A Map or a Matrix? Identity Management Is More Complex By the Day

A newly published white paper on how advertisers and brands can recognize unique customers across marketing platforms underscores just how tough this important job is for data-driven marketers.

As technologists and policymakers weigh in themselves on the data universe – often without understanding the full ramifications of what they do (or worse, knowing so but proceeding anyway) – data flows on the Internet and on mobile platforms are being dammed, diverted, denuded, and divided.

In my opinion, these developments are not decidedly good for advertising – which relies on such data to deliver relevance in messaging, as well as attribution and measurement. There is a troubling anti-competition mood in the air. It needs to be reckoned with.

Consider these recent developments:

  • Last week, the European Court of Justice rendered a decision that overturned “Privacy Shield” – the safe harbor program that upward of 5,000 companies rely upon to move data securely between the European Union and the United States. Perhaps we can blame U.S. government surveillance practices made known by Edward Snowden, but the impact will undermine hugely practical, beneficial, and benign uses of data – including for such laudable aims as identity management, and associated advertising and marketing uses.
  • Apple announced it will mandate an “opt-in” for mobile identification data used for advertising and marketing beginning with iOS 14. Apple may report this is about privacy, but it is also a business decision to keep Apple user data from other large digital companies. How can effective cross-app advertising survive (and be measured) when opt-in rates are tiny? What about the long-tail and diversity of content that such advertising finances?
  • Google’s announcement that it plans to cease third-party cookies – as Safari and Mozilla have already done – in two years’ time (six months and ticking) is another erosion on data monetization used for advertising. At least Google is making a full-on attempt to work with industry stakeholders (Privacy Sandbox) to replace cookies with something else yet to be formulated. All the same, ad tech is getting nervous.
  • California’s Attorney General – in promulgating regulation in conjunction with the enforcement of the California Consumer Privacy Act (in itself an upset of a uniform national market for data flows, and an undermining of interstate commerce) – came forth with a new obligation that is absent from the law, but asked for by privacy advocates: Companies will be required to honor a browser’s global default signals for data collection used for advertising, potentially interfering with a consumer’s own choice in the matter. It’s the Do Not Track debate all over again, with a decision by fiat.

These external realities for identity are only part of the complexity. Mind you, I haven’t even explored here the volume, variety, and velocity of data that make data collection, integration, analysis, and application by advertisers both vital and difficult to do. As consumers engage with brands on a seemingly ever-widening number of media channels and data platforms, there’s nothing simple about it. No wonder Scott Brinker’s Mar Tech artwork is becoming more and more an exercise in pointillism.

Searching for a Post-Cookie Blueprint

So it is in this flurry (or fury) of policy developments that the Winterberry Group issued its most recent paper, “Identity Outlook 2020: The Evolution of Identity in a Privacy-First, Post-Cookie World.”

Its authors take a more positive view of recent trends – reflecting perhaps a resolve that the private sector will seize the moment:

“We believe that regulation and cookie deprecation are a positive for the future health and next stage of growth for the advertising and marketing industry as they are appropriate catalysts for change in an increasingly privacy-aware consumer environment,” write authors Bruce Biegel, Charles Ping, and Michael Harrison, all of whom are with the Winterberry Group.

The researchers report five emerging identity management processes, each with its own regulatory risk. Brands may pursue any one or combination of these methodologies:

  • “A proprietary ID based on authenticated first-party data where the brand or media owner has established a unique ID for use on their owned properties and for matching with partners either directly or through privacy safe environments (e.g.: Facebook, Google, Amazon).
  • “A common ID based on a first-party data match to a PII- [personally identifiable information] based reference data set in order to enable scale across media providers while maintaining high levels of accuracy.
  • “A common ID based on a first-party data match to a third-party, PII-based reference data set in order to enable scale across media providers while maintaining high levels of accuracy; leverages a deterministic approach, with probabilistic matching to increase reach.
  • “A second-party data environment based on clean environments with anonymous ID linking to allow privacy safe data partnerships to be created.
  • “A household ID based on IP address and geographic match.”

The authors offer a chart that highlights some of the regulatory risks with each approach.

“As a result of the diversity of requirements across the three ecosystems (personalization, programmatic and ATV [advanced television]) the conclusion that Winterberry Group draws from the market is that multiple identity solutions will be required and continue to evolve in parallel. To achieve the goals of consumer engagement and customer acquisition marketers will seek to apply a blend of approaches based on the availability of privacy-compliant identifiers and the suitability of the approach for specific channels and touchpoints.”

A blend of approaches? Looks like I’ll need a navigator as well as the map. As one of the six key takeaways, the report authors write:

“Talent gaps, not tech gaps: One of the issues holding the market back is the lack of focus in the brand/agency model that is dedicated to understanding the variety of privacy-compliant identity options. We expect that the increased market complexity in identity will require Chief Data Officers to expand their roles and place themselves at the center of efforts to reduce the media silos that separate paid, earned and owned use cases. The development of talent that overlaps marketing/advertising strategy, data/data science and data privacy will be more critical in the post-cookie, privacy-regulated market than ever before.”

There’s much more in the research to explore than one blog post – so do your data prowess a favor and download the full report here.

