New shop, limited digital history

One primary objective

Determine which digital touchpoints most effectively move nearby consumers from awareness to an in-store visit, then use that evidence to prioritize LADD Coffee’s marketing effort.

Supporting questions

• Which channels generate the most qualified local discovery?
• Which menu items create the strongest visual/content hooks?
• Which digital actions are most associated with store-intent signals?
• Which customers can be encouraged to return?

A journey-based approach

The strategy can be organized around a simple customer journey:

Discover → Consider → Visit → Return

What is already visible online

Public sources provide several useful early signals:

• LADD Coffee LLC was filed in California in March 2026.
• A June 2026 local feature described LADD as a newly opened Korean specialty café in West Covina.
• Public descriptions emphasize cream-top drinks, matcha, desserts, and a minimalist café environment.
• The physical location is listed as 987 S Glendora Ave., Unit 3, West Covina.

These signals are useful for positioning, but they are not enough to evaluate marketing performance.

What we have vs. what we need

The key gap is first-party behavioral and transaction data.

Build a practical data foundation

• Google Business Profile performance
• Website or landing-page analytics
• UTM-tagged campaign traffic
• Instagram/TikTok content metrics
• POS transactions by day/time/product
• Promotion or QR-code redemptions
• Email/SMS opt-ins
• Repeat-customer indicators

Recommended unit of analysis

Where privacy and platform limitations allow, aggregate performance by:

Date × Channel × Campaign/Content × Customer Action

This structure supports meaningful comparisons without requiring personally identifiable customer data.

Prioritize channels by customer role

Figure 1: Proposed channel priorities for LADD Coffee’s launch-stage digital strategy

Google: capture intent

Optimize the business profile for:

• Accurate hours and location
• Menu/product photos
• Signature-item language
• Review generation and response
• Direction and call actions

Social: manufacture discovery

Use Instagram and TikTok to make the product visually memorable:

• Cream-top preparation
• Matcha and specialty drink builds
• Dessert pairings
• “What to order first” content
• Customer reactions and UGC
• West Covina / SGV local discovery hooks

Give discovery somewhere to convert

A lightweight website or landing page should answer the questions a first-time customer has immediately:

The website should function as a conversion and measurement layer, not merely an online brochure.

KPIs by stage

Example structure in R

campaign_data <- tibble(
  date = as.Date(c("2026-08-01", "2026-08-01", "2026-08-02")),
  channel = c("Instagram", "Google", "TikTok"),
  impressions = c(4200, 1800, 5100),
  actions = c(210, 175, 255)
) |>
  mutate(
    action_rate = actions / impressions,
    channel_group = case_when(
      channel == "Google" ~ "Intent",
      TRUE ~ "Discovery"
    )
  )

campaign_data

# A tibble: 3 × 6
  date       channel   impressions actions action_rate channel_group
  <date>     <chr>           <dbl>   <dbl>       <dbl> <chr>        
1 2026-08-01 Instagram        4200     210      0.05   Discovery    
2 2026-08-01 Google           1800     175      0.0972 Intent       
3 2026-08-02 TikTok           5100     255      0.05   Discovery    

What a useful future sample should contain

A meaningful evaluation period should capture:

• Weekdays and weekends
• Multiple dayparts
• Several content formats
• Paid and organic traffic
• More than one promotion or creative treatment
• Product-level purchase variation
• Enough time to observe repeat behavior

The first goal is not a huge dataset. It is a consistent dataset.

Core definitions

Action Rate

[ = {} ]

Offer Redemption Rate

[ = {} ]

Repeat Purchase Rate

[ = {} ]

Start simple, then increase sophistication

Phase 1 — Descriptive

• Channel performance
• Content performance
• Product mix
• Day/time patterns
• Funnel conversion

Phase 2 — Diagnostic

• Campaign comparisons
• Cohort behavior
• Promotion lift
• Creative-level differences

Phase 3 — Predictive

Once sufficient data exist:

• Visit/purchase propensity
• Repeat-purchase classification
• Customer or product segmentation
• Demand forecasting

First 90 days

1. Own local search

Treat Google Business Profile and local-search accuracy as core conversion infrastructure.

2. Build product-led social content

Use signature beverages and desserts as the center of short-form creative.

3. Establish first-party measurement

Launch a simple owned landing experience with GA4, UTMs, event tracking, and opt-in capture.

4. Connect marketing to transactions

Use trackable offers, QR codes, POS tags, or campaign-specific promotions to create attribution signals.

5. Create a test-and-learn rhythm

Review performance monthly and move effort toward channels and creative that produce meaningful customer actions.

From visibility to learning

A stronger digital strategy should help LADD Coffee:

• Increase qualified local awareness
• Make signature products easier to discover
• Reduce friction between discovery and visit
• Create an owned audience for retention
• Learn which channels and content influence behavior
• Make future marketing investment more evidence-based

The most valuable early outcome is not simply more impressions — it is a system that tells LADD Coffee what is working.

Research notes

This proposal uses publicly available information to establish basic business context. No private sales, POS, web analytics, advertising, or customer-level data from LADD Coffee were available during preparation.

Reproducibility

The channel-priority chart and planning tables in this deck are generated directly from R objects contained in the QMD file.

Future project links

Published presentation: https://mjshawell.github.io/IBM-6540-IBM-6300/Improving-LADD-Coffee-Digital-Strategy.html#/title-slide

GitHub repository: https://github.com/mjshawell/IBM-6540-IBM-6300