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Customer & Operations Intelligence

Predict what happens next. Act before it costs you.

ShuttlePro Intelligence turns customer, order, conversation and document data into predictive signals your teams can use—before a customer churns, a risky order is fulfilled or a service issue escalates.

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Retail & Ecommerece focused

Connected to CRM Workflows

Available by implementation scope

What is ShuttlePro Intelligence?

ShuttlePro Intelligence is the predictive and analytical layer of ShuttlePro. It analyses customer, commerce, conversation and document data to identify likely outcomes, explain risk and feed timely actions into ShuttlePro CRM, ShuttleBot AI and connected ecommerce workflows.

Four intelligence capabilities

Move from operational data to earlier action

Start with the use case that has the clearest business impact, then extend the intelligence layer as data quality and operational adoption improve.

01

Predictive Customer Analytics

Estimate purchase likelihood, churn risk, repeat-purchase probability and customer value so marketing and retention teams can act before behaviour becomes an outcome.

02

High-Risk Order Detection

Score unusual orders before fulfilment, show the main risk signals and route selected orders to a focused review workflow instead of checking everything manually.

03

Real-Time Sentiment Analysi

Understand sentiment, urgency and complaint signals while customer conversations are happening, then prioritize or escalate them inside the support workflow.

04

Document & Image Intelligence

Use computer vision and pattern matching to extract, verify and match delivery receipts or operational documents, with manual review for uncertain cases.
Predictive customer analytics

Know which customers need your attention next

Create practical customer scores and segments from historical behaviour, then use them to guide retention, campaigns and service priorities.
  • Purchase-likelihood and repeat-purchase scoring
  • Churn or inactivity-risk identification
  • Product, category and customer-value signals
  • Actionable segments for marketing and retention workflows
Customer Intelligence
Active Model

CR

Customer profile

Commerce, service and interaction history

Purchase likelihood

High

Purchase likelihood

Purchase likelihood

Medium

Service follow-up advised

Purchase likelihood

Apparel

Recent category activity

Purchase likelihood

Priority

epeat purchase history

Suggested next action

Prioritize the open support issue, then include this customer in the relevant retention segment.
Predictive customer analytics

Review the right orders before fulfilment

Detect orders that differ from expected customer, address, value, timing or delivery patterns, then focus manual review on the cases that carry the most risk.

  • Configurable risk rules combined with anomaly signals
  • Risk score, confidence and clear reason codes
  • Human review queue with approve, hold or investigate actions
  • Feedback captured to improve future scoring
Conversation sentiment intelligence

See dissatisfaction before it becomes escalation

Analyse customer communications in real time to identify sentiment, urgency, complaint likelihood and emerging service themes across channels.
  • Positive, neutral and negative sentiment signals
  • Urgency, frustration and complaint-likelihood detection
  • Priority changes, supervisor alerts and ticket triggers
  • Sentiment trends by brand, topic, queue or period
Conversation sentiment intelligence

Turn delivery receipts into structured operations data

Analyse customer communications in real time to identify sentiment, urgency, complaint likelihood and emerging service themes across channels.
  • Image quality checks before processing
  • Extraction of required receipt or document fields
  • Order and delivery record matching
  • Confidence thresholds and manual exception queues

One platform, three product layers

CRM manages the work. AI handles conversations. Intelligence guides decisions.

The products are distinct, but they create more value when customer conversations, operational workflows and predictive signals stay connected.

Operational layer

ShuttlePro CRM

Manages customer conversations, ownership, tickets, teams, customer context, performance and audit history.

Explore ShuttlePro CRM →

Conversational layer

ShuttleBot AI

Understands customer requests, retrieves approved business information, performs controlled actions and hands off exceptions.

Explore ShuttleBot AI →

Intelligence layer

ShuttlePro Intelligence

ShuttlePro Intelligence Predicts behaviour, scores risk, analyses sentiment and turns documents into structured operational signals.