And let’s keep the competition concerns open and continuing. There’s more at stake here than simply a broken customer identity or the receipt of an irrelevant ad.

Influencer Marketing Can Have Great ROI and You can Prove It

In my previous post, I discussed how influencer marketing will become a prominent marketing tactic in 2020. In this post, I would like to share what is working and what influencer marketing needs to do to become a trusted channel.

In my previous post, I discussed how influencer marketing will become a prominent marketing tactic in 2020. In this post, I would like to share what is working and what influencer marketing needs to do to become a trusted channel.

Designing an effective influencer-based campaign must take into account the objectives of the campaign, whether it is a product or service, and the length of the product purchase cycle. As a result, execution varies. However, a clear consensus is emerging that the most successful campaigns focus on co-developing content, where the influencers are given the flexibility to determine the right way to introduce their audience to the sponsor’s brand. In these instances, brands work with influencers to design content that interacts with their product or service in an entertaining or informative way. When done well, the influencer’s credibility transfers onto the sponsor’s brand. A great discussion on this can be found on Scott Guthrie’s podcast.

A Successful Influencer Marketing Campaign

One example of an influencer campaign that I really love is the Liquid- Plumr “Will it clog” campaign. In this campaign, Liquid-Plumr worked with Vat19 to create funny and interesting clogs for Liquid-Plumr to tackle, like a pile of gummy bears. For Vat19’s audience, this was completely aligned with their theme of creating entertaining experiments. For Liquid-Plumr, not only was it great brand exposure, but it also built significant brand trust among viewers. As the challenges became more and more insane, viewers were impressed with how effective the product was at tackling tough clogs. I recently had the opportunity to hear Bryan Clurman, brand manager for Liquid-Plumr, share the team’s experience, and the lift in sales he showed was impressive.

I assume Liquid-Plumr detected the increase in sales because it was an impressive viral campaign lifting historically flat sales. In this aspect, this case is atypical. Many influencer campaigns are effective, but struggle to show it. Ask a typical marketer working on influencer campaigns and they will confess their most pressing challenge is measuring impact. Currently, most common attribution metrics rely on the same pixel/cookie-based tracking that has been used for digital ads over the last two decades. While this method has some clear benefits, we also know that there is usually a non-trivial gap between actual impact and that which can be directly attributed using cookies. (Let’s forget, for the moment, that the industry-wide death of cookies has already begun.) In my experience, this gap increases with longer sales cycles or when driving brand recognition is the primary goal, as opposed to immediate sales. The further the sale is from the ad exposure, the greater the chance that direct attribution will be lost.

The Magic of the Middle Funnel

An important part of the total ROI solution lies in the middle of the sales funnel. Activities here are closer to the initial ad/brand exposure. For example, assume you are looking for a washing machine for a new home, where your actual purchase may not happen for weeks. While conducting research, you come across a recommendation from a trusted influencer. You interact with the content and may click on a link to the brand website. There, you might look at reviews and product features, but you are still not ready to purchase. These engagement activities have economic value. We know this, because as engagement with a brand increases, sales should increase. However, middle of the funnel measurement is often neglected.

While paying more attention to middle funnel metrics is one step, the other is generating more compelling middle funnel activities. If an effective influencer campaign leads to a clickthrough, can the brand extend that co-branded experience on its own digital property? Not only will that cobranded experience keep the viewer engaged, it is also great for ROI tracking. Even if pixel tracking is lost at this stage, a statistical algorithm can now be employed to correlate the increase in co-branded engagement with eventual sales.

The truth of the matter is, influencer marketing does not have a measurement challenge. Influencer marketing ALSO has a measurement challenge.

What that means is there is nothing uniquely perplexing about influencer marketing ROI. However, influencer marketing is still very new and therefore, the burden of proof is higher. As with all successful marketing ROI plans, it requires a focused approach that clearly defines the objectives and actively seeks opportunities to encourage measurable engagement.

4 Tips Aimed at Defending Digital Marketing’s Value

For B2B marketing, it isn’t always as easy to quantify success as we would like, even with the near-infinite measurability of digital marketing. Here are ideas for defending your digital marketing’s value.

“Half the money I spend on advertising is wasted; the trouble is, I don’t know which half.”

John Wanamaker’s famous quip may be less true today than it was when he said it — we have so many ways to track and assess advertising and marketing performance. And yet, those same tools — largely digital tools — have also created unrealistic expectations for many marketers. This especially true for B2B marketers for whom sales aren’t consummated after a website click.

So we’re left in a state where the data available to us (and boy, there’s a lot of data!) doesn’t tell the whole story. This can often put marketers at a disadvantage when talking to the C-suite crowd.

Their interest is in profit and loss. Clicks, likes, and follows aren’t a currency they care about.

The question is, what can you do as a marketer to demonstrate the value your team’s work delivers?

Tie Digital Marketing to Business Outcomes

Begin by admitting that you can’t rely on process metrics alone – the clicks, likes, and follows I mentioned above. You must tie your work to business metrics. Ideally, that’s profit, but you can also demonstrate a positive return if your work impacts other key performance indicators, like revenue, cost savings, lead quality, or lead volume.

