Resource Label: App analytics

If marketers want accurate results of their mobile marketing campaigns, they need to understand the impact of their campaigns across devices and platforms. Without cross-device attribution to generate true return on ad spend (ROAS) and marketing ROI, marketers simply cannot really know whether they’re doing well or not.

Worse, without a cross-device attribution solution, marketers might erroneously think that one ad campaign is better than another. And that means that companies risk both investing in the wrong channels and campaigns and divesting from the right ones prematurely. That’s literally a worst-case scenario.

As an example, take a look at these two campaigns:

As a mobile app marketer, if you measured the performance of your mobile marketing campaigns only against user behavior in-app, Campaign 2 would appear to be driving higher marketing ROI. In fact, it’d be a no-brainer, and you’d immediately redirectly more ad spend to that channel and media partner.

But, of course, you’d be making a fatal mistake.

In reality when you see the bigger picture of both mobile and web activity taken into account, Campaign 1 returns 2X ROAS compared to 1.5X ROAS and is the much better marketing investment.

The reality is that all too often such crucial data points go unnoticed by companies that either can’t or don’t integrate cross-device data into their marketing analytics ROAS and ROI calculations. Without cross-device — or cross-channel on the same device — insights, marketers just don’t have the tools to accurately evaluate, measure, and optimize performance across marketing channels.

The result is that mobile marketers waste ad spend, lose insights, and worst of all, cost their companies conversions and sales … because they’re turning off channels and partners that are actually working.

Simultaneously, in some cases, marketers are also frustrating users with disjointed cross-device experiences.

The reality is that the cross-device challenge is only becoming more urgent. In the post-COVID world, we are living with a multitude of devices, constantly switching from smartphone to tablet to computer throughout the day in our home offices. People are already using more devices than ever — owning about four on average — and typical conversion paths can wind their way through all of them.

The problem?

Less than a third of marketers use mobile data to identify users across devices. As a result, when consumers engage with a brand across devices, user profiles become fragmented and marketers lose their ability to construct a full view of the buyer journey in order to understand the channels and campaigns driving their highest return.

(And, by the way, there are privacy-safe ways of doing this, using only first-party data.)

Fortunately, a single view of each customer can be created by deterministically matching an organization’s first-party data across all devices and tying it back to each individual customer. This single-view approach can then be leveraged to optimize marketing more effectively. In fact, one study showed that marketers who are able to optimize campaigns based on cross-device attribution insights can reduce Cost Per Action (CPA) by 30-50% and increase mobile app ROI by 50-100%.

Plus, the benefits of optimizing campaigns based on cross-device insights extend beyond ROI measurement and ad spend decisions. Where possible, marketers can automate retargeting campaigns using cross-device information to optimize their messaging — for instance, using desktop creatives that reflect a user’s browsing behavior in-app.

I mentioned it already, but it’s worth reiterating: marketers who leverage cross-device analytics to optimize their marketing return should always do so in a privacy-safe way. Cross-device data can be used to draw sensitive inferences from users and, if not handled securely, can lead to unexpected and unwelcome use of user data. One of the biggest challenges marketers face in generating cross-device insights to optimize their marketing channels is protecting user privacy.

That’s why Singular can enable marketers to integrate cross-device attribution without an SDK by securely passing an identifier during the attribution process via a server-to-server integration. That helps marketers get a full view of their users, using every touchpoint in the conversion path to spot patterns, measure the true performance of their campaigns and, ultimately, hit their marketing goals.

And, it ensures that marketers don’t have to invade customer privacy with third-party data or invasive data-collecting SDKs.

Interested in cross-device measurement?

Request a demo of the Singular solution here.

For top mobile brands, marketing analytics has become dramatically more important in recent years as the emergence of a multitude of tools to track advertising spend, attribution, creative performance, and in-app analytics has advanced mobile app marketers’ ability to measure their user acquisition (UA) efforts across channels and optimize their campaigns.

But you can get too much of a good thing. And you can find that more tools doesn’t equal better performance.

Digital marketing analytics are on the rise — but utility is stagnant

Performance marketing is hard. That’s one of the reasons why we’ve seen the rise of growth stacks and marketing analytics stacks. It’s precisely because of the challenges that digital marketing teams face in corralling and crunching performance data that we’ve seen the growing importance of growth stacks and marketing analytics.
And that’s driving an increase in the purchase and use of marketing analytics to support data-driven decision-making. In fact, a recent survey of CMOs commissioned by Deloitte and The American Marketing Association found that companies plan to increase their spending on marketing analytics by 376% in the next three years.

