Date post: | 20-Aug-2015 |
Category: |
Marketing |
Upload: | r2integrated |
View: | 573 times |
Download: | 1 times |
Why are we talking about search in a discussion about paid social?
The early days of search
• Marketers’ dream comes true
• We built it, they came
• Few tools, big results
• Highly targeted, reactive – Customers flocked to us
• At low cost
Search was brilliant.
Why are we talking about search in a discussion about paid social?
The middle years
• Battle for top rankings intensifies
• Fierce competition for keywords
• The age of SEO begins
• Fuzzy metrics
• ROI uncertain
• Enter paid search
Why are we talking about search in a discussion about paid social?
Today
• SEOs have tried all sorts of things
• Diminished search experience
• Algorithms (Vince, Panda)
Most brands have to pay for reach and scale
• Brands can’t control search results
• Paid Search is a multi-billion dollar a year industry.
Current Landscape
Social Marketing Today
• Social reach in decline
• Battle for the newsfeed
• Algorithms control eyeballs
Brands have lost control of who
sees their social content. Sound Familiar?
• Confusing metrics
• If brands build content, consumption is not guaranteed. This is a problem.
• Enter Paid Social
Current Landscape
Search Marketing Today/ Social
Climate Visibility in decline Reach in decline
Battle Zone Newsfeed Front Page
Control Algorithms Algorithms
Targeting Gender, age, geo Keywords
Metrics Confusing Fuzzy
Current Landscape
Social Marketing Today
• Followers, retweets
• Fans, likes, comments, shares
• Likes, comments, shares
This model leaves lots of unanswered questions
Current Landscape
Who is on my social channels?
Why is it important to know?
How you can find out?
What to do with the information?
Current Landscape
How can I reach them?
• Location
• Age, Gender
• Interests
• Behavior
• Education Levels
• Connections
• Keywords
• Interests
• Location
• Gender
• Language
• Devices
• By job title and function
• By industry and company size
• By seniority
FACEBOOK TWITTER LINKEDIN
Current Landscape
What content is working?
Why is it important to know?
How you can find out?
What to do with the information?
Current Landscape
Who is sharing it?
Why is it important to know?
How you can find out?
What to do with the information?
Current Landscape
Limitations
The customer journey is not limited to just social channels
Analytics should give deep insight across channels
Multichannel/Full Lifecycle approach to managing the customer experience
The future of social advertising
Audience Profiles
• The best audience profile is data-driven
• Utilize a combination of native targeting on social networks in addition to your first-party data
• First-party data
• Social network native targeting
The future of social advertising
Audience Profiles continued…
3 KEY WAYS OF TARGETING AUDIENCES ON FACEBOOK AND TWITTER:
1. Core Audiences
2. Custom or Tailored Audiences
3. Look-a-like Audiences
The future of social advertising
Social Retargeting
Why retarget?
How do you retarget?
What are the use causes?
The future of social advertising
Forecasted campaigns and predictive analytics
• A single source of truth bringing data together
• Adopt consistent ad tech and marketing tech solution
• No single user journey is the same
• Leverage other unique data points such as rich analytics data as early signals that correlate to a conversion rate to address data sparsity issues.
The future of social advertising
Predictive analytics to inform ad buying
Some company have access to hundreds of analytics metrics
Identify ones that have a high correlation with conversion rate and leverage as revenue signals. This may differ from industry vertical to vertical.
Common revenue signals are:
1. Page views in the first visit
2. Total time spent on multiple visits
3. Bounce rate
4. Total page views across multiple visits
5. Time spent onsite in first visit
The future of social advertising
Best practices for targeting in a multi-channel ecosystem
• Common business objectives
• Leverage customer profiles & audience targeting
• Segment audiences at scale and test different levers that influence performance
• Leverage early predictors of performance using web analytics
• Optimize channels holistically to maximize digital ROI
• Experiment and iterate
Case Studies
Lead Generation for Education client
OBJECTIVE: TEST AND MAXIMIZE FULL POTENTIAL OF 1ST PARTY CRM DATA
Solution: Test overlay of core native targeting with Look-a-like targeting
Results: Benchmarked against Core native targeting on Facebook
Click through rate increased +447%
Conversion rate increased +51%
Cost per lead improved 46%
Solution: Look-a-like audiences generated from Custom Audiences
Results: Benchmarked against Core native targeting on Facebook
Conversion rate increased +199%
Cost per lead improved by 59%
-66%
-45%
-5%
3%
4%
166%
100%
-4%
1%
-14%
-100% -50% 0% 50% 100% 150% 200%
ROAS CPO
Case Studies
Online sales for eCommerce client
LOOKALIKE AUDIENCES
LIKES & INTERESTS
LOOKALIKE AUDIENCES
+ LIKES & INTERESTS
CUSTOM AUDIENCES
WEBSITE CUSTOM AUDIENCES
Custom audiences & other Audience Targets relative to avg. CPO and ROAS
0%
2%
4%
6%
Search Behavior Lifetime Website Custom Audiences Last 60
Days
Search Behavior Last 7 Days
CTR CVR
Case Studies
Online sales for Travel client
ROAS: 5.69 6.64 8.11
OBJECTIVE: DRIVE INCREMENTAL SCALE BEYOND SEARCH, COST-EFFICIENTLY
Solution: Social retargeting through Website and CRM Custom Audiences
Results:
Case Studies
Online sales for Travel client
0.41% 0.61% 0.50% 0.56%
1.02%
3.06%
8.25%
9.70%
0%
2%
4%
6%
8%
10%
12%
Engaged Newsletter
Subscribers
High Value Customers Lower Tier Loyalty Program Higher Tier Loyalty Program
CTR CVR
ROAS: 5.69 12.12 15.55 24.85