2013

PARTICIPATION ACTIVATED:

A Trends Reportby Performics@Performics

We’re creating over 2.5 quintillion bytes of data each day. For years, search engines, search management platforms, advertisers and agencies have been collecting this data, coining it “Big Data” – but it has only been actionable to marketers on a small scale.

2013 is when marketers will activate participant data across screens, at scale, in real-time, setting in motion a spiral of shares, likes, comments, clicks, leads and sales. This is traceable, individual, optimized data that feeds back into (and out of) the cloud. Performics’ Participation Activated Report sets out the major themes to watch. By embracing these themes, marketers can better drive participation in 2013.

Par•tic•i•pa•tion Acti•vat•ed:The Process of Turning Engagement into Action

“This is the year of breaking data out of the cloud.”

THEMES

AUTOMATED INTUITION

THE HUMAN ALGORITHM

SEARCH APP-IFICATION

Advertisers activate Big Data to customize ads at scale, by intent, across devices, in real-

time to better Connect with participants

Participants move beyond traditional search results to

Discover alternative (human!) authority for

crowd-sourced decision making

Search engines seek to Empower

participants by giving them niche content—e.g. integrated vertical search portals directly

in the results

Advertisers Leverage Big Data to Customize Ads by Intent, at Scale, across Devices to Connect with Participants

AUTOMATED INTUITION

Remember the scene in Minority Report where Tom Cruise walked into a mall and a Gap ad scanned his retina, personalizing a custom message: “Hey Tom, how’d those assorted tank tops work out for you?” This is the application of Big Data for retail. In many ways, this future is here.

Expanding e-commerce platform advances, robust analytics tools and attribution modeling allow unprecedented application of Big Data. Marketers are beginning to deliver highly customized Minority Report-type ads, at scale in a completely automated way. We’re calling this trend Automated Intuition.

Search by Intent

Retargeting

Mass Personalization

Real-Time Bidding

SEM Platforms

Data / Analytics

Teams

Social Profiling

MICRO-TRENDS

AUTOMATED INTUITIONMicro-Trend 1: Search by Intent

Location: 41.85˚ N, 87.65˚ W

Location: 42.01˚ N, 87.45˚ W

Time: 8:30AM

Time: 9:07PM

With Google Enhanced Campaigns, marketers can deliver search ads based on user intent. What intent does a user have when he searches for “coffee?” Well, that depends on where he is, what time it is and what device he’s on. He may want a coffee shop or home coffee delivery. Enhanced Campaigns enable marketers to make cross-device, automated inferences based on participant data , at scale

For years, marketers have been managing campaigns separately by device: (1) desktop, (2) tablet, (3) smartphone. Meanwhile, participants have been moving across devices, fluidly consuming content. Because participants think of devices as fluidly connected, Google Enhanced Campaigns (rolling out in late Q1 and Q2) enable marketers to manage devices as fluidly connected . AdWords will now be designed to manage hybrid desktop and mobile campaigns per context (intent), not per device.

AUTOMATED INTUITIONMicro-Trend 2: Retargeting

SearchEx: If a marketer knows that a searcher has engaged with that marketer on its site, that marketer can bid up for that searcher and serve her customized copy:

User on Your Native Site

User Later Searches

User on Your Native Site

User Later on Facebook

Through retargeting, marketers can make automated inferences to engage specific participants that have already engaged with them. Emerging search capabilities soon coming out of beta enable advertisers to customize bids, copy and landing pages for searchers cookied on the advertiser’s site. In social, Facebook Ad Exchange (FBX) enables advertisers to reach users based on their browsing histories (via cookies). Advertisers will be better able to engage Facebook users to complete lower-funnel activities—like booking a flight after searching, buying a product after filling a cart or subscribing to a service after browsing.

FacebookEx: A user who places products in her shopping cart on a retailer’s site but doesn’t purchase later sees a Facebook ad promoting 10% off the products in her cart:

AUTOMATED INTUITIONMicro-Trend 3: Mass Personalization

In 2012, Google unified its Privacy Policy to allow permission to share valuable user data across all Google services. This sharing enables advertisers to better customize ads and search listings to each individual user across Google properties (i.e. targeting search ads based on searcher profiles discovered by peoples’ browsing histories). But this is just the tip of the iceberg.

In 2013, Google+ could become a global user ID that links everything a user does online. Yahoo! Axis, which enables participants to integrate content across desktop and mobile, could connect user habits cross-device. Advertisers could then feed personal data into campaigns to create Minority Report-type ads at scale.

User on Your Native Site User Later Searches

*Of course this all hinges on whether Tom Cruise thinks it violates his privacy

Customers Increasingly Seek to Discover Alternative Authority for Crowd-Sourced Decision Making

THE HUMAN ALGORITHM

People are moving beyond the traditional algorithms of Bing and Google to places like Amazon, TripAdvisor, Quora or Facebook to get search results influenced by their friends or peers.

What these people see and say has significant impact on your brand. Search marketers must think less about pleasing algorithms and more about pleasing participants. We’re calling this trend The Human Algorithm.

