Customer Engagement Software Buyer’s Guide for Customer Support

Who This Guide Is For

You need this guide if you’re responsible for customer support outcomes and the tools behind them, and you’re trying to decide whether “customer engagement” software is worth buying—or which kind to buy.

Buyer personas this is for

1) VP / Director of Customer Support

You care about:

• Deflection without destroying CSAT
• Faster first response and resolution times
• Agent productivity and retention
• Omnichannel coverage without multiplying tools
• AI that actually reduces workload, not just adds dashboards

Your pain points:

• Ticket volume is growing faster than headcount
• Knowledge is scattered across chat, email, voice, CRM, and call notes
• Your team spends too much time triaging instead of solving
• Leadership wants “AI transformation” but won’t fund a seven-tool stack

2) Head of Customer Experience / Customer Operations

You care about:

• Journey-level visibility
• Identifying friction before customers complain
• Voice-of-customer insights tied to action
• Cross-functional visibility into support, product, and revenue issues

Your pain points:

• Support data is available, but not usable
• Surveys and conversations live in separate systems
• You can’t connect sentiment to churn or escalation risk fast enough
• Your current stack tells you what happened, but not what to do next

3) Contact Center / CX Platform Owner

You care about:

• Integrations
• Security and compliance
• Routing, orchestration, and channel consistency
• Practical implementation effort

Your pain points:

• Vendor demos look great, but deployment is messy
• Every “native” feature still needs services work
• Data hygiene is poor, so AI outputs are unreliable
• Procurement is pushing for one platform while operations needs flexibility

4) Customer Success / Support Operations Leader at a Growth Company

You care about:

• Fast deployment
• Self-service
• Shared inboxes and live engagement
• Payback in under 12 months

Your pain points:

• You do not have a large admin team
• You need value in weeks, not quarters
• You can’t justify enterprise-suite bloat
• Your team wants simple workflows, not a “digital transformation program”

Who should NOT read this guide

Skip this if:

• You are only shopping for a basic help desk with no interest in engagement, orchestration, AI, or analytics
• You need a single-use point solution only for surveys, only for chat, or only for call recording
• You are not responsible for support operations, customer experience, or contact center tooling
• You expect software to fix broken processes, bad data, or undertrained agents by itself

---

What Customer Engagement Software Does

Customer engagement software in the customer support category is the layer that helps you interact with customers more intelligently across channels. It goes beyond ticket logging. It helps you:

• Respond faster
• Route more accurately
• Personalize interactions
• Predict risk
• Automate repetitive work
• Learn from conversations and behavior

In practice, this category includes platforms that handle one or more of the following:

• Omnichannel messaging and routing
• AI chatbots and self-service
• Agent assist and next-best-action guidance
• Conversation intelligence and sentiment analysis
• Customer journey analytics
• Customer communications management
• Experience management and feedback loops
• Customer data unification

Before vs. after

Why buyers invest

The business case is usually not “customer engagement” in the abstract. It is one of these:

• Reduce cost per contact
• Increase containment/deflection
• Improve first-contact resolution
• Reduce churn and save accounts
• Shorten time to resolution
• Improve agent efficiency
• Unify fragmented customer data

Dollar-value example

A mid-market support team handles 250,000 contacts/year at an average fully loaded cost of $6.50 per assisted contact. That is about $1.625M/year in direct support cost.

If a customer engagement platform:

• Deflects 12% of contacts to self-service,
• Reduces average handle time by 18% on another 20% of contacts,
• Cuts escalation rate by 10%,

the annual savings can easily reach:

• $195,000 from deflection alone
• $58,500 from handle-time reduction
• $32,500 from fewer escalations

That is roughly $286,000/year before you count churn reduction or agent capacity gains.

The key point: if your current cost structure is inefficient, even a modest improvement pays back quickly. But if your data is bad and your workflows are immature, software alone will not generate those savings.

---

Market Landscape 2026

The 2026 market is defined by one thing: AI is no longer a feature; it is the buying filter.

Five years ago, buyers asked whether a platform had chat, routing, or surveys. Today the real questions are:

• Can it understand intent?
• Can it summarize and recommend?
• Can it orchestrate next steps across channels?
• Can it work with your data, not just vendor-generated prompts?
• Can it prove business impact in weeks, not vanity dashboards in quarters?

