This is a companion blog post for my talk at MozCon Local next Tuesday on Powerful Ranking Reports using Google Data Studio. I won’t go into nuts and bolts here — I’ll save that for those who can make it out to the show. But I will go into the new frontier for ranking data usage.

Let’s face it. Ranking is not a great SEO metric anymore for a myriad of reasons. To name a few:

• Results are too unique to individuals
• Google is constantly messing with fine-tuning their algorithm
• Ranking may get you traffic, but it doesn’t guarantee you sales

These are widely accepted facts in the search industry, but to paraphrase one of our clients:

“Perception is reality for a lot of business owners who wonder why the shop across the road shows up #1 ‘widgets’ and they don’t.”

Even so, search experts know better. Chasing keyword rankings for vanity’s sake is a fool’s errand. Especially when there’s profitable traffic to be had elsewhere.

But there are still great uses for ranking data beyond just chasing a single glory phrase. Today, I’ll walk you through 3 of them and the reports we’re using at Portent to address SEO progress through ranking.

The Ranking Map

The age of personalized search has made local ranking factors way more prevalent. Your storefront’s proximity to the person searching, the accuracy of your store listings in the search engines, your content’s relevance for a given city or neighborhood name, and ratings in the marketplace are a big deal now.

So instead of using ranking data for individual keywords, many ranking tools will allow you to see rank for target keywords in a given location. When you aggregate those numbers across a handful of important keywords for your business, you get a ranking map like this one:

This helps you prioritize which store locations to work on listing accuracy for, which to start campaigns for gathering reviews from past satisfied customers, and – in some cases – show you how intense your competition is on the local level.

The Keyword Funnel

Another way to use ranking data that’s very valuable is grouping your keywords together by which part of the customer journey a person is in when they search for it.

It’s the traditional Awareness > Consideration > Conversion marketing funnel we’re all used to, but for search.

Here’s an example of how you would assign keywords to each part of the funnel:

• Awareness – “widgets”
• Consideration – “acme widgets vs. other widgets”
• Conversion – “where to buy acme widgets”

You’d repeat this process for all of the keywords you’re targeting and then take your average rank and the total search volume available in each part of the funnel.

This helps you understand what type of content you need most in each keyword theme: Educational content for people just getting to know your brand, comparative content to help them understand the nuances of various products in the marketplace and why they should buy it from you, or promotional content to share your latest deal when your prospect is ready to buy.

Keywords by Landing Page

Since web analytics platforms no longer get granular keyword data from search engines, it’s important to understand which page is obtaining the ranking for a given keyword and whether or not that’s the most appropriate page for somebody searching to land on.

Then you can cross-reference that page’s performance (I recommend using the Landing Pages report in Google Analytics for this) for the conversions you care most about and see if perhaps you’re not attracting the right audience to that content.

So, remember, don’t fall into the trap of gunning for individual rankings. But there are so many creative ways we can repurpose this data to help us make better decisions as search marketers that it’d be a shame not to integrate some in our daily workflow.

The post How to use keyword ranking reports without them using you: A 3-step guide appeared first on Portent.

Imagine your company being truly data driven.

What does that look like?

Your teams getting the right data they need at the right times to tailor their campaigns to key audiences. Your data platforms talking to each other and getting critical pieces of information integrated. Your marketing strategy being based on actual customer behaviors and anticipated changes to that behavior instead of hunches.

It’s the dream, right?

The Data Driven Marketing Problem

Dreams don’t always match up with reality though.

A recent survey showed that only 11% of CMOs considered their team’s use of analytics to be sophisticated or cutting edge:

CMO Survey Results Courtesy of SpencerStuart

With all the talent and technology available in the marketplace today, there just isn’t an excuse to be behind the curve any more.

What does getting ahead mean to your business?

Well, the same CMOs polled in the survey above expected impact from data to manifest itself in a number of ways. But primarily, better customer experiences, better marketing strategy, better online marketing and even better product development:

Image Courtesy of SpencerStuart

Why hire an agency for analytics?

