and behavior. But most of these assets remain trapped in reports, static segmentations and poorly intelligent campaigns.\n\n**Data monetization is not about selling spreadsheets or creating more dashboards.** It is about turning signals into decisions that increase revenue, margin, conversion, recurrence, commercial efficiency and customer value.\n\nPredictive AI makes it possible to identify where there is intent, propensity, elasticity, offer opportunity, risk of loss and growth potential before the market notices.
Most companies live with the same frustration: 89% do not monetize their own data. Most companies already have the data they need to grow more: transactional history, navigation, customer profile, channel behavior, campaigns, prices, categories, inventory, recurrence, abandonment, repurchase and commercial interaction.
The problem is that this data rarely becomes incremental revenue.
In practice, monetization fails because data is used too late, in a fragmented way or without connection to the next business decision.
The company personalizes the email, but does not influence the journey, the product, the price, the channel, the POS or the next commercial action.
The offer is pushed by what is in stock or by the campaign rule, not by real purchase propensity.
Customers are activated by rules, not by the real moment of intent, repurchase, abandonment or higher probability of conversion.
The dashboard shows what happened, but does not guide who to approach, what to offer, through which channel and with which incentive.
The company has data, but continues losing margin, conversion, ticket, recurrence and commercial efficiency.
> The real pain is simple: the data exists, but it still does not move at the speed of the opportunity or anticipate what will happen to guide decisions before the market.
Many data initiatives stop at reports, fixed clusters, generic propensity models or personalization platforms that treat similar customers as if they were the same.
These approaches help organize information, but they do not necessarily increase revenue.
The problem appears when the company cannot answer operational questions with precision:
Without prediction, monetization becomes guesswork.\ Without activation, it becomes a dashboard.\ Without incremental measurement, it becomes a vanity metric.
>Data only generates revenue when it changes a decision: who to approach, when to approach, what to offer, how much to incentivize, where to allocate effort and how to measure the real result.
infinity6 applies proprietary predictive AI and recommendation engines to transform transactional, behavioral and operational data into actionable growth decisions.
The solution does not depend on a single front. Monetization can happen in CRM, e-commerce, marketplace, physical retail, industry, pricing, supply, assortment, campaigns, digital channels or commercial operations.
*Applications:* recommendation, purchase propensity, cross-sell, up-sell, personalization, reactivation and offer prioritization.
*Applications:* discount optimization, bundles, promotions with lower margin erosion and propensity-driven pricing.
*Applications:* demand forecasting, repurchase anticipation, prioritization and trend identification.
*Answers:* who to activate, when, with which offer, channel and incentive. Connects data, prediction and activation to generate measurable commercial action.
Monetization happens when transactional, behavioral and operational data stop feeding only reports and start guiding commercial decisions in real time.
The solution operates as an intelligence layer above current systems, connecting ERP, CRM, e-commerce, marketplace, CDP, OMS, WMS, media, store and digital channels. Data can be anonymized, handled through hash identifiers and activated in an LGPD-friendly way, with no exposure of PII.
01. Business data
The foundation starts with the signals the company already has: purchase history, navigation, search, cart, abandonment, campaigns, products, categories, price, margin, inventory, channel, store, recurrence, commercial response and anonymous session behavior.
This data reveals intent, affinity, elasticity, risk, repurchase and hidden revenue opportunities.
02. Predictive engine
The proprietary engines identify patterns that fixed rules, BI and traditional segmentations do not capture.
AI calculates purchase propensity, product recommendation, affinity between customer and SKU, price elasticity, cross-sell opportunity, up-sell potential, risk of loss, repurchase probability and the best activation moment.
Clusters are not static. They relearn at every cycle, as customers, products, channels and contexts change.
03. Prescriptive decision
Prediction becomes a practical business recommendation.
AI indicates who to activate, when to activate, with which product, which offer, which channel, which incentive and which priority. It also guides where to reduce discounts, where to protect margin, where to increase commercial pressure and where there is higher incremental potential.
Each recommendation can be measured against a baseline, control group or A/B test, separating real impact from vanity metrics.
