Our sixth chapter discusses the challenges of tracking and analytics in search engine marketing in a conversation with Analytics Pros General Manager Chris Bridges.
Getting on Track with SEM
As if analytics for SEM weren’t convoluted enough, tracking is getting tougher. In a digital marketing ecosystem where data fragmentation is the norm, buyer journeys becoming more complex. According to McKinsey, consumers are changing the way that they shop for products. In contrast to a traditional marketing funnel, where consumers start with a set of potential brands and methodically reduce that number to make a purchase, multiple touch points are the new norm.
McKinsey studied this trend across 20,000 customers in five industries and three continents. Their “research showed that the proliferation of media and products requires marketers to find new ways to get their brands included in the initial-consideration set that consumers develop as they begin their decision journey.”
That’s why the advertising community is seeing new paid marketing channels and optimization techniques evolving.
An Evolving Ecosystem
“Because of the shift away from one-way communication — from marketers to consumers — toward a two-way conversation, marketers need a more-systematic way to satisfy customer demands and manage word-of-mouth,” says McKinsey.
Adding to this tracking challenge is the fact that social media platforms and devices are seemingly multiplying. Given today’s data ownership and licensing ecosystem, it’s challenging, to say the least, to track audiences across their many different touch points.
Yet while the customer journey changes, marketers are also compiling huge amounts of data - data pertaining to users’ clicks, including their location, device, time-of-day clicked and demographic info. Tracking millions of clicks and impressions and actually deriving meaningful insights from them remains a challenge for many marketing teams. In other cases, ironically, marketers struggle with data scarcity as they launch new products or launch existing products in new territories in the absence of empirical data. These dual data challenges can be bridged by predictive advertising management platforms that use data science to extrapolate deep insights from big data and predictively model performance for untested programs.
But how do predictive advertising management platforms handle the other ambiguities we’ve discussed? How will the future of SEM analytics address the increasingly fragmented, and increasingly difficult-to-track customer journey? Just as today’s paid advertising landscape is dramatically different from what it was just three years ago, the future is less than clear. So how can marketing leaders prepare themselves?
Q&A - Chris Bridges of Analytics Pros
To explore this question, we interviewed Chris Bridges, GM of Analytics Services at Analytics Pros, one of the world’s leading Google Analytics consultants. Bridges’s experience spans 25 years of business intelligence, engineering, and analytics experience. He has implemented tracking frameworks across billions in PPC spend, collectively, at companies like Conversant, IAC and more. Here’s a look into the future of analytics for predictive advertising management, through his eyes.
Q: What’s the biggest trend that you’ve seen in the last few years, overseeing SEM analytics for some of the world’s largest online media brands like About.com, Investopedia and UrbanSpoon?
CB: Companies have been relying on more and more traffic sources. Over time, we’re going to continue to see this trend accelerate. What’s been perplexing (and is going to continue to be perplexing) is the “dance” between traffic sources.
What I mean is that there is limited overlap or cross-pollination of information. Despite the proliferation of data, it’s been challenging for companies to form a complete customer picture. This trend is at risk of continuing.
Q: What’s the end result?
CB: Marketers don’t have the insight that they need to make optimal judgment calls. They may be focusing on some channels more than others, “just because.” Hidden inefficiencies arise. Marketing dollars are going to waste, and CMOs may not see what they’re wasting.
Meanwhile, competitors with strong analytics capabilities are gaining competitive advantages where others are missing out. Bad marketing can destroy once-healthy businesses.
Q: There has to be a silver lining to this. What is it?
CB: Technology is changing every day. The tools are out there. The question is how we can piece them together.
I’ll use Google Analytics Premium as the example. BigQuery is changing the equation a lot, and I don’t think that’s known yet. Just a few short years ago, when I was consulting on analytics for large companies where I worked, there weren’t any data stores to use in synthesizing and analyzing information. They would just take click data and compare it with SEM spend. There was no insight into the layers in between—the human story that connects dots between marketing spend, engagement, and ROI. They’d analyze performance down to the keyword. How was one word trending? Was it leveling out?
