# Chatbot Welcome Messages [Image] Chatbots are often the first touchpoint for users seeking help or information. For product designers and managers, the challenge is crafting a welcoming experience that feels helpful and human while directing users efficiently toward their goals. Chatbot Welcome Message Testing uses a design stack of UX metrics: comprehension, sentiment, engagement, and success to measure how effectively the chatbot’s first interaction supports user needs. This approach replaces subjective opinions with measurable insights. With these findings, designers and managers can make informed design decisions, prioritize improvements, and demonstrate the impact of changes on business outcomes. For example, testing NetApp’s data storage landing page chatbot revealed high comprehension but low engagement, highlighting where tone and message flow could be refined to make the conversation more inviting and effective. * * * ### **Define Goals for Your Chatbot Welcome Message** A chatbot welcome message should balance user needs like clarity, relevance, and trust with business goals such as lead generation, support efficiency, and engagement. Measuring how people interact with the welcome message ensures it feels approachable while achieving conversion or support goals. **Audience** To define user needs, you first need to establish who your audience is. In the case of our NetApp example, we targeted database professionals and tech decision makers who might be interested in their data storage solutions. **User Needs** As a visitor interacting with a chatbot welcome message, the five most important needs would be: 1. The chatbot should be simple to interact with, offering clear next steps without confusion. (Chatbot should be [**Useable**](https://glare.helio.app/define/user-needs/usable)) 2. The message should quickly surface relevant information or assistance based on context. (Message should be [**Insightful**](https://glare.helio.app/define/user-needs/insightful)) 3. The tone should feel conversational and inviting, encouraging users to respond. (Tone should be [**Engaging**](https://glare.helio.app/define/user-needs/engaging)) 4. The chatbot should clearly represent the brand, respecting privacy and user intent. (Chatbot should feel **Trustworthy**) 5. The interaction should save time, resolving questions or connecting users to the right place quickly. (Interaction should be [**Efficient**](https://glare.helio.app/define/user-needs/efficient)) These five ensure the welcome message feels helpful, approachable, and purposeful, setting the tone for a smooth, human-like experience. **Business Goals** Here are the five most important business goals for chatbot welcome messages: 1. **Increase Engagement Rates** – Encourage more visitors to interact with the chatbot through compelling greetings. 2. **Guide Conversion Paths** – Direct users toward high-value actions such as demos, trials, or support requests. 3. **Reduce Support Costs** – Automate simple inquiries to free up human agents for complex issues. 4. **Personalize the Experience** – Tailor messaging based on user type, referral source, or browsing behavior. 5. **Collect Behavioral Insights** – Use chatbot data to understand user intent, refine messaging, and improve flows. These goals help the business increase engagement, reduce friction, and personalize the customer experience through effective chatbot messaging. * * * ## **Choose Metrics to Test Your Chatbot Messages** For NetApp’s chatbot welcome message, a design stack of four UX metrics was chosen to measure how effectively the chatbot greets visitors and supports their next steps. This stack — Expectations, Sentiment, Success, and Intent — was established by mapping user needs directly to measurable outcomes: - [**Insightful**](https://glare.helio.app/define/user-needs/insightful) **→** [**Expectations**](https://glare.helio.app/define/ux-metrics/attitudinal-metrics/expectations)
 The chatbot’s first message should clearly communicate its purpose and what it can help with. Expectations measures whether users immediately understand the chatbot’s capabilities and relevance. - **Trustworthy →** [**Sentiment**](https://glare.helio.app/define/ux-metrics/attitudinal-metrics/sentiment)
 Visitors should feel comfortable engaging with the chatbot and confident in its responses. Sentiment captures whether the chatbot creates a positive emotional impression, such as feeling helpful, friendly, or reliable. - [**Usable**](https://glare.helio.app/define/user-needs/usable) **&** [**Efficient**](https://glare.helio.app/define/user-needs/efficient) **→** [**Success**](https://glare.helio.app/define/ux-metrics/behavioral-metrics/success)
 The chatbot should make it easy for users to complete simple tasks or find the right information. Success evaluates whether participants can achieve their goal — like finding support or learning about a product — without frustration. - [**Engaging**](https://glare.helio.app/define/user-needs/engaging) **→** [**Intent**](https://glare.helio.app/define/ux-metrics/behavioral-metrics/intent)
 The chatbot’s welcome experience should encourage users to continue interacting. Intent measures whether participants express interest in following up, asking another question, or exploring more features after their first exchange. * * * ## **Establish Hunches to Direct Your Testing** Chatbots can make or break a visitor’s first impression by setting the tone for interaction and support. Starting with hunches about how users may interpret, trust, or engage with the chatbot helps shape the questions needed to confirm whether it’s truly helping users connect with the brand and find what they need. **Example: NetApp Data Storage Landing Page Chatbot**

