Transform Data’s Impact: Pick The Right Success KPI!

Your analysis provides clear data that the campaign was a (glorious) failure. It could not be clearer. The KPI you chose for your brand campaign was Trust, it had a pre-set target of +5. The post-campaign analysis that compares performance across Test & Control cells shows that Trust did not move at all. (Suspiciously, there […]

The post Transform Data’s Impact: Pick The Right Success KPI! appeared first on Occam’s Razor by Avinash Kaushik.

Your analysis provides clear data that the campaign was a (glorious) failure.

It could not be clearer.

The KPI you chose for your brand campaign was Trust, it had a pre-set target of +5. The post-campaign analysis that compares performance across Test & Control cells shows that Trust did not move at all. (Suspiciously, there are indications that in a handful of Test DMAs it might have gone down!)

Every so often, the story is just as simple as that.

You do the best you can with a marketing campaign (creative, audience, targeting, channels, media plan elements like duration, reach, frequency, media delivery quality elements like AVOC, Viewability, etc.), and sometimes the dice does not roll your way when you measure impact.

You would be surprised to know just how frequently the cause for failure is things that have nothing to do with the elements I mentioned above.  In future Premium editions we’ll cover a bunch of these causes, today I want to cover one cause that is in your control but often a root cause of failure:

Judging a fish by its ability to climb a tree!

AKA: You picked the wrong KPI for the campaign.

[Note 1: I’m going to use the phrase Success KPI a lot. To ensure clear focus, clear postmortems and clear accountability, I recommend identifying one single solitary metric as the Success KPI for the initiative. You can measure seven additional metrics – say for diagnostic purposes -, but there has to be just one Success KPI. Close accountability escape hatches.]

[Note 2: Although the guidance in this article applies to companies/analytics teams of all sizes, it applies in particular to larger companies and large agencies. It is there that the largest potential for mischief exists. It is also there, with an army of brilliant Analysts, that the highest potential for good exists.]

[Note 3: This article, part 1 of 2, was originally published as an edition of my newsletter The Marketing < > Analytics Intersect. In part 1, below, we’ll sharpen our skills in being able to recognize the problem, and five of the twelve rules for success. If you are TMAI Premium member, check your inbox for TMAI #313 for part 2 with the remaining rules and additional guidance. If you can’t find it, just email me. Merci.]

Be sure to save the summary visual at the end for implementing it in your company/agency.


The Subtle Art of Picking Bad KPIs.

Example 1.

Let’s say I work at Instagram, specifically in the Reels team. We want Reels to, say, crush TikTok. The team runs a $250 mil multi-platform campaign to increase Awareness of Reels. The campaign Success KPI was chosen to be: Incremental Reels Videos Created.

Good campaign. Bad Success KPI.

If you truly build Awareness creative, then judge success using the KPI Awareness. No?

Fish swim.

[Yes, long-term success of Reels will only come from Instagram users uploading Reels, but that was not the problem the creative was solving for. If the goal was Incremental Reels Videos Created, you would build an entirely different creative, you would target the campaign, potentially, to a different audience, you might create a different media plan, you would… run a different campaign.]

Creating a performance Success KPI for a brand campaign is a particularly common, and heartbreaking, mistake. Sophisticated brand measurement is hard. It feels simpler to pick what’s easy to measure, but you are going to make the fish feel bad when you judge its ability to climb trees AND you don't accomplish the desired outcome.

Example 2.

Let’s say I ran the campaign mentioned at the top of this email for my employer American Express.

If you look at Brand Trackers published by numerous industry sources, it becomes apparent in two minutes that American Express does not have a Trust problem. Americans trust American Express in massive quantities.

If you run a trust campaign for American Express, that campaign is going to fail. You are solving a problem that’s not a problem.

Bad Success KPI because of, technically speaking, high baselines.

Example 3.

Your new, Extremely Senior Leader is obsessed about doing Marketing that makes people fall in love with our brand. So, they conceive of a multi-million dollar Social campaign and demand the success KPI be: Brand Adoration.

