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WHITNEY PARKERSENIOR DIRECTOR,DIGITAL MARKETING & MARKETING STRATEGY

CASE STUDY: MEASURING SOCIAL MEDIA ROI

ABOUT ME

2,000 EXPERTS26 COUNTRIES

CORPORATEINVESTIGATIONS

CYBER SECURITY

COMPLIANCE & DUE DILIGENCE

PHYSICAL SECURITY

12 YEARS IN DIGITAL

MARKETINGB2B & B2C

SOCIAL MEDIALEAD GEN

& ECOMMERCE

NONPROFITS UNIVERSITIESTHINK TANKS

BICYCLESSERVICES

SAAS TECHCOURSES

IDEAS

ABOUT KROLL

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CASE STUDY OVERVIEW

Expand Kroll’s Market Share in Investigation Services

Increase Social Referral Traffic for Global Fraud Report (downloadable asset) on Kroll.com

Identify Top Twitter Influencers for Fraud Report content to drive paid traffic.

Use persona targeting Linkedin Promoted Posts and Ads to drive paid traffic

Business

Objective

Increase Web Lead Pipeline (MQL) for Investigative Services in FY16

Tactic #1

Tactic #2

Marketing

Strategy #1

Use Facebook look-alike audiences and retargeting to drive paid trafficTactic #3

Marketing

Objective #1

Use Twitter, Linkedin and Facebook organic posts to drive un-paid trafficTactic #4

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GOALS

Determine what to measure and what tools to measure with

Configure tracking process, analytics tool, marketing automation platform and CRM to capture data

Use data to make real time incremental adjustments

Measure ROI from organic and paid social media traffic

Tell a story about our results and document lessons learned

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TOOLS USED FOR MEASUREMENT

HOOTSUITE & NATIVE AD PROGRAMS

• CLICKS

• CLICK RATE

• ENGAGEMENT

• SHARES

• RETWEETS

• LIKES

GOOGLE ANALYTICS

• LP CONVERSIONS

• LP CONVERSION RATE

• PAGEVIEWS

• SHARE OF TRAFFIC FROM SOCIAL OVER TIME

PARDOT (Marketing

Automation)

• UNIQUE FORM SUBMISSIONS (ASSET DOWNLOADS)

• QUALITY AND ENGAGEMENT OF LEADS (GRADES AND SCORES)

• CHANGE IN LEAD VOLUME OVER TIME

SALESFORCE(CRM)

• NEW OPPORTUNITIES

• WON CASES

• CAMPAIGN INFLUENCE ON OPPORTUNITIES

• RECORDS COST OF CAMPAIGN(S)

• PROVIDES DATA FOR: COST PER LEAD AND RETURN ON INVESTMENT

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TRACKING PROCESS

Using UTM tracking codes on all of our links, we mapped out defined values for each tactic (in a Google Spreadsheet) in our strategy including:

UTM_campaign (all links had the same campaign name)

UTM_source (used to denote the website property – Twitter vs. FB vs. Linkedin)

UTM_medium (used to denote the broad type – social vs. search. vs. email)

UTM_content (used to describe the content of the ads and differentiate messaging)

We also used custom parameter tracking in Google Analytics to capture additional data including:

Persona (we wanted to understand in Linkedin for example, if IT Professionals, Lawyers, or Compliance Officers might respond better to this type of campaign)

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OTHER CONFIGURATION AND SET-UP

GOOGLE ANALYTICS Enabled Goal Tracking for the target asset along with a Conversion Funnel that included the Landing

Page for the report

We researched Custom Parameters to ensure we could also capture “Persona” information in our tracked URLs

PARDOT We created a custom field for “Persona” so that this parameter data could be passed from GA to

Pardot.

We enabled and configured GA integration so allow UTM parameters to be passed from GA to Pardot.

A decision was made NOT to override existing UTM data from GA if it was already recorded on a Lead record, effectively implementing a first touch attribution model. This was a discovered limitation of Pardot.

Enabled Lead Scoring and Grading to pass only MQLs over to Salesforce for follow-up by our sales team.

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OTHER CONFIGURATION AND SET-UP

SALESFORCE

We created a custom dashboard to display a mix of reports with campaign data, including:

Number of Marketing Qualified Leads generated by the campaign

Marketing Qualified Leads generated by campaign source, grouped by source and medium

We recorded the campaign costs for each paid channel in the Campaign Record for each child campaign

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JUST MAKING SURE

YOU’RE STILL AWAKE!

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HOW WE ANALYZED CAMPAIGN DATA

DURING THE CAMPAIGN

Each day we reviewed our GOOGLE ANALYTICS, PARDOT, AND SALESFORCE campaign data to understand four things:

1. WHAT WAS OUR LANDING PAGE CONVERSION RATE BY TACTIC? (Google Analytics)

2. HOW MANY LEADS/CONVERSIONS WERE EACH TACTIC DRIVING? (Google Analytics OR Pardot)

3. WHAT WAS THE OVERALL LEAD QUALITY FOR EACH CAMPAIGN TACTIC? (Pardot)

4. WHAT WAS THE OVERALL COST PER MARKETING QUALIFIED LEAD BY TACTIC? (Salesforce)

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OUR DATA: LP CONVERSION RATE BY TACTIC

Facebook retargeting campaigns led to the highest LP conversion rates (up to 4x)

All social tactics, paid and organic, had higher-than-average LP conversion rates

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OUR DATA: LEAD VOLUME BY TACTIC

Facebook retargeting campaigns led drove the highest volume of total Leads, was easier and more cost effective to scale

All social tactics, paid and organic, contributed to about of our overall Lead pipeline for the campaign

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Linkedin campaigns led drove the highest quality of total Leads, as indicated by Pardot Grades (profile match) and Scoring (engagement on our website)

Linkedin paid vs. organic traffic were comparable in terms of quality, but the ads drove more new Leads.

Facebook Leads were also high quality, but were generally not new Leads (since we were using retargeting and email lists to build audiences)

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OUR DATA: LEAD QUALITY BY TACTIC

OUR DATA: COST PER LEAD (MQL) BY TACTIC

We monitored directly on our various social media ad platform analytics tools, and then compared manually with our Salesforce data to see how many of the total Leads were surpassed our grade/score threshold to become “Marketing Qualified”

Twitter campaigns had the highest Cost Per “Marketing Qualified” Lead, since none of them met our qualification threshold.

Facebook had only a few reach Marketing Qualified status, and so similarly, ended up with a higher total Cost Per Lead.

Linkedin ads, while had the highest Cost Per Conversion, due to the higher quality relative to other channels, the cost for an MQL was lowest of the three.

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SO WHAT?

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CALCULATING SIMPLE ROI

REVENUE COST ROI

COST

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CALCULATING SIMPLE ROI - LINKEDIN

$15KREVENUE

$1KCOST

14x ROI

$1KCOST

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LESSONS LEARNED AND IMPROVEMENTS

Our long sales cycle makes it difficult to calculate ROI on campaigns with much accuracy until as early as 6 months to a year later.

The most actionable campaign metric for our company that enabled faster decision-making was cost-per-lead (MQL).

Our current marketing automation and CRM tools are only set up for first touch attribution modeling if we’re using UTM campaign codes to track social engagement. More sophisticated analysis of ROI would require some investment in configuring our tools to capture more touch points in the buying cycle.

Setting up the trackable URLs for every link on social channels takes some preparation and discipline!

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QUESTIONS?

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