What changed

There are four major shifts:

1) AI is moving from assistance to orchestration

The market has moved from:

• “Bot answers common questions”

to

• “System detects intent, routes intelligently, summarizes context, drafts replies, and triggers the right workflow”

That matters because the highest-value use cases are no longer just front-door chat. They are:

• Guided self-service
• Predictive routing
• Agent assist
• Case summarization
• Journey orchestration
• Sentiment-triggered escalation

Vendors like Salesforce Einstein, Talkdesk Navigator, Verint Da Vinci™ AI services, U-Connect by Uniphore, Uniphore X Platform, AI for Service by Kore.ai, and Salesforce Service Cloud are competing on how much work AI can take off the agent’s plate.

2) Buyers want a platform, but they still buy point solutions

The market is split between:

• Platforms: Salesforce Service Cloud, Zendesk Sunshine, Freshworks Platform, Oracle Customer Experience (CX), Qualtrics XM Platform, Genesys DX, Uniphore X Platform, Verint, five9, Talkdesk AI
• Specialized layers: CallMiner Visualize, Advanced Analytics by Tethr, Engagement AI by ChurnZero, Live Dashboard by Reamaze, Staircase AI by Gainsight, Pointillist, Quadient Inspire, Esker, Equiniti

The platform pitch is strong because procurement likes consolidation. But point solutions often win when the buyer has a narrow pain point:

• conversation intelligence
• customer journey analytics
• account-risk detection
• compliant customer communications
• high-volume order processing automation

3) Data unification is now mandatory

Customers do not experience your stack as separate tools. They experience:

• one company
• one brand
• one support history

That is why products like Zendesk Sunshine, Salesforce Service Cloud, Oracle CX, Freshworks Platform, and Qualtrics XM Platform emphasize unified profiles and system-of-record positioning.

If your support data is siloed, AI will be mediocre. Garbage in, confident garbage out.

4) Compliance and trust are buying criteria, not checkbox items

Enterprise buyers increasingly require:

• SOC 2
• ISO 27001
• PCI-DSS
• HIPAA
• GDPR
• data residency / regional controls
• auditable workflows

This favors vendors with strong governance stories, including Verint, Genesys Cloud CX / AppFoundry, Five9, Oracle, Salesforce, Qualtrics, and Quadient. It also means lower-friction tools can lose deals if they cannot answer security questions cleanly.

How the vendor landscape is structured

Layer 1: Core customer engagement platforms

These are the broad suites that anchor the buying decision:

• Salesforce Service Cloud by Salesforce Einstein
• Zendesk Sunshine by Zendesk
• Freshworks Platform by Freshworks
• Oracle Customer Experience (CX) by Oracle Corporation
• Genesys DX by Genesys Cloud
• Five9 Digital Engagement by five9
• Qualtrics XM Platform by Qualtrics
• Uniphore X Platform by Uniphore
• Verint Da Vinci™ AI services by Verint

Layer 2: AI and automation intelligence

These add AI capability, analytics, and orchestration:

• Salesforce Einstein
• AI for Service by Kore.ai
• Talkdesk Navigator by Talkdesk AI
• U-Connect by Uniphore
• Engagement AI by ChurnZero
• Staircase AI by Gainsight
• Advanced Analytics by Tethr
• CallMiner Visualize by CallMiner
• Verint Web & Mobile by Verint

Layer 3: Journey, experience, and voice-of-customer analytics

These are best when the core issue is insight:

• Pointillist by Genesys Cloud CX
• Qualtrics XM Platform
• XM for Customer Experience by Qualtrics
• Advanced Analytics by Tethr
• CallMiner Visualize
• Staircase AI by Gainsight

Layer 4: Communications and document/output automation

These matter when support includes formal communications:

• Quadient Inspire platform by Quadient
• Esker Sales Order Processing Automation Software
• Intelligent Customer Resolution & Complaints Technology by Equiniti

Layer 5: Flexible/opensource and ecosystem play

These are for teams that want control:

• Chatwoot
• AppFoundry by Genesys Cloud CX
• Freshworks Platform
• Zendesk Sunshine

Direct market take

If you want a single-system strategy, start by comparing:

• Salesforce Service Cloud
• Zendesk Sunshine
• Freshworks Platform
• Oracle CX
• Genesys DX
• Five9 Digital Engagement
• Qualtrics XM Platform
• Verint
• Uniphore X Platform

If you already have a core platform and want to improve outcomes, the better path is often a specialized add-on:

• CallMiner Visualize or Tethr for interaction intelligence
• ChurnZero Engagement AI or Gainsight Staircase AI for customer health and risk
• Talkdesk Navigator or Kore.ai AI for Service for orchestration and self-service
• Quadient Inspire for compliant communications
• Reamaze Live Dashboard or Chatwoot for lightweight engagement and shared inbox use cases

What to avoid

Avoid vendors that:

• Lead with “AI” but cannot explain training data, governance, or outcomes
• Have broad “platform” messaging but no real workflow depth
• Force you into a professional services dependency just to launch basic use cases
• Cannot distinguish between analytics, automation, and engagement
• Hide implementation complexity behind a polished demo

---

The 9 Things That Matter Most

Below is the evaluation framework I’d use if I had to choose in one week and live with the decision for three years.

1) Use-case fit and workflow depth — 18%

Why it’s critical: A tool that is “good at everything” but weak in your specific workflow is a trap. Support engagement is not one market; it is chat, voice, email, self-service, journey orchestration, sentiment analysis, and customer communications.

What to evaluate:

• Which workflows are native?
• Does it support your highest-volume use case first?
• Can it handle escalation, handoff, and follow-up cleanly?
• Are the AI actions embedded in workflow or bolted on?

What good looks like:

• You can describe your top 3 workflows and the vendor shows each one end-to-end
• The product has built-in support for your priority channels
• AI is action-oriented, not just observational

Red flag: The demo is impressive, but your real use case requires custom development.

Ask the vendor:

• Which workflows do customers usually deploy in the first 60 days?
• What parts of this are native vs. custom-built?
• What customer types fail to get value from your product?

---

2) Omnichannel coverage and orchestration — 15%

Why it’s critical: Customers do not care about your internal channel ownership. They want one coherent experience across chat, email, SMS, voice, web, mobile, and social.

What to evaluate:

• Native channel support
• Seamless channel switching
• Unified conversation history
• Routing consistency across channels

What good looks like:

• One customer thread across channels
• Channel switching without losing context
• Real-time orchestration based on intent, not just queue rules

Red flag: The vendor claims omnichannel but actually offers disconnected channel modules.

Ask the vendor:

• How does context persist when a customer moves from chat to voice?
• Which channels are native and which require integrations?
• How does routing differ by channel?

---

3) AI quality and governance — 15%

Why it’s critical: AI can reduce workload or create dangerous overconfidence. In customer support, hallucinations and bad routing create escalations, churn, and compliance risk.

What to evaluate:

• Summarization accuracy
• Intent recognition
• Explainability
• Human override
• Model governance and audit logs
• Data usage policy

What good looks like:

• You can see why the AI made a recommendation
• Agents can edit/approve before sending
• The system tracks confidence and escalation triggers

Red flag: “Generative AI” with no governance story and no answer to “what data trains the model?”

Ask the vendor:

• What data is used to train or tune the AI?
• Can we disable AI outputs in sensitive workflows?
• How do you measure AI precision and drift over time?

---

4) Data unification and context richness — 12%

Why it’s critical: Support quality depends on context. If the platform cannot see history, intent, order data, product usage, and account risk, every interaction starts blind.

What to evaluate:

• Customer profile depth
• Data model flexibility
• Native connectors
• Identity resolution
• Real-time data availability

What good looks like:

• One customer profile with interaction history and external attributes
• Clean mapping between accounts, contacts, cases, and journeys
• Context appears automatically inside the workflow

Red flag: The system stores interactions but doesn’t actually unify the customer.

Ask the vendor:

• How do you deduplicate identities across systems?
• What data is available in real time vs. batch?
• How much data prep is required before AI works well?

---

5) Analytics, reporting, and actionability — 10%

Why it’s critical: Dashboards are not value. Actionable insight is value. Your team should know which issues are rising, which agents need help, and where customers are getting stuck.

What to evaluate:

• Root-cause analysis
• Journey analytics
• Conversation intelligence
• Custom reporting
• Alerting and workflow triggers

What good looks like:

• Analytics tied to specific operational actions
• Trend detection across topics, sentiment, and outcomes
• Exportable, exec-ready reporting

Red flag: A pretty dashboard that never changes behavior.

Ask the vendor:

• What action can I trigger from an insight?
• How quickly do trend alerts appear?
• Can we tie experience metrics to churn, renewals, or costs?

---

6) Security, privacy, and compliance — 10%

Why it’s critical: This category touches personal data, payment data, and often regulated information. One weak control can block procurement or create legal exposure.

