Pebbles on a rocky beach are granular. The white sugar that we all hate to love is granular. The stars of the Milky Way that smudge together into a glorious sheet of light are, under closer inspection by a powerful telescope, also granular.
And so is the very best of digital and mobile marketing.
Why?
“Granularity sustains profitable scale,” says Singular’s Vice President of Customer Strategy Victor Savath. “Without granularity, you can scale… but it’s hard to monitor quality.”
Granularity is important both cross-channel and within channels, Savath said recently at UNIFY conference, where he interviewed experts from Yelp, Kabam, Postmates, Nexon, and N3twork on the topic. It’s important for creative. Granularity is also important for bids and CPIs. It’s critical to evaluating publishers and sub-publishers. And it’s something that impacts your daily budgets.
But exactly what is granularity?
And what does it achieve for digital marketers?
And … how has the concept of granularity changed with iOS 14.5 and SKAdNetwork?
Granularity in digital marketing can be defined as the ability to dissect big blocky chunks of marketing activity and ad buys to see the smaller building blocks. For example:
If your ad campaign is spread over 15 different agencies, you can view each one individually
If each agency uses multiple ad networks, you can see how each is performing
If each ad network employs different publishers and sub-publishers in your campaign, you can dive into sub-publisher metrics
If you’re using varying creatives and forms of targeting, you can see how each performs
As users or customers engage, you can see their journey and react personally to their preferences and needs
As you can see in the video from UNIFY, experts from top mobile companies had a lot to say about the concept of granularity. Here are eight things they highlighted:
1. Granularity tells you how to maximize channels
Clearly, seeing which ad network or publisher is providing the best results is a good thing. But it’s sometimes even more important to really understand what’s working within a network or publisher.
“Obviously Facebook is the biggest social channel, but Pinterest, which is often overlooked, is an interesting play,” says Yelp’s Head of Performance, Eyal Grundstein.
The key to unlocking performance for Yelp on Pinterest was experimentation… and granularity.
Initial generic campaigns produced generic results, but when Yelp started targeting “odd things” like nail salons, click-through rates jumped 5X. Another finding: tattoos are huge on Pinterest, because people search for tattoos that they’ll consider. Targeting on tattoos and showing tattoos in the ads boosting conversions 10X.
“You can be granular not only in the targeting but also in the copy,” Grundstein says.
2. Granularity tells you which publishers are performing
Most ad networks fulfill impressions and conversions for their clients by purchasing inventory from publishers or sub-publishers. When this happens, sometimes advertisers lose the ability to optimize for maximum performance because they either lack the capability or are not looking below the top line campaign numbers to the sub-publisher results.
Hint: some will be rock stars; some will be duds.
“We have a two to three times per week process of pruning out the low performers,” says Eric Seufert, Platform at N3twork. “We kill them at the line-item level if they’re not performing.”
That process does vary from week to week, Seufert says, as publishers change. There’s some natural variance between good, acceptable, and bad, so some level of discretion is warranted. Still, the overall learning remains: advertisers need to be able to probe down to sub-publisher levels to really fine-tune performance.
3. Granularity helps you avoid ad fraud
Granularity is table stakes for avoiding fraud, says Grundstein. Impression-level data, for instance, is an absolute must.
It’s also a way to tie the technicalities of adtech to the ground-truth realities of customers, users, and your product. And there’s no better way, says Warren Woodward, Nexon’s Executive Director of User Acquisition, to really see what’s going on.
“Show me this ad in the wild,” Woodward will often ask his ad partners. “It’s amazing how many sources break down when you ask them… where is your traffic? Can you show it to me?”
And, just as source-level data allows you to pinpoint top performers, it also allows you to isolate potential fraud. Especially when you explicitly state your goalposts in the ad insertion order:
“This game that usually has a 90% tutorial completion… if we see a source as over ‘x’ number of installs and [it] deviates from that norm by over 50%… we’re going to consider that incentivized or some other type of fraud,” says Woodward.
4. Granularity helps you avoid bidding against yourself for adspace
Granularity on the publisher level helps us to “strategize and understand where not to overbid or bid against yourself,” says Yelp’s Head of Performance, Eyal Grundstein. “For example, if you’re buying on two different DSPs and they’re both buying on Mopub… they will bid up against each other potentially, especially on a particular placement if there is enough volume or if it is relevant enough.”
In other words, the ad space is complex and busy. And if you’re a significant advertiser, you’re probably using anywhere from ten to over a hundred advertising partners, which means you could potentially have campaign collisions.
There’s only one thing less cool than ad fraud, and that’s bidding against yourself.
5. Granularity helps you customize to different geographies
Country and regional level data is critical when marketing, says Kabam’s Director of User Acquisition, Andy Park.
“How people consume media across geos is different,” Park says, noting that people in China like to like and comment on ads, particularly on Tiktok, the country’s top video platform. “[One] ad got 37,000 likes and 600 comments in two days.”
Creatives come in many different sizes, shapes, and user experiences, Park says. The key is being able to present different creatives to different audiences, and react appropriately depending on which ones work.
This also enables regional targeting, says Postmates’ Director of User Acquisition Patrick Witham.
“We operate city-level targeting,” Witham says, while noting that there are some limitations with ad network data for geotargeting.
Separating campaigns for different geographies can also make overall campaign analytics more challenging, he added, and does put some limits on scale. However, tighter targeting almost always leads to better results, and “specificity drives conversions.”
6. Granularity allows you to “try wild things” and still be successful
Some of the best things you’ll do in marketing are crazy.
At least, at first glance.
“Our approach has been to build tools that allow us to be radically experimental,” says N3twork’s Seufert. “We’re building about 50 videos a week… we deploy them to test and then deploy more universally.”
Some of those videos are going to be incredible. Some are going to be horrible. But by building the engine to enable creativity at scale and fast failure, N3twork is opening itself up to those rare oddball explosions of lightning in a bottle that drive mass conversions.
Nexon’s Woodward agrees.
“Try wild things,” he says. “You want something that’s going to stand out… when you have a completely different experience, it’ll be the biggest winner or a complete loser.”
One example for Nexon was an ad that featured almost no gameplay — an extreme rarity in the mobile game ad world. Instead, it simply showed fans talking about the game. Essentially, it broke every rule… and it was the company’s biggest winner.
“It carried about a quarter of our user acquisition,” says Woodward.
7. Granularity helps you avoid poorly performing genres of publishers
Sometimes you want to avoid one publisher in particular. Sometimes, though, you want to avoid an entire genre of publishers.
That’s exactly the scenario that Kabam’s Park found himself in (watch the video for full details… including precisely what he was trying to avoid.
Some things just don’t work for your company, your brand, your product, or your app. And granularity enables you to avoid them.
8. Granularity helps you test creative versus creative
Every marketer wants to know which ad units are performing. That’s table stakes… and yet also an example of granularity.
Smart marketers also want to know their conversions from different creative types: banner, text, interstitial, video… and playable ad. You just might be surprised at what you find.
For example, playable ads doubled Nexon’s app installs from one particular source, says Executive Director of User Acquisition Warren Woodward.
“Now we’re making as many playables as possible,” Woodward says. “If you’re not games, think about other ways you can make interactive ad units. The rest of us are… you won’t be in the game if you’re not.”