[A KPI like that instinctively makes Analysts cringe because what’s Brand Adoration anyway. What does that even mean? Do we just make something up? If we do, how would we ever know if we did something meaningful, how we are doing compared to competitors/industry, what kind of creative/media do we even use to build “Brand Adoration,” and what are the core drivers of Brand Adoration, and if you don’t know, what are you actually doing spending all this money? I am going to set all this aside for a future TMAI Premium editions!]

You’ll measure that KPI using a question (or five) that will be presented in both the Test & Control cells. Will anyone who is not an employee of your company or in your Team's orbit even understand what the question is?

Let’s say, you ask Do you adore PayPal? Will the responding human know how to process this question?

Let’s say, you try an even more clever trick and ask PayPal is my preferred choice for financial transactions of a personal nature, and I would never use any other service, choose Yes or No.

Would the responding human understand that you are measuring brand adoration and give you a valid answer?

This is a bad Success KPI because no responding human can understand what you are asking – then the signal you accumulate to assess the campaign success or failure is a false signal.

And, it is the analytics person/team/agency's mistake.

Example 4.

A little grab bag for you…

When you are trying to drive long-term profit, picking Conversion Rate as a Success KPI for a campaign would be a mistake.

For your Display Advertising campaigns, picking any Success KPI close to buying (ex: Revenue) usually is a mistake. (Assisted Conversions – over a 30 or 90-day period, depending on your business – might be better.)

Anointing Conversion Rate (or dare I say even Revenue) as the Success KPI for your Email newsletter is a double mistake. It will cause your team to use newsletters in the spirit of pushy spam, and it will stop newsletters from truly becoming a strategically valuable owned asset, as Email is magnificent at See and Think, not so much Do.

I could keep going on. I have a hundred thousand more stories of judging a fish by its ability to climb a tree.


12 Rules for Picking the Right Success KPI.

While there is enough responsibility to spread around, I rest accountability for this common mistake on the Analyst/s. Marketers, CMOs, Finance peeps should know the implications of picking an imprecise Success KPI, but the Analyst is the expert and, hence, I expect them to take the lead.

To help you do that, here are 12 rules I codified for our team to use when we pick the Success KPI for a campaign. Each of these rules helps address a common error, collectively they also help you/leaders think through the campaign strategy, consider if they are solving the right problem, and so much more beyond just the KPI.

Ready to be A LOT MORE influential in your company?

Here are 12 rules brilliant companies use for picking the right Success KPI (and do Marketing that matters):

1. Is it an industry standard KPI?

It sounds like bad news that I’m saying you are not a special snowflake, that your campaign/tactic/magnificently brilliant idea is not so very incredibly unique that you need to make up a Metric to measure its success.

When you use an industry standard KPI, you have access to standards and benchmarks – providing you the super cool benefit of being able to assess your own performance in a much bigger context. This choice also comes with guidance on best practices for measuring this KPI – so that you don’t have to invent a methodology/technique that has no benefit of the industry’s collective wisdom.

Bonus: If you use an industry standard KPI, very often you’ll get access to research related to drives of that KPI that your Creative, Media and Strategy teams will kill for. If they know the drivers, they can internalize at a deeper layer what it takes to drive success.

Try not to make up a KPI, try not to make up the formula/question/methodology for a Success KPI. On that note…

2. (If it is a made up metric:) Is the KPI definition clear and understandable by a non-employee (aka consumer)?

For brand marketing, you and I assess success using a question we ask consumers.

When we make up our own metrics, the questions come from our best expertise, they might then get changed by a non-expert (Director of Marketing, CEO) because they like the sound of a particular word or phrase. But, phrased like that… Only your Director, and five people who say yes to everything the Director says, actually understand the question and answer choices. People taking the survey are super confused or putting their own interpretation on what you are asking. Now, their answers are suspect and – regardless of if their campaign results are indicated as a Big Success or Big Failure by the data – the measurement is imprecise.

