Want to Put Your Paid Marketing to the Test? Turn It All Off
A little reset goes a long way.
- To uncover the true business impact of your marketing efforts, turn off your paid marketing.
- Hitting pause on paid allows your marketers and analytics teams to reset and reestablish a baseline for all media – organic and paid.
- While it may seem radical, this paused spend approach gives control back to your team, delivers trustworthy data, and eliminates bad incentives that can impede your goals.
Incrementality: the most fashionable word in the mobile app marketers lexicon for five years running. Incrementality is no longer an aspirational objective, it’s table stakes – and for good reason. In an ecosystem brimming with fraud and misattribution, mobile growth and performance marketing teams lean on incrementality testing to quantify and validate the true business impact of their paid tactics, not just average cost per acquisition (CPA) and return on ad spend (ROAS).
As the popularity of (and appetite for) incrementality has grown, so have the options for implementing such tests. You’re likely familiar with the most popular methodologies available to you: intent-to-treat (ITT), PSA and ghost ads. However, there's another option worth considering that most advertisers overlook. It’s one that ad networks will never recommend and internal marketing orgs are hesitant to acknowledge.
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Turn off all your paid marketing — all of it.
Although this may seem radical (especially in today’s mobile-dominated world) this methodology has no barrier to entry, requires no additional costs (in fact, it comes with a cost savings), has no biases, offers advertisers the most control, and can be implemented across all operating systems.
So, how does it work? At a high level, the philosophy behind this approach is very similar to that of the elimination diet. In the case of the elimination diet, you remove all potentially troublesome foods from your diet, and slowly reintroduce them back in one at a time to understand the effects of each. Similarly, when you turn off all your paid spend, you’re resetting and reestablishing what your true, organic baseline is in terms of your business’ digital presence. The goal is to remove any governable variables from what is soon-to-be your control. The time required for this “calm” period – the period in which all of your paid marketing is turned off – is completely dependent on the advertiser, which is why it's important to leverage your analytics and data science team.
That team will have recommendations on the correct time frame, and can help you cement your paid reintroduction plan (by channel, OS, country, etc). As you reintroduce channels, remember that you need to do so one-by-one, and that spend needs to stay consistent during this phase. This will allow you to measure the impact that paid has to Organic (did it stay flat, go up or go down) but also the Total (did it go up with paid, and did it grow at the same volume as is attributed to paid). This measurement and analysis are both things that your data science teams can help with. Ultimately, this is going to be tailored to each advertiser, but that's what makes this methodology so great: you and your team have all the control, transparency, and data.
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How the Paused Spend Approach is Different
If you move forward with this approach, you should understand how it compares to other methodologies and their limitations:
- Although intent-to-treat or holdout tests have no cost and can be implemented on the client side, they often produce noisy and potentially biased results.
- PSA, on the other hand, has essentially zero noise as it serves ads to the test and control groups, allowing advertisers to obtain data on both. Unfortunately, PSA testing is costly and, if not implemented correctly, can create selection biases as test and control groups may be optimized differently.
- The ghost ads methodology is widely considered the “best” approach. It solves for the gaps in the previous two: no noise, limited-to-no bias, and - best of all - free to run. However, this approach is less transparent, uses self attribution from the ad partner running the test, and can only be implemented at the partner vs. portfolio level. It also relies on identifiers (such as device IDs) that – thanks to changes in iOS – are increasingly less accessible.
It would be naive to not acknowledge and address the hurdles that come with the paused spend approach. As an approach it requires patience, strong analytical skills, strong strategy, and confidence in your attribution windows and internal data pipelines. You can’t simply rely on an ad partner to implement the test and provide results for you. This approach is for the advanced marketer who has tested other methodologies and experienced their limitations first hand.
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The Big Benefit: Corrected Incentives
If you do fit the aforementioned persona, and you think you’re ready to try the paused spend approach, there might still be a question lingering in your mind, namely: “if pausing spend is a superior approach, why has no one ever recommended it to me?” Skepticism is to be expected. Especially since this approach runs contrary to what many people believe to be true.
