It’s not you, it’s your MMP: How to know it’s time to switch

By Einav Mor-Samuels
It’s not you, it’s your MMP - How to know it’s time to switch

Prepare yourself for the quickest palm reading you ever experienced in your life:

You already work with an MMP, so you understand the value of marketing measurement, but you’re either not getting enough value from your current MMP or have outgrown it and require a more comprehensive platform to support your growing needs. 

How vigorously are you nodding your head right now?

If this resonates with you at all — read on. If it doesn’t — read on anyway, as some of the following findings might surprise you.

And if you are contemplating switching MMPs, brace yourself for our next blog, where we’ll tackle the top MMP migration concerns you might already be battling with right now, and even have a few of our clients share their own migration stories.

But first, let’s break down the three most common pain points that push brands to ask themselves: did I partner with the best MMP for my needs, and how do I know when it’s time to switch?

1. You’re throwing money away (and might not even know it)

Throwing money away

a) Because of lack of efficiency: You don’t have a primary, unified source of truth to rely on

Let’s call it what it is. 

Your data flow is decentralized and distributed across several sources, each network receives its own postback data, and you need to bring these threads together to be able to make any sense of your campaigns’ performance from a holistic standpoint. 

Your data sources can include: 

  • SKAN values
  • Consented cohorts
  • MMP attribution data
  • Mixed media modeling and incrementality estimates
  • Ad networks’ attribution data (an unstandardized wild west in its own right) 

But unless you’re able to combine these sources, eliminate duplications (that will inevitably occur), clean and standardize your data — there’s no way for you to understand the bigger picture. 

Without working out of a consolidated dashboard with standardized data, it’s impossible to identify cross-channel and cross-platform trends, and in turn — use them to inform your budget decisions and strategic growth plans. 

The solution? A Single Source of Truth (SSOT)

You need an MMP that will allow you to look at your campaigns’ performance using one dataset with deduplicated install and LTV data. 

An SSOT is your “one ring to rule them all”.

It allows you to combine multiple data streams into one deduped source of data, so you can finally have an accurate and complete view of your campaigns’ true performance. 

An MMP that enables you to slice and dice your metrics across all applicable platforms and devices, will not only offer the ability to draw actionable insights, but also continuously optimize your ad spend and avoid spilling money on the wrong campaigns.

b) Because you lack breadth of visibility

Lack breadth of visibility

Although mobile is the most data-rich marketing channel in existence, the sheer amount of data that needs to be analyzed when assessing campaign performance can be overwhelming and hard to gauge – it calls for comprehensive campaign measurement that doesn’t leave a stone unturned. 

Not being able to measure effectively, across all devices, taking into account the entire spectrum of attribution, and without relying on a single source of truth is a recipe for errors, misplaced budgets, and plenty of missed opportunities for optimizing your LTV and ROAS.

The solution? All-comprehensive marketing measurement

An MMP that offers a holistic, accurate, and unbiased attribution will help you pinpoint the true value of specific channels, media sources, publishers, campaigns, and even creatives, so you can focus on ensuring strategic growth. 

What do we mean by all-comprehensive?

Your MMP has to offer you a single dashboard for all sources that includes: 

  • Cross-platform and device measurement – including mobile, desktop, CTV, OTT, and OOH). On the CTV front, you need the ability to measure performance across all CTV devices, including smart TVs, game consoles, and streaming boxes.
  • Multi-touch attribution – measuring all marketing touchpoints and optimizing media based on both last touch and earlier touchpoints that drive installs.

c) Because you lack depth of visibility

Lack depth of visibility and can't see the full picture

Relying solely on last clicks and last impressions (when view-throughs are reported) offers a very partial view of the performance of your media sources, and an incomplete picture of which ad networks are performing well and which are underperforming.

How can you piece together the high-level insights you need in order to make informed strategic decisions and plan ahead? For example, know if a specific channel is contributing to your SQO count?