Non-employees – aka normal people – need to be able to clearly and quickly understand what you are asking in your brand measurement surveys. Both the question AND the answer choices.

For performance marketing, you can see this confusion practiced when you create compound metrics. I bet your CMO dashboard has Social Engagement on it – only you understand what that metric actually is, and the convoluted formula ensures no one will ever know why Social Engagement went up or down. Not a good success KPI.

3. Is the Success KPI a business metric or a third-order driver metric?

You might have noticed above that I’m a fan of understanding the drivers of success (driver metrics) and not just the Big Thing we are trying to move (success KPI).

But, there is a special type mistake I see often made: The driver metric is chosen as the Success KPI.

An example of this is choosing Conversion Rate – certainly a driver metric – as the Success KPI vs. Profit. Yes, perhaps Profit will go up if you have a higher Conversion Rate, but the team could just use coupons or targeting low-value customers to drive the Conversion Rate and Profit will never go up.

Another example of this is that we want to influence Trust in our company, and we end up picking Product Quality as the Success KPI. Yes, Product Quality will improve Trust over time, but the coefficient is probably petite.

To correctly identify the impact of your campaign, pick the business outcome you want as the Success KPI and not one of the many driver metrics.

4. Is the Success KPI the goal set in the creative brief?

The creative is the ad we see on TV or TikTok, it is the lines of text in your Bing ad, and it is the (hopefully not annoying) image, text, animation, call to action, in your Display ad currently running in the Sacramento Bee.

Creative teams love big challenges and are motivated by solving existential issues. Hence, when Marketers / Leaders write creative briefs, they end up briefing the team for Big Things.

Make the world believe we are as good as Apple in quality… We are trying to get customers to think we are an innovative company… Our goal is to have the world believe that we are a force for good when it comes to climate change… The campaign investment is meant to help shift the perception that we are committed to our customers in the long run!

These are all fantastic things to shoot for (if your reality matches these aspirations).

The challenge occurs when the Success KPI for all of the above campaigns is set as In-Store Sales. Or, Lifetime value. Or, Most Valuable Brand in the world.

When there is a conflict between what the creative brief is (what the ads are being built for) and the measured Success KPI, the latter is an extremely poor choice because it will invariably show failure.

Brief the creative team for an outcome that actually matters to the business, and then set that exact same outcome as the Success KPI. Clear alignment between input and output.

5. Does the KPI have headroom?

I love this one. Not only as a great rule, but also to force Marketers to be clever.

What’s headroom?

Let’s consider this brand question: Is Apple an innovative company?

The answer: Yes (68%).

That is a very high baseline. If 68% of the people think anything positive of a company, there is likely no one else left in the world to persuade.

[In the case of Apple, there are a fair number of people who love to dislike Apple. That further means, purely from a measurement perspective, no headroom.]

You cannot move an unmovable metric.

No matter how much money you spend.

Even IF the campaign had great creative, it was well delivered, on the right channels, with optimal reach and frequency. The campaign will look like a failure. And, it was not the Marketing team’s fault.

Before you pick a Success KPI, do a bit of research to understand headroom. If you have less than six or eight points, don’t solve that problem (because data is indicating that it is not a problem!).

Pick something else. Unaided Awareness of Apple Tags is just 12 points. Solve that problem. Lots of headroom!

[Note: The concept of headroom applies to performance marketing as well. You might be maxed out for the audience you can reach in a particular channel. You already have max possible Click Share on Google. There might not be any more new customers to entice across the East Coast of the US. Etc. Assess headroom available across your performance Success KPIs as well.]

[Note:
​​​​ Premium subscribers will recognize assessment of headroom as another clever manifestation of the win before you spend Minerva (Pre-Flight) Check outlined in TMAI #273.]


Scoring Success KPIs.