But what about iOS 14.5 and SKAdNetwork?
Old-school mobile marketing relied on granular device-level data to get detailed data on impressions, clicks, installs, and post-install activity, and on iOS that is no longer all relevant. (For now Android is business as usual.) On devices running iOS 14.5 and later (which is now almost all devices) you can only get that level of data if people opt in to tracking … which you can only know after they install your app.
For that reason — and the fact that 75-85% of people are not allowing tracking via the App Tracking Transparency pop-up — your best source of data arrives via Apple’s SKAdNetwork data.
The good news for mobile marketers: it’s deterministic.
The bad news: it’s incomplete by design, it arrives at variable times, it’s adopted with different practices and policies across different ad networks, and it’s explicitly not device level in order to be privacy-safe.
This doesn’t mean marketing optimization on iOS is over. It does mean that mobile growth marketers need to use new ways of measuring advertising and marketing success and learn to be comfortable with a certain missing layer of granularity. However, using Singular SKAN, marketers can still get good, usable, reliable data on which to base marketing optimization tactics.
Summing up
Granularity isn’t just a nice-to-have. It’s an incredibly useful attribute for marketers who want to scale profitably.
The good thing: it’s easy to get on Android.
The challenge: you have to work for it — and earn it — on iOS, and in some cases, you simply can’t get it.
Are you a mobile marketer? Would you like to get more ROI from your mobile app business by using your data and measurement in your mobile analytics platform better? Then you probably want to understand how Average Revenue per User (ARPU) and Average Revenue per Paying User (ARPPU) can be used to make better investment decisions for mobile user acquisition. In other words, how you can turbo-charge your app install and re-engagement campaigns.
Quick note:
As always, we recommend using ROI (not ARPU or ARPPU) as your key metric for any effort to measure and optimize app marketing. ROI is critically important because it alone tells you in dollars and cents whether what you’re doing makes economic sense. However, ARPU and ARPPU can also be valuable marketing analytics because they provide guidance on appropriate CPIs for planning. They are critical components of ROI calculations. However, you need to use them in context. High ARPU is great. High ARPPU is wonderful. But not so great, and not so wonderful if your cost of user acquisition is higher.
Let’s start with simple definitions of ARPU and ARPPU
ARPU measurement defined
ARPU is one of the most useful measures in mobile analytics. ARPU is your average revenue per user, meaning that ARPU measures total revenue driven by your app divided by your number of app installs. Singular helps you calculate this for all app installs, including paid app installs, organic app installs, or total/paid/organic installs for a particular time period. Plus, you can use Singular to further slice and dice your mobile analytics and measure ARPU data by country, vendor and campaign.
ARPPU measurement defined
ARPPU is similar to ARPU, obviously, but it measures average revenue per paying user. ARPPU is a measure that was originally designed for subscription-based apps, like a game that you pay a fee to use every month. The core idea was to be able to understand the quality of paying game users by eliminating the free or non-revenue users from the math. As you might expect, this measure is particularly valuable for freemium model apps or businesses where a small number of users drive the lion’s share of your revenue. Another place ARPPU is relevant is where you have in-app purchase revenue. ARPPU data tends to be particularly relevant for game businesses that focus on sales of virtual in-app purchase sales (IAPs). Some chose to think of ARPPU as a measure of active users, but it’s literally a measure of active payers.
ARPU measurement and install campaign vendor allocation decisions
As you’re probably already aware, ARPU is a powerful metric for both overall and comparative business analysis. Examining ARPU data across all of your installs, or broad classes of installs like organic versus paid, helps you understand both overall business viability and the quality of your app experience. It also helps you compare different games or apps in your portfolio against each other when deciding where to invest in growth.
ARPU highlights problems and successes quickly and easily. If, for example, you expected to drive a thousand dollars per user per year but your ARPU is running at just $50 a year, you clearly have experience, payment, engagement, or other product problems that need to be addressed immediately.
Of course, some apps are primarily designed not to drive revenue, but rather to improve overall customer experience.
That can be non-game apps for industries like hospitality, where augmenting customer experiences is seen as a way to drive loyalty and brand preference. An example would be a companion app for a hotel. Such apps often have relatively low revenue goals – perhaps to simply break even — or no direct revenue goals. In this case, you can compare your ARPU to your acquisition cost to see if your app is meeting this admittedly modest goal, or you can assign revenue from your core activities to the contribution your app is making.
But ARPU data is primarily used to compare vendors and campaigns to each other and determine the quality of users that you’re getting. By examining ARPU data from different ad networks, for example, you can assess if certain media sources are attracting higher or lower quality users or customers for your app. That knowledge in hand, you can quickly make the appropriate ad spend allocation decisions.
The good news in case you’re now sure how to calculate ARPU or ARPPU: both ARPU and ARPPU are metrics you can get easily in the Singular unified analytics platform.
Real-world example: average revenue per user/per paying user
To make it a little more real, let’s look at an example of how ARPU data can help you make better media allocation decisions.
Suppose you worked with just three media vendors to drive installs for your app. All were using the same creative in the same campaign. Over the course of 90 days, you found the following ARPUs:
Network A is delivering the highest ARPU, at 1.3% above Network B and 155% more than Network C, and clearly both Network A and Network B are attracting a higher quality user than Network C, at least for your business or app. That’s important to know because even if Network C offers a bit lower cost per app install (CPI) than Networks A or B, it may not make up for the difference in revenue per game user. If your cost per install for Network A were $5, then the CPI for Network C would have to be less than $1.95 for it to be as cost effective as Network A.
ARPU is a valuable directional measure to consider for gaming budget allocation. But it needs to be considered in the context of ROI.
If we assume, for example, that Network C charges $4 per install, then putting more money into Network C is far less profitable than putting it into Networks A or B. That’s because the ARPU from Vendor C is far lower. But without ARPU, you might rely on CPI to make your allocation decisions. Many companies do, and end up pouring more dollars into channels and vendors that are actually less efficient at driving revenue on equivalent cost.
Obviously here we are focusing on a component of ROI as a way of comparing relative ROI figures.
In the analysis above we focused on differences between networks’ ARPU. But the same method of analysis can also be used to compare campaigns and creative executions.
Using ARPPU to analyze your game business
ARPPU is most useful for app businesses with revenue coming from a small fraction of total users. The classic example, of course, is a freemium game. ARPPU helps because it assesses your app monetization process and buyer flow. When only a small fraction of users are payers, ARPPU will be far easier for you to see the effects of a new monetization process on existing buyers.
Here’s what we mean.
A 10% improvement in average revenue per payer driven by a better monetization process for an app with 1,000,000 installs but only 30,000 payers would be easy to spot in a test. Half your buyers go through the test process, the other half the control, and the outcome reveals a 10% difference. But if you had used ARPU, you would be dividing the revenue difference across 500,000 installs, and so the impact would seem negligible.
See below:
In this example, a 2.5 cent change in ARPU in your test versus the default standard process doesn’t look like much. In fact, you might think nothing really has happened: it’s only 2.5 cents more.
But if you look at ARPPU, the impact of your changes becomes obvious. When you’re just looking at paying users, the difference is almost a dollar. Clearly, ARPPU is useful in certain circumstances for apps with far more users than payers.
