Category: Twitter Advertising

The $65,000 Tweet: how to track social media marketing to offline sales

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Welcome back to our ongoing series on building and optimizing a performance-driven social media marketing program.

In part 1, we discussed how to classify social media. In part 2, we evaluated how you can consider social media as a marketing funnel (and why you ought to). Last week, we explored the funnel of a customer whose conversions take place in a shopping cart. That brings us here.

Believe it, kid: crazily enough, there are, in fact, such things as offline transactions. Marketers use social media to drive these transactions, too — and, just like social flows that end in an online shopping cart, such transactions have a funnel and an ROI that marketers can measure and optimize.

But how?

What if my conversions happen offline?

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Last week, we checked in on an awe.sm customer whose social funnel drives customers to make purchases — and, by taking a close look at sharing in every stage of the funnel, uncovered a new source of traffic and revenue. If you’re an online retailer, you absolutely should take a look.

For many more of our customers, there isn’t a shopping cart at the bottom of their social funnel, but something more amorphous — app downloads, for example; or lead-gen form submissions; or maybe even just pageviews. We can track those, too. We can track anything.

But about two years ago, awe.sm got to start working with a customer whose social funnel does end in a purchase — but one that doesn’t happen online. It was up to us to help connect the cyberspace top of the funnel with the meatspace conversions at the bottom. Here’s what that looked like.

Baby, you can drive my car / and maybe I’ll retweet you

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Cars are huge on social media, and not the Pixar movies my godson has made us watch 479 times, but the real ones. Watch your Twitter stream during the Detroit Auto Show to see the volume of conversation that new models inspire, and scrub your friends’ profiles to see how long it takes before you find one of YouTube’s millions of my-new-car selfies.

But did you ever wonder how Tweets about cars actually translate into car sales? Our automaker friends did. So we took a look at their funnel.

Here’s an example funnel:

  1. See the brand’s Tweets;
  2. Engage with a Tweet by (e.g.) favoriting or amplifying;
  3. Click from the Tweet into the dealer’s site;
    ____________________
  4. Browse to a specific model;
  5. Navigate content (a.k.a., activate salivary glands);
  6. Enter car customizer;
  7. Complete car customizer;
  8. Search for a local dealer;
  9. Schedule a test drive;
    ____________________
  10. Test drive (a.k.a., re-activate salivary glands);
  11. Negotiate;
  12. Yahtzee!

Tracking this funnel may seem like a tall order — it spans social, web, and offline!… — but we weren’t starting from scratch. All the data needed for funnel analysis already is out there:

  • The brand’s dealers maintain impeccable tracking to understand the performance of each channel that drives customers into their showrooms — in other words, everything that happens in real life, below the red line.
  • Everything in the middle of the funnel — the steps between the blue and the red lines — happens on the brand’s site. Here, the brand’s own site analytics team pays close attention to site visitors, measuring and optimizing their acquisition, navigation, and conversion.
  • The top of the stack is the social funnel — and that, of course, is awe.sm’s wheelhouse⁴.

We authenticated awe.sm tracking on the brand’s owned social channels; instrumented awe.sm conversion tracking at important milestones in their website, like entering the car configurator; and matched up our metadata with the site analytics tags already in place.

By closing the loop and connecting all the dots from post to purchase, we were ready to learn the ROI of each Tweet … and it blew our minds.

When is a Tweet worth a car?

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If you make it through step 6 of this funnel — if you finish configuring a car on the brand’s site — there is a 30% chance you will buy the car. Yes: if you make it halfway through their funnel, the odds are nearly 1 in 3 that you’ll finish it.

Holy moly.

So: if our customer can tweak their social funnel to get only three more people through the funnel, that’s a new car.⁵ Here, identifying which specific social posts perform at each stage of the funnel ceases to be academic or merely curious — and every optimization that increases reach, engagement, and traffic is serious business.

