Targeting specific audiences with Rules
How rules shape different shopper experiences inside Liva 7
Overview
Personalisation rules in Liva 7 do not target audiences directly. There are no demographic filters, no segment pickers and no audience‑specific rule settings. Instead, rules influence audiences indirectly by shaping the environment the engine works within.
This article explains how that indirect targeting works, how rules interact with signals and session behaviour and how Liva 7 adapts recommendations for different types of shoppers.
How Liva 7 understands audiences
Liva 7 builds a real‑time understanding of each shopper using behaviour, context and session signals—not predefined segments. Every visitor is treated as a dynamic profile that evolves as they browse.
Liva 7 interprets audiences through:
- behavioural signals (views, clicks, dwell time)
- page context (product, collection, cart)
- session behaviour (recency, sequence, depth)
- anonymous personalisation (no identifiers required)
This creates fluid, behaviour‑driven audiences that adapt moment by moment.
Liva 7 targets behaviour, not demographics.
How rules influence different shopper types
Rules apply globally, but different shoppers experience them differently because their behaviour and context vary.
Returning shoppers
- Stronger signals mean more personalised output
- Exclusions prevent outdated or irrelevant items resurfacing
- Pinned items appear alongside personalised recommendations
New shoppers
- Limited signals mean rules provide structure
- Pinned items help guide early discovery
- Exclusions prevent irrelevant categories appearing too early
High‑intent shoppers
- Deep‑funnel behaviour amplifies relevance
- Exclusions remove noise
- Pinned items reinforce merchandising priorities
Rules shape the boundaries; behaviour determines the path.
How rules interact with signals
Signals determine relevance.
Rules determine eligibility.
This creates predictable behaviour across all audiences:
- If a shopper shows strong interest in an excluded category, the engine finds the next best alternative
- If signals are weak or broad, pinned items help guide discovery
- If signals conflict with rules, rules always take priority
Rules ensure the engine adapts without breaking your merchandising constraints.
Eligibility always overrides relevance.
How rules influence session behaviour
Session behaviour evolves as shoppers browse. Rules influence this evolution by shaping what the engine is allowed to consider at each stage.
Rules help:
- stabilise early‑session recommendations
- prevent irrelevant items from entering the session
- keep pinned items visible across multiple pages
- guide fallback behaviour when signals are weak
This creates a consistent experience even as the shopper’s intent becomes clearer.
Rules provide structure while sessions provide context.
How rules affect anonymous personalisation
Anonymous shoppers still receive personalised experiences based on real‑time behaviour and page context. Rules ensure that even without identifiers:
- excluded items never appear
- pinned items guide early discovery
- fallback behaviour remains controlled
This keeps anonymous personalisation safe, predictable and aligned with your brand.
Anonymous personalisation follows the same rule boundaries as identified sessions.
When merchants typically use rules to shape audiences
Rules are especially useful when you want to influence how different types of shoppers experience your store without manually building segments.
Common use cases include:
- guiding new shoppers toward hero products
- preventing returning shoppers from seeing discontinued items
- shaping discovery for broad‑interest visitors
- controlling sensitive or restricted categories
- stabilising recommendations during campaigns
Rules influence audiences without requiring audience management.
How rules support long‑term audience strategy
Rules help maintain consistency across all shopper types by ensuring:
- brand priorities are always respected
- excluded items never surface
- pinned items support discovery for new visitors
- returning visitors see cleaner, more relevant output
- fallback behaviour aligns with your merchandising approach
They allow you to shape audience experiences without needing explicit targeting tools.
Rules create predictable experiences across all audience types.