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Singular helps data-driven marketers connect, measure, and optimize siloed marketing data, providing the vital insights they need to drive ROI. Our unified analytics platform tracks billions of dollars in digital marketing spend to optimize revenue and lifetime value across industries including commerce, travel, gaming, entertainment, and on-demand services.
If you’d like to learn more or see a demo of the Singular unified analytics platform, get in touch.
We’re pleased to announce that Singular has won the 2019 Technology Innovation Award for marketing analytics in North America from Frost & Sullivan.
Past recipients of Frost & Sullivan awards include Google, Verizon, Cisco, and IBM. Frost & Sullivan is a global research consultancy. 98% of the Fortune 1000 are clients, and the company creates original research for dozens of industries and sectors.
That research included investigating five key technology attributes including Industry Impact, Product Impact, Scalability, Visionary Innovation, and Application Diversity.
Frost & Sullivan’s study also examined five future business value criteria: Financial Performance, Customer Acquisition, Technology Licensing, Brand Loyalty, and Human Capital.
“Against the backdrop of extensive primary and secondary research across the entire value chain, Frost & Sullivan is quite pleased to recognize Singular as the Technology Innovation Leader in the marketing analytics industry,” David Frigstad, Chairman of Frost & Sullivan, wrote in a letter of congratulations.
“Achieving excellence in technology innovation is never an easy task, and it is made even more difficult considering today’s competitive intensity, customer volatility, and economic uncertainty—not to mention the difficulty of innovating in an environment of escalating challenges to intellectual property,” Frigstad wrote. “In this context, your selection as recipient of this Award signifies an even greater accomplishment.”
It’s important to note that this was an independent study. Singular did not pay for it to be produced; we did not request that this report be created, and we did not apply for a technology innovation award.
And all of which, of course, makes winning that much sweeter.
Global brands have lauded Singular’s marketing intelligence platform as one of the strongest and most irreplaceable tools in their arsenal that has helped them obtain a clearer picture of their marketing effectiveness and maximize their return on investment.
– Frost & Sullivan report
More than anything else, Singular is focused on the success of our customers — the best marketers in the world. That makes this external validation of our recent progress particularly gratifying.
[Singular] has been an integral partner to some of the most innovative companies worldwide that have achieved phenomenal success with their marketing efforts.
“We’re very excited to get this award from Frost & Sullivan,” says Singular CEO Gadi Eliashiv. “It confirms that the most important thing we’ve been working on over the last year — our customers’ growth and success — is actually happening.”
Intelligent data that drives insights for growth requires three key ingredients:
Accuracy
Granularity
Actionability
In order to obtain all three ingredients, you need to ensure the reliability of API integrations with each of your marketing platforms. This is where you find the Singular difference. Singular is the only measurement partner to have two separate API integrations with Twitter, along with over 1,000 additional marketing platforms, providing you the most comprehensive solution for ROI down to the creative level.
This is what we call “dual integration.”
WTH is the Dual Integration approach?
Before you can understand the importance of API integrations (and dual integrations) you first should understand the type of data you need to collect in order to have anything meaningful for your campaign optimization efforts. Simply put, there are two key data sets you need to collect from your marketing platform, whether that is from Twitter, Snapchat, Pinterest, Facebook, Google, Vungle, Unity, Amazon: you name it.
First, you need your campaign analytics data (aka pre-install data) to answer questions like:
“How much did I spend on this campaign?”
“How many impressions did that creative get?”
“How many clicks came from each publisher?”
Second, you need your attribution data (aka post-install data) to answer questions like:
“How many installs did that campaign generate?”
“What was the revenue on this creative asset?”
“How many people went to level two as a result of this keyword?”
Only by combining these two datasets with a robust cost aggregation solution can you really know your ROI by campaign, by creative, by keyword, and by individual ad. This gives you the power to optimize at the most granular as well as aggregate levels, providing your best opportunity to maximize profitability.
To do this manually, you would need to standardize the hierarchies (some sources offer only campaign and ad level, while others go right down to the keyword) and the taxonomies (names and terms differ) across every source, and then calculate your ROI by each dimension … every single time you need it.
Sounds like a pain in the @$$?
Good thing Singular has already done it for you!
This is the dual integration approach
Singular has spent years building API integrations for both sides of the puzzle across over 1,000 additional marketing platforms, and automatically combines this data to show you ROI at the most granular levels.
Unlike other analytics platforms who are only accountable for your “pre-install data” or other attribution providers who are only accountable for your “post-install data,” Singular is accountable for both. Which is why we are the only Twitter measurement partner to have integrations that collect BOTH datasets, just as we do for hundreds of other marketing platforms: so we can do dual integration for you, out of the box.
Inherent flaws with tracking links
You might be asking: So why can’t I just use tracking links to collect this data? My attribution provider uses tracking links and says they can do campaign ROI.
Great question! While the tracking link is the easiest way to collect the necessary macros for a given network, this method has some inherent flaws.
It is not retroactive
You are only receiving data at the time of the click, therefore if the numbers reconcile after the time of the click, this will not be reflected in your reporting.
Not all networks support passing all macros
For example, you might be able to receive campaign cost and clicks, but you may not get site ID or publisher ID.
No creative assets!
Singular is the only solution on the market to provide you the most complete reporting of your creative asset ROI across the most visual networks. However, creative assets and their performance can only be reported by an API integration.
Data loss and discrepancy is HIGH
In a recent study, we compared a number of customers who were using Singular along with a third-party attribution provider. In observing their “campaign data” collected via our API integration against the same data set collected via the tracking link by the third-party attribution provider, we saw a 31% discrepancy … with the numbers reported from our API integration matching identically to the number on the final bill.
Of course, we too sometimes rely on the tracking link for those marketing platforms that do not offer an API to collect campaign analytics. However, in the rare case that we cannot collect data via an API, we will also rely on alternate integration methods to ensure accuracy of the data.
For example, a daily email report, or a CSV file upload to an S3 bucket.
We understand every marketer is different, and how you look at your data may be completely different from your competitors. We are flexible and here to ensure the data you see in Singular matches your internal systems.
Heck, we even have a bi-directional API to push and pull data to your source of truth.
Already a Singular customer and looking to take advantage of our dual integration with Twitter? Check out the help center for details on how to configure your Twitter integration.
I’m guessing very, very, very few people can say yes to that question. Maybe fewer than 1,000 on the entire planet. So if you’re trying to grow — and grow aggressively — it makes a lot of sense to listen to someone who can answer with a yes.
Last week our CEO Gadi Eliashiv shared twoarticles by UserAcquisition.com’s Dave Riggs in one of Singular’s Slack channels. In them, Riggs talks from personal experience about the tech marketers need when they start scaling user acquisition for hyper-growth.
The most important point?
World-class measurement: something that our very best clients (and the best marketers in the world) whole-heartedly agree with.
The key differentiator between okay UA, good UA, and great UA comes down to data and measurement. Invest in it. The very best UA teams have sophisticated technology setups that allow them to slice and dice any data by any segment imaginable.
– Dave Riggs
That starts with an MMP, Riggs says: a mobile measurement platform. But it extends far beyond just an MMP. To do a world-class job of scaling user acquisition, you need five critical components in your marketing technology stack, he says.