Ubiquitous Search

People-Powered Accuracy

Collective Relevancy

Data Management

Established Search Portals

“Answer” Platforms

User-Generated

Content

MICRO-TRENDS

THE HUMAN ALGORITHMMicro-Trend 1: Ubiquitous Search

Search is ubiquitous; it’s everywhere. When participants turn to sites like Amazon for reviews from real humans, they may not call it “searching.” But, in fact, they are searching. And as a search engine, Amazon is as important as Google. To illustrate, a bike shorts brand could rank #1 organically on Google for “bike shorts.” It could have a perfectly optimized paid search program. It could deliver the right messages at the right time in display, social and email. It could have poor Amazon reviews and not sell any bike shorts.

Participants are increasingly dependent on answers from sites like Amazon, Quora and TripAdvisor from context-rich human opinions. Success in the age of The Human Algorithm hinges on activating participation—reviews, comments, likes, shares to engage participants across the path-to-purchase.

THE HUMAN ALGORITHMMicro-Trend 2: People-Powered Accuracy

Social networks are clearly capitalizing on The Human Algorithm trend. In January 2013, Facebook rolled out Graph Search. Graph Search results are influenced by humans; they include information that the searcher’s friends have shared (people, photos, interests, places, “Likes”). To illustrate, a user can search “NY restaurants my friends like” to see results for restaurants in NY liked by their Facebook friends.

Additionally, Twitter rolled out the Twitter Human Computation Engine in January 2013, which puts a human layer on Twitter search to increase accuracy. Contributors look at trending queries and evaluate them for relevancy, effectively annotating Twitter’s search algorithm.

Facebook Graph Search (Jan. 2013) Twitter Human Computation Engine (Jan. 2013)

Twitter: “How would you know that #bindersfullofwomen refers to politics, and not office accessories? . . . [W]e need to teach our systems what these queries mean as quickly as we can [by sending them] to real humans to be judged.”

THE HUMAN ALGORITHMMicro-Trend 3: Collective Relevancy

How will traditional engines embrace The Human Algorithm ? They’ll continue to expand social integrations to bring more human into their algorithms. Bing revealed major social integration in 2012—the right-hand “sidebar” column that displays recommendations/advice related to the searcher’s query from friends, experts and enthusiasts—with emphasis on Facebook and Twitter. Sidebar also features a box for searchers to pose questions to their Facebook friends. In January 2013, Bing also announced a five-fold increase in Facebook-influenced results.

Bing Social Search

The challenge for the traditional engines is that Facebook, Amazon and Twitter “own” most of the participant data that enables The Human Algorithm. However, Microsoft has the Facebook partnership, and Google will continue to build social data of its own through Google+.

As Users Move to Niche Sites, Engines Adjust w/ Vertical Search App-ification

SEARCH APP-IFICATION

Participants are increasingly moving to vertical sites for product discovery. 33% of shoppers now start their searches on Amazon vs. 18% on a search engine (Forrester). This is reminiscent of the old days when people used vertical-specific portals to search.

Google is well aware that this participant trend is a threat to its “one-stop shop” status. Thus, Google is working on a series of “apps” within the search page for vertical queries. We’re calling this trend Search App-ification.

Generic Irrelevance

Feed Evolution

Vertical Gardens

Listings Integration

Vertical-Specific Data

“Niche” Discovery

Utility-Based Search

MICRO-TRENDS

SEARCH APP-IFICATIONMicro-Trend 1: Generic Irrelevance

Can you imagine a world without Google? A world where people ditch mass search engines for niche engines? A world where people use restaurant search engines to find restaurants, hotel search engines to find hotels, skinny tie search engines to find skinny ties? It’s not that crazy. We know the importance of vertical search result relevancy.

The biggest threat to Google isn’t Bing. It’s a million little niche engines—Citysearch, TripAdvisor, RetailMeNot (coupons), SkyScanner (flights), TinEye (images), Pipl (people), MixTurtle (songs) and SlideFinder (Powerpoint slides). Participants who have specialized needs are, in a way, finding the established engines irrelevant —and looking elsewhere for “better” results.

SEARCH APP-IFICATIONMicro-Trend 2: Feed Evolution

In Search App-ification, the “apps” push down traditional results. In the below example, the Google-controlled Shopping Ads (PLAs) draw searchers’ eyes from the top-ranked Amazon organic listing. If you’re a retailer trying to make it solely in traditional SEO, what do you do? Because traditional organic results are increasingly losing real estate, you’ll have to insure your presence in these continuously evolving “apps” and product feeds:

• Google Local/Maps listings (local SEO)

• Google+ Business Pages (social SEO)

• YouTube videos (video SEO)

• Google Shopping (PLAs) (feeds/CSE): App-ification accelerated in Feb. 2013 when Google purchased Channel Intelligence (product feeds optimization) to bolster Google Shopping

• Travel/hotel sponsored content

• Top-sponsored ads to trump “app” results

AdWords Ad

Shopping Ads (PLAs)

Organic Listing

SEARCH APP-IFICATIONMicro-Trend 3: Vertical Gardens

Of course, innovation often encounters legal barriers. In a ten-month investigation ending in January 2013, the FTC explored allegations that Google “manipulated its search algorithms to harm vertical websites and unfairly promote its own competing vertical properties.” For example, in the below screenshot, the Google hotels “app” pushes all the organic listings below the fold. However, the FTC eventually determined that this practice could be “justified as innovation that improved Google’s product and the experience of its users.”

The FTC gave Google the green light to continue to prominently feature its own properties. App-ification is already happening in verticals like shopping, travel, hotels, weather, local and coupons. Is your vertical next?

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