04. i6 Signal activation
Recommendations are delivered into the current process through API, file, dashboard, CRM, e-commerce, campaign tool or i6 Signal.
With each activation, the models learn from the real response of the customer, channel or operation, creating a continuous monetization cycle: data, prediction, decision, activation and result.
>The team stops asking only “what happened?” and starts acting on “what is the next best action?”.
What is data monetization with predictive AI? It is the use of transactional, behavioral and operational data to generate incremental revenue through predictions, recommendations and actionable decisions. Instead of only analyzing the past, AI identifies where there is higher purchase propensity, cross-sell opportunity, up-sell potential, price elasticity, risk of loss, repurchase or incremental margin.
Does data monetization mean selling data? Not necessarily. In most cases, data monetization means using the company’s own data to increase revenue, improve conversion, reduce waste, protect margin and create new sources of value. This can happen in CRM, e-commerce, marketplace, physical retail, pricing, assortment, campaigns, digital channels or partner relationships.
Do I need a mature data lake to start? No. The foundational model i6-RecSys-Base.g1 is already pre-trained on 1.45 billion records from e-commerce, banking, telecom and retail. infinity6 connects to what already exists, such as purchase history, products, campaigns, inventory, price, channel and digital behavior, and fine-tuning happens on the business data.
How is LGPD handled? Operation with anonymized data is native. The solution can work with hash identifiers and behavioral attributes, with no PII traffic. The goal is to generate predictive intelligence from behavior, purchase, navigation, channel and context signals, without exposing unnecessary personal information.
Do the models become a black box for the commercial team? No. Each recommendation can return the factors that support the decision, such as propensity, affinity, recent behavior, purchase history, elasticity, channel, context and expected response. This turns the model into a sales and decision argument, not an opaque imposition.
Does it work for anonymous customers without login? Yes. i6 RecSys generates purchase propensity for anonymous traffic based on session pattern, navigation, search, origin, context, similarity and aggregated behavior. Even without login, AI can identify intent signals and recommend products, offers or journeys with higher conversion probability.
Why is BI not enough to monetize data? BI shows what happened. Monetization requires deciding what to do now. The difference is turning analysis into action: who to approach, with which product, in which channel, at which moment, with which incentive and with which expected return. Data only becomes revenue when it changes a commercial decision.
Which data can be used for monetization? Purchase, navigation, search, cart, abandonment, CRM, campaigns, products, categories, price, margin, inventory, availability, channel, store, region, frequency, recurrence, commercial response, anonymous session, repurchase, churn and inactivity data can be used.
Which areas can monetize data? Marketing, CRM, e-commerce, marketplace, sales, trade, pricing, supply, product, digital channels, physical retail, industry and partner relationships. Any area that makes recurring decisions can capture value with predictive data.
What are the main use cases? Purchase propensity, product recommendation, cross-sell, up-sell, journey personalization, cart recovery, campaign optimization, pricing, bundles, assortment, commercial prioritization, demand forecasting, customer reactivation and partner intelligence.
How do you measure whether monetization generated real results? Measurement should compare recommended actions against a baseline, control group or A/B test. The main metrics are incremental revenue, incremental margin, conversion, average ticket, campaign ROI, CRM cost reduction, repurchase increase, commercial efficiency and share of revenue generated by the models.
How does AI increase revenue from data? AI increases revenue because it identifies opportunities before they are obvious to the operation. It points out customers with higher propensity, products with higher affinity, offers with higher conversion probability, discounts with lower margin erosion, more valuable audiences and activation moments with higher response probability.
How does this page connect with Behavior & Conversion? Behavior & Conversion goes deeper into how navigation, search, cart, intent and journey signals can be used to influence purchase, personalize experience and increase conversion in digital channels.
How does this page connect with Demand & Supply? Monetizable data also exists in the operation. Demand, turnover, inventory, stockout, assortment and replenishment signals can generate efficiency, commercial intelligence and new revenue opportunities.
How does this page connect with Predictive Operations? Data monetization requires recurring activation. Predictive Operations shows how to turn predictions into routines, alerts, prioritization and operational decisions in daily execution.