Admit to Marketing’s Uncertainties

Get your peers and upper management to buy into the fact that nearly all B2B marketing includes some amount of uncertainty. As noted earlier, our sales are more complex and there’s rarely a “Buy” button for prospects to click after consuming a piece of your content or connecting with you via social media.

Make Metrics Work for You

For many of us, this is the holy grail. Unfortunately, it’s not always easy.

You may have to work backward by, for example, diving into your CRM data to examine the profiles of converted prospects.

  • How much content have they consumed?
  • Where have they interacted with you on social media?
  • Are they email subscribers?
  • Have they attended industry events at which your executives have presented?

This won’t necessarily paint a causal effect, but can help you make the case that your marketing work is making a difference.

Seek Ongoing Incremental Improvement

Though this again will require metrics data that can be hard to establish with confidence, it’s worth tracking your progress any way you can. For example, is the percentage of converted leads who began their relationship with your firm via the website increasing or decreasing, compared to other methods? If you don’t know, can you create the tools you need to gather this information?

Ideally, we’d all spend 100% of our resources on reaching and converting our ideal prospects. But don’t shy away from investing in the systems that will let you do so more consistently, and with more accountability.

How to Use Google Analytics to Improve Google Ads Performance

Google Analytics can be a treasure trove of information to help improve the performance of your Google Ads campaigns. However, trying to figure out all of the the various metrics within Google Analytics can be a big stumbling block for advertisers.

Google Analytics can be a treasure trove of information to help improve the performance of your Google Ads campaigns. However, trying to figure out all of the the various metrics within Google Analytics can be a big stumbling block for advertisers.

The sheer volume of numbers and data available can quickly get overwhelming.

The Key to Finding Value in Google Ads Metrics

Both Google Analytics and Google Ads metrics and reports should be looked at in the context of your business. Are you using the platform effectively enough in ways that benefit your business? What is it you value most, when it comes to your company?

These are a couple of questions you might want to focus on as you comb through your Google Analytics metrics. Understanding what you want to accomplish with your ad campaign can help you narrow down metrics that matter to your bottom line.

  1. What audience demographics do you wish to attract?
  2. Are visitors able to find the thing they are looking for after clicking your ad?
  3. Is your landing page delivering the type of conversions you are after?
  4. From which channels would you like to direct most of your traffic?

Let’s look at how certain Google Analytics metrics and reports can help with Google Ads.

Give Visitors a Great Experience

Do you know what visitors hate the most about clicking on an ad? Not finding what they need. This can ultimately hurt your brand, if your Google Ads campaigns are frustrating prospective customers.

Sure, your Google Ads conversion rate can help give you this insight, but it doesn’t give you the full story. If your ads are not converting as well as you’d like, then you need to dive into Google Analytics to see what’s going on.

First, take a look at your your landing page bounce rate. That’s the number of visitors who see your landing page and then leave without clicking to a second page. A high bounce rate means your landing pages are not living up to the promises you’re making in your ads.

Gain Insight Into Your Website Design

What good does it do to drive prospective customers  to your website, if they have a difficult time with the navigation?

If you are having difficulty getting your conversion rate up to where you would like, it could be an issue with website design. Part of the problem might be that your website design makes completing the path to a conversion overly tedious.

You can review this using the Google Analytics Users Flow report. The Users Flow report will show you how people are navigating through your website, starting with your landing page. You may see that prospective customers are getting distracted and clicking to pages that are not in your sales funnel. Use this information to redesign your landing pages and subsequent pages in the sales funnel to reduce drop off and increase the overall conversion rate of your Google Ads campaign.

Find Your Top Performing Audience Demographics & Interests

The Google Analytics demographics and interest reports can you give you great insight into your top performing audiences. Review these reports to see which audiences are performing best.

Then use the audience data to improve your Google Ads campaigns. Modify your demographic targeting, adjust bids, and even launch new campaigns to target the audiences you know perform best based on the Google Analytics data.

Summary

To be successful with Google Ads often requires using data that’s not available within the Google Ads reports. But one of the best sources of advertising performance-enhancing data is Google Analytics.

Review your landing page bounce rates to see how well you’re matching your landing page message to your ad copy. Use your Users Flow reports to see if your prospective customers are getting distracted on your website. And use your demographics and interests reports to improve the targeting in your Google Ads campaigns.

Want more tips to improve your Google Ads performance? Click here to grab a copy of our “Ultimate Google Ads” checklist.

 

 

3 Ways to Better Manage Marketing Automation So the ‘Shiny Object’ Doesn’t Stab You

I presented at the All About Marketing Tech Virtual Conference & Expo on the topic of targeting and automation. One of the themes I hit upon was about how companies are hindering their marketing automation success with needless complexity.

On Thursday, I will be presenting at the All About Marketing Tech Virtual Conference & Expo on the topic of targeting and automation. One of the themes I plan to hit upon is about how companies are hindering their marketing automation success with needless complexity. This topic falls squarely in the “land of shiny objects,” which is a recurring theme in many of my posts.