Attribution (an MMP)
A cost data solution (could be same as #1)
A database/data warehouse
A BI platform
A real-time visualization tool
Obviously, Singular plays in both slot #1 and slot #2. And Riggs has some high praise for Singular:
If you want a provider that offers both cost and attribution tracking, I recommend Singular. There’s nothing better.
– Dave Riggs
That’s pretty exciting for us to see here at Singular. But even more exciting are the reasons Riggs provides:
Extreme accuracy
“Far more network integrations than competitors”
All your tracking in one place
No gaps in reporting
Those are great reasons. Even greater, however, is how scaling user acquisition successfully at such extremes feels when you have the right tools: safe.
Let me repeat that: safe.
This is extremely important. After all, you’ve gone from $100/day, or $36,500/year to an almost unimaginable $250,000/day. If you kept your foot on the gas pedal all year at that rate — unlikely unless you’re achieving the most rarified heights of mobile success — that’s an astounding $91.25 million/year.
Some of Singular’s clients spend twice that. And more.
Feeling safe at such extremes of spending is almost more important than words can convey. (And actually being safe is even more critical.) Mistakes at this kind of spend velocity run into the millions of dollars very quickly.
So how do you scale your user acquisition spend 2,500 times? With the right technology … including Singular.
And how does that make you feel?
Picture it.
An expanse of open road appears before you. You accelerate and feel yourself pulled deeper into the plush leather. Your heart beats faster. Meanwhile, you appreciate the sweet complexity and design that holds you in perfect equilibrium, while the world around you flies past at breakneck speed. Thanks to world-class engineering, you know you’re perfectly safe, even as you accelerate. You’re blaring your horn and laughing like Cruella DeVil, as you ride up on the shoulder, leaving all the basic and intermediate fools in your dust.
– Dave Riggs
If you need that feeling today, let’s talk. Contact us, or request a demo. You deserve to feel safe. And if you need more info, here are the links to Dave’s articles again.
What do brands need most out of their marketing data in 2019?
Actionable insights, consumer privacy protection, and full marketing data unification, chief marketing officers say.
I recently asked 199 CMOs, VPs of marketing, and other marketing leaders what their biggest challenges for marketing data will be in 2019 for a story in Inc. (There was far too much to write there, so this post became necessary.) Tops on marketers’ lists of priorities? Actionable insights in an avalanche of data. But just behind it in today’s climate of consumer privacy breaches was privacy – and trust.
Here’s how Felicity Carson, CMO for IBM’s Watson division, put it:
“Among all the marketing data challenges, the biggest in 2019 will be how marketers instill trust in data – both for the marketing discipline and customers – balanced with the need to improve customer experience.”
The 800-lb gorilla in the room?
Marketers have far too much data already. That’s a consumer privacy risk, but it’s also a potential marketing intelligence nightmare.
“Marketers are drowning in data from various analytics systems,’ says Jo Ann Sanders, VP of Product Marketing at Optimizely. “What marketers are going to have to do going forward … is to go beyond analytics data … and adopt new, agile test and learn practices.”
Marketers don’t need more data.
What they need are actionable insights drawn from the data they already have. Marketers’ third priority, unifying all their marketing data, will help.
“With the exponential growth of data over the past decade and into the new year, it’s becoming harder daily to turn information into action,” says SurveyMonkey CMO Leela Srinivasan. “While more data has the potential to deliver more meaningful insights, prioritizing an action plan to address it is critical.”
Consumer privacy and data security
Insights are essential for growth, that’s clear.
But a strong brand untainted by consumer privacy breaches is also essential for growth. Anyone who feels otherwise, just ask any company that experienced a privacy breach in 2018 … and look at its stock price impact.
That’s why, almost shockingly, marketers’ second-biggest concern has now become consumer privacy, the security of consumer data that brands now possess, and regaining the trust of their customers.
“The single biggest challenge B2B marketers face in the coming year will be balancing privacy and personalization to regain the trust of their audiences,” says Penny Wilson, CMO of social media marketing platform Hootsuite. “That starts with respecting [consumers’] privacy, being open and transparent about when and why data is collected, and then leveraging the data that customers are willing to share to create personalized one-to-one experiences that deliver unique value.”
“Going forward, brands must focus less on maximizing reach, and more on generating transparent, quality engagements that add value to their customers,” Wilson adds.
This is not business as usual for marketers and advertisers, who have typically wanted as much data as possible. In fact, a new “social contract” between brands and consumers will become so important, says Lloyd Adams, SVP at SAP North America, that data ethics will become more important than data analytics.
Unifying marketing data: a top-3 priority
What else do marketers care about?
Not far behind privacy/security/trust, marketers rank unifying marketing data as a top-three priority.
The challenge is obvious.
In a universe of 7,000 marketing and advertising technology tools, marketers are both doing and learning so much more from their prospects and customers. But most of those actions and insights are being generated in siloed, dispersed systems.
“The problem is, we’ve put too many tools in place to collect and analyze marketing data that are too hard to use and it’s causing a lot of frustration,” says Tim Minahan, CMO of Citrix. “Marketing professionals are spending way too much time searching for information and clicking through multiple pages in applications to gather the insights they need to design, execute, and measure effective campaigns.”
The result is not pretty.
“Everyone’s data is a mess,” says Peter Reinhardt, CEO of Segment.
Identifying insights from your marketing data and then unifying them for a single view of customers – and a unified understanding of marketing success – is critical to cleaning it up.
“Data lives in different places — sales, customer service, digital marketing,” says Selligent Marketing Cloud CEO John Hernandez. “The biggest data-related challenge [for 2019] will be consolidation and a full 360-degree view of the customer relationship.”
That’s a difficult challenge, Hernandez says, and CMOs agree.
And in fact, not only is it hard right now … it’s getting harder.
“The biggest issue with marketing data is federating it into a meaningful whole picture,” says Eric Quanstrom, CMO of Cience. “As CMO, I live in (literally) a dozen different dashboards, daily. And that number is growing.”
Marketing data used to be fairly simple: survey data, market data, customer data, product use data, and probabalistic reporting on ad performance in traditional channels. New digital channels offer deterministic reporting possibilities, but with web and mobile and apps and wearables and IoT – to say nothing of platform proliferation like email and social and messaging and search – it’s getting harder.
And all of that proliferation leads to siloed data sets.
The problem with siloed data is that fragmentation subverts complementarity, says Rebecca Mahoney, CMO at MiQ. When data isn’t complementary and doesn’t add up to a complete picture, the marketing results is an inability to detect new opportunities, or see weak links in existing marketing campaigns, she says.
Data lakes may not save marketers, says Daniel Jaye, founder of Aqfer, a data lake provider.
In fact, they can actually exacerbate the problem because most data lakes inevitably become data swamps, Jaye says. Widespread data proliferation, chaotic file partitioning and sharding practices, and the lack of traditional data management tools all cost marketers the opportunity to achieve integrated insights.
Marketing intelligence unifies data insights
But there is hope.
Good marketing data practice does result in growth.
“With a holistic view of data, powered by marketing intelligence, campaign performance will drastically improve, and otherwise unidentified business opportunities will become unlocked,” Mahoney says.
