Boostt.ai uses behavioral signals (browsing, purchase patterns, engagement, recency/frequency, and trend data) to assign AI-driven personas that reflect motivations and intent—not just age, income, or location. This enables more relevant product recommendations and messaging.

Persona assignments group customers into interpretable clusters based on behaviors and preferences (e.g., “premium upgrader,” “deal seeker,” “research-heavy comparer,” “quick replenisher”). You can use personas to tailor recommendations, creative, offers, and cadence for upsell/cross-sell campaigns.

Boostt.ai predicts likely next actions—such as second purchase, category expansion, upgrade intent, or churn risk—using propensity-style scoring models and real-time trend signals. These predictions help you decide which products to recommend and when to trigger a cross-sell or upsell.

Boostt.ai can recommend:

• Next-best product (complementary cross-sell)
• Next-best upgrade (premium tier, larger size, better model)
• Replenishment (repeat purchase timing)
• Bundles (high-affinity combinations)
• Content + product (education-driven conversion for research-heavy personas)

Product discovery raises Average Order Value (AOV) by surfacing relevant add-ons, bundles, and upgrades customers actually want—based on persona and purchase behavior—so conversions improve through relevance, not margin-eroding promotions.

It means you don’t retarget everyone the same way. Boostt.ai uses signals (product views, time on site, cart behavior, repeat browsing, category interest) to retarget customers with specific recommendations and messages that match their intent stage—turning “tire-kickers” into buyers.

Yes. Boostt.ai can vary messaging angle (value vs. premium vs. convenience), creative themes, and offer strategy by persona—so each segment gets a pitch that fits how they buy.

By mapping demand signals and purchase affinity by persona, Boostt.ai can highlight:

• which products resonate with which customer types,
• which bundles drive higher conversion/AOV,
• emerging category trends,
• likely replenishment windows,
• and where demand is rising or falling—supporting smarter inventory planning and product roadmap decisions.

Common KPIs include:

• AOV (Average Order Value)
• Basket attach rate (add-on uptake)
• Conversion rate (overall + retargeted)
• Revenue per customer / ARPU
• Repeat purchase rate
• Time-to-second purchase
• Upsell take rate (upgrade adoption)
• Margin impact (especially if offers are used)

Most teams can start with a quick win: persona assignment + a few high-impact recommendation triggers (browse retargeting, post-purchase cross-sell, replenishment reminders). From there, you expand into more advanced decisioning (next-best-action, multi-step journeys, offer optimization).

It works for both. eCommerce uses cases include add-ons, bundles, upgrades, replenishment, and category expansion. Services and subscriptions can use persona + propensity to drive upgrades, add-on services, renewals, and plan expansion based on predicted intent.