Anticipate intent, prescribe the right argument and convert more — proprietary AI that learns individual behavior and activates the next action on the right channel.
Transformation · Behavior & Conversion
Most companies do not lose conversion because they lack traffic. They lose conversion because they interpret too late the signals of intent, context and behavior that were already happening throughout the journey. \n\nEvery search, click, page view, abandonment, return, compared product, accessed channel and session pattern reveals a possible intent. **The problem is that, without prediction, these signals only become insight after the opportunity has passed.** \n\nPredictive AI applied to behavior makes it possible to anticipate undeclared intentions, personalize the next action and influence conversions with more precision, without relying only on coupons, fixed rules or generic campaigns.
Customers rarely declare exactly what they want. They leave signals.
The way they navigate, search, compare, return, abandon, click, ignore, add to cart or interact with an offer indicates intent, doubt, urgency, price sensitivity, product affinity and conversion probability.
In practice, marketing, CRM, e-commerce, growth, sales and digital channels deal with recurring pain points:
>The real pain is easy to understand: undeclared intent already exists in the journey. The challenge is to anticipate that intent with enough precision to change conversion before the opportunity disappears.
Rule-based personalization treats different customers as if they were the same within the same segment. “If cart was abandoned, send a coupon.” “If product X was viewed, recommend product Y.” “If the customer entered the category, show the standard campaign.”
These rules are easy to operate, but they ignore the questions that truly change conversion:
Generic propensity models also fail when they use weak signals, frozen clusters or recommendations without contextual adherence.
Without prediction, the company reacts to abandonment.\ Without explainability, the team does not trust the recommendation.\ Without real-time activation, intent passes by.
Without context, personalization becomes a superficial campaign variation.
> Conversion does not depend only on knowing who the customer is. It depends on understanding what they are about to do. To change the conversion game, AI needs to anticipate intent, explain the recommendation and activate the next best action while the journey is still alive.
infinity6 applies proprietary predictive AI, recommendation and pricing engines to transform live behavior into early decisions for personalization, influence and conversion.
The solution connects navigation, purchase, search, cart, channel, product, price, context and historical signals to identify undeclared intent and recommend the next best action.
*Applications:* purchase propensity, product recommendation, journey personalization, cross-sell and up-sell, cart recovery and anonymous traffic activation.
*Applications:* unnecessary coupon reduction, discount optimization, personalized bundles and margin protection.
*Answers:* who to activate, what to offer, which argument to use and why. Combines propensity, context and explainability to increase conversion with precision.
The solution operates as an intelligence layer above current channels, connecting e-commerce, CRM, app, CDP, media, marketplace, WhatsApp, POS and campaign tools.
Each behavior event can recalibrate propensity and update the next best action. The goal is to transform live signals into activation while there is still an opportunity to influence conversion.
01. Behavior data
The foundation starts with the signals generated throughout the journey: navigation, search, product view, accessed category, cart, abandonment, return, frequency, origin, device, campaign, purchase history, price, inventory, channel and anonymous session behavior.
This data reveals intent, doubt, affinity, urgency, elasticity, price sensitivity and conversion probability.
02. Predictive engine
The proprietary engines identify patterns that fixed rules and traditional segmentations do not capture.
AI calculates purchase propensity, affinity between customer and SKU, conversion probability, abandonment risk, incentive elasticity, cross-sell opportunity, up-sell potential and the best activation moment.
Prediction considers context, timing, similarity, history, recent behavior and undeclared intent signals.
03. Prescriptive decision
Prediction becomes a practical conversion recommendation.
AI indicates which product to recommend, which offer to present, which argument to use, which channel to activate, which incentive to apply and which journey to prioritize.
Each recommendation can come with feature-level explainability, showing the factors that support the decision, such as category affinity, recent engagement, similarity with high-value profiles, price elasticity or correlation with higher-margin items.
04. i6 Signal activation
Recommendations are delivered into the current flow through API, e-commerce, CRM, app, WhatsApp, campaign tool, POS, dashboard or i6 Signal.
Activation can influence storefront, recommended product, message, argument, incentive, offer order, campaign or commercial approach.
With each interaction, the models learn from the customer’s real response, creating a continuous cycle: behavior, prediction, recommendation, activation and conversion.
> The team stops asking only “who visited?” and starts acting on “which intent is forming now and which action increases the chance of conversion?”.
What is behavior prediction? Behavior prediction is the use of AI to analyze navigation, search, purchase, cart, abandonment, channel, context and history signals, with the goal of anticipating intentions and indicating the next best action to increase conversion, personalization and customer value.
How can AI identify undeclared intentions? AI identifies patterns in indirect signals, such as navigation sequence, compared products, interaction time, origin, visited category, recurrence, abandonment, similarity with other profiles and historical response. These signals help estimate intent even when the customer does not declare what they want.
Does it work for anonymous traffic without login? Yes. The solution can generate propensity for anonymous visitors based on session pattern, device, origin, navigation, search, context and similarity with known clusters. Even without login, there are enough signals to improve recommendation and activation.
How does AI influence conversion? AI influences conversion by indicating the right product, the right offer, the right argument, the right channel and the right incentive for each moment of the journey. This makes it possible to act before abandonment, improve relevance and reduce dependence on generic campaigns.
How does the solution avoid cannibalizing full-price sales? The elasticity engine helps identify who needs an incentive and who would buy without a discount. Customers with high propensity can receive a value argument, recommendation or proof of fit instead of an unnecessary coupon.
What does feature-level explainability mean? It means each recommendation can bring the factors that support the decision, such as category affinity, recent behavior, similarity with high-value customers, purchase history, elasticity, channel, context and conversion probability. This increases confidence and adoption by the commercial team.
Why are fixed personalization rules not enough? Fixed rules treat similar customers as if they were the same and do not adapt well to context, timing, intent, elasticity and behavior change. Predictive AI recalculates propensity as new signals appear throughout the journey.
Does AI replace the CRM or e-commerce platform? No. The solution operates above current channels. CRM, e-commerce, app, marketplace, WhatsApp, CDP or campaign tool remain the activation environments. AI adds the predictive layer that defines who to activate, when, with which offer and why.
What data is needed to start? The minimum data usually includes navigation events, search, product views, cart, abandonment, purchase history, products, categories, price, channel and origin. Additional data such as inventory, margin, campaigns, CRM, recurrence and commercial response increases precision.
How do you measure conversion impact? Impact should be measured against a baseline, control group or A/B test. Common metrics include conversion rate, incremental revenue, average ticket, incremental margin, coupon reduction, cross-sell conversion, up-sell, cart recovery and share of revenue generated by the models.
How long does it take to see impact? It depends on traffic volume, event quality and activation speed. In digital operations with relevant volume, the first learning cycles can happen in a few weeks and material results tend to appear as recommendations go into production.
How does this page connect with Data Monetization? Behavior and conversion are one of the most direct ways to monetize data. Navigation, intent, purchase and abandonment signals can be transformed into incremental revenue, margin and commercial efficiency.
How does this page connect with Demand & Supply? Digital behavior anticipates demand. Search, navigation, cart and intent help predict pressure on products, categories and channels before the sale happens, supporting inventory, assortment and replenishment.
How does this page connect with Predictive Operations? Conversion also depends on operations. Behavior signals can anticipate stockout risk, channel pressure, replenishment needs and operational prioritization before the problem impacts the customer experience.