It’s true that not every marketer will have a single marketing cloud for all their marketing technology and data needs. And even most marketing cloud customers also use additional tools to engage and understand their customers.
That suggests that centralization of marketing insights, particularly on paid but also on organic marketing efforts, is what will help marketers the most. Engagement happens where the customers are and data lives in the tools a brand uses to connect with them. Marketing intelligence aggregates then insights from the entire gamut of customer engagement into one single unified view.
Marketers are also concerned about the quality of the data that they have, and its accuracy. 13% said that accuracy was a top concern in 2019. Another 12% said they have too much data.
“In many ways, marketing has too much data on its hands,” says David Meiselman, the CMO of corporate catering company exCater.
As Citrix CMO Tim Minahan said above, we’ve put too many tools in place to collect and analyze marketing data. The result is frustration.
A potential savior?
Artificial intelligence.
“We … believe marketing and customer engagement will be an excellent first use-case for enterprise AI,” says Patricia Nagle, CMO at OpenText. “AI systems can analyse structured and unstructured data to identify opportunities for marketing outreach, customer support, and other actions that enhance overall customer experience.”
That’s true, and AI is a tool that marketing is already seeing results from in fraud reduction, creative reporting, and other areas.
But it does some with some dangers as well.
“Deep learning models have been shown to be vulnerable to imperceptible perturbations in data, that dupe models into making wrong predictions or classifications,” says Prasad Chalasani, Chief Scientist at MediaMath. “With the growing reliance on large datasets, AI systems will need to guard against such attacks on data, and the savviest advertisers will increasingly look into adversarial ML techniques to train models to be robust against such attacks.”
And finally … all the other quotes
When you ask 200 top marketers for their insights, you get a lot of insights. And they’re too good to bury.
So here are many of the additional quotes that marketers provided, broken down into categories that I’ve chosen. Some of them are partially referenced above, but are given in complete form here. Each of the responses is answering a simple question:
What are brands’ biggest challenges with marketing data in 2019?
Marketers need: Data accuracy and quality
Peter Reinhardt, co-founder and CEO of Segment
The biggest challenge for marketing data in 2019 will be data correctness. Everyone’s data is a mess. Consumers are bombarded with tons of noise, much of it based on wrong data, names, and locations. As a result, customers are burned out. It doesn’t matter how much a company invests in personalization if the underlying data is incorrect. For businesses to truly succeed in 2019 and beyond, they need to prioritize making sure their data is clean and accurate.
The biggest challenge with marketing data in 2019 will be determining how good the data really is. Before this rise in cookie awareness people weren’t really flushing the cookies or clearing their cache as much, which lent itself to long-standing good demographic data. Now, the data isn’t as deep, therefore not as reliable. In 2019, businesses will need to learn to re-target.
Not surprisingly, war against fraud will be the biggest challenges mobile marketers will have to face. In addition to common fraud cases like SDK spoofing and click spamming, more and more new types of fraud will stop developers from obtaining real users. Our job as mobile marketers is to keep educating ourselves about different types of fraud and the pattern of each fraud cases, so that we can take a proper action when we find them.
Marketers need: Actionable insights and marketing intelligence
The best tools solve the disconnect between data, insight, and action, incorporating multiple sources of data to execute, allocate, attribute and optimize digital campaigns across channels.
Marketing data still struggles with insights and it would be amazing to see more of a focus on this essential craft. There are endless tools for gathering the WHAT – numbers and histories and basic information about your customers – but very little that helps us figure out the WHY. The big challenge in 2019 (and likely for a few more years) is going to be training people to understand how to read the what to get to that why.
Phil Gerbyshak, Digital Selling and Marketing Strategist
With all the data collected, the biggest challenge with all the marketing data is finding the most meaningful data, and then figuring out the most actionable insight from that meaningful data. Too often reports for reports sake are created, even with AI to help us find the patterns. Taking the time to think about what you want to accomplish and setting up your data accordingly will challenge marketers and delight stakeholders in 2019 and beyond.
There is so much data out there that ‘big data’ is no longer the priority; there is a need for actionable data that means something to marketers. The other challenge is that the biggest winners on the platform side are increasingly closed and stingy with their access, which may be necessary for consumers and benefits the platforms but hurts marketers. Finally, marketers will have to grapple with a savvier base of consumers who are constantly reading mainstream press coverage about data abuses; marketers will need to determine how cautious they want to be with collecting and accessing consumer information.
What is the biggest data challenge for marketers in 2019?
Building actionable results based off of accurate data. We continue to see an inability of our clients to properly read analytics and come to assumptions. I hope continued AI and machine learning will add tools to assist.
Felicity Carson, CMO, IBM Watson Customer Engagement
Among all the marketing data challenges, the biggest in 2019 will be how marketers instill trust in data – both for the marketing discipline and customers – balanced with the need to improve customer experience by identifying meaningful patterns buried deep within the deluge of data. Compounding this challenge is the need to break down compartmentalized martech and adtech stacks that house this information, coupled with the need to have contextualized understanding of aggregated customer data across the organization such as commerce and digital teams. Marketing teams will need to rely on AI to achieve this level of high performance at scale, particularly in the new era of the ‘Emotion Economy’ that requires organizations to engage with customers in relevant ways on issues that personally matter to them.
The biggest challenge with marketing data in 2019 will be to decipher which of the outlier [datapoints] are leading indicators into new market growth.
Leela Srinivasan, CMO of SurveyMonkey
Today, we have access to more data than ever before, but with the exponential growth of data over the past decade and into the new year, it’s becoming harder daily to turn information into action. A study by IDC bleakly projects that by the end of 2025 only 15% of global data will be tagged; of that, only 20% will be analyzed and approximately 6% will be useful.
While more data has the potential to deliver more meaningful insights, prioritizing an action plan to address it is critical. In 2019, B2B marketers will be laser-focused on finding a way to cut through the massive troves of data available and identify the insights that matter most.
The biggest challenge with marketing data is the ability to find the useful insights to create concrete actionable next steps. We have so much data, but not enough of an efficient way to sift through the noise accurately for truly useful data.
The biggest challenges we have as a startup are: having easy-to-use data that doesn’t require a ton of training like a Nielsen or IRI platform and being able to triangulate all of our data among a two-person team. Right now, we are using about 8 different vendors to analyze one or more stages within the customer lifecycle, and while it’s great to have all of this data, it’s not easy to triangulate it. It would be great to have 1 platform that could assess all or most of our marketing program’s performance, but those platforms usually come with a large price tag that we can’t afford.
One of the biggest challenges marketers will face in 2019 is how to better analyze consumer behavior and turn those insights into effective marketing. Consumers spend 40 hours a month and three hours a day in apps, mobile time spent will surpass time spent in TV in 2019, so marketers need to change their traditional planning behaviors for this brave new world. They must integrate their traditional teams with their digital teams, combine their video teams into one cohesive team, and integrate the data across all channels so that they can be smarter about how they find their most valuable customers. It is also getting harder with privacy, however, companies with strong technology especially predictive algorithms can predict users intentions based on less data. We are just scratching the surface on data analysis and with new data privacy laws, this challenge will only get harder.
