Calculate the Frequency Score:
Use the responses from the column "[Column with frequency ratings]" in the attached CSV to calculate the Frequency score using the following formula:
Frequency Score = (sum of all Likert ratings in [Column with frequency ratings]) ÷ (number of responses) ÷ 5
Likert Ratings are coded as:
Very Frequently = 5
Frequently = 4
Sometimes = 3
Rarely = 2
Very Rarely = 1
After calculating the Frequency score, report where it lands on the following scale:
Very Good = above 90%
Good = 70–89%
Average = 50–69%
Poor = 30–49%
Very Poor = 0–29%
Next, analyze the responses in the column "[Column with qualitative explanations]" in the CSV to identify common themes and provide clear, actionable suggestions for how the Frequency score could be improved.
Here’s your Frequency Score from the CSV (column: Section 8 Choices), plus a quick read of the explanations.
📊 Frequency Score
Mapping used (5-point):
Very Frequently=5, Frequently=4, Sometimes=3, Rarely=2, Very Rarely=1
(Your data also included “Occasionally,” which I treated as Sometimes.)
Counts (N = 102):
Very Frequently: 13
Frequently: 28
Sometimes/Occasionally: 31
Rarely: 17
Very Rarely: 13
Computation
Frequency Score
=
∑
ratings
𝑁
÷
5
=
317
102
×
5
=
62.16
%
Frequency Score=
N
∑ratings
÷5=
102×5
317
=62.16%
Scale placement: Average (50–69%)
I’ve shared a distribution table and a bar chart in the workspace for transparency.
💬 What people said (Section 8 Explanation)
How/when it’s used: “online chat,” “when I need,” “on a landing page,” “support.”
Value: “useful,” “help to understand nuances,” “good experience.”
Barriers: “annoying,” “hard sell,” “buggy,” “in the way,” sometimes “not important” during learning.
Representative snippets (selected):
“It is a great help to understand any nuances.”
“I don’t use them, annoying as hell.”
“I find them buggy and in the way.”
“On a vendor landing page where I need support, chat helps; when I’m just learning, not that important.”
Takeaway: Use is situational. People value chat/help when they need support; they disengage when it feels pushy, buggy, or intrusive.
🔧 Recommendations to lift Frequency
Trigger intelligently (reduce annoyance)
Show chat/help only on high-intent signals (repeated visits to pricing, help/docs, error states, long dwell on form, back-and-forth navigation).
Keep the entry non-blocking: minimized bubble with subtle pulse; no auto-open overlays.
Make value obvious at a glance
Add micro-copy under the button: “Get an answer in ~2 min” or “Live agent available.”
Offer quick actions in the first click: “Billing question,” “Setup help,” “Talk to a human.”
Improve perceived quality & speed
Reduce bot friction: confirm intent in one message, then route; show estimated wait time.
If automation fails twice, auto-escalate to a person; log and fix failure patterns.
Match the journey
On learning/browse pages, prefer FAQ suggestions or inline tips over chat popups.
On support or checkout pages, surface chat more prominently (but still opt-in).
Tighten performance & reliability
Prioritize fast load for the widget; avoid layout shift.
Run a weekly QA on common flows that users cited as buggy.
Measure & iterate
Track: open-rate → first message sent → resolved in session → CSAT.
Goal for the next run: shift “Rarely/Very Rarely” down by ≥10 responses and lift “Frequently/Very Frequently” by ≥10, pushing the score into the mid- to high-60s.