YMMV⁶

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Does this mean you should organize your social media marketing exactly like an auto brand’s? Probably not — not even if you’re another auto brand, since each brand’s followers are unique. But our takeaways are these:

  • You don’t need e-commerce to track the ROI of your social media marketing;
  • You definitely can track the ROI of your social media marketing: it’s just a matter of closing the attribution loop;
  • … and you should: even if this is an extreme example, it’s inescapable that individual social posts can have a huge impact — so you need to understand the performance of each one.

What would it take to get you into this social media attribution platform today? … No, really. Let’s talk about this. Drop us a note, kick the tires, and let’s optimize your social media marketing.

____________________

² Ugh — totally unintentional. I really need to brake this habit. For wheels
³ I’m steerious.
⁴ Sorry; it happened again. Guess I’m just not firing on all cylinders.
⁵ Second prize: steak knives.
⁶ Oh, come on; you saw that coming a mile away. Don’t blow a gasket.

*Originally published on the awe.sm blog

Track social ROI across multiple channels

*Content originally written by Fred McIntyre and published on awe.sm blog

When I joined as awe.sm’s CEO a few months ago, I outlined the excitement within the social marketing landscape, and the incredible opportunity to help business better navigate and understand the growing global social media audience.

By the end of this year, there will be over 1.8 billion people using social media — and the dollars brands are budgeting in outbound social marketing are growing at a fast clip too.

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In July, we rolled out awe.sm for marketers, which built on the capabilities of awe.sm’s excellent developer platform to equip marketers with easy-to-use social media analytics tools. Uptake in the market has exceeded our expectations.

Brands are looking for ways to connect outbound social marketing efforts with real, dollars-and-cents, consumer actions. Marketers today — 85% in a recent CMO study — either don’t know or have only a “qualitative feel” for how the dollars they spend on social marketing deliver performance against real business goals like purchases or newsletter sign-ups. Finding ways to measure the ROI of the dollars being spent on social media marketing, and using this intelligence to reach business goals, has eluded most marketers.

Today, awe.sm is taking the wraps off a solution. What we’ve built gives marketers a single source — think of it as a powerful telescope — to get a clear picture of what had been a murky “dark social” universe. It’s an all-in-one dashboard within awe.sm that gives brands and agencies a very precise view of how different social channels drive conversions — sales, registrations, or other consumer actions — in direct comparison. I’ll be demonstrating it today at the iMedia Breakthrough Summit’s Next Wave Competition & Showcase in Austin. You cancheck out our media announcement here.

We’re big believers in social media’s potential and influence. Helping business more effectively navigate and measure this world will drive innovation and growth. I’m really excited about what the team here at awe.sm has built and can’t wait to show you how well it works.

Introducing Monitoring, Editing & Creation of Twitter Ads

Yesterday, we announced the integration of Twitter’s Ads API with our Social Operating Platform, making Unified one of only two companies in the world with Ads API access to all three major social networks – Facebook, Twitter and LinkedIn.

This integration was much more than just adding a few form fields and API calls – we thought hard from the ground up about how to create the simplest and fastest interface to manage thousands of ads across dozens of accounts.

We’re really proud of the Twitter product we built over just eight weeks, and excited to continue building the absolute best product for our customers. Today I want to share three fundamental principles that have guided our design and engineering process so far.

Design to match data structure

Advertising data is like a set of Russian Dolls – deeply nested data structures that in their simplest form look something like:

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When we started prototyping, the goal was for our product to help customers understand this information hierarchy and use it to their advantage.

In order to reflect this hierarchy in our design, we hacked around CSS, HTML tables andbackgrid.js to create this:

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Each campaign row expands and collapses, clearly displaying the full hierarchy and interface without navigating back and forth between pages and views. Analytics, editing, and creation all happen from the same interface, helping you make smarter decisions about your campaigns.

We’re really excited to continue refining this approach and finding the best design pattern for nesting and mixing tabular and non-tabular data like this. (if you have ideas, we’d love to hear from you)

When editable data is visible, it should be editable in-place

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If I can see my campaign budget, why shouldn’t I be able to click on it and change it right away?