Tim Minahan, Chief Marketing Officer and SVP, Citrix
Every marketing challenge can be whittled down to a mathematical equation – whether it’s measuring customer sentiment, tracking conversions, or weighing the return on a particular campaign. Data-driven marketing can eliminate much of the he-said/she-said friction that has historically muddied sales and marketing relationships. It can cut through emotional biases and drive the right course of action to reach and win the market and deliver the best results. The problem is, we’ve put too many tools in place to collect and analyze marketing data that are too hard to use and it’s causing a lot of frustration. Marketing professionals are spending way too much time searching for information and clicking through multiple pages in applications to gather the insights they need to design, execute, and measure effective campaigns.
To tackle this problem, marketing organizations need to tap into intelligent technologies like machine learning that can make data-driven marketing smarter and easier to execute. Machines can recognize patterns and analyze things with greater speed and efficiency and automatically deliver insights and intelligence that humans can use to make more informed decisions and engage customers and prospects in the most optimal way. And beyond tools that automate tasks and make marketing more efficient, we need to equip our teams with solutions that enable them to push the envelope. Like using artificial intelligence and machine learning to see data in new and innovative ways. Or leveraging augmented reality to create entirely new worlds where we can interact with customers in insanely personal ways.
As marketing tools and automated solutions continue to flood the market, the biggest challenge marketers will face is applying data to create timely, emotionally-reciprocal experiences. More and more consumers desire a human to human connection and want to communicate with an empathic human rather than a bot or an algorithm. The year ahead will be a pivotal milestone for marketers and brands, the ones that use their data to better understand consumer behavior and leverage it to create more personalized, human connections will succeed, while the ones that do not will risk losing loyal customers.
In 2018, if you’re a marketing professional that listened to recommendations of marketing experts everywhere, you collected a lot of customer data and used it to formulate campaigns. The problem of moving marketing forward into 2019 is two-fold; security and recognizing changes in data before solid patterns are defined. Data security, obviously, will be most important for maintaining trust between marketing and their audience, whereas recognizing emerging patterns in the data deluge will mean the difference between cutting-edge marketing or just ‘catching up’ marketing in the new year.
Sid Bharath, growth marketing consultant for tech startups
The biggest challenge with marketing data is figuring out what signals to pay attention to and how to prioritize them. With an explosion of data, the bottleneck moves to how fast you can execute on what the data tells you, and unless you have unlimited resources, you need to prioritize them.
The biggest challenge with marketing data in the coming year will be gaining actionable insight from a flood of data generated via a diverse and numerous set of online (and offline) channels, including social media, website, email, events, PR and advertising.
The Marketing Data Scientist will be focused on deriving detailed insight about customer behavior and producing reliable predictive and prescriptive insights based on complex data models and machine learning. These models will evolve from historical analysis into real-time applications that transform how products are delivered to customers.
Gennady Gomez, Director of Digital Marketing, Eightfive PR
As marketing data becomes not only more accessible but also much more bountiful, there will be an exponential increase in analysis paralysis. As a result, we’ll start to see the focus in martech shift from data mining to insights reporting, driven by data science and machine learning. These new breed of tools will be critical for marketers as they sort through, identify, and filter actionable data.
Until our ability to glean insights from all this data catches up to our ability to capture it, we’ll face the same issue as a city with plenty of cars and not enough roads: traffic. Don’t confuse having more data with having more insights.
The biggest data-related challenge will be consolidation and a full 360-degree view of the customer relationship. As it stands, data lives in different places — sales, customer service, digital marketing — and migrating it into a single platform and making sense of it all is going to be difficult. I hope that in a year’s time, we’ll see a lot of progress and proof that leveraging data to focus on delivering personalized, more relevant experiences is the optimum path for better engagement, stronger sales pipelines, and more meaningful marketing results.
Emerging technology has improved marketing strategy, but the challenge marketers are facing is the daunting task of managing a large number of applications. In the next year, more CMOs need to take a platform approach. Investing in a platform that can be integrated into an existing CRM allows organizations to easily unify their revenue teams, and with the addition of AI incorporated into the platform, unified teams have insight into the behavior of modern buyers with the use of real-time data.
Connecting the dots between marketing touch points and giving proper attribution has been and will remain a major challenge for marketers in 2019 … it can be hard to get a full 360-degree view of the true marketing and sales funnel … it is still nearly impossible to combine data from multiple touch points … to paint a full picture of marketing efforts and sales results.
Many marketers have so much data from multiple domains on hand, but no way to streamline and manage it in one centralized location to gain valuable insights.
Eric Quanstrom, CMO, Cience
The biggest issue with marketing data is federating it into a meaningful whole picture. As CMO, I live in (literally) a dozen different dashboards, daily. And that number is growing.
Daniel Jaye, Founder, Aqfer
2019 will be the year enterprises discover that serverless data lakes are a thing, and that they inevitably become data swamps due to widespread data proliferation, chaotic file partitioning/sharding practices, and the lack of traditional data management tools. As marketers are still floundering to piece together the data and figure out whether or not campaigns truly succeeded—they will realize that they can’t keep their heads in the sand on data any longer, and must work to get a better grasp on data management in order to get to the truth about their customers.
The biggest challenge will be how to tie all the data together to clearly identify what marketing channels are working or not working. There are multiple touch points to a buyer’s journey and it’s very common to see multiple marketing channels involved in the buyer’s decision, which makes it hard for analytics tools to attribute accurately what marketing channel contributed the most.
The biggest challenge in 2019 will be the ability to bring together … multiple sources of data to connect the dots, make informed decisions and act quickly on those insights.
The marketing technology landscape is increasingly fragmented and that’s not going to slow down. But marketers will need to find a solution to stop isolated data sources from negatively impacting their marketing capabilities in 2019. By integrating key marketing technologies such as CRM, marketing automation and ABM platforms, marketers can start to share data across these applications and get the complete customer view that they crave.
Businesses have a wealth of valuable marketing data available to them, but complications arise when this data remains in siloes pertaining to the different departments within that business. This prevents the data from being complementary, and businesses cannot detect potential weak links or new opportunities. With a holistic view of data, powered by marketing intelligence, campaign performance will drastically improve, and otherwise unidentified business opportunities will become unlocked.
In 2019, marketers will be faced with the challenge of data implementation. Marketers know how valuable data is, but struggle to make sense of it as they’re faced with the challenge of navigating numerous fragmented platforms and systems to get accurate and quality data. The goal is to gather data from multiple sources that work together to achieve optimum success, but there isn’t one standard way to streamline data. Eventually, unlocking this will give marketers the capability to improve context, relevance, and develop creative that resonates.
The key is to centralize … data and connect every interaction to a single customer ID. Then you can actually understand how your customer behaves across channels and devices. But even more importantly, that data has to connect to your marketing execution platforms directly, so you actually use those insights to power your marketing.
The increase and abundance of data that is available now due to integrated marketing platforms will demonstrate the flaws in various deep learning models. Deep learning models have been shown to be vulnerable to imperceptible perturbations in data, that dupe models into making wrong predictions or classifications. With the growing reliance on large datasets, AI systems will need to guard against such attacks on data, and the savviest advertisers will increasingly look into adversarial ML techniques to train models to be robust against such attacks.