This theme in my posts and the 1:10 p.m. ET session, “Using Automation + Targeting to Engage and Convert,” focuses on how tempting technology can be to the marketing practitioner and how it can lead to the desire to do too many things — to detrimental effect. However, there are three things you can do to manage automation better.

Step 1 in Marketing Automation

First, make sure you have a customer strategy. If you do not have a solid strategy, then you will be automating a bunch of tactics. Unless these tactics sit under a cohesive strategy, they may work against each other.

For example, a price-focused customer acquisition program may hurt long-term brand development or pricing power. When you add automation to this scenario, it will supercharge the tactic and potentially cause greater harm.

Step 2

Second, make sure you have a test-and-learn agenda. Automation is a very data and metrics-driven process and it is managed by humans, using those same data points and metrics.

Successful marketing automation involves iterative learning to drive growth. Therefore, knowing what you are trying to achieve through automation and running multiple tests to better understand the underlying dynamics is critical.

What tends to happen, however, is that too many objectives are pushed through the automation system and the ability to learn is muddled by an excess of data and a dearth of focus.

The advice I often give is:

“Because you can do something through automation, it does not mean you should.”

Creating a learning agenda you can manage and identifying the critical metrics needed for evaluation are critical first steps before automating a marketing function.

Step 3

Third, make sure you have a pivot plan. A pivot plan anticipates how you will modify your automation program and lists the levers at your disposal.

For example, if results are not coming in as expected, you may alternate content, alternate segments or redefine the automation goals.

Doing all three at once will most likely leave you as clueless as when you began. While this seems like marketing management 101, it is easy to lose sight of this with automation. Automation generally promises rapid decision-making over volumes of interactions and self-learning capabilities.

As a result, it is tempting to get out of the way and let it do its magic. In the near to mid-term, despite automation’s usefulness, this will not substitute for strategic and management thinking.

Conclusion

I am in no way discouraging the use of marketing automation. It is not only the future, but it is also the present and is driving positive results.

Successful marketers need to start experimenting with the technology now.

However, marketing automation is also not so wonderous and awe-inspiring that we forget that it needs management and strategy. That, in turn, means balancing lofty automation goals with what you can managerially digest.

How I Cut the Cord and Learned to Love OTT

Just how many months — no, years — does it take for a logical, clear-headed, money-conscious, well-informed consumer to overcome inertia, cut the cord in his home television habits, and move to OTT?

Just how many months — no, years — does it take for a logical, clear-headed, money-conscious, well-informed consumer to overcome inertia, cut the cord in his home television habits, and move to OTT?

I’ll let you know when it happens.

Yes, I’m one of those Americans — a dwindling number, but we’re still a force. Being charged a couple hundred dollars every month with our stripped-down, no add-ons triple-play (phone/TV/Internet) packages, because there’s no cable competition (in my building) and Spectrum knows it. We don’t even have access to Verizon or AT&T, or RCN, either. Such a dilemma.

Thank goodness for Mom and Dad. They don’t pay my bills. But they donated to me their Roku device when they upgraded their own TV sets. They also added me to their Netflix account as a gift, and now my viewing habits — finally — are changing. Scheduled television via cable at home is clearly on the wane. On linear TV via cable, I watch local news and live sports, mostly — and even some of that I can stream.

As stuck as I am in my ways … I’m about to go bold. And do the deed. Snip! (Well, we’ll see.)

In the meantime, advanced television is clearly on the rise.

“Ad spend on over-the-top (OTT) streaming video will increase 20% this year to $2.6 billion, according to a Winterberry Group study of U.S. ad spend data,” reports eMarketer. “Despite OTT’s surge, it’s still small — compared with the $69.2 billion that Winterberry Group estimates U.S. advertisers will spend on linear TV. For some advertisers, measurement challenges prevent them from investing more in OTT.”

A recent Direct Marketing Club of New York program included a panel of experts who parsed some of the challenges. With OTT, you have two worlds colliding — traditional television and traditional digital — and the user (me) has an expectation that online video, if I’m to watch it as programming, had best carry the quality of linear television. I even want my online video advertisements — hey, it’s ad-financed content on many platforms — to carry the quality of a TV ad, rather than a GIF. Still, I’m open to new ad formats here — I’m starting to enjoy 6-second ads, thanks to digital training. And I’m actively searching and browsing, often on a second device concurrently, some of it prompted by content and ads.

We Need Industry Standards …

What metrics matter to whom? Audience reach and eyeballs may coo the traditional TV media buyer (and seller), who simply wants those same or similar metrics digitally. And that may be fine for CMOs who live and breathe “passive” awareness, but addressable television’s real prize is data: user data, dwell time — and demographics — that shed light on a brand’s customers, one device or cross-device, and one view or continued view (start viewing a program on one device, and finish viewing on another) at a time. Here, “active” engagement metrics matter, such as clickthroughs, conversions, and attribution. These data drive the algorithms that target and tailor the advertising.

And remember the Big Data “ouch” when mobile, social, and local users flooded the market? Same goes here: “Data is overabundant, non-standardized, and non-harmonious,” said one panelist. We need to codify, standardize, and become screen-agnostic in our reporting. Certainly, people expect viewing on a TV to be different than viewing on a smartphone. Marketers need to know device use metrics to see how ad delivery may need to differ. Yet the user metrics do need to be agnostic — audience and engagement metrics need to be settled upon for the marketplace to trust, verify, and grow. That’s because in OTT and Advanced Television, “data is the most important ROI.”