It would not surprise you to learn that smart teams codify their thinking (frameworks FTW!), and implement a process that ensures that thinking is applied 1. at scale 2. at the right moment, and 3. is understood by all.

That’s the real success to winning influence with data. To make it easier for teams I've led to implement the rules for success KPIs framework, we use the following checklist (with part 1 rules)…

12_rules_for_picking_success_kpis_part_1

[Click image above for a higher resolution version. It is pretty easy to type it all up in Excel, but if you need an Excel version, just email me.]

A thoughtful assessment, upfront. Simple and clear to all the cross-functional teams involved (and not just the Analytics team).

Rules 1 through 8 are mandatory, all of them have to be met for a KPI to be anointed a success KPI. The scoring in light blue row above. Rules 9 through 12 are for Analysis Ninjas, those who want to go above and beyond, those who do not leave things to chance, those looking for coming as close to guaranteeing success as possible. The scoring is in the darker blue row.

The KPI candidate with the best score wins! :)

In a future blog post, we can cover the process to put in place to ensure this happens at scale in your company/non-profit.


Bottom line.

Measuring the wrong thing should be the last reason to get a false signal of the impact of a campaign. False positive or false negative.

Measuring the right thing, and ensuring there is a process and framework in place to discuss that up front, ensuring every good and bad dimension of thinking can be put on the table up front, is a gift of immeasurable proportions to your employer/client.

Pick the right Success KPI.

It won’t guarantee campaign success, it will ensure that you’ll know when success occurs that it is real, and when failure occurs, there are clear lessons to learn for doing better in the future.

Pick the right Success KPI.

How good is your team, your agency, at ensuring that you are picking success KPIs that deliver in-depth insights, and optimal accountability? Please share via comments below. Merci.

[Quick reminder: If you are a TMAI Premium subscriber, part 2, with rules six through twelve and bonus content, is in your inbox. If you can’t find it, just email me.]

The post Transform Data's Impact: Pick The Right Success KPI! appeared first on Occam's Razor by Avinash Kaushik.

Increase Analytics Influence: Leverage Predictive Metrics!

Almost all metrics you currently use have one common thread: They are almost all backward-looking. If you want to deepen the influence of data in your organization – and your personal influence – 30% of your analytics efforts should be centered around the use of forward-looking metrics. Predictive metrics! But first, let’s take a small […]

The post Increase Analytics Influence: Leverage Predictive Metrics! appeared first on Occam’s Razor by Avinash Kaushik.

Almost all metrics you currently use have one common thread: They are almost all backward-looking.

If you want to deepen the influence of data in your organization – and your personal influence – 30% of your analytics efforts should be centered around the use of forward-looking metrics.

Predictive metrics!

But first, let's take a small step back. What is a metric?

Here’s the definition of a metric from my first book:

A metric is a number.

Simple enough.

Conversion Rate. Number of Users. Bounce Rate. All metrics.

[Note: Bounce Rate has been banished from Google Analytics 4 and replaced with a compound metric  called Engaged Sessionsthe number of sessions that lasted 10 seconds or longer, or had 1 or more conversion events or 2 or more page views.]

The three metrics above are backward-looking. They are telling us what happened in the past. You'll recognize now that that is true for almost everything you are reporting (if not everything).

But, who does not want to see the future?

Yes. I see your hand up.

The problem is that the future is hard to predict. What’s the quote… No one went broke predicting the past. :)

Why use Predictive Metrics?

As Analysts, we convert data into insights every day. Awesome. Only some of those insights get transformed into action – for any number of reasons (your influence, quality of insights, incomplete stories, etc. etc.). Sad face.

One of the most effective ways of ensuring your insights will be converted into high-impact business actions is to predict the future.

Consider this insight derived from data:

The Conversion Rate from our Email campaigns is 4.5%, 2x of Google Search.

Now consider this one:

The Conversion Rate from our Email campaign is 4.5%, 2x of Google Search.

Our analysis suggests you can move from six email campaigns per year to nine email campaigns per year.