Hunch

Question

UX Metrics

The chatbot’s opening message (“What would you like to chat with us about today?”) may feel too generic, failing to establish trust or show relevance to the visitor’s specific goals.

“How helpful did the chatbot’s first message feel in getting you started?”

Intent

Visitors may expect product guidance or demo scheduling options upfront, but the bot doesn’t clearly direct them to those paths immediately.

“What kind of help were you expecting the chatbot to offer when it first appeared?”

Expectations

The chatbot placement in the bottom corner could be easily overlooked on a page already dominated by bold messaging and visuals.

“Did you notice the chatbot when you first arrived on the page?”

Success

Because NetApp’s site targets enterprise customers, the tone of the chatbot might seem overly casual, which could undermine the credibility expected from a B2B data storage leader.

“How professional or trustworthy did the chatbot feel in tone and presentation?”

Sentiment

Visitors may feel unsure whether they’re chatting with a real person or an automated assistant, affecting confidence in getting meaningful answers.

“Did you feel like you were chatting with a person, an automated bot, or weren’t sure?”

Sentiment

These hunches assess whether NetApp’s chatbot welcome message meets user needs for trustworthiness, insight, engagement, and efficiency and whether the bot experience aligns with the professionalism of the brand’s overall design and messaging. * * * ## **Turn Hunches into Test Questions** Turning these metrics into participant questions transforms design assumptions into measurable signals. Each metric uses a specific question type paired with a clear example from NetApp’s chatbot experience: - [**Expectations**](https://glare.helio.app/define/ux-metrics/attitudinal-metrics/expectations) **(5-pt Likert scale)**
 *Question type:* Agreement scale.
*Example:* “The chatbot’s welcome message matched what I expected when visiting this page.” (Strongly Disagree → Strongly Agree) [Image] - [**Sentiment**](https://glare.helio.app/define/ux-metrics/attitudinal-metrics/sentiment) **(Multiple-choice impressions)**
 *Question type:* Impression checklist.
 *Example:* “Which of the following words best describe your impression of the chatbot’s welcome message?”
 (Positive: Helpful, Friendly, Clear, Professional. Negative: Confusing, Pushy, Unhelpful, Distracting) [Image] - [**Intent**](https://glare.helio.app/define/ux-metrics/behavioral-metrics/intent) **(Multiple-choice action selection)**
 *Question type:* Action preference.
*Example:* “Which of the following would you most likely do after seeing this chatbot message?”
 (e.g., Start a conversation with the bot, Explore the page further, Dismiss the message, Leave the site) [Image] - [**Success**](https://glare.helio.app/define/ux-metrics/behavioral-metrics/success) **(Click test directive)**
 *Question type:* Task-based click test.
 *Example:* “Where would you click to ask the chatbot a question about data storage solutions?” [Image] * * * ## **Calculate UX Metric Scores from User Feedback** We tested NetApp’s data storage landing page chatbot with 100 data and tech professionals. Those responses were analyzed and converted into UX metric scores on a 0–100% scale. Each metric in the design stack was calculated using first-click data and survey responses, then benchmarked against this scoring scale: - **Very Good** = 90% and above - **Good** = 70%–89% - **Average** = 50%–69% - **Poor** = 30%–49% - **Very Poor** = below 30% NetApp’s Results: - [**Intent**](https://glare.helio.app/define/ux-metrics/behavioral-metrics/intent) **(58% — Average):** Users showed moderate willingness to interact with the chatbot, though some were uncertain about its purpose or value upon seeing it. - [**Success**](https://glare.helio.app/define/ux-metrics/behavioral-metrics/success) **(43% — Poor):** Few participants successfully completed key tasks, such as finding storage solutions or initiating sales conversations through the chatbot. - [**Expectations**](https://glare.helio.app/define/ux-metrics/attitudinal-metrics/expectations) **(86% — Good):** The chatbot’s tone, timing, and placement aligned well with what users expected from a B2B landing experience. - [**Sentiment**](https://glare.helio.app/define/ux-metrics/attitudinal-metrics/sentiment) **(97% — Very Good):** Users responded positively to the chatbot’s design and clarity, often describing it as approachable, modern, and easy to understand. [Image] These