You’ve heard the adage, “‘nobody is perfect” — which is often used to excuse the mismatch we make between the perceptions we have and the actions we take throughout our daily lives. But performance marketing, de facto the most data-driven and data-intensive discipline there is, is a different story. If you approach performance marketing properly, and make full use (and sense) of the billions of impressions, clicks and actions you generate with each campaign, then you can approach perfection in the decisions you make and the outcomes you influence — and that in near to real-time.

Currently, approaches to performance marketing focus on equipping marketers to “see” Big Picture trends and make obvious decisions based on these basic observations. As a result, marketers are overwhelmed by the task of monitoring and measuring a mountain of data points and performance “signals.” But it’s not enough to collect and collate only the simplest surface data such as cost per acquisition only by supplier and date. Performance marketers must strive to distil granular data, including creatives and sub publisher information, into deeper insights. This is the way to unlock new performance opportunities and this paves the way to achieving what I call “Perfect Performance Marketing.” It’s all about making data-informed decisions with the confidence that you have collected and considered all the data possible, which allows you to make a perfect decision.

With this in mind (and based on my personal experience managing performance marketing for some of the largest mobile spenders globally), I have developed a framework to guide marketers on the path to make Perfect Performance Marketing decisions.

Why settle for less than perfect?

Perfect Performance Marketing is quite different from how many performance marketers do their job right now every time they optimise campaigns without knowing the full context of the factors at work and driving the results. Simply put, each time performance marketers rely on a metric and dimension that fails to give them the “full picture” of performance, they are pursuing a strategy quite counter to Perfect Performance Marketing. Unfortunately, falling into this trap is all too easy.

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Ask yourself: Are you optimising to revenue without taking the cost user acquisition into consideration as well? Are you optimising to cost per registration without taking retention rates and LTV into consideration? Are you optimising to supplier conversion figures without taking the de-duped figures from 3rd attribution into consideration? If your answer is “yes,” then you are making imperfect decisions that can significantly reduce your chances of success.

In practice, you make an imperfect, and potentially dangerous, decision every time you reduce investment in a channel without excluding poor-performing sub publishers, keywords and/or creatives. Imperfect decisions are also the unfortunate outcome when you invest in one channel without assessing the return every alternative channel could possibly provide.

 

The framework

Perfect Performance Marketing? It’s perfect decision making in action.

Perfect decision making in action – making a decision based on every possible variable that can move the needle. This is what I define as perfect performance marketing.

Let’s review the three key components of the framework you master in order to optimise to metrics and practices that deliver perfect results.

(Completeness + depth + certainty) x frequency

 

1. Completeness

Completeness is about having all your metrics in one place, in other words a full picture of the entire funnel.

To visualise this, compare the level of completeness to the width of your spreadsheet or pivot table. You increase the level of completeness in your performance marketing intelligence when you increase the number of metrics you can access on demand. Keep in mind it’s not just any metrics; it’s key metrics that unify cost vs. behaviours in order to determine the true performance of your campaign.

 

reporting table that shows data for cost, installs, rejected installs, revenue, return on ad spend, retention and predictive LTV across Facebook, Google and Organic traffic

Take life-time-value (LTV). In its most basic form LTV is all the revenue a single user will generate from the time they download the app until they abandon the app altogether. As a rule, LTV is a metric that marketers monitor over a specific period, depending on their app and campaign, to determine revenue and plan budgets based on a highly predictable cash flow.

However, no organisation can afford to wait months, let alone weeks, to understand what is boosting LTV, or cutting it short). This is why it’s crucial to have visibility into a mix of early funnel metrics along with LTV. It’s the combination that will allow you to identify key correlations between user behaviour that occur within the first few days and LTV, which spans the entire customer lifetime.

What’s more, it yields a complete view that allows markets to optimise much sooner. It’s an approach that doesn’t just make good business sense; it’s also best practice followed by most of the top-grossing games companies to move the needle on their app campaigns. The same strategy is now gaining traction with marketers across non-gaming sectors. For instance, we’ve seen how one restaurant booking app used this practice to optimise to predictive LTV and reduce CPAs by a whopping 74%.

 

2. Completeness + Depth

Depth is about the level of granularity you can apply to the metrics. Think of it as the number of dimensions or breakdowns in your spreadsheet. Whilst striving for ‘completeness’ allows you to map the user journey and shifts in performance every step of the way, it’s the focus on ‘depth’ that will help you identify the precise variables that are causes of the outcomes you observe. Naturally, the more variables you evaluate, the deeper your insights.