I didn’t have to finish my blog at any particular time today — thanks to TV on demand, anywhere. Oh wait a minute, I gotta shut my laptop: the season finale of “RuPaul’s Drag Race” starts in 10 minutes, and I’ve been looking forward to it for two weeks! Inertia, indeed.

Why KPIs Lack Insight and What Marketers Can Do About It

I have a love-hate relationship with KPIs. When done right, they are mission-critical to defining success and can focus the organization on the right priorities. When chosen poorly, KPIs can be disconnected with ground realities and be a constant source of frustration for team members trying to impact them.

I have a love-hate relationship with KPIs (key performance indicators). When done right, they are mission-critical to defining success and can focus the organization on the right priorities. When chosen poorly, KPIs can be disconnected with ground realities and be a constant source of frustration for team members trying to impact them.

However, poorly designed KPIs are not my primary gripe, at least not in this post. My main concern is that even well-designed KPIs are simply not deeply insightful, but they are often used as if they are.

Well-designed KPIs are full of contradictions. On the one hand, they are expected to be simple, easy to communicate, and intuitive. On the other hand, they’re expected to provide actionable information and be a reliable measure of important success criteria.

Anyone who has worked on developing KPIs knows that it is a game of balance and compromise, based on business objectives. The need for actionable information battles with the desire for simple metrics. The desire for intuitive metrics battles metrics that push status quo thinking or properly reflect the diversity of business interactions.

After many years working with and helping clients identify KPIs, I have found ways to manage their dichotomous nature, but never overcome it. If there is a brilliant mind out there who has solved for this, I would love to hear about it. For now, I will assume that this dichotomous struggle is a law of nature.

This leads me back to my main point. Marketers need to stop viewing KPIs as major source of insights. They are, as the name illustrates, only “indicators.” While this seems like an obvious statement, it is surprising how often KPIs have become the primary source of insights for most companies.

Take digital analytics, for example. Most companies using the web analytics platform use default metrics, such as clickthrough and page views, as their primary measures to understand web activity. While these metrics may indicate increased interest in content, they rarely tell you how satisfied the visitor was with the content or how valuable it was in decision-making. It is rare for companies to set up custom metrics and reporting, which might provide better insights. It is even rarer for companies to download raw web data into a data management tool and truly analyze visitor interaction with content, even though these solutions exist. Instead, most companies use the default web KPIs to derive custom insights into behavior on their website.

Another example can be found about how companies use social channel data. There are some great social analytics tools out there. When I come across most implementations, however, they are mostly set to track high-level sentiment analysis and rarely deliver deep insights. However, the underlying data is often volumes of highly informative, unprompted, free-form feedback. It has the added benefit of being free of interviewer bias or agenda-setting.

Recently, I was working on a project for a client that viewed their products as very innovative. Yet, when mining nearly 1,000 instances of social data, we found only one unprompted mention of innovation. Upon further investigation, we found that innovation was meaningless to the consumer. Instead, it was performance, excitement, and fun that consumers talked about most often. The customer was conveying what innovation really meant to them, while the company was still thinking in terms of engineering sophistication. This insight was un-minable from the standard social KPIs. Even traditional survey-based market research may not have captured this insight, as it would have relied on coming up with the right questions to uncover this disconnect between the company and its customers.

These examples demonstrate the need to dig deeper for better insights and I risk the label of “Captain Obvious” by making this assertion.

So, let me add to this. Well-designed KPIs, because of their simplicity and action orientation, often lull us into overestimating their insightfulness. This link is unconscious and habitual.

When I have asked marketers “What is your (Social, Web or Customer) data telling you?” A common response is, the (relevant) tracker is telling us [fill in the blank].

In reality, the answer to the question is rarely found in the tracker or KPIs. Even if they can point to a KPI that is helpful, the underlying explanation is still often conjecture or a hypothesis. In fact, the better aligned the KPI story is with commonly accepted wisdom, the more likely it is to be seen as data-driven thinking.

In other words, we find an interesting KPI trend and create a believable story around this trend and that becomes data-driven thinking when it is still just conjecture. It takes great discipline to put on the brakes and look for deeper and corroborating evidence and that is what KPIs really calls for.

I want to make clear that this post is not advocating for the elimination of KPIs. They are very helpful tools for aligning the organization and most of us understand that they are only indicators. When done well, however, they are insidiously brilliant at creating the illusion of deep insight; especially if the resulting story is a good one. Truly data-driven marketers should be aware of this and be ready to dig deeper before letting a KPI drive strategic decisions.

Understanding Your Google Ads Metrics With the Latest Interface

How do you know what the metrics in Google Ads mean and which ones matter the most? The latest version of Google Ads’ interface has a particularly large number of metrics, so it’s easy to get overwhelmed when you first log on.

How do you know what the metrics in Google Ads mean and which ones matter the most? The latest version of Google Ads’ interface has a particularly large number of metrics, so it’s easy to get overwhelmed when you first log on.