View Intelligence Capabilities →

From signal to action

Intelligence should change a workflow, not create another dashboard

Every implementation should define how data becomes a signal, who reviews it, what action follows and how the outcome improves future decisions.

Connect

Bring together relevant customer, order, conversation or document data.

Analyse

Apply configured rules, prediction models or pattern matching.

Explain

Show the score, confidence and main contributing signals.

Act

Route the insight into a CRM queue, ticket, alert or approved workflow.

Learn

Capture outcomes and human feedback to measure and improve performance.

From signal to action

Intelligence should change a workflow, not create another dashboard

Every implementation should define how data becomes a signal, who reviews it, what action follows and how the outcome improves future decisions.

01

Define the outcome

Select a specific decision to improve, such as identifying risky COD orders or escalating dissatisfied customers earlier.

02

Assess data readiness

Review sources, history, identifiers, quality, permissions and the outcome labels needed for validation.

03

Run a controlled rollout

Introduce scores and recommendations with clear confidence thresholds, human review and measurable acceptance criteria.

04

Measure and expand

Track accuracy, overrides, business impact and model drift before adding new actions, brands or intelligence capabilities.

From signal to action

Intelligence should change a workflow, not create another dashboard

Every implementation should define how data becomes a signal, who reviews it, what action follows and how the outcome improves future decisions.

CRM capability

Unified Inbox

Use sentiment and customer-priority signals inside the team conversation queue.

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CRM capability

Ticketing & Complaints

Turn risk, sentiment and operational exceptions into tracked follow-up work.

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CRM capability

Audit Trail

CRM capability Audit Trail Review score-driven actions, manual overrides and document decisions.

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Solution

Ecommerce Ops & COD

Apply order-risk and document signals to fulfilment and post-purchase workflows.

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Solution

Multi-Brand Operations

Compare intelligence signals while maintaining separate brand queues and ownership.

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WhatsApp use case

WhatsApp AI Chatbot

See how AI handles repetitive customer queries before handing exceptions to teams.

Explore →

Frequently asked questions

ShuttlePro Intelligence FAQs

Clear answers about product scope, implementation, data and operational control.

ShuttlePro Intelligence is the predictive and analytical layer of ShuttlePro. It uses customer, order, conversation and document data to produce signals such as purchase likelihood, churn risk, order risk, sentiment and document-match confidence. Those signals can then be used inside CRM and ecommerce workflows.

ShuttleBot AI handles customer conversations, retrieves approved business information and performs controlled actions. ShuttlePro Intelligence analyses broader customer and operational data to predict behaviour, identify risk, detect sentiment patterns and support decisions.

Yes. A focused starting point is recommended. For example, a business may begin with high-risk order detection or sentiment-based escalation, validate the results and workflow, and then add other capabilities.

Not by default. The recommended first stage is to assign a risk score, explain the main risk signals and send selected orders to a human review queue. Automated actions should only be introduced after accuracy, thresholds and operational rules have been validated.

What data is required for predictive customer analytics?
The exact requirements depend on the chosen prediction. Relevant data may include customer history, orders, returns, delivery outcomes, product interactions, support conversations, ticket outcomes and campaign activity. ShuttlePro reviews data quality and identifiers before implementation.

How is sentiment analysis used in customer support?
Sentiment analysis can add urgency and sentiment signals to conversations, prioritize dissatisfied customers, trigger supervisor alerts and contribute to reporting. It should support human decisions and quality review rather than automatically judge an agent or customer.

What can document and image intelligence automate?
The first planned use case is delivery receipt processing. The system can check image quality, extract required fields, match the receipt with an order, identify inconsistencies and send low-confidence cases to a manual review queue.

Pricing depends on the chosen capability, data sources, processing volume, integration complexity and workflow scope. The first step is an implementation discussion and data-readiness assessment. You can also review the main ShuttlePro CRM pricing separately.

Start with the decision that matters most to your business

Share your current workflow, available data and the outcome you want to improve. We will help define a focused first implementation for customer prediction, order risk, sentiment or document automation.
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