The real story may not just be the numbers. Marketers can get strategic with data science.
Keyword-level data is important. But there’s potentially more to the marketing analytics story. The problem that I’ve observed is that marketers are time-strapped. They’re spending all their time doing routine analytics instead of figuring out how to tell that complex buyer journey story.
The silver lining is that new technologies are emerging to course-correct this imbalance in resource allocation. BigQuery is one example solution that is changing the equation a lot. Predictive advertising management platforms are also emerging to streamline the SEM bidding process, as well. The tools exist.
Q: If the tools exist, why are marketers struggling to connect the dots?
CB: The answer to this question is deceptively simple. With tracking and analytics, there’s far too much that can go wrong. From errors appending tracking parameters to non-standard business intelligence implementations, campaign tracking can break very easily.
When tracking breaks, marketing teams have trouble fixing it. What happens to information about tracking parameters when people leave their roles at companies? What happens when someone on your team sets up something wrong, without even realizing it?
Far too many things about analytics exist in a black hole when it really shouldn’t.
Q: So what’s the right path forward, to create an analytics strategy that supports the future of SEM?
CB: It comes down to one word: precision. One company that I’ve worked with, for instance, has created more than 100 audiences that they regularly track in Google Analytics—and they’re creating more on a rolling basis.
Tracking and analytics strategies will vary between different companies. The key is to create an analytics strategy that is finely tuned to your specific audience. SEM and bid management aren’t just about targeting anymore. You need to make sure that you’re continuing to retarget and engage audiences across your conversion funnel.
Q: Speaking of retargeting—why should marketers be paying closer attention to this paid channel tactic from an analytics standpoint?
CB: It’s one of the most underutilized opportunities in marketing today. While many businesses are going through the motions of retargeting, they’re often missing the mark when it comes to precision. Strategies aren’t informed by analytics. Rather, marketing teams are operating from rough guides.
Inputs of data sources may not fully capture the intricacies of user behavior. Marketing teams are losing patience when it comes to the time required to fully analyze what their audiences are doing on-site. Marketing teams are typically met with combinational reports that they create themselves, showing we bought this traffic, revenue was generated, we think it’s on average related to this stuff, we think that there’s a lift, but there isn’t any direct association, always.
It’s time for a change in mindset.
Q: What does that change in mindset need to be, especially for companies that are looking to be more predictive in their advertising strategies?
CB: It’s about teams communicating the right information to the right people.
A major company here in Seattle will have many, many projects going on at once, and the teams don’t always talk to each other. And then when it’s time to release changes to the site, or to the apps, to make analytics things happen, changes do happen, then they get pushed down and forgotten.
And then, oh wait, why didn’t that happen? Because also, engineering or development is disconnected from the teams that are responsible for doing product change. So teams are all in indirect competition for resources.
Technology will play a bigger role than ever before. As predictive intelligence improves, marketing leaders can rely on their platforms of choice to do the heavy lifting. The flow of information needs to be crisp.
That’s what I see missing time and again at companies. There needs to be more effort devoted to creating an organization structure that leads to great product work.
Better analytics and strategic use of data will help marketers better find their best prospects in a crowd.
As a result of the constant changes in the customer journey, it’s more important than ever to be able to not just manage multiple touch points, but to also strategically see higher-level insights beyond individual quantitative data. Strategically using analytics means deploying next-generation, predictive advertising management solutions such as data science algorithms that glean deeper insights from large chunks of data and create predictive models in cases of data scarcity. It also means using smarter strategies that go beyond day-to-day number crunching to connect the dots, such as retargeting, which can significantly increase the relevance of your messaging.
About the Author
Andrew Park is a content marketing manager at QuanticMind. A UC Berkeley graduate and lifelong Bay Area resident, Andrew has done tours of duty in editorial, PR and marketing, and now works with the QuanticMind team to communicate the importance of data science and machine learning in digital advertising.More Content by Andrew Park