Each page has a table full of data, including a graph of metrics and various reports. It’s a little like looking at an airplane cockpit for the first time, with all its lights, switches and gauges. However, experienced advertisers know that all the information in Google Ads allows you to dig into your campaign performance and find ways to improve it.

Which Metrics Really Matter?

The most important Google Ads metrics include the following:

  • Cost-per-click (CPC)
  • Clickthrough rate (CTR)
  • Conversion rate
  • Cost-per-acquisition (CPA)

CPC

CPC is an advertising model in which an advertiser pays a website owner each time a user clicks on an ad. First-tier search engines like Google Ads typically use a CPC model, because advertisers can bid on key phrases that are relevant to their target market. In comparison, content sites typically charge per 1,000 impressions of the ad.

CTR

CTR, or clickthrough rate, is the ratio of users who click a link to the total number of users who view the ad. CTR generally indicates a marketing campaign’s effectiveness in attracting visitors to a website.

Conversion Rate

Conversion rate is the ratio of goal achievements to the number of visitors. It’s essentially the proportion of visitors who take a desired action as a result of your marketing activity. The specific action that a conversion rate monitors depends on the type of business you’re promoting. For example, online retailers often define a conversion as a sale, while services businesses consider other actions, such as a request for a quote, a demo sign up or a report download, when measuring conversion rate.

CPA

CPA, or cost per action, is the total cost of your ads divided by the number of conversions. Again, the specific action depends on the type of business you’re promoting. For example, CPA for online retailers is typically the cost per e-commerce sale. Services businesses typically measure CPA as a cost per lead. This number is critical, because it tells you if your campaigns are profitable or not.

How Can Metrics Help You Improve Performance?

Poor metrics can indicate courses of action that can help you improve your Google Ads campaign performance.

CPC

A high CPC could mean that you need to raise the quality scores for your ad, which could reduce the cost of each click. You can also accomplish this by using ad scheduling and geotargeting to ensure your website doesn’t show ads during times or in locations where you don’t do business. Additional strategies for reducing CPC include using demographic targeting, in-market audiences and remarketing to narrow your audience to just the people who are interested in your business.

CTR

A low CTR can indicate that you need to review the keywords and ad copy in your Google Ads account. For example, you should ensure that you’re only bidding on keywords that relate to your offers. You should also perform A/B testing on your ads to determine the factors that interest your prospects the most, whether it’s features, benefits or some emotional trigger. You can also improve CTR by ensuring that your ad takes up as much room as possible by implementing ad extensions.

Conversion Rate

A low conversion rate can indicate that you need to take a closer look at your landing pages, where visitors go when they click on an ad. These pages should be very clean and quick to load to ensure visitors don’t lose interest after they click. Your ads should always send visitors directly to a dedicated landing page, rather than just your home page or even a general landing page.

CPA

A high CPA means that you aren’t getting a good return on investment (ROI) from your ad spend. Possible causes of a high CPA include a high CPC or low conversion rate, which often means a poor choice of keywords and ad copy. Concentrate your budget on high-converting keywords with a high intent to buy.

Conclusion

Google Ads provides many metrics that can tell you how to improve website performance. However, this information can also be daunting to interpret if you don’t know what it means.  Follow the tips above to monitor your key metrics and make adjustments to improve your Google Ads performance.

Want more tips to improve your Google advertising? Get your free copy of our “Ultimate Google AdWords Checklist.”

 

Nostalgic for the Future: Data That is ‘Close to You’

Last week, I had a dream — and in it, Karen Carpenter and I were friends. The following night, I had a similar dream — and this time it was Carly Simon. I literally went to bed the next night hoping for a Roberta Flack visitation.

Last week, I had a dream and in it, Karen Carpenter and I were friends. The following night, I had a similar dream and this time it was Carly Simon. I literally went to bed the next night hoping for a Roberta Flack visitation. As a result of these slumbering vocalists and songwriters, I’ve spent a good part of my leisure time over the New Year holiday listening to all their songs on my iPod. It’s yesterday, once more.

Who knows why we dream what we dream?

Sometimes, it just happens that when we’ve experienced enough in life, in play, in work some situations are bound to come around again, next week or decades later. I mean, I owned all that vinyl way back then and now I can stream it all again.

Greatest Hits: Lifecycles of Data-Inspired Marketing

So when Marc Pritchard of Procter & Gamble last week at the Consumer Electronics Show talked about “a world without ads,” I said to myself “oh, I’ve heard this song before.” And he’s right to say it.

In the world of data and direct marketing, a quest for wholly efficient advertising and a mythical 100-percent response rate actually is a 100-year science. Thank you, visionaries, such as Claude Hopkins.