What to evaluate:

• SOC 2, ISO 27001, GDPR, PCI-DSS, HIPAA, etc.
• Access controls
• Audit logging
• Data retention and deletion
• Regional hosting/data residency

What good looks like:

• Clear security documentation
• Granular permissions
• Strong auditability
• No ambiguity about subprocessors and data use

Red flag: Security answers that depend on “we can discuss that later with our team.”

Ask the vendor:

• What certifications are current and in scope?
• Can we control retention and deletion by data type?
• Where is data stored and processed?

---

7) Implementation complexity and time to value — 8%

Why it’s critical: A six-month deployment can kill momentum. If the first use case takes too long, internal champions lose power.

What to evaluate:

• Configuration vs. custom build
• Migration effort
• Admin overhead
• Integration dependencies
• Change management needs

What good looks like:

• First value in 4–8 weeks for mid-market use cases
• Clear milestone plan
• Limited custom code for standard workflows

Red flag: The vendor says “implementation depends” but cannot define a typical launch path.

Ask the vendor:

• What is the fastest real customer deployment you’ve seen?
• What did that customer already have in place?
• What are the top 3 reasons implementations slip?

---

8) Extensibility and ecosystem — 6%

Why it’s critical: No support stack lives alone. You need CRM, telephony, knowledge, analytics, CDP, billing, and identity integrations.

What to evaluate:

• APIs
• Prebuilt connectors
• Marketplace ecosystem
• Webhooks and eventing
• Developer documentation

What good looks like:

• Strong API coverage
• Reusable integrations
• Marketplace apps for common adjacent needs

Red flag: “Open” platform that still requires vendor professional services for basic integrations.

Ask the vendor:

• Which integrations are supported out of the box?
• How much can be done via API without services?
• What’s the most common integration failure?

---

9) Commercials and contract flexibility — 6%

Why it’s critical: Pricing in this category can hide in seats, usage, channels, modules, interactions, and service fees. Cheap entry points often become expensive real-world deployments.

What to evaluate:

• Pricing metric
• Annual vs. monthly commitments
• Overages
• Bundle dependencies
• Services and support costs

What good looks like:

• Simple pricing model
• Predictable scaling
• Clear differentiation between software and services

Red flag: A low initial price that requires multiple paid add-ons to be usable.

Ask the vendor:

• What causes price to increase most often after year one?
• Which features are mandatory vs. optional?
• What are the hidden implementation or admin costs?

---

Use-Case Matching: Which Type of Buyer Are You?

1) Growth-stage company scaling support quickly

Typical profile: You have 10–100 support agents, rising ticket volume, and limited admin resources.

What matters most:

• Speed to launch
• Ease of use
• Shared inbox + automation
• Good default workflows
• Affordable entry price

Prioritize:

• Chatwoot
• Reamaze Live Dashboard
• Freshworks Platform
• Zendesk Sunshine
• Genesys DX if digital engagement is core
• Talkdesk Navigator if routing/orchestration is a major pain point

Avoid:

• Heavy enterprise suites that need months of configuration
• Platforms that require a dedicated solution architect just to launch chat and triage

---

2) Enterprise support organization with complex channels

Typical profile: You run voice, digital, and self-service at scale with strict compliance and multiple systems.

What matters most:

• Omnichannel orchestration
• Strong governance
• Deep integrations
• Auditability
• AI that can be controlled

Prioritize:

• Salesforce Service Cloud
• Five9 Digital Engagement
• Verint Da Vinci™ AI services
• Genesys DX
• Oracle Customer Experience (CX)
• Uniphore X Platform
• AppFoundry by Genesys Cloud CX for ecosystem expansion

Avoid:

• Point solutions that cannot support enterprise governance
• Tools with weak identity, role-based access, or audit trails

---

3) Support org focused on retention, expansion, or account risk

Typical profile: Your support data should help customer success, account management, and leadership spot churn before it happens.

What matters most:

• Relationship intelligence
• Sentiment and topic detection
• Risk scoring
• Executive-ready summaries
• Account-level views

Prioritize:

• Engagement AI by ChurnZero
• Staircase AI by Gainsight
• Advanced Analytics by Tethr
• CallMiner Visualize
• Qualtrics XM Platform
• XM for Customer Experience by Qualtrics

Avoid:

• Tools that only improve case handling but don’t surface account risk or customer health signals

---

4) Regulated or document-heavy organization

Typical profile: You operate in banking, insurance, healthcare, government, or any environment with strict communication controls.