 

reporting table which shows cost, installs, rejected installs, revenue, return on ad spend, preditctive LTV

A standard approach is to collect and group performance data by partner, country and OS. But that’s also what most every other business is doing as well. If you want to win big, then you must aim higher. This means going much deeper in the data in order to deliver positive improvements in performance.

Consider the green lights and red flags that tell you to dial your ad spend up or down accordingly. Now imagine a scenario where your campaign performance assessment reveals a specific ad network is driving huge volumes, but poor results. Your first reaction might be to pull the plug on all campaigns running on that ad network. However, if you take this action without looking at sub-publisher level performance, a granular dimension that yields greater depth and valuable insights, you’re likely making a decision you’ll regret. A deeper dive into the data might have revealed that the ad network was a scalable — and valuable channel — if only you had excluded the poor performing sub-publishers from the data set.

Facebook is another example. It’s easy to imagine a scenario where Facebook appears to be a strong performing channel, delivering results that are well worth the ad spend. However, increasing budget without increasing depth — viewing variables through the lens of age, gender or placement breakdowns — blinds you to any poor performing segments that might be holding you back from achieving even greater results.

As a rule, to achieve maximum depth, you must strive to break down your metrics by every variable and dimension possible. Description is King, so go for depth and detail in how you name, tag and track your campaigns. Add labels and strive for visibility into performance that goes beyond what default ad network labels that favour short-hand descriptions over the level of detail you need to know what works and why.

3. Completeness + Depth + Certainty

Certainty, the last building block in this framework, is about pacing and forecast. At a basic level, you can assess your pacing and forecasts in a linear fashion by evaluating how much budget you have allocated to a specific supplier and the results they have delivered vs. how much budget/time left in the campaign.

Table to show forecast and pacing of ad spend across Facebook and Google, including dimensions budge, actual cost, forecasted cost, target revenue, forecasted revenue and pacing %

To improve the accuracy of your forecast, you need to increase the granularity and frequency you apply to the metrics that feature in your forecast. Don’t just build your forecast by media partner. Apply your forecast down to the operating system and country level, and refresh the model on a regular basis.

To be certain your budget optimisations are on the mark, use your forecasts to build out a variety of scenarios that put your predictions and preconceived notions to the test. What happens if you spend less on Facebook and more on Google? What is the outcome if you spend less in the U.S. and more in Asia? Play out all possible parallel scenarios to understand impacts, gauge outcomes and assess risks and rewards. It’s much more than an exercise; it’s a discipline that delivers the level of certainty essential for perfect decisions — thus satisfying a key requirement for Perfect Performance Marketing.

4. (Completeness + Depth + Certainty) * Frequency

Employing this framework equips you to be a perfect performance marketer. But, in a world where change is the only constant, it’s not enough to have a comprehensive overview of the data at a specific moment in time. You need it fresh and much more frequently than that.

It’s easy to demand data often, but systems or organisational processes rarely deliver it. In my agency days, we thought we were supremely lucky to get a daily report. On the few occasions that we did get a report, it was very high-level, so not particularly high-value.

Achieving frequency is critical, but it can also be costly requiring investments to build out internal capabilities, or boost BI teams — or both. It’s why more companies are choosing an alternative route, investing resources in purpose-built unified analytics platforms to automate the process of combining data from a variety of different — and often disparate — sources. This ensures real-time insights that are essential to perfect performance marketing.

Can we truly make Perfect Performance Marketing decisions?

If perfection is about making decisions which have considered every possible variable and KPI, then the answer is a resounding ‘Yes!’. You have indeed made the most informed decision possible. The next question to consider is: Are your current insights, reporting or analytics set up in a way that is sufficient (and truly able) to provide access to every variable and KPI you could desire?

Of course, the answer will depend on KPIs you track or value — and that will depend on your business, your objectives and your audience. It’s why I can’t possibly offer you a single spreadsheet or template at the end of this post that will equip you to evaluate everything in every decision you make. Unfortunately, this is exactly what you are required to do to succeed as a performance marketer in a data-driven discipline where imperfect decision-making is no longer an option (or an excuse). The framework and formula I have developed provides a solid starting point. Use it to evaluate what your current system can deliver vs. the amazing the results that can be achieved when you internalise the principles of Perfect Performance Marketing.

 

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