• The 19th Century shopkeeper knew each customer, and conversed regularly. Ideally, each customer’s wants and desires were noted and needs anticipated to the extent that the customer was fulfilled accordingly. (Aaron Montgomery Ward and Richard Warren Sears.)
• Direct marketing originally through print, catalogs and mail, and then broadcast sought to replicate this model remotely. Measurement, attribution and response were put to science. Creativity served the science, or science served the creativity in either direction. Segmentation, analytics and differentiated communication flowed. (David Ogilvy, Stan Rapp and Alvin Eicoff, among others).
• In digital, social and mobile, direct marketing is rejuvenated this time “data-driven marketing.” Some have described this as data-inspired storytelling, or direct marketing on steroids. How responsible data collection can be used to identify prospect needs and wants, and funnel tailored communication through to sale, service and repeat purchase. (Jeff Bezos, among others.)
• And now the product itself can be designed to communicate to the customer smart appliances, smart cars, and the parts and products inside, with sensors and Internet connections and mobile app interfaces all being able to let the user know, it’s time for consideration or some other product lifecycle action.

Post-Advertising: A Reverence for Data

In all these examples, the constant is “I want to know you, so I can serve you the customer” and the facilitator is data. We exist to create and serve a customer. Period. Anything less is not sustainable. Data, in these models, is sought, analyzed and revered. It is also transparent, and its use and application has consumer buy-in. That premise is as true now in the Internet age, as it was in the direct response era before it. We all need to excel in data reverence, first, and then data analysis and application.

Advertising does have a role here, of course. Not every product sells itself and not every product meets customer satisfaction fully. The best advertising, and even the best data behind it, cannot save a bad product. There is always a need for advertising and marketing to inform the consumer, and a brand promise that serves to attract and retain beyond the product.

Every generation has its pop heroes. Tonight, I may just dream of Adele.

Factors for Marketers to Consider in Attribution Rules

At the end of each campaign effort, a good database marketer is supposed to study “what worked, and what didn’t,” using attribution rules. Call it “Back-end Analysis” or “Campaign Analytics.” Old-timers may use terms like “Match-back.” Regardless, it is one of the most important steps in 1:1 marketing that is synonymous with what we used to call “Closed-loop Marketing.”

At the end of each campaign effort, a good database marketer is supposed to study “what worked, and what didn’t,” using attribution rules. Call it “Back-end Analysis” or “Campaign Analytics.” Old-timers may use terms like “Match-back.” Regardless, it is one of the most important steps in 1:1 marketing that is synonymous with what we used to call “Closed-loop Marketing.” (refer to my first article on Target Marketing from 11 years ago, “Close the Loop Properly”).

In fact, this back-end analysis is so vital that if one skips this part of analytics, I can argue that the offending marketer ceases to be a 1:1 or database marketer. What good are all those databases and data collection mechanisms, if we don’t even examine campaign results? If we are not to learn from the past, how would we be able to improve results, even in the immediate future? Just wild guesses and gut feelings? I’ve said it many times, but let me say it again: Gut-feelings are overrated. Way more overrated than any cheesy buzzword that summarizes complex ideas into one or two catchy words.

Anyhow, when there were just a few dominant channels, it wasn’t so difficult to do it. For non-direct channel efforts, we may need some attribution modeling to assign credit for each channel. We may call that a “top-down” approach for attribution. For direct channels, where we would know precisely who received the offers, we would do a match-back (i.e., responders matched to the campaign list by personally identifiable information, such as name, address, email, etc.), and give credit to the effort that immediately preceded the response. We may call that a “bottom-up” method.

So far, not so bad. We may have some holes here and there, as collecting PII from all responders may not be feasible (especially in retail stores). But when there was just direct mailing as “the” direct channel, finding out what elements worked wasn’t very difficult. Lack of it was more of a commitment issue.

Sure, it may cost a little extra, and we had to allocate those “unknown” responders through some allocation rules, but backend analysis used to be a relatively straightforward process. Find matches between the mailing (or contact) list and the responders, append campaign information — through what we used to call “Source Code” — to each responder, and run reports by list source, segment, selection mechanism, creative, offer, drop date and other campaign attributes. If you were prudent to have no-mail control cells in the mix, then you could even measure live metrics against them. Then figure out what worked and what didn’t. Some mailers were very organized, and codified all important elements in those source codes “before” they dropped any campaigns.

Now we are living in a multi-channel environment, so things are much more complicated. Alas, allocating each of those coveted responses to “a” channel isn’t just technical work; it became a very sensitive political issue among channel managers. In the world where marketing organizations are divided by key marketing channels (as in, Email Division vs. Direct Mail Division), attribution became a matter of survival. Getting “more” credit for sales isn’t just a matter of scientific research, but a zero-sum game to many. But should it be?

Attribution Rules Should Give Credit Where Credit’s Due

I’ve seen some predominantly digital organizations giving credit to their own direct marketing division “after” all digital channels took all available credit first. That means the DM division must cover its expenses only with “incremental” sales (i.e., direct-mailing-only responses, which would be as rare as the Dodo bird in the age of email marketing). Granted that DM is a relatively more expensive channel than email, I wish lots of luck to those poor direct marketers to get a decent budget for next year. Or maybe they should look for new jobs when they lose that attribution battle?

Then again, I’ve seen totally opposite situations, too. In primarily direct marketing companies, catalog divisions would take all the credit for any buyer who ever received “a” catalog six months prior to the purchase, and only residual credit would go to digital channels.