But, beyond the benefits mentioned above, what many marketers ignore is that the pause spend methodology removes any internal or external incentives. And while you may not think it, there are lots of incentives at play around your mobile marketing. There’s an external incentive from your ad partners for you to spend more. There’s an internal marketing team incentive to show positive ad performance at all costs. There’s an internal finance incentive to spend the allocated budgets. You get the picture.
This misaligned incentive structure is reminiscent of the early days of mobile app install campaigns. During that time, the incentive for app marketers revolved around install volume and efficiency. Ad partners and nefarious actors were well aware of this, and so began the days of rampant click/install fraud. On paper, marketers saw what they had hoped and they had no incentive to dig deeper or understand the data further. The high volume and unbelievable efficiency for the dollars they were spending brought internal praise to the team, which got them more dollars from finance, which made them spend more money with those ad partners.
And it’s the reason why we lean on incrementality tests today.
When you remove incentives from the equation, as you do with the paused spend approach, the focus and clarity of the test aligns with the original goal of the test: quantify and validate the true business impact of paid marketing tactics.
Reset your baseline, understand where your business stands, and remove misaligned incentives and when you restart your Mobile Marketing programs, you’ll do so from a place of understanding, insight, and control that could help launch you to new heights.
Personalized Advertising in a Post-User World
iOS changes and regulatory shifts make app ad targeting a challenge. Now what?
- Operating system changes have given users unprecedented control over their data online.
- Advertisers and marketers need to adapt their paid acquisition approaches, as personalization of ads based on demographic information is increasingly difficult and inefficient
- The best place to learn about your customers — both current and future — is within your product, which is why product and advertising teams need to work closer
- Develop your internal data tracking, personalize user journeys, and uncover “aha moments” to turn first-time purchasers into lifetime customers
You can't track mobile users like you used to — at least not across the iOS advertising landscape. In the past, anyone could pay a premium for a clever algorithm to munch up users' behavioral data and spit out the perfect bait and hook. It worked, but with the latest changes, company mindsets need to change, too. Personalized advertising helped targeting get precise and narrow, but it may have narrowed the thinking of an entire industry. Prevailing sentiment has put the focus on uncovering only the warmest leads, deprioritizing broader metrics like installs. It’s time for a paradigm shift.
Broadening Your Mindset and Your Model
The old advertising landscape enabled marketing teams to lean on automated optimization. Aside from targeting downstream goals like return on ad spend, we still mostly see user acquisition (UA) marketing teams brushing superficial layers of the user journey.
Because of these recent privacy changes, paid acquisition targeting is now less precise and veers toward more contextual and topics-based: take Google’s Topics API Privacy Sandbox proposal, as one example.
This industry shift means app marketing teams should follow suit. Because while tracking and predicting the behavior of individuals in advertising may become obsolete, the same personalized mindset can be translated to what’s really important: products.
Marketing and product teams can collaborate on hypothesis-rich and cushy user journeys, measuring the results of their testing, and holding hands through benchmarking and iteration.
And in a marketplace where growth hacks only get you so far, the most efficacious and effectively amplified products win. So how do you approach the change?
The new age requires a new approach.
Marketing and product teams should:
- Collaborate more closely
- Design hypothesis-rich and cushy user journeys
- Measure results through testing
- Hold hands through benchmarking and iteration
Product marketers and advertisers occasionally spend their time chasing lightning-in-a-bottle. Such strokes of brilliance are not only rare, but rarely replicable. More importantly, they can introduce unpredictable friction. Instead of swinging wildly, consider what’s best for the user, which is:
- They know what this product or service is - "I understand this."
- They're told why it will be valuable for them - "I may need or want this."
- They're onboarded with feelings of familiarity and safety - "This product is what I was expecting so far!"
- They've developed a trust that deepens through their experience - "This largely is what I expected, and yes, it's delivering on the wants and needs that they promised."