You could staff up to do analytics internally. Nothing wrong with that. You could hire another general business consultant to do it. Sure.

But will that get you the results you want?

Going back to the CMO survey once more, they were asked who is currently using data to its fullest and affecting how their organizations make decisions. 58% said SEO/SEM marketing practitioners were having the largest impact:

Image Courtesy of SpencerStuart

Why? Because search (SEM), by nature, is an incredibly accountable channel. We have better data collection methods and more data points than almost anybody and we’ve grown addicted to using analytics to get better results for our clients.

Portent has been doing SEO/SEM since search engines were in their infancy. As search engines have evolved, we’ve evolved too. And we worked with businesses in every major vertical to improve their return on marketing investment.

There’s no secret to why we’ve done this so well for 20 years: It’s just a combination of marketing savvy in all channels and using all the great data available.

The post Leading Edge Analytics – Data Driven Marketing appeared first on Portent.

Thanks to everybody who attended my talk at SMX West on closed-loop analytics! It covered enriching your web analytics data with customer information from your CRM or marketing automation platforms.

A Quick Closed-Loop Analytics Primer

I did a quick whiteboard interview with Colin Parker, Director of Marketing at Portent. We covered the benefits to investing the time and connecting the data sets you have on your customers:

My SMX Deck

SMX made the decks from my talk and every other talk available in Slideshare format.

Resources from the Talk

I made a tool for querying the HubSpot API (work for other CRMs too with minor modifications) and brings it into a Google Sheet in a format that Google Analytics will be able to read and import.

Get the Data Export Tool

Note: You will need to make a copy of the tool as it’s read-only.

The post Integrating Analytics & CRM Platforms – SMX West 2016 appeared first on Portent.

Tracking the B2B Customer Lifecycle

I talked through an ideal integration in true Moz fashion, with a whiteboard:

I mentioned a few articles at the beginning that fully encapsulate the issues B2B marketing folks face and some good rhetoric on it, if nothing else:

• Marketing Profs’ Key B2B Marketing Challenges & Strategies
• Google’s Zero Moment of Truth series
• Pardot’s Closed Loop ROI Reporting

But where all of those guides fall short is giving practitioners an actual use case to mimic with their platforms.

A Guide to Integrating Analytics & CRM Platforms

With the help of Che-Crawford’s lovely Fifth Element fan art, I presented a demo for establishing a connection between Google Analytics and HubSpot using Google Tag Manager.

Here’s the recording, for those of you who missed it:

A Quick CRM Integration Reference Deck

And if you don’t have time enough to watch the video, tab through the deck quickly:

Stay tuned for more on analytics integrations. It’s quite a bit beyond 101, but let’s face it: our livelihood as tech marketers relies on building sophisticated campaigns. Our audiences demand it!

The post Mozinar Recap: Integrating Analytics & CRM Systems appeared first on Portent.

• Acquisition
• Behavior
• Conversion

It’s built into Google Analytics, for God’s sake:

And it’s a tried and true methodology for measuring.

Problem is, we’re measuring to take action. If the lifecycle begins and ends with acquisition and conversion, we’re missing a lot of workflow steps as marketers. Every campaign needs planning and post-mortem phases.

That’s the real job — not the numbers, but what we did to attain those numbers and what we’re going to do in the future to beat those numbers.

So I think we need to evolve the ABCs into something a little more critical, a little more holistic and a little more, well, musical. (You weren’t expecting that word to go there, were you?)

Back to Basics

Campaign planning and optimization is very much a creative exercise. And for me, creativity is joined at the hip to music.

Whenever I feel like getting back to basics in music, I think of the first thing I ever learned — the C major scale on the piano:

It’s insanely simple, yet it is the key for unlocking complicated music theory.

Instead of starting with ABC, like the alphabet, the C major scale ends with ABC:

There’s some key work to put in before the ABCs and I’m going to tie that part of the scale, now, to marketing campaign creation and execution.

Campaign Planning

Context — What are we doing? Who will buy this?

Difference — Where do we fit in the landscape of competitors? What makes us unique?