The solution? Built-in, top-down measurement solutions

An MMP that offers holistic methods such as incrementality, predictive modeling, and media mix modeling (MMM) will allow you to gain a valuable perspective on how to spend your media budget more efficiently. 

These methods put complex statistical models to use in order to estimate the impact of your paid media, all while being personal identifiers-free — so you can make the most of this new user-level data deprived reality.  

Media mix modeling enables you to measure the impact of your campaigns and helps you determine how various elements contribute to your bottom line.

MMM calls for intricate probabilistic models that are based on multivariate regressions that take into account large amounts of historical data — such as ad spend, impressions, blended installs, and conversions — so you can gauge what’s influencing the performance of your marketing spend. 

Incrementality allows you to identify incremental revenue drivers in the battle to optimize your budget allocation. Incrementality measures the incremental impact of your remarketing efforts, helps you make smarter decisions, and limits the cannibalization of your organic user engagement. 

This method can be supplemented by last-touch attribution to extrapolate which elements have had a positive, incremental impact on your conversions. 

Predictive modeling uses machine learning to identify and map correlations between early user engagement indicators and expected user value — offering you accurate predictive insights during the early stages of your campaign’s lifetime.

With the recent limitations around user level data, the interest level in top-down measurement methods has spiked — thanks to the fact they’re not dependent on personal identifiers such as IDFA or web cookies, and can also measure activities that don’t provide actual touch data (e.g. billboard, TV, and podcast advertising).

d) Because of fraud and mis-attribution

Fraud and mis-attribution

Preventing ad fraud is one of the most effective ways to ensure your hard-earned budget isn’t wasted away.

Any type of abnormal trends could be a smoking gun indicating your numbers are plagued — a spike in click volumes, impressions or even suspiciously amazing conversion rates from a new source.

That said, being able to prevent fraud effectively calls for exhaustive market intelligence, scale, and advanced machine learning capabilities — which your MMP might not be mature or established enough to offer.

Buying your own fraudulent traffic and doing so over and over again on a growing scale — will lead your business to the dreaded bleeding cash cycle.

And if you thought your current MMP got you covered despite actively ignoring crucial fraudulent activity stages — think again. Only 16% of fake installs can be detected post attribution.

Assuming your current MMP doesn’t offer post-attribution fraud prevention in addition to covering real-time install and in-app event fraud — well, Houston, we have a problem.

The solution? End-to-end fraud protection

To help you break the budget-draining fraud cycle, your MMP has to be able to remove bad players from the platform to ensure its integrity. It has to partner only with ad networks who take fraud prevention just as seriously, and offer post-attribution fraud detection.


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2. You’re not privacy-ready, while the world around you is quickly gearing towards it

Not privacy ready while the world is quickly gearing towards it

a) Because of limited iOS measurement (and lack of actionable insights)

Being able to tap into tangible insights in the age of user privacy is no walk in the park. Even with the new and improved SKAN 4.0, navigating your way through the SKAdNetwork maze is not for the faint of hearts. 

With a limited number of campaigns, no creative data, fewer conversions, and a limited measurement window — Apple’s SKAN brings with it heaps of limitations, making it a daunting frontier for marketers to conquer when after iOS campaign insights. 

The solution? Robust iOS 14+ measurement

To be able to squeeze every drop possible out of the SKAN lemon, you need MMP that offers a custom SKAdNetwork solution that’s not limited to predefined models.

It has to offer: 

  • Retroactive updates for post-install ATT consent
  • Aggregated conversion modeling to account for null values
  • Advanced conversion value modes for revenue event funnels
  • Event frequencies that use up less Conversion Values capacity
  • Aggregated advanced privacy – so you can control availability of user-level attribution data in real-time while complying with Apple’s privacy policy in full.

b) Because you don’t have privacy-preserving measurement solutions

 Lack of privacy-preserving measurement solutions

Rising scrutiny around data privacy, driven by privacy regulations, is making it increasingly complex for marketers to collect, store, analyze, and share user-level data.