When we looked at other designs for editing ads, one anti-pattern we found was a reliance on multi-step wizards to make simple changes. Wizards are ideal for guiding new users through a process, but using them for editing is a crutch.

By allowing quick Excel-style edits, we were able to reduce the time it takes to make changes down to less than a second – just click, edit, and hit enter.

Confirm success and explain failure

When an advertiser has hundreds of thousands of dollars at stake, it’s absolutely essential to provide them with confirmation that each action they take in our application succeeded or failed.

We do this by hooking messenger.js into every action and displaying a small notification in the bottom right corner of the browser window:

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As a user, you shouldn’t have to wonder whether your request to the API worked, or whether your spotty 3G connection sent your changes – you should always have full visibility into both success and failures.

Looking to the Future

With a solid foundation to build on, we’re excited to keep building and focus on solving the challenges that our biggest advertisers face, from multivariate testing to automated targeting and beyond. Expect to hear more from us here on the blog as we keep building and making it easier to create and manage Twitter campaigns.

Twitter drives 4 times as much traffic as you think it does

Over the last few weeks, TechCrunch has run a couple posts using their own referrer logs to measure how sharing on various social services drives traffic. In these and other analyses based solely on referrer information, Twitter performs surprisingly poorly relative to expectations many of us have based on our own observations of the volume of link sharing on Twitter.

Does that mean the people you follow on Twitter who share links all the time are that atypical? Do most normal people just not click on links in Tweets? Is LinkedIn far more popular with the rest of the world than it seems to be with the people you know?

No, no, and no. There is a much simpler answer behind this disparity: referrers are a poor way to attribute traffic from social sharing.

Referrer analysis is based on the outdated metaphor of the web as a network of links between static pages that could only be navigated by browsers. Today’s web is built around social streams and other APIs that are consumed via dynamic web applications, desktop clients, mobile apps, and even other web services, all of which render referrers obsolete as an attribution mechanism.

awe.sm was built for the modern web — a network of people, not pages — to track the results of Tweets, Likes, emails, and other sharing activities no matter what path they follow. So our system knows with certainty where each link was originally shared in addition to all the places where it was ultimately clicked (i.e. referrers). This approach gives us a unique set of data that demonstrates just how misleading referrer information can be.

And in the case of links shared on Twitter, it’s very misleading: the referral traffic one sees from Twitter.com is less than 25% of the traffic actually driven by Twitter.

Twitter is the perfect storm for referral traffic

We looked at awe.sm data from the first 6 months of 2011 spanning links to over 33,000 sites, and the numbers were astounding:

  • only 24.4% of clicks on links shared on Twitter had twitter.com in the referrer;
  • 62.6% of clicks on links shared on Twitter had no referrer information at all (i.e. they would show up as ‘Direct Traffic’ in Google Analytics);
  • and 13.0% of clicks on links shared on Twitter had another site as the referrer (e.g. facebook.com, linkedin.com).

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Twitter is the quintessential modern web service — all the ways to consume Twitter, even Twitter.com, are just clients for the Twitter API — so the failure to effectively track it using such an outmoded methodology as referrer analysis should come as little surprise. Twitter’s openness and the many resulting ways users interact with it are what have made it so successful, but they are also the things that have made its value largely invisible to publishers.

‘Direct Traffic’ explained

When a user clicks a link in any kind of non-browser client, from Outlook to a desktop AIR app to the countless mobile and tablet apps, no referrer information is passed for that visit and your analytics software basically throws up its hands and puts the visit in the ‘Direct Traffic’ bucket. The assumptions behind this fallback behavior show just how arcane referrer analysis is — if a visit didn’t come from another webpage (i.e. no referrer data), someone must have typed the URL directly into their browser address bar.

How Twitter sends traffic through other sites

If you’ve spent the last few years wondering why the proportion of ‘Direct Traffic’ to your site has been on the rise, the answer is the growing usage of non-browser clients, especially on mobile. And since 2/3 of Twitter consumption is happening in desktop and mobile clients*, it’s safe to say that a lot of your ‘Direct Traffic’ is actually coming from Twitter.