Explanation — Explain C & D to key decision-makers in the company that can fund the campaign.

Fine-Tune — Firm up the philosophy for specialists and managers that will drive the campaign.

Gameplan — Get your nuts and bolts in order.

Acquisition — Take this campaign to the people!

Behavior — Observe how the people consumed your campaign.

Conversion — Make some money! Once that’s all said and done, we need a campaign reporting and evolution phase. We’ll use the same scale there.

Campaign Post-Mortem

Context — Stack up our performance against our goals and/or previous campaigns. (Only use goals for your context if they were set in some basis of reality. Past performance is the best context.)

Difference — Did it exceed or fall short of our expectations? Put hard numbers on that, not percentages.

Explanation — Explain why your campaign performed the way it did to your higher-ups. Don’t just vomit C & D at them!

Fine Tune & Fix — Tweak your campaign to compensate for what went right or what went wrong.

Gameplan — Give your team nuts and bolts, Pt. II.

Acquisition — Take this new campaign to the people.

Behavior — Observe how they consumed your campaign. Particularly the things you changed!

Conversion — Make more money!

It’s a constant process. And if you’re doing it right, you should reach each subsequent octave with higher and higher results.

The post The C Major Scale of Analytics appeared first on Portent.

During the webinar, I made mention of several links and resources that would be available afterward. Here’s the link bundle containing everything:

http://portent.co/measuring-intent

If you don’t have time for either of those things, I’ve also provided a bare bones transcript of the webinar here for your reading pleasure!

Intro

In your traditional marketing funnel you have three stages: awareness, consideration, and conversion.

Those map neatly to your Google analytics reports: acquisition, behavior, and conversion.

The top and bottom of the funnel are incredibly easy to measure. In fact, they aren’t even dependent on your Analytics setup. If you have log files for your server, you can figure out how many people came to your site. Your cart software can show you just how many people purchased on your site.

The middle of your funnel is incredibly hard to measure. Why? Because at bare minimum it requires customization through event tracking. But even if you set up robust event tracking in your Analytics platform, it’s still hard to determine what pushes someone over the edge from visitor to customer.

Today we’re going to talk about: mapping out intent indicators on your site, setting up event tracking, and ultimately building an intent scoring model that you can use for remarketing purposes.

Intent indicators

Do you have blog articles on your site? Do you have videos on your site? Then you have intent indicators.

Go through your site to find any action a potential customer could take that you don’t consider a conversion.

It’s almost like a content audit, but you’re taking  stock of activities instead.

Grading activities

This is the fun part. Once you’ve got every possible non-conversion activity in a list, rank them from least perceived intent to most perceived intent.

If you work in house, do this twice: once for you and once for your potential customers.

If you work at an agency, do this three times: once for you, once for your client and once for your potential customers.

When you’re done, take the average for each interaction.

Congratulations! You’ve just created the scaffolding for your intent model.

Then it’s all about deciding what constitutes high, medium and low intent. Since the average value of an action among these 7 activities is 3.5, look at it in terms of 1 activity, 3 activities and 5 activities.

Once you have these scoring ranges setup, that becomes the framework for your remarketing campaigns.

Setting up event tracking

Now the issue becomes, do you have a way to measure all of these activities? If they don’t have unique page views on your site associated with them, then you probably don’t.

This is where Google Tag Manager can save your bacon (or bacon-like meat substitute).

A few tags in Google Tag Manager will give you programmatic click tracking on every page of your site with automatic events being passed into Google Analytics associated with those.

Goal values

Once the events are flowing into Google Analytics, you can configure goals around them and assign dollar values to those goals.

Don’t worry, these aren’t real dollar values but just a vehicle to get your new event scoring model into Google Analytics and have it start aggregating scores on each visitor.

Building your intent segments

Over time the goal values will build up leaving you with the ability to bucket visitors in three different segments based on their level of intent.

Remarketing based on intent

Thanks to Analytics and its seamless integration with AdWords, you’re able to import your segments as remarketing lists.

Then you can build campaigns around the visitor’s level of intent and serve them up appropriate content.