To complicate things even further, inefficient data synthesis processes — where data correlation across separate data sets requires heavy lifting by data scientists — is a costly and time-consuming endeavor. 

The solution? A data clean room

Being able to analyze restricted user-level attribution while preserving privacy and compliance is quite literally a game-changer. 

An MMP that offers a data clean room is in essence giving you access to your very own “Switzerland of data”. It’s a neutral, safe space for 1st-party user data to be leveraged collaboratively.

Within its environment, two parties (or more) can securely share and analyze that data with full control of how, where, and when it can be used. 

While user-level data goes into the data clean room, aggregated insights come out in a co-mingled audience group called a cohort. This gives you access to much-needed insights in a regulatory compliant space that keeps your consumers’ privacy intact. 

And if your MMP doesn’t offer a data clean room, you’re missing out on one of the safest and most effective ways to make the most of our new, data-deprived reality.

3. You’re not feeling supported

Not feeling supported

Whether it’s insufficient resources, mismatched time zones, long response times, or lack of training — poor support is a major pain point for marketers. 

Not only does it impact their ability to complete deployment and onboarding in a timely manner, but also prevents them from making the most of their investment, leading to wasted time, wasted budgets, and missed opportunities.

The solution? Uncompromising, high-availability customer care

Your MMP needs to offer timely, attentive, knowledgeable, and cross-timezone support, onboarding, and training to allow your business to fully adopt the platform and make the most of everything it has to offer.

One great way to learn if your MMP truly is customer-centric, is to ask about customers’ influence on the product roadmap.

MMPs that make time to listen to their customers and actually apply their feedback to their product are the kind of MMP you want to partner with.

Back that customer-focused mindset up with solid resources and expert knowledge needed to support your growing needs — and there’s no longer a need to compromise on far-from-optimal deployment, rarely available Cs and support, zero to very little optimization, and the building frustration that ensues.

If your MMP can’t offer you seamless and fully customized onboarding, a joyful customer experience, or continuous growth driven by a dedicated team of experts, it might be a good time to hit pause, reevaluate, and reconsider your options.

Handy resources to evaluate the big switch

Now that we’ve covered all the major pain points that might come up working with the wrong MMP, let’s equip you with a few additional resources to help you in your reassessment process:

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TL;DR – Summing things up

Summing things up

The most common telltale signs you need to switch MMPs are:

  • You’re throwing money away – because of lack of visibility, lack of efficiency, fraud, mis-attribution, poor top-down measurement, or disparate & siloed sources of data.
    • The solution: All-comprehensive attribution that includes cross-platform and device measurement (mobile, desktop, CTV, OTT, and OOH), top-down measurement (incrementality, MMM, and predictive analytics), multi-touch attribution, end-to-end fraud protection, and a single source of truth.
  • You’re not privacy-ready, while the world around you is – because of limited iOS measurement — making it harder than ever before to tap into tangible insights, and lack of privacy-preserving measurement solutions — making it increasingly complex to collect, store, analyze, and share user-level data.
    • The solution: a robust and custom SKAdNetwork solution that’s not limited to predefined models. 
    • A data clean room that will allow you to analyze restricted user-level data while preserving your users’ privacy in full.
  • Far from adequate level of support – due to insufficient resources, restricted time zones, long response times, or lack of training.
    • The solution: partnering with a customer-obsessed MMP that takes exceptional customer care just as seriously as you do, if not more. This could mean the difference between far-from-optimal deployment, ongoing frustration, and zero to very little optimization — to seamless onboarding, a joyful customer experience, and continuous growth.

Einav Mor-Samuels

With extensive experience in digital marketing, Einav is a Content Writer at AppsFlyer. Over the course of the past 15 years, she has gained ample experience in the mobile marketing landscape, researching market trends, and offering tailored solutions to customers' digital problems. Einav fuels her content with data-driven insights, making even the most complex of topics accessible and clear.
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