Marketing Analytics Process

1. Objective & Marketing Problems

Objective:

Strengthen Stern & Cohen’s client acquisition strategy by using data to understand what drives leads and case conversions, and where the firm's highest-value clients are coming from.

Marketing Problems:

• We're allocating a lot of budget on marketing (Google Ads, TV, billboards), but don’t know which channels are driving the most valuable cases

• Lead tracking is fragmented across platforms

• No unified marketing dashboard or attribution model

• Difficult to predict high-value leads from the moment they contact us

Key Questions:

• Which marketing channels (TV, Google, billboards, social, referral) bring in the highest-value clients?

• What lead characteristics predict a signed case or a large settlement?

• How do referral vs. digital leads compare in conversion rates?

• Are we overspending on low-performing channels?

2. Data Requirements

Data to Collect and Analyze:

• Lead Data: Name, source (TV, Google, billboard, etc.), date of contact, method (call, form, chat), referral (yes/no), notes

• Case Data: Lead ID, signed Y/N, attorney assigned, case type, location, settlement amount

• Marketing Spend: Campaign name, channel, spend per month

• Website & Digital Performance: Clicks, form submissions, ad spend, bounce rate, call tracking

• TV/Billboard Reach Estimates (via media agency reports)

Database Layout:

• Leads: lead_id, name, contact_method, source, referral_flag, date

• Cases: lead_id, case_signed, settlement_amount, attorney_id

• Spend: campaign_id, channel, month, dollars_spent

• Digital Analytics: source, clicks, forms, calls, impressions

Bias & Checks:

• Duplicate leads or misattributed referrals

• Channel bias (e.g., calls from billboards logged as “Google” despite not being first point of contact)

• Missing follow-up if a lead doesn’t convert

• Time lag between contact and case resolution distorting ROI

3. Cleaning & Processing

• Standardize lead sources and entry points

• Remove duplicate leads and tie them to case records

• Normalize settlement values (remove outliers, adjust for multi-case clients)

• Convert dates to a consistent format for time series

• Join lead and case tables by lead ID

• Calculate time from lead to case opening

• Flag missing attribution data for manual review

4. Analyses, Models, & Visuals

Hypotheses to Test:

• Leads from Google have a higher signing rate than TV leads

• Referral clients yield higher average settlements

• Cases from billboard leads take longer to convert

• Certain campaigns (e.g., 76ers arena) generate more brand searches, not direct leads

Visualizations:

• Bar chart: Signed cases by lead source

• Heatmap: Conversion rate by channel and case type

• Line graph: Monthly ad spend vs. new signed cases

• Histogram: Settlement distribution by source

Models (R Pseudocode):

model <- glm(case_signed ~ source + referral_flag + contact_method + campaign_channel, data = full_data, family = binomial)

lm_model <- lm(settlement_amount ~ source + attorney_id + case_type, data = signed_cases)

Expected Findings:

• Referrals will have higher conversion and settlement rates

• Google and TV may bring more leads, but not necessarily higher-quality ones

• Certain billboard placements (like those near work-heavy industrial zones) may perform better

• Marketing ROI is likely uneven across channels, especially with brand-only impressions

5. Recommendations (if analysis confirms expectations)

• Shift spend toward higher-ROI sources like referrals, strategic Google campaigns, and top-performing billboard zones

• Reallocate TV budget if conversion rates are poor or attribution is too difficult

• Build a lead scoring system to flag high-potential clients based on intake answers

• Create a monthly dashboard that tracks cost per signed case and average settlement by channel

1. Combine with Strategy

• Use lead scoring to prioritize intake follow-up

• Create content campaigns targeted at high-converting case types

• Adjust paid search strategy based on referral or conversion trends

• Test call-to-action adjustments (e.g., “Free Consultation” vs. “Get Help Now”) on different channels

2. Repeatability

• Monthly reporting cadence: track spend, leads, signings, and settlements

• Quarterly case value analysis by lead source

• Update models as more case data is closed and settlement amounts are finalized

3. Integration into Business Routine

• Set KPIs:

  • Cost per signed case

  • Average settlement per channel

  • Lead-to-sign ratio by channel

  • Number of untracked leads

• Create alerts for underperforming campaigns

• Make marketing analytics a standing agenda item in team meetings

1. Follow-Up Analytics

• A/B test intake scripting to improve lead conversion

• Run regression analysis on attorney performance vs. case outcomes

• Use NLP (natural language processing) on intake call transcripts to detect urgency or claim strength

2. Analytics Culture

• Build a test-and-learn mindset

• Train intake team to properly log lead sources and client stories

• Make performance transparent across campaigns and departments

• Celebrate wins from data-backed decisions, not just gut instincts