Finally consider this one:

The Conversion Rate from our Email campaign is 4.5%, 2x of Google Search.

Our analysis suggests you can move from six email campaigns per year to nine email campaigns per year.

We predict it will lead to an additional $3 mil in incremental revenue.

The predicted metric is New Incremental Revenue. Not just that, you used sophisticated math to identify how much of the predicted Revenue will be incremental.

Which of these three scenarios ensures that your insight will be actioned?

Yep. The one with the Predictive Metric.

Becaues it is hard, really hard, to ignore your advice when you come bearing $3 mil in incremental revenue!

Starting your Predictive Metrics journey: Easy Peasy Lemon Squeezy.

In a delightfully wonderful development, every analytics tool worth its salt is adding Predictive Metrics to its arsenal. Both as a way to differentiate themselves with their own take on this capability, and to bring something incredibly valuable to businesses of all types/sizes.

In Google Analytics, an early predicted metric was: Conversion Probability.

Simply put, Conversion Probability determines a User’s likelihood to convert during the next 30 days!

I was so excited when it first came out.

Google Analtyics in this instance is analyzing all first-party data for everyone, identifying patterns of behavior that lead to conversions, now looking at everyone who did not convert, and on your behalf giving a score of 0 (no chance of conversion) to 100 (very high chance of conversion).

Phew! That’s a lot of work. :)

What’s particularly exciting is that Conversion Probability is computed for individual Users.

You can access the report easily in GA: Audience > Behavior > Conversion Probability.

google_analytics_conversion_probability_report

An obvious use of this predicted behavior is to do a remarketing campaign focusing on people who might need a nudge to convert, 7,233 in the above case.

But, there are additional uses of this data in order to identify the effectiveness of your campaigns.

For example, here is the source of traffic sorted by Average Conversion Probability

conversion_probability_report_3

In addition to understanding Conversion Rate (last column) you can now also consider how many Users arrived via that channel who are likely to convert over the next 30 days.

Perhaps more delightfully you can use this for segmentation. Example: Create a segment for Conversion Probability > 50%, apply it to your fav reports like the content ones.

There is so much more you can explore.

[TMAI Premium subscribers, to ensure you are knocking it out of the park, be sure to review the A, B, O clusters of actionable recommendations in #238: The OG of Analytics – Segmentation! If you can’t find it, just emial me.]

Bonus Tip: I cannot recommend enough that you get access to the Google Merchandise Store Google Analytics account. It is a fully working, well-implemented real GA data for an actual business. Access is free. So great for learning. The screenshot above is from that account.

Threee Awesome New Predictive Metrics!

With everything turning over for the exciting world of Google Analytics 4 you get a bit more to add to your predictive metrics arsenal.

Conversion probability is being EOLed with GA 4, but worry not as you get a like-type replacement: Purchase Probability

The probability that a user who was active in the last 28 days will log a specific conversion event within the next 7 days.

Currently, purchase/ecommerce_purchase and  in_app_purchase events are supported.

You can do all of the same things as we discussed above for Conversion probability.

To help you get closer to your Finance team – you really need to be BFFs with them! – you also get a predictive metric that they will love: Revenue Prediction

The revenue expected from all purchase conversions within the next 28 days from a user who was active in the last 28 days.

You can let your imagination roam wild as to what you can do with this power.

Might I suggest you start by looking at this prediction and then brainstorm with your Marketing team how you can overcome the shortfall in revenue! Not just using Paid strategies, but Earned and Owned as well.

Obviously in the rare case the Revenue Prediction is higher than target, you all can cash in your vacation days and visit Cancun. (Wait. Skip Cancun. That brand’s tainted. :)

There’s one more predicted metric that I’ve always been excited about: Churn Probability.

The probability that a user who was active on your app or site within the last 7 days will not be active within the next 7 days.

What’s that quote? It costs 5000x more to acquire a new User than to retain the one you already have? I might be exaggerating a tad bit.