scores produced an overall test score of 71% — Good. While the chatbot succeeds in creating a positive emotional impression and meeting user expectations, low task success and only moderate intent reveal opportunities to strengthen its usefulness and clarity of purpose. Refining conversation prompts and guiding users toward key actions will help improve engagement and conversion outcomes. Click here to check out the [raw survey data and UX metric scores for NetApp's chatbot welcome message](https://my.helio.app/report/01K6EM2DEPMGBM2RG9Q5P64G5V). * * * ## **Draw Signals from Your Design Stack** Here’s how signals were surfaced from the NetApp's chatbot test results by following these five steps: **1\. Focus on poorly scoring metrics** [Image] The test of NetApp’s chatbot welcome message revealed poor task success (43%) and average intent (58%), suggesting that while visitors noticed the chatbot, many failed to complete an intended action or understand how to proceed. Despite these challenges, Expectations (86%) and Sentiment (97%) scored very well, indicating that users had a strong emotional response to the chatbot’s tone and helpfulness, even if its usability fell short. **2\. Identify patterns across metrics** [Image] The metrics reveal a clear pattern: NetApp’s chatbot performs well in tone and first impression but struggles with guidance and clarity. Users respond positively to the chatbot’s personality and presentation but don’t know what actions it can perform or how to reach their goals. This gap between emotional satisfaction and practical success suggests that while the design captures attention, it doesn’t effectively convert it into task completion. **3\. Determine if user needs are being met** [Image] - [**Usable**:](https://glare.helio.app/define/user-needs/usable) *Not met* — users struggled to identify next steps or complete intended actions within the chatbot. - [**Insightful:**](https://glare.helio.app/define/user-needs/insightful) *Partially met* — while messaging feels relevant, the chatbot doesn’t always surface the right assistance at the right time. - [**Engaging:**](https://glare.helio.app/define/user-needs/engaging) *Met* — the friendly tone and approachable style encourage user interaction. - **Trustworthy:** *Met* — the chatbot’s design and communication reinforce NetApp’s professionalism and reliability. - [**Efficient:**](https://glare.helio.app/define/user-needs/efficient) *Not met* — task success is low, showing users spend unnecessary effort to achieve goals. **4\. Compare outcomes to your business goals** - **Increase Engagement Rates:** Supported — users are drawn to interact, reflected by strong positive sentiment and good expectations scores. - **Guide Conversion Paths:** Poor — confusion about available actions limits users from reaching desired outcomes like demos or support. - **Reduce Support Costs:** Weak — failure to complete tasks means users may still need human assistance. - **Personalize the Experience:** Partially met — users appreciate the tone but need clearer contextual guidance. - **Collect Behavioral Insights:** Limited — low task success reduces the quality of data available for insight generation. **5\. Surface signals & establish a direction** **Signals derived from the data:** 1. The chatbot’s personality works — but its usability doesn’t. Users enjoy the interaction but can’t accomplish key actions. 2. Expectations are met but not exceeded. The chatbot feels aligned with brand quality, yet users want faster, more specific responses. 3. Emotional response outpaces practical value. High sentiment indicates users like the chatbot, but low success reveals functional barriers. **Direction based on business context:**
 To support NetApp’s goals of increasing engagement and guiding conversion paths, design improvements should focus on: - Streamlining task flows with clearly labeled quick-reply buttons and suggested actions. - Enhancing contextual intelligence to offer personalized, relevant prompts early in the interaction. - Preserving the chatbot’s positive tone and brand alignment while emphasizing clarity and efficiency. Based on the signals and design direction, we created an updated version of the design with the expected UX metric improvement: [Image] The direction is clear: *NetApp's chatbot creates a great first impression but leaves users without clear next steps. By pairing its engaging tone with stronger guidance and action-focused prompts, the chatbot can transform positive sentiment into measurable success.* * * *