But there’s a problem.

While most companies have increased focus (and budget) on their marketing analytics stack and the sophistication of analysis tools has grown, for many companies, the investments have yet to pay off. More money spent on marketing analytics hasn’t translated into more growth.

One reason: more tools doesn’t mean more usage. According to data derived from the same survey of CMOs, the use of marketing analytics in decision-making has actually remained stagnant for the last five years. In other words, we have the tools, but we’re not using them.

Why?

Personnel is one issue. Time is another. And perhaps training as well. Marketers surveyed in the study cited a lack of qualified people who can utilize marketing analytics tools as well as a lack of tools to actually measure success through analytics as the primary factors preventing their companies from using more marketing analytics.

Fragmentation follies?

But in a way, it’s not marketers’ fault.

A separate survey commissioned by Google found that only 13% of digital marketers were confident in their ability to measure marketing performance data. The number one reason marketers gave for why they have such a hard time exposing marketing performance data is a lack of integration between their marketing analytics tools.

It’s like telling time: if you have one clock, you “know” exactly what time it is. Two or more, and if there’s any differences, you’re not really sure any more which is right.

Plus, there’s inefficiencies in a large growth stack with multiple redundancies.

Fragmentation in the modern-day marketing stack creates workflow inefficiencies as well as holes in performance data. Simple tasks like aggregating performance data across channels often require marketers to toggle between multiple dashboards and manually update metrics in unwieldy Excel files or other tools. The process is particularly painful for marketers who want to analyze performance data not merely by click-through rates or raw install metrics but rather by the actual quality of those users, as measured by ROI. (In other words, by metrics that matter.)

That’s one reason why Singular is named the way it is.

Singular’s name serves as a constant reminder of our mission: to build a single marketing analytics platform that unites all the disparate data feeds, enabling marketers to do their jobs more efficiently and more effectively. In other words, a single source of marketing truth.

In essence, when your marketing analytics are centralized under one single source of truth, your stack’s output becomes smaller and more manageable, making reporting, analyzing and optimizing performance across channels less error-prone and time-consuming.

This isn’t removing data. It’s not ignoring signals.

It’s integrating them all into a unified whole.

Take cross-device attribution, for instance. In the past, we’ve seen marketers make investments in the wrong marketing campaigns because cross-device data was not properly integrated into their marketing analytics. This highlights the fact that just because a campaign drives high performance metrics on one device or platform does not mean it drives high performance metrics on other devices or platforms. When user engagement data across devices and platforms is taken into account, marketers are able to expose the true ROI metrics of their campaigns to drive better spending decisions and optimizations.

Analytics-Driven Experimentation, Without the Data Deluge

At the core of effective mobile user acquisition strategies is experimentation.

As new channels, media formats and targeting strategies emerge, they can lead to outsized returns for marketers, especially in their early days. For example, when they first launched in 2016, Apple’s auction-based Search Ads drove low CPIs and high conversion rates as demand for Search Ads remained relatively low and curious users noticed the new ad slots.

(By the way, check out the Singular ROI Index for the highest performing networks, including new entrants.)

The advantage of testing new channels and media formats is clear, but marketers have to do so in a way that is deeply integrated with existing analytics systems. The common problem? Thanks to the lack of standardization among ad networks, extracting detailed data from partners for analysis often requires custom integrations and constant maintenance. Definitions and standards differ. Normalization and standardization is essential.

This can add layers of complexity to your analytics stack. And that’s why outsourcing partner integrations to a third-party analytics and attribution provider (Singular!) will allow for smarter testing and analysis without the data deluge.

And that has rewards.

Using this approach, one Singular customer says their team can now dedicate roughly 20% of their time to experimentation with new partners that they think could be promising, including BD, relationship management, media buying and testing. The remaining 80% of their time is spent investing in larger platforms they know well. In both cases, Singular serves as the analytics backbone, providing the most detailed and flexible performance data across nearly 2000 networks, thereby allowing the team’s stack to stay small, nimble and powerful.

Small. But nimble. And powerful.

That’s the power of activated insights driven by consolidated data.