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Your Product Is a Targeting and Segmentation Hub
Without the help of targeted advertising, where can you uncover details about users and their wants? The best place to gather actionable data and insight about your potential customers is very close to you: it’s your own product.
Unlike other channels, you have total control of your product journeys and if you prepare for it, the ability to gather granular intelligence on your customers’ actions. You can observe their behaviors, tracking relevant metrics (time on screens, where and when purchases happen, etc.), and note patterns with a level of detail you can’t outside of your platform. Where do you start?
First, create a persistent user ID system that allows you to cater to your customers with messaging that suits their specific moods and motivations. You may not be able to tag and track your users with personalized advertising approaches like before, but with a user ID system, you can create highly personalized advertising experiences via Liveops (call center services), push notifications, email, and other channels.
Experiences That Go Deeper
Your product experience map might be both intuitive and intimidating. Most employees can talk through a relatively simple product experience, but the nuances of scrolling, clicking, habit-changes, unusual churn, and other obscure behaviors is usually best understood by the most data-science-aware minds. Human behavior is not always intuitive, especially when looking at rows and rows of data. A data pro will be able to pull notable clusters of users from a mountain of numbers, which you can then assign behavioral personas. Those personas will make for deeper user journeys and greater success across the funnel.
Form hypotheses for who you think customers are. Gender and age may be less important than, for example, "baseball fans" or "competitive collectors." Maintain focus on contexts and motivations. Use your IDs to further explore and test this. This shared knowledge should then permeate both marketing and user journeys from start to finish.
Deploy marketing tactics that speak to users based on their point in the life cycle, driving toward a goal. Have fun flexing brand voice and culture in our many user-facing channels, but use those elements as flavor and keep your eyes on the prize: goals and desired actions.
That doesn’t mean you should blast users with "Return to the product and spend money" messaging. Instead, uncover “aha moments” to build revenue-driving interactions. Consider the casino app category. When a user spins a slot machine and hits, that user is thrilled. Crafting messages and campaigns around that moment can excite users, prompting them to return to the product. That’s much better aligned with their motivations than purely business-focused messages delivered errantly. Put simply, communicating that "we have a new hot machine! Return and spin to win," satisfies a user’s desire and pursues a business goal. "Try this new coin bundle at $29.99" is far less considerate of their experience. Discover your “Return and spin to win” and use the methods we’ve described to serve it at the right moment.
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From First-time Purchaser to Lifelong Customer
As advertisers, we can easily get caught up in immediate, short-term revenue returns, but the nature of your offering may not always lead to quick cash, especially in the app world. When you’ve outfitted your users with personal IDs, allowed them to spend time with your product, and understood their paths and motivations, there will be many opportunities to communicate with them and put them on a path to monetization beyond days one through seven.
Optimizing for tight goals is the fastest way to drive up CPM (cost per mile) and will become difficult with ATT opt-in and conversion timer barriers anyway. Opening targeting to upper-funnel conversions can be intimidating. App advertisers have gotten used to install-optimized campaigns driving downloads, purchase-optimized campaigns bringing us the most efficient purchasers. It’s hard to now consider that optimizing for installs may be a path to success when we covet returns. However, optimizing for upper-funnel events is not just a method for reducing your cost per install. It provides the opportunity for you to reach desirable users you may have “ignored” in your search for only the most premium.
If you're dipping your toes in, allow yourself a month or more to monitor users. Notice how users take very strong actions that show they could be ripe for revenue conversion. Borrow a principle from the e-commerce world: user consideration-to-conversion can be as simple as an email nudge: “We saw you added this to your cart. Here’s a coupon to complete your purchase.”
By making your product a data center, you’ll know when to deliver these nudges and give customers what they want when they want it. And by opening your marketing up to a broader base, you’ll find yourself with access to more data and events. In the process, you’ll foster deeper relationships that transcend the one-and-done transactional targeted advertising models of the recent past.