We ’ll go through the following in the next 20 minutes or so and then leave time at the end for q&A.
So instead of taking Ambien to help me sleep at night, we ’ve enlisted these top technology companies to help us decipher insights and take action. Please note that this is not to say that these are the tops ones or that other companies can’t perform similar or quite frankly even better metrics, these are just the ones we chose to focus on
-Do the more “Likes” directly effect the campaign costs? Or More importantly do more like ’s directly effect sales? And really, what is the value of a like and why should I care if I have 500 likes vs. 5000 likes
Here are 2 recent articles I found where industry pundints are trying to answer that very question. In article 1, they state an average fan is worth about $136.38, although for some very successful social marketers the value can be dramatically higher, while for some less successful companies it can be virtually zero. Syncapse came up with the figure by asking 4,000 fans of 20 of the top brands on Facebook — including Nokia, BlackBerry, Victoria’s Secret, Adidas, Nike, Coca-Cola, Starbucks and McDonald’s — why they were fans of those companies or brands, and about their past and future purchasing behavior. Syncapse then tried to estimate what the value of each fan’s spending would be to a company, as well as the value of continuing to have that fan as a customer over time. The key findings of the report are likely to come as music to the ears of advertisers that have been pursuing a Facebook-based social media strategy. According to the survey: On average, fans spend an extra $71.84 they would not otherwise spend on products they describe themselves as fans of, compared to those who are not fans. Fans are 28 percent more likely than non-fans to continue using a specific brand. Fans are 41 percent more likely than non-fans to recommend a product they are a fan of to their friends. Article 2: Virtue has determined that, on average, a fan base of 1 million translates into at least $3.6 million in equivalent media over a year. Vitrue arrived at its $3.6 million figure by working off a $5 CPM, meaning a brand's 1 million fans generate about $300,000 in media value each month. Using Vitrue's calculation, Starbucks' 6.5 million fan base -- acquired in part with several big ad buys -- is worth $23.4 million in media annually.
Click Equations exposes all the raw queries. This data we can see the bounce rate, query by query rather than the overall keyword. By using the raw queries as new keywords and used more specific landing pages for each keyword, we increased the relevance of the landing page to the query. The bounce rate for the new keyword variations dropped substantially while the newly-focused generic keyword’s bounce rate fell as well. You are giving the user exactly what they are asking for and they are more likely to stick around.
-Aperture has an 80MM offline database which they convert to get more targeted uniques for your campaign Most offline solutions have access to 250K so we chose aperture because of it ’s larger sample size enabling more audience coverage with less uniques needed
The last demographic we were buying against was length of residence. We know that users purchase their mattresses every 7 years and this data from Aperture is confirming it. Serving impressions to people living in home for 4-6 years but Aperture showed more purchases for people in their home for 7-10 years and over 14 years
Informed targeting made key audiences respond! ?????
A ha! In our exploring and digging around we found this. Looking at Operating systes.. We noticed that the share of operating systems, we see that this client has over 32% of their traffic coming from mobile devices. (BUILD) However, only 6% of their conversions come from mobile devices, half of which is from iPads As mobile share of visits had grown over the last 2 years, conversion rate had steadily fallen with an inverse coorrelation. So this is the problem. They don’t convert on mobile and mobile is becoming an ever increasing avenue of traffic and this is what’s causing an overall drop in conversion rates.
This is a two-pronged approach to counter this trend. Love the iPhone AND hate the iPhone. Stop the bleeding now by not targeting mobile devices and cut this out of our campaigns- spending no media. However, clearly this clients customers have gone mobile. SO …In the meantime deploy a mobile experience through an app or eCommerce. try to get those conversions up .
1. Digital Trends & Insights: Putting Them To Work For Your Clients
6. WHAT IS THE VALUE OF A “LIKE”? HOW DO YOU QUANTIFY SEARCH RESULTS USING A BID MANAGEMENT TOOL? HOW DO YOU UTILIZE OFFLINE DATA TO OPTIMIZE YOUR ONLINE CAMPAIGN? HOW TO PUT GOOGLE ANALYTICS TO WORK FOR YOUR MEDIA BUYS?
8. Campaign Management Platforms Decipher Insights From Dashboards & Take Action
9. WHAT IS THE VALUE OF A “LIKE”?
11. Evaluation of a “Like” Different Studies, Different Numbers $136.38 = $3.60 =
12. Added Value How to Serve it to a Client
13. Direct Relationship Between “Likes” and CPA Better CPA, Higher the “Likes”
14. Client: Pisco Porton Creative Optimized Based on FB Insights
15. HOW DO YOU QUALIFY SEARCH RESULTS USING A BID MANAGEMENT TOOL?
16. Maximizing PPC Efficiency AND Effectiveness Approaching Campaigns from All Angles
17. The Value of the Query vs the Keyword Optimizing Landing Pages Based on User Intent Jobs Jobs in DC Nurse Jobs Tuscon Jobs /JobSearch 42% Bounce Rate
18. The Value of the Query vs the Keyword Optimizing Landing Pages Based on User Intent Tuscon Jobs Nurse Jobs Jobs in DC Jobs /Nursing /DC /Search 11% Bounce Rate 19% Bounce Rate /Tuscon
19. Client Impact on Campaign ROI Better Relevance Led to Better Performance
21. Results Are Win-Win-Win Client, Users, Agency 1 st Clients 1 st Users 1 st Agency
22. HOW DO YOU UTILIZE OFFLINE DATA TO OPTIMIZE YOUR ONLINE CAMPAIGN?
23. Accurate Measurement Demo Data enables Site Measurement with as Few as 10k Uniques 250K 80MM Vs. Larger Off-Line Database = Larger Sample Statistically Significant Measurement @ Placement Level = 10% Panel-Based Solution Audience Coverage Uniques Needed .01% 10,000 10,000,000
24. Case Study Major Mattress Retailer
25. Demographic Data Comparison: Age Impression Index vs. Conversion index = Over-indexing
26. Home Ownership Index Demographic Data Comparison: Home Ownership Impression Index vs. Conversion Index
27. Home Ownership Index Demographic Data Comparison: Home Ownership Impression Index vs. Conversion Index
28. Revised Media Plan Skew Towards Rental Sites
29. Aperture Data on Rental Site Rental Site Over-Indexing on “Owners” 75 Index 133 Index 123 Index 82 Index
30. 7-year mattress cycle Demographic Data Comparison: Length of Residence Impression Index vs. Conversion index Length of Residence: Impressions Length of Residence: Clicks
31. Campaign Execution & Optimization Letting Data Dictate Optimizations
32. HOW DO YOU PUT GOOGLE ANALYTICS TO WORK FOR YOUR MEDIA BUYS?
33. Google Analytics is Ubiquitous
34. Google Analytics is Extensive and Detailed Understanding Your Site Users is Key
35. Case Study Online Food Delivery Service
36. Monitor Dashboards to Identify Issues Conversion Rates Had Been Falling Site-wide; Why? Geography? Seasonality? Website Bugs? Media Mix? Demographic Change? Product Mix?
37. Problem Identified: Mobile Conversions Mobile: Second in Traffic, Last in Conversions
38. Problem Identified: Mobile Conversions Mobile: Second in Traffic, Last in Conversions
39. Two Pronged Approach to Counter Love the iPhone and Hate the iPhone. Stop Targeting Most Mobile Devices Create Apps and Mobile eCommerce
40. Short Term Results are Strong Long Term Results are TBD
43. Lori Goldberg SVP, Client Services [email_address] 646-254-6562