# data[].attributes.body
Example: “Engagement on Instagram dropped 40% after we switched from video posts to static images.”

This explains the cause of a pattern.

# data[].attributes.body
Example: “Our TikTok campaign last month had 50,000 impressions, 3,200 clicks, and a 6% click-through rate.”

This is about summarising past campaign results.

# data[].attributes.body
Example: “Based on current growth, our café’s cold brew ad will likely hit 100,000 views in the next 2 weeks, with about 1,200 conversions.”

This uses existing patterns to forecast.

# data[].attributes.body
Example: “Since TikTok ads are converting at 3× the rate of Instagram, we should reallocate 60% of our ad budget to TikTok next month.”

This gives a clear actionable recommendation.

# data[].attributes.body
Tracking Instagram engagement for a café’s cold brew posts over 6 months. The chart shows peaks in summer and drops in winter.

Business use: Helps the café know when to spend more on ads.

# data[].attributes.body
Comparing people who first followed the café’s TikTok in June vs July. June followers return for repeat purchases twice as often as July followers.

Business use: Helps identify if certain campaigns create more loyal customers.

# data[].attributes.body
Scanning 1,000 Twitter comments about the cold brew campaign. 70% are positive (“looks refreshing”), 20% neutral, 10% negative (“too expensive”)

Business use: Helps the café adjust messaging — focus on affordability concerns.

# data[].attributes.body
Looking at customer data and discovering 3 clear groups: (1) Students buying small sizes daily, (2) Professionals buying large coffees in the morning, (3) Weekend shoppers buying novelty drinks.

Business use: Helps the café design tailored offers for each segment.

# data[].attributes.body
A café runs a summer campaign. Using time series, they spot sales spikes in December. Cohort analysis shows December’s new customers stick around longer than January’s. Sentiment analysis of TikTok comments reveals people love the product but complain about price. Cluster analysis groups customers into students, professionals, and families — each with different needs. Together, these analyses guide where to spend ad money and how to adjust messaging.

# data[].attributes.body
A local café launches a summer campaign for its new cold brew coffee, posting across Instagram and TikTok.

Descriptive analysis shows that TikTok ads generated 50,000 impressions and 3,200 clicks, while Instagram only delivered 12,000 impressions and 600 clicks.

Diagnostic analysis reveals why: engagement on Instagram dropped after switching from video posts to static images, while TikTok’s algorithm boosted the video ads.

Time series analysis shows that engagement spikes on weekends and in the hottest weeks of December, when demand for iced coffee is naturally higher.

Cohort analysis compares followers gained in December vs January: December’s new followers are twice as likely to come back for repeat purchases.

Sentiment analysis of TikTok comments reveals 70% are positive (“refreshing, perfect for summer”) but 15% complain about the $5.95 price point.

Cluster analysis sorts customers into three groups:

1. Students buying small iced coffees daily,

2. Professionals grabbing large coffees on weekday mornings,

3. Families buying novelty drinks on weekends.

# data[].attributes.headline
Paste your URL link to your prompt in this column

# data[].attributes.subject
Paste your URL link to your prompt in this column

# data[].attributes.attachment_link.description
Meet Gemini, Google's AI assistant. Get help with writing, planning, brainstorming, and more. Experience the power of generative AI.

# data[].attributes.body
Prompt: help me create a step by step data analysis process for the focus area that i have chosen

# data[].attributes.attachment_link.description
A conversational AI system that listens, learns, and challenges