The post Webinar Recap: Measuring Intent With Google Analytics appeared first on Portent.

That kind of integration is the holy grail of web analytics — truly closed-loop reporting, from the time a visitor first hits your website, to when they become a lead and, ultimately, a paying customer.

Here at Portent, we use HubSpot for marketing automation and as a checkpoint before our leads are passed into our CRM — Salesforce. So, naturally, we were chomping at the bit to integrate.

Researching the HubSpot integration

If you’ve ever looked into this kind of integration before, Google’s documentation for integrating with popular CRMs is non-existent. There really are no best practices for this. It’s the wild west out there.

A lot of reputable folks have written about integrating with Salesforce, but nobody framed it within the scope of a pre-Salesforce marketing automation pitstop. So I had to cobble an approach together from a few sources.

Finding a Unique Identifier

I started at e-nor’s blog. They wrote an excellent post last year on integrating with Salesforce before UA came out of BETA.

In it, they describe the process of reading Google’s __utma cookie and passing that value into Salesforce as a Visitor Id and into GA as a Custom Variable.

That got my creative juices flowing. If they were reading the __utma cookie, why couldn’t I read another cookie for the same purpose?

Leveraging Google Tag Manager

But this roll-out would be a real pain in the neck if I couldn’t do it programmatically. (I was envisioning a scenario where I’d have to ask my developers to insert an additional piece of script every time a new landing page went up.) Nope, I needed to use Google Tag Manager (GTM) for this.

That’s where Justin Cutroni came in. He wrote an amazing walkthrough on specifying a User ID with Google Tag Manager.

Essentially, you can define a macro in GTM that scrapes a First Party Cookie set on your site and repurpose it in any tags you want to fire, not just User ID.

Putting the pieces together

That got me thinking: HubSpot has its own tracking pixel. Are they already setting an ID in a cookie that I could scrape?

I’m sure you can see where I’m going with this. The answer is yes! They are!

They set a cookie called hutk — a 2-year cookie. And it already gets passed into HubSpot along with every form submission:

From there, it was easy to set a macro in GTM to scrape it:

Then, I pass that value into GA as either a User ID (shown below) or as a Custom Dimension:

The beautiful results

This is what it looks like when it’s all said and done. (I’ve passed in the hutk as a Custom Dimension in this example.)

The same ID associated with my form submission on the site can be tied back to my sessions that led up to that action:

So what’s next?

The possibilities are endless, really. But two things I’m really looking forward to are:

Lead scoring

Sorting our HubSpot contacts into buckets by lead quality and passing that information back into GA keying on the UID. You can do this by going to Admin > Data Import in GA and using either the User Data or Custom Data options:

PII Viewer

David Simpson launched a Chrome Extension that allows you to take any data exports from HubSpot or any other CRM and inject information back into the analytics UI without actually passing Personally Identifiable Information (PII) into GA — which would violate their terms of service.

Notice now that I can see a user’s email address alongside their behavior in GA. Really powerful stuff!

The post Integrating Google Analytics & HubSpot appeared first on Portent.

Search Engine Land broke the story, Google themselves confirmed it:

“Today, we are extending our efforts to keep search secure by removing the query from the referrer on ad clicks originating from SSL searches on Google.com.”

Queries, Not Keywords

Marin Software’s CMO Matt Ackley — on the heels of the report — quickly made the distinction between search query data and keyword data noting:

“Keyword data and search query data are not the same thing. If Google stops passing the full set of referrer data on paid search clicks, this will impact search queries, not keywords. Platforms that rely on Keyword IDs, as Marin does, would not have their bidding capabilities disrupted.”

Google’s statement corroborated that, mentioning:

“For generating reports or automating keyword management with query data, we suggest using the AdWords API Search Query Performance report or the AdWords Scripts Report service.”

So if it’ll still be available via API — and we “will continue to have access to … the AdWords search terms report and the Google Webmaster Tools Search Queries report” — what’s the problem?