What matters most:

• Compliance
• Audit trails
• Communications governance
• Document generation/personalization
• Data retention controls

Prioritize:

• Quadient Inspire platform
• Verint Da Vinci™ AI services
• Oracle Customer Experience (CX)
• Equiniti Intelligent Customer Resolution & Complaints Technology
• Salesforce Service Cloud
• Genesys Cloud CX ecosystem via AppFoundry

Avoid:

• Lightweight support tools with vague compliance claims
• Anything that can’t answer retention, residency, and audit questions quickly

---

Vendor Evaluation Questions

Use these in an RFP or vendor call. Do not accept vague answers.

Features

• Which support channels are native today?
• How does a customer move between channels without losing context?
• What workflows are fully automated vs. human-assisted?
• How does the platform handle escalation rules?
• What self-service capabilities are included out of the box?
• Can agents edit, approve, or reject AI-generated responses?
• How does the system detect intent, sentiment, and urgency?
• What reporting exists for first-contact resolution, deflection, and CSAT?

Integration

• Which CRMs, contact center systems, and knowledge bases are prebuilt?
• What APIs are available for cases, profiles, conversations, and outcomes?
• Can we sync customer identity across multiple systems?
• How do you handle real-time data vs. batch data?
• What integration work typically requires professional services?
• Can the platform trigger external workflows via webhooks or events?

Security

• Which certifications are current and in scope for this product?
• Where is data stored, and can we choose region or residency?
• How are permissions, roles, and access controls configured?
• Do you log administrative actions and AI outputs?
• What are your retention and deletion controls?
• Do you use customer data to train models by default?

Pricing

• What is the pricing metric: seat, interaction, usage, module, or bundle?
• What costs increase as we scale?
• Which features are included vs. add-ons?
• What implementation fees should we expect?
• Are there overage charges for interactions, messages, or AI usage?
• What does renewal pricing typically look like?

Implementation

• What is the typical time to first value for a customer like us?
• What internal resources are required from our side?
• What are the most common causes of launch delays?
• How much data cleanup is needed before go-live?
• What training do agents, admins, and managers need?
• How do you support change management?

Support and success

• What does post-sale support actually include?
• Do you provide named CSMs, admins, or solution architects?
• What is your SLA for support response and escalation?
• Can you share examples of customers in our industry with similar scale?

---

Common Mistakes to Avoid

Mistake #1: Buying “AI” instead of buying a workflow

If you start with AI features instead of a business problem, you’ll end up with an expensive demo and no operational change.

Consequence: low adoption, wasted budget, zero measurable ROI. Do instead: define the workflow first: deflection, routing, agent assist, risk detection, or journey orchestration.

Mistake #2: Assuming omnichannel means unified

Many vendors support multiple channels but still leave them operationally disconnected.

Consequence: duplicated effort, broken context, poor customer experience. Do instead: test a real channel switch live in the demo.

Mistake #3: Ignoring data cleanup

Support data is usually messy: duplicate contacts, inconsistent case tags, outdated macros, and junk fields.

Consequence: weak AI results and broken reporting. Do instead: budget 2–4 weeks for cleanup before launch, longer if you have multiple systems.

Mistake #4: Overbuying platform before proving value

Large suites are tempting because they promise future flexibility.

Consequence: long implementation, unused modules, political compromise. Do instead: prove one high-value use case first, then expand.

Mistake #5: Underestimating compliance requirements

A vendor can look enterprise-ready and still fail a security review.

Consequence: procurement delays, legal risk, forced rework. Do instead: collect security, privacy, and residency answers before you shortlist.

Mistake #6: Choosing the cheapest tool for a complex environment

Low seat price is not low cost if integrations, services, and workarounds pile up.

Consequence: hidden TCO and poor scalability. Do instead: price the full deployment, not just licenses.

Mistake #7: Letting reporting drive the purchase instead of operations

Dashboards are easy to sell. Operational changes are not.

Consequence: insight with no action. Do instead: require every report to map to a decision, trigger, or workflow.

Mistake #8: Not involving frontline agents in evaluation

If agents hate the tool, adoption dies.

Consequence: shadow processes and low ROI. Do instead: have real agents test real scenarios before signing.