Marketers are equipped with more consumer data than ever before, which can give them valuable insights into their customer base. Many are eager to use AI to automate and personalize communications, but lack the proper infrastructure and data know-how for AI to work properly. There are countless marketing AI platforms available, but until brands are able to properly segment their datasets and make their data truly work for them, they won’t have the ability to conduct innately intelligent marketing. What does this kind of marketing look like? All the marketer needs to do is set the framework, and the AI takes it from there to create personalized messaging for consumers. In 2019, we can expect to see a push from brands to organize their data within a framework that allows them to hyper-personalize communications.
Analytics continue to be a critical way to review the impact of marketing on business objectives. In 2019 the continued adoption of dashboard reporting and analysis systems will improve how we measure marketing programs and tactics. With better understanding of all marketing functions, organizations can take a more strategic approach and focus on what’s performing best. We also believe marketing and customer engagement will be an excellent first use-case for enterprise AI. AI systems can analyse structured and unstructured data to identify opportunities for marketing outreach, customer support, and other actions that enhance overall customer experience.
We are collecting and analyzing real time data using AI powered platforms … we are telling the story, giving shape and voice to the billions of data points that would otherwise be a bottomless inkwell of unrealized potential.
Marketing data and insights [will become] cheaper due to marketing AI platforms. It will no longer take big budgets, multiple data vendors, and a team of analysts to get actionable insights.
Marketers need: Data security, consumer trust, privacy
Lloyd Adams, SVP, SAP North America
Data ethics will become more important than data analytics.
In 2019, marketers will struggle with the social contract of data exchange between consumers and brands. They’ll wrestle with these questions: How will users be compensated beyond personalization? How can marketers do this without being “creepy”? And, as more biometric data emerges, how can marketers use in an ethical manner?
Making better and more critical decisions about what to collect, how to protect it, how to combine it, and how to use it. The idea of a “360-degree view of the customer” has encouraged brands to collect as much data as possible, but this should never be a goal because it raises risks such as privacy concerns, data breaches, GDPR compliance, and customer distrust. Marketers and customer experience leaders need to focus on prioritizing their data needs, better assessing risks, and developing a data strategy that prioritizes and centers on what data is essential and how it will be used over accumulating more of it.
The single biggest challenge B2B marketers face in the coming year will be balancing privacy and personalization to regain the trust of their audiences. In many ways, 2018 was a tumultuous year for brands, marketers, and customer experience leaders. Concerns around fake news, fake followers, and data privacy led individuals to question their trust in politicians, media outlets, social networks, and businesses alike. Those same concerns extended to how brands — both B2C and B2B — forge relationships with customers, and the data they use to do so. The priority for B2B marketers in 2019 must be to reassure customers – and their customers’ customers – their data is safe and secure. This has to be achieved in the changing climate of customer expectations. Increasingly customers — be they businesses or individuals — are expecting content that is important, interesting and timely to them. That starts with respecting their privacy, being open and transparent about when and why data is collected, and then leveraging the data that customers are willing to share to create personalized 1:1 experiences that deliver unique value. Going forward, brands must focus less on maximizing reach, and more on generating transparent, quality engagements that add value to their customers.
The biggest challenge marketers will face in 2019 is activating their first party data and growing its use. Brands are facing a privacy paradox as customers increasingly expect personalized service, but both data regulations and consumer privacy controls whittle away at third-party data and tracking. To remain compliant and provide great customer experiences, brands will increasingly rely on data customers willingly provide in the course of direct interactions across engagement channels–websites, apps, messages, social and in-store interactions and more. The best brands will go beyond gaining opt-ins, subscriptions and followers, and use these interactions to collect context and content preferences for each individual.
Tifenn Dano Kwan, CMO, SAP Ariba
A greater focus needs to be on being data compliant as well as on the ease of leveraging data.
The biggest challenge with marketing data stems from the fact that marketers have so much data available to them today, which they’re able to use to reach customers in a very precise way, yet there’s currently a huge lack of communication between brands and customers as to how and why that data is being used. Without more transparency with customers around why personalized outreach is happening and how it’s benefiting them, the immediate reaction is one of distrust, uncertainty, and even fear or anger. In 2019, brands need to focus on clear messaging that explains to customers why they’re using personalization tactics, how their privacy is protected, and what they stand to gain from it.
With ad-blockers becoming ubiquitous and privacy concerns reaching a fever pitch, marketers need to rebuild the trust that has been lost with consumers. Some of the most successful marketing campaigns in recent times have been honest and authentic, sometimes to the point of distress: KFC’s apology for running out of chicken and Nike’s partnership with controversial quarterback Colin Kaepernick. The biggest challenge in 2019 and beyond will be to create a new normal between marketers and consumers.
May 25, 2019 will mark 1 year since GDPR came into force in the EU. This privacy law sets concrete standards on how EU citizen data has to be treated in addition to strict guidelines on consent. Successful senders will have taken stock of this law and enacted internal processes to ensure compliance with European law.
As data protection regulations like GDPR become increasingly prevalent in 2019, marketers will struggle to target customers with individually customized online advertising. The most successful marketers will be those who can deliver individualized experiences, without individual user data. Computer vision and other contextual analysis technologies will be necessary to anonymously align ads with the likeliest potential customers.
Esteban Contreras, Senior Director, Hootsuite
We have more data than ever before in history and that in itself is a challenge. One of the most important problems to overcome is how to effectively handle and leverage data – data engineering, data analysis and data science – with legitimate consumer empathy. We need to consider privacy by design. The ethical use of big and small data is ultimately about creating value (e.g. personalizing and contextualizing experiences) without misleading or dehumanizing anyone.
Marketers need: Less data (or at least, smarter data)
The biggest challenge with marketing data will be having too much of it, specifically for B2C organizations.
Marketers crave access to information, but we’re drowning in a virtual quagmire of data. We want to get granular, digging deeper into every data point, but we’re stuck with analysis paralysis — unable to prioritize actions that will have the greatest impact on our KPIs.
Marketers will need a cutting-edge customer experience platform that recommend strategic actions tied to specific KPIs.
Noise. There is a lot of it and it is increasing exponentially. As data volumes increase, the tools we will need to filter it all and extract the valuable components will have to increase in their abilities accordingly.
The sheer volume of data being collected is itself posing a challenge to marketers. As we move into 2019, the ability to separate out meaningful patterns and relevant trends from the vast quantities of background noise will be a real test for many marketing teams.
Because there’s just more and more data with disparate KPIs coming from different sources, the biggest challenge will be in deciding what to ignore and what to value.
I predict a big challenge for marketers in 2019 will be remembering to use data as only part of the story and keep an eye on the bigger picture. How many times have any of us been hit with poor targeting because of something else we’ve liked or clicked on? It will be important for marketers to remember that the people they are marketing to are real, complex people who should be considered as more than just a few data points.
The biggest challenge for marketers is focusing on the data that matters. There is a lot of noise out there and each team member needs to know which data is significant to their success. But, it’s not just about having the data but putting it into context to make strategic revenue-driving business decisions. If you aren’t able to execute on your marketing data, it is useless.