Google Analytics Implications

Well, for one, we’ve probably seen the last of the Matched Search Query dimension in Google Analytics. If they’re indeed removing the query from the referrer, that’ll mean it won’t come across to GA as a part of AdWords auto-tagging.

Matched Search Query can be applied to numerous insightful GA reports, including GA’s powerful Multi-Channel Funnels suite — the only place where you can discover how a given paid query impacts performance of non-paid channels:

Tumbling CPCs

Google’s party line on this has been to bill it as “security enhancements for search users.”

But if they really cared about user security, this would’ve happened a lot sooner.

What’s actually going on then?

I’m convinced it is part of a broader effort by Google to shore up falling CPCs. They’ve been chipping away at this over the past few years.

In their most recent earnings report, they spelled it out for their shareholders:

“Average cost-per-click, which includes clicks related to ads served on Google sites and the sites of our Network members, decreased approximately 11% over the fourth quarter of 2012 and decreased approximately 2% over the third quarter of 2013.”

So they’re stopping the bleeding. How?

Look closely at what they’ve done of late:

• Introducing Enhanced Campaigns — Taking away Tablet bid granularity. They’re getting those clicks at Desktop rates now.
• Putting Emphasis on PLAs — Feed-driven performance without a clear 1:1 relationship between products and non-branded queries. Susceptible to repeated comparison-shopping clicks.

In both cases, Google has taken control and insight away from the advertiser, while bolstering click cost and volume through attractive (and unavoidable) ad products for consumers.

A (not provided) Tomorrow

How is the query data related to Enhanced Campaigns and PLAs? Well, it’s not — on the surface.

But query data in Google Analytics makes for much more intelligent advertisers. A query that does well in terms of CTR in AdWords might not do well in terms of engagement and sales on your site. Informed advertisers build negative keyword lists through this, which kills volume for Google, raises Quality Scores, and in turn, lowers CPCs for advertisers.

I think they’re testing the waters with how much information they can take away from advertisers without it negatively affecting our willingness to spend. Don’t be surprised if the query information is just phase one in Google obfuscating keyword information in the referrer.

The advertiser in me hopes I’m wrong, the conspiracy theorist in me fears I’m right.

Ultimately, Google answers to two people above all: searchers and shareholders. Advertisers will always come in a distant third.

The post (not provided) for Advertisers™ Beta appeared first on Portent.

And the link bundle mentioned throughout Michael’s presentation today:

http://portent.co/nl-segments

Check back soon for video!

The post Portent Webinar Recap: Next-Level Segmentation appeared first on Portent.

This isn’t one of those hoaxes like with Andy Kaufman or SEO.

LCA was a doomed model for a number of reasons. But for me, it no longer applies to the world we live in now. And it barely applied before.

LCA will only stay dead, though, if we can decide on its successor.

I’ll get to that.

But first, a little history lesson.

The Last Click problem

When web analytics was in its infancy, LCA worked. People could only reach your site so many ways. You had word-of-mouth. And you had, maybe, a house email list.

That was kinda it.

Search engines were still in a relative stone age. The results weren’t super great and AdWords hadn’t even considered quality score yet. Social networks were crawling out of the primordial ooze. I hadn’t even heard of MySpace yet. Blogging platforms like WordPress hadn’t really taken shape. Plus, there weren’t a lot of great email lists you could buy or sponsor.

In light of all that, LCA made sense.

If somebody reached your site, the last place they reached it from was probably also the first. So it was logical to give the last click credit for a lead or a sale.

But now? The ways people can reach your site are virtually endless.

Search engines have employed machine learning to provide you almost predictive results based on your apparent intent, current location and search history. AdWords has so many different ad formats I’ve lost track.

Social networks have reached critical mass. Even my Grandmother has a Facebook profile. They’re faster sources of news for us than Newspapers or TV.

That doesn’t even factor in the growth of the web and potential sites that could write about your business. Or the growth of mobile apps!

Google did a study in 2012 where they looked at the purchase journeys of 3,000 shoppers across a number of verticals.

All 3,000 of them took different paths to that purchase.

Would that have happened in 2006? I doubt it. Would that have happened in 2000? No chance.