---

Evaluation Timeline & Process

6-week evaluation plan

Weighted evaluation matrix template

How to use the matrix

• Score each vendor from 1 to 5
• Multiply by the weight
• Require evidence, not opinions
• Do not let brand reputation override measured fit

What good evaluation looks like

By the end of week 6, you should know:

• Which use case you are solving first
• What data you need to connect
• What implementation effort is realistic
• Whether the platform is operationally useful or just feature-rich

---

Implementation Reality Check

Typical implementation timelines by company size

Key success factors

• One executive owner
• Clear first use case
• Clean data model
• Named technical lead
• Agent training with live scenarios
• Change management for managers, not just users
• A measurable baseline before go-live

What vendors won’t tell you

• The “standard implementation” is often only standard for customers with simple data
• AI quality depends heavily on how disciplined your support taxonomy is
• Integrations are rarely “plug and play” in real enterprise environments
• You will likely need to redesign some workflows, not just configure software
• Adoption problems are usually management problems, not software problems

Budgeting reality

Plan for:

• 2–4 weeks of data cleanup for most mid-market deployments
• 4–8 weeks for integration testing if multiple systems are involved
• Training and change management time that often equals the config effort
• Ongoing admin ownership after launch; this is not set-and-forget software

---

Red Flags — When to Walk Away

---

How This Guide Was Created

This guide was created using a vendor-neutral market analysis of the customer engagement software landscape in the customer support category, with emphasis on workflow fit, AI maturity, integration depth, compliance, implementation reality, and buyer use cases.

Review date: September 2026 Update frequency: Quarterly, or sooner if major product, pricing, or market shifts occur. Independence disclaimer: This guide is published by commercial.ai as an independent software directory resource. It is not sponsored by any listed vendor and is intended to help buyers compare solutions objectively.

Customer Engagement solutions in the catalogue (26)

• Advanced Analytics by Tethr — AI-powered conversation intelligence for customer experience and agent performance.
• AI for Service by Kore.ai — Empower customers and agents with agentic AI for personalized, efficient service.
• AppFoundry by Genesys — Extend Genesys CX platforms with a marketplace of integrations, applications, and services.
• CallMiner Visualize by CallMiner — Visually explore conversation intelligence data to create shareable presentations and drive action.
• Dixa by Dixa — The AI-driven customer service platform for consumer brands.
• Engagement AI by ChurnZero — AI for smarter insights and stronger customer relationships.
• Five9 Digital Engagement by five9 — Seamless omnichannel customer interactions across digital and voice channels.
• Freshworks Platform by Freshworks — A flexible, AI-powered enterprise platform for all Freshworks applications.
• Intelligent Customer Resolution & Complaints Technology by Equiniti — Automate complaints, ensure compliance, and enhance customer engagement with intelligent technology.
• Live Dashboard by Reamaze — Monitor live customer activity, browsing, and shopping data for proactive support.
• Oracle AI for Customer Experience (CX) by Oracle — AI-powered CX to personalize customer interactions and drive business outcomes.
• Oracle Customer Experience (CX) by Oracle — Go beyond CRM with a connected suite of cloud applications for sales, service, marketing, and commerce.
• Pointillist by Genesys — Measure, monitor, and orchestrate customer journeys to optimize CX and improve digital containment.
• Quadient Inspire platform by Quadient — The world's leading platform for personalized, compliant customer communications.
• Qualtrics XM Platform by Qualtrics — A system of action to manage and improve customer, employee, product, and brand experiences.
• Sales Order Processing Automation Software by Esker — Automate sales order processing with AI and RPA for optimized data recognition and verification.
• Salesforce Einstein by Salesforce — AI-powered customer service for smarter, faster, and more personalized experiences.
• Salesforce Service Cloud by Salesforce — AI-powered customer service for smarter, faster, and more personalized experiences.
• Staircase AI by Gainsight by Gainsight — Real-time, AI-powered customer engagement insights across all conversations.
• Talkdesk Navigator by Talkdesk AI — Generative AI for intuitive, conversational customer journey orchestration.
• U-Connect by Uniphore — Omnichannel customer engagement platform for voice, chat, email, and social media interactions.
• Uniphore X Platform by Uniphore — AI-native platform for enhanced customer and employee interactions.
• Verint Da Vinci™ AI services by Verint — Intelligent AI services for superior customer engagement and support.
• Verint Web & Mobile by Verint — Unify digital data to track and improve the customer experience across web and mobile.
• XM for Customer Experience by Qualtrics — Software to immediately surface customer friction and guide frontline teams to better serve clients.
• Zendesk Sunshine by zendesk — Unify customer data across all touchpoints for a complete, actionable view.