Now, can we at least agree that either of these cases is far from ideal? When the game is rigged from the get-go, what is the point of all the backend analyses? Just a façade of being a “data-based” organization? That sounds more like a so-called “free” election in North Korea, where there are two ballot boxes visibly displayed in the middle of the room; one for the Communist Party of the Dear Leader, and another box for all others. Good luck making it back home if you put any ballot in the “wrong” box.

Attribution among different channels, in all fairness, is a game. And there is no “one” good way to do it, either. That means an organization can set up rules any way it wants them to be. And as a rule I, as a consultant, tend not to meddle with internal politics. Who am I to dictate what is the best attribution rule for each company anyway?

Here’s How I Set Up Attribution Rules

Now that I am a chief product guy for an automated CDP (Customer Data Platform) company, I got to think about the best practices for attribution in a different way. Basically, we had to decide what options we needed to provide to the users to make up attribution rules as they see fit. Of course, some will totally abuse such flexibility and rig the game. But we can at least “guide” the users to think about the attribution rules in more holistic ways.

Such consideration can only happen when all of the elements that marketers must consider are lined up in front of them. It becomes difficult to push through just one criterion — generally, for the benefit of “his” or “her” channel — when all factors are nicely displayed in a boardroom.

So allow me to share key factors that make up attribution rules. You may have some “A-ha” moments, but you may also have “What the … ” moments, too. But in the interest of guiding marketers to unbiased directions, here is the list:

Media Channel

This is an obvious one for “channel” attribution. Let’s list all channels employed by the organization, first.

  • Email
  • Direct Mail (or different types of DM, such as catalog, First Class mail, postcards, etc.)
  • Social Media (and specific subsets, such as Facebook, Instagram, etc.)
  • Display Ads
  • Referrals/Affiliates
  • Organic Search/Paid Search
  • Direct to Website (and/or search engines that led the buyers there)
  • General Media (or further broken down into TV, Radio, Print, Inserts, etc.)
  • Other Offline Promotions
  • Etc.

In case there are overlaps, which channel would take the credit first? Or, should “all” of the responsive channels “share” the credit somehow?

Credit Share

If the credit — in the form of conversions and dollars — is to be shared, how would we go about it?

  • Double Credit: All responsible channels (within the set duration by each channel) would get full credit
  • Equal Split: All contributing channels would get 1/N of the credit
  • Weighted Split: Credit divided by weight factors set by users (e.g., 50% DM, 30% EM, 20% General Media, etc.)

There is no absolutely fair way to do this, but someone in the leadership position should make some hard decisions. Personally, I like the first option, as each channel gets to be evaluated in pseudo-isolation mode. If there was no other channel in the mix, how would a direct marketing campaign, for example, have worked? Examine each channel and campaign this way, from the channel-centric point of view, to justify their existence in the full media mix.

Allocation Method

How will the credit be given out with all of those touch data from various tags? There are a few popular ways:

  • Last Touch: This is somewhat reasonable, but what about earlier touches that may have created the demand in the first place?
  • First Touch: We may go all of the way back to the first touch of the responder, but could that be irrelevant by the time of the purchase? Who cares about a Christmas catalog sent out in November for purchases made in May of the next year?
  • Direct Attribution: Or should we only count direct paths leading to conversions (i.e., traceable opens, clicks and conversions, on an individual level)? But that can be very limiting, as there will be many untraceable transactions, even in the digital world.
  • Stoppage: In the journey through open, click and conversion, do we only count conversions, or should the channel that led to opens and clicks get partial credit?

All of these are tricky decisions, but marketers should not just follow “what has been done so far” methods. As more channels are added to the mix, these methods should be reevaluated once in a while.

Time Duration (by Channel)

Some channels have longer sustaining power than others. A catalog kept in a household may lead to a purchase a few months later. Conversely, who would dig out a promotional email from three weeks ago? This credit duration also depends on the type of products in question. Products with long purchase cycles — such as automobiles, furniture, major appliances, etc. — would have more lasting effects in comparison to commodity or consumable items.

  • Email: 3-day, 7-day, 15-day, 30-day, etc.
  • Direct Mail — Catalog: 30-day, 60-day, 90-day, etc.
  • Direct Mail — Non-catalog: 7-day, 14-day, 30-day, 60-day, etc.
  • Social: 3-day, 7-day, 15-day, etc.
  • Direct Visit: No time limit necessary for direct landing on websites or retail stores.
  • General Media: Time limit would be set based on subchannels, depending on campaign duration.

Closing Thoughts

The bottom line is to be aware of response curves by each channel, and be reasonable. That extra 30-day credit period on the tail end may only give a channel manager a couple extra conversions after all of the political struggles.

There is really no “1” good way to combine all of these factors. These are just attribution factors to consider, and the guideline must be set by each organization, depending on its business model, product composition and, most importantly, channel usages (i.e., how much money bled into each channel?).

Nevertheless, in the interest of creating a “fair” ground for attributions, someone in a leadership position must set the priority on an organizational level. Otherwise, the outcome will always favor what are considered to be traditionally popular channels. If the status quo is the goal, then I would say skip all of the headaches and go home early. You may be rigging the system — knowingly or unknowingly — anyway, and there is no need to use a word like “attribution” in a situation like that.