So today, with all those different paths, how could LCA possibly measure which channels should get credit for a sale?

It can’t.

How did Last Click survive?

We kept it alive.

It’s the default in Google Analytics and a number of other analytics platforms.

The status quo is easy. The status quo is comfortable.

All my historical reports are LCA. Introduce another attribution model and it feels like revisionist history—even if the revisions are (*gasp*) more accurate.

LCA does a really great job showing us our brand equity in each channel. Closing clicks come after people are aware of your product or service and you’ve persuaded them to commit.

That commitment is nice. Ultimately, though, it’s patting yourself on the back.

LCA does a really poor job showing us our brand growth in each channel. Which clicks are introducing and persuading people to buy in the first place? LCA is blissfully ignorant on that count.

If it’s ignoring all but the bottom of the marketing funnel, why in the hell are we basing our budgets and ROI on it?

I think it’s time we paid our respects and moved on.

Okay, Last Click sucks. What now?

LCA is dead, but where do we go from here? I’ve been mulling this over for a while.

I started by taking Portent client data over the last year and running it under different attribution models and looking at two things:

• % Change from LCA by Channel
• $ Change from LCA by Channel

Big shout out to Tim Gillman (a.k.a Timtern) for pulling the raw data on this.

My goal was to settle on a model that is aware of the whole marketing funnel. And one that—more importantly—fits customer behavior.

What did I use for this? Google Analytics’ Attribution Model Comparison Tool. It’s fantastic.

I actually arrived at two different ones for ecommerce and lead generation.

For ecommerce: Position Based Attribution

People who buy online are crazy. They open lots of tabs. They go to lots of different sites. They read reviews.

What kind of attribution model solves for ecommerce customers? Position Based Attribution (PBA).

The u-curve model gives nearly equal credit to the first discovery click and last closing click, but it also sprinkles in a little credit for all those flighty comparison shopping clicks in the middle.

% Change from LCA

So how does the PBA model compare to LCA? Which marketing channels get more credit for sales?

• Social Networks—where people get information from their friends—was the biggest winner
• Organic Search—where good content gets you better visibility—also saw huge gains

But % Change doesn’t tell you how much more money each channel was responsible for making.

$ Change from LCA

• Organic Search—content driven—was over $5M more valuable to our clients in 2013 under PBA
• Paid Search—where you can target intent closely—was over $4M more valuable

For lead generation: Time Decay Attribution

People who fill out lead forms online are a little more pragmatic.

They’re interested in your service, but less impulsive. You have to build trust with them. Especially with expensive services and long-term contracts. With each click, they learn more about you, become more satisfied that you’re the right solution and get more okay with the idea of handing their phone number over to you.

What kind of attribution model solves for lead generation customers? Time Decay Attribution (TDA).

This ramp model gives more credit to a channel as it gets closer to the lead without completely ignoring the clicks that came before it.

% Change from LCA

Which marketing channels get more credit under this model?

• Email—where you can develop a good drip campaign that builds interest in your service—is growing in influence
• Social Networks—much like in the case of ecommerce — get a lot more credit, as one of your friends using a service is a great trust factor

Lead Change from LCA

But again, the % difference versus LCA doesn’t show how many more leads each channel was responsible for driving.

• Referral—especially from review blogs and forums—drove over 3K more leads for our clients in 2013
• Organic Search—where informative content is king—was also a big winner with over 1K more leads

Life after Last Click

I think we can all agree LCA is six feet under. But there are lots of options for attribution models. I’ve posed two that I think are really great here.

Different models reward—and encourage more investment in—channels that are unsung heroes at all points in the marketing funnel.

What models are you using? Let’s work together as marketers to move ourselves (and our clients) on to better attribution in 2014!

Can’t get enough attribution talk? Still confused about how to justify your marketing budget? Tune in for Ian Lurie’s free webinar “Attribution-fu: Measuring Cross-Channel Impact the Old Fashioned Way” on Thursday, January 30.

The post R.I.P Last Click Attribution appeared first on Portent.