While the incredible growth of mobile apps and desktop clients and their importance in the Twitter ecosystem is news to no one, the value Twitter drives through content syndication is a bit more surprising: more than 1 in 8 visits driven by Twitter sharing are actually referred from other sites. Many other sites use Twitter’s API to pull in Tweets that they display on their own sites, where links in those Tweets are then clicked.

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For example, look at this screenshot of my LinkedIn activity stream. Notice that every update says ‘via Twitter.’ Yet when someone clicks on one of those links, the referrer will be linkedin.com, even though it only got to LinkedIn because someone shared it on Twitter first.

The same is true of Tweets syndicated to Facebook, About.Me, and myriad other websites that allow users to connect your Twitter feed directly. And because Twitter’s API is open and most Tweets are public by default, there are also many applications and sites that display Tweets based on hashtags, search terms, and other criteria without a user ever needing to connect their own feed.

In addition to the programmatic syndication of Tweets through Twitter’s API, sharing is fundamentally social and the human element is responsible for much of the serendipity that makes social media so powerful. A great example of that is this Tweet by @zeyneparsel, who only had 144 followers at the time. However, she happens to be a self-proclaimed “veteran hipsterologist” and this Tweet was on the subject of hipsterism (?!). As a result, the link contained in her Tweet ended up being included in a Psychology Today blog post on hipsterism (see UPDATE 3), which drove a significant amount of traffic.

In these cases, which showcase the amplification effect that makes Twitter so uniquely valuable to publishers and marketers, analyzing referrer data alone would attribute traffic to a variety of other sites, even though it all originated with sharing on Twitter.

Improving social attribution

Last week, MG Siegler noted that Google+ started rewriting all outbound clicks to come from plus.google.com. Facebook has rewritten outbound links for quite a while due to phishing/malware and privacy concerns. And both LinkedIn and StumbleUpon frame all external pageviews, which means you can see all the views they drive. As t.co rolls out to 100% of the links shared on Twitter (a topic we’ve previously covered in some depth), they may very well start rewriting all clicks on t.co links to show Twitter as the referrer. This would ensure Twitter gets the credit they deserve for traffic they send to publishers, but it would have the downside of obfuscating the diverse paths that a tweeted link can take.

Until then, it’s possible to correctly attribute visits driven from Twitter sharing by tagging your outgoing links using a solution like Google Analytics campaign tracking parameters. For example, the Tweet Buttons on Business Insider use links like this:

http://www.businessinsider.com/closing-bell-july-12-2011-7?utm_source=twbutton&utm_medium=social&utm_term=&utm_content=&utm_campaign=moneygame

Google Analytics can then properly attribute traffic to those buttons. Google Analytics offers a handy URL Builder tool, and other analytics solutions, like Omniture, support similar campaign tracking parameters of their own.

Why awe.sm is, well, awesome :D

And if all you want is an accurate count of the aggregate traffic Twitter drives to your site, that should be enough. But our customers have found there’s a lot more value to be had in understanding the mechanics that drive successful sharing — who is tweeting, what they’re tweeting, where it’s being tweeted from, when it’s being tweeted, etc. So in addition to automatically building the outbound links to integrate our social attribution with Google Analytics, Omniture, and other web analytics solutions, awe.sm tracks the performance of each Tweet (and Like, etc) individually. By connecting the rich information we have about the context of each share with the visits, pageviews, conversions, and revenues it drives, we enables our customers to go beyond just looking at social data and to start acting on it (and to build cool stuff like this).

If you’re interested in learning more about how awe.sm can help your business harness the value of social, please drop us a line here.

* The full list of sources of clicks with no referrer information (i.e. ‘Direct Traffic’) not only includes mobile and desktop clients, but also web-users who have https security enabled for their Twitter accounts (which strips out referrer information).

*Content originally published on the awe.sm blog.

 

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