For mobile app/game developers in particular (or for content sites, or any entity for whom recency/frequency is a do or die proposition). Churn is a constant obsession and now you can proactively get churn probability. Make it a core part of your analytical strategy to understand Behavior, Sources, Users, who are more/less likely to churn and action the insights.

GA 4 does not simply hand you these metrics willy-nilly. The algorithms require  a certain number of Users, Conversions etc., in order to ensure they are doing sound computations on your behalf.

These three predictive metrics illustrate the power that forward-looking computations hold for you. There are no limits to how far you can take these approaches to help your company not only look backwards (you’ll be stuck with this 70% of the time) but also take a peek into the future (aim to spend 30% of your time here).

And please consider segmenting Purchase Probability, Revenue Probability and Churn Probability!

Bonus Tip: If you would like to migrate to the free version of Google Analytics 4 to take advantage of the above delicious predictive metrics, here’s a helpful article.

Predictive Metrics Nirvana – An Example.

For a Marketing Analyst, few things come close to nirvana in terms of forward-looking predictions from sophisticated analysis than to help set the entire budget for the year including allocation of that budget across channels based on diminishing returns curves and future opportunity and predict: Sales, Cost Per Sale, and Brand Lift.

Here’s how that looks from our team’s analytics practice…

predicted_budget_channel_allocation_sales

Obviously, all these cells have numbers in them. You’ll understand that sharing them with you would be a career-limiting move on my part. :)

I can say that there are thirteen different element sets that go into this analysis (product launches, competitor behavior, past analysis of effectiveness and efficiency, underlying marketing media plan, upcoming industry changes, and a lot, lot, lot, of data).

Supercool – aka superhard – elements include being able to tie Brand Marketing to short, medium, long-term Sales.

Forward-looking allocations are based on simulations that can take all of the above, to answer low, medium, high-risk plans – from which our senior leader gets to choose the one she believes aligns with her strategic vision.

[Note: Strictly speaking what we are doing above is closer to Predictive Modeling, even though we have a bunch of Predictive Metrics. Potato – Potahto.]

I share our work as a way to invite your feedback on what we can do better and in the hope that if you are starting your Predicted Metrics practice, that it might serve as a north star.

From experience, I can tell you that if you ever felt you as an Analyst don’t have influence, that your organization ignores data, then there is nothing like Predicted Metrics to deepen your influence and impact on the business.

When people use faith to decide future strategy, the one thing they are missing is any semblance of what impact their faith-based strategy will have. The last three rows above are how you stand out.

BOOM!

The Danger in Predicting the Future.

You are going to be wrong.

A lot, initially. Then less over time as you get better and better and predicting the future.

(Machine Learning comes in handy there as it can ingest so much more complexity and spit out scenarios we simply can’t imagine.)

But, you will never be exactly right. The world is complicated.

This does not scare me for two reasons, I urge you to consider them:

1. Very few companies drove straight looking out of the rear view mirror. But, that is exactly what you spend time trying to do every single day.

2. Who is righter than you? The modern corporation mostly runs of faith. You are going to use data, usually a boat-load of it. It is usually far better than faith. And, when you are wrong, you can factually go back and update your models (faith usually is not open to being upgraded).

So. Don’t be scared.

Every time you are wrong, it is an opportunity to learn and be more right in the future – even if perfection will always be out of reach.

Bottom Line.

My hypothesis is that you are not spending a lot of time on predictive metrics and predictive modeling. Change this.

It is a great way to contribute materially to your company. It is a great way to invest in your personal learning and growth. It is a fantastic way to ensure your career is future-proof.

Live in the future – at least some of the time – as an Analyst/Marketer.

I’ll see you there. :)

As always, it is your turn now.

Please share your critique, reflections, tips and your lessons from projects that shift your company from only backwards looking metrics to foward looking metrics that predict the future.

The post Increase Analytics Influence: Leverage Predictive Metrics! appeared first on Occam's Razor by Avinash Kaushik.