I think the core problem moving into 2019 where marketing data is concerned is that with ever increasing amounts of data, marketers are still looking at each marketing silo individually or in small affiliated clusters as opposed to cross-analyzing insights across the entire marketing, sales and customer service value-chain. Often silo based marketing teams will look individually at social data, demand-gen, PR, sales and customer service rather than studying where they intersect and interact. This has always been faulty and is continuing to be a greater problem as each and every silo continues to yield more data.
Jo Ann Sanders, VP of Product Marketing, Optimizely
The biggest challenge marketers will face in 2019 is getting access to the right data to know definitively what their digital users want. Marketers are drowning in data from various analytics systems that provide a historical view of the past. They then ideate ways to improve based on this past data, spend resources to deploy updates, and then re-measure to see if their ideas worked. This process of guessing at what will improve conversion metrics can take weeks or months.
What marketers are going to have to do going forward to succeed so they can keep pace with rapid innovation is to go beyond analytics data that tells them where they have been and adopt new, agile test and learn practices. This will take the guesswork out of what users want, and better ensure that they are rolling out winning user experiences quickly.
With the proliferation of marketing data the challenge will be how to use the latest tools to narrow 1000 points of data down into the few key quality ones that are needed to improve business operations and to better communicate with your customer.
David Meiselman, CMO, exCater
In many ways, marketing has too much data on its hands. The challenge is to figure out which data helps the most to optimize targeting, messaging, and conversions. Traditionally, marketers would work their way through countless A-B tests to determine what works. Today, machine learning and artificial intelligence is helping us accelerate that process to detect correlations and causal factors to improve marketing outcomes across the board, from which people to target for highest conversion to what message to send when for greatest effect.
Tim Minahan, CMO, Citrix
The problem is, we’ve put too many tools in place to collect and analyze marketing data that are too hard to use and it’s causing a lot of frustration. Marketing professionals are spending way too much time searching for information and clicking through multiple pages in applications to gather the insights they need to design, execute, and measure effective campaigns.
For many years we’ve been trying accumulate as much data as possible, and we’re now ironically in a difficult position of potentially having too much data. We need to remember to sit back and discover what is actually happening at different points in time, so that we can figure out how it all fits together. We also can never forget that the most important data point when building attribution is still “Where did you hear about us?”
Marketers need: Many more things
These quotes don’t fit an exact category. But they’re too good to not use.
Jenni Schaub, Strategic Planning Director, DEG Digital
But always still remember, a mountain of data is not a replacement for empathy
Stephanie Smith, Co-Founder and President, MOJO PSG
Thoughtfully exploring and formulating the question you’re actually trying to use data to answer is a key challenge that marketers must face in 2019. Without taking the time to define the problem we’re solving for, we end up wasting a lot of time swimming in seas of data and even potentially misusing the data we uncover. Marketers must also find a balance between using data to inform, rather than dictate, decisions, as the marketing craft will always be a blend of art and science.
Measurement is hard. For SMBs and mid-market companies, it is harder, and the stakes are often higher. Although last click measurement is an archaic way to measure performance and impact, many marketers still rely on it because they don’t see accessible alternatives, as sophisticated tools are often difficult to set up or not flexible enough to work within their data model.. Yet, marketers are hungry for change and searching for a better way to provide visibility and optimize their campaigns. I see 2019 as the year modern marketers stop relying on vanity metrics and outdated measurement models and start looking at what is actually driving sales. Further, marketers need to embrace multi-channel adoption and prioritize creating connected stories across all touchpoints.
The biggest challenge for marketers will be navigating the evolution of what marketing is in a post-truth world. In 2019, marketers will need to ask themselves the difference between truth and propaganda.
Summary
Thanks to all the marketers who participated in this research, which was initially for my column in Inc, but grew beyond that.
Clearly, there’s a significant change in marketing data policy coming. Marketers know that it’s not about quantity of data but quality. They also know that insights on next best actions is the thing they need their data to reveal. And they are more than cognizant now that consumer privacy matters, and companies that violate their customers’ and prospects’ trust do get punished, both financially and in reputation.
We have to give the last word to Jolene Rheaulot from The Bid Lab.
She said this, which every marketer should remember:
The biggest challenge with the breadth of marketing data available to a company is to keep the data human.
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Quietly rolled out by Apple on March 29th, 2018 with their iOS 11.3 release, SKAdNetwork is an API that validates advertiser-driven mobile app installs. In Apple’s documentation, it’s stated that SKAdNetwork’s objective is to help marketers to measure the success of an ad campaign while maintaining user privacy.
What’s different about the SKAdNetwork API?
SKAdNetwork is a class that belongs to the StoreKit framework; Apple’s In-App Purchase Payment System that manages transactions for In-App Purchases. After installing the app, Apple shares only 5 items with the advertiser: ad network ID, transaction identification, ad campaign ID, app ID installed, and attribution code to link all.
There are two key postbacks associated with SKAdNetwork:
Initiating Install Validation: This Informs an ad network when users install and launch an app after viewing an ad. Ad networks initiate validation by providing signed information, including a campaign ID, when displaying the ad. Later, if the ad results in a conversion, Apple notifies the ad network with a postback that includes the same campaign ID.
Verifying an Ad Conversion: When a user installs and launches an app as a result of your ad, you receive a postback request that validates the installation. The request is sent to the ad network URL provided in registration.
What does this mean for advertisers?
It’s still too early to predict how SKAdNetwork will play out. Adding to the mystery, Apple has been very hush-hush about their motives and the rollout of SKAdNetwork. However, we think there are a few possible ways this could play out:
1. Apple doesn’t actively push SKAdNetwork, it doesn’t garner significant adoption, and nothing changes in the mobile marketing space.
One possible scenario could be that Apple doesn’t actively push SKAdNetwork to advertisers, resulting in minimal adoption. In this scenario, there wouldn’t be any significant change in the way that app marketers manage their attribution.
2. Apple pushes SKAdNetwork and Google follows suit with their own version.
Another scenario is that Google follows suit with its own version of the ad network API. This scenario could play out a few different ways:
Apple and Google don’t build out a robust attribution solution, which results in a lack of adoption by app marketers. Apple has made its mark in the world thanks to being an extraordinary and innovative hardware company, but they have never been accountable for providing analytics and insights to app marketers. If Apple and Google do not develop all the features that are necessary for an end-to-end attribution solution, (e.g. data extraction, all postback types, flexible attribution windows, easy BI integrations) then the industry will not adopt their solutions.
Apple and Google develop all the functionality needed for a robust attribution solution, leaving third-party mobile app attribution providers to potentially die-off in their current form. Who can compete with the operators of the mobile app stores we attribute from anyway? However, advertisers may still lose out in this scenario because they might encounter more complexities coming from running attribution on two separate platforms. The winners in this scenario would be third-party mobile app attribution providers that offer value-added services such as connecting multiple networks into a single view and aggregating all necessary features into a single API.
3. Apple pushes SKAdNetwork but Google does nothing.
In a third possible scenario, Apple could actively push SKAdnetwork to advertisers, while Google doesn’t follow suit with their own version. This would still result in complexities for advertisers who would need to manage attribution programs in silos across different OSs.
In this scenario, marketers would turn to attribution providers who could help them gather data from multiple sources, standardize it, and aggregate it into a single ROI dashboard.
So what’s going to happen?
It’s unfortunately too early to say, but one thing is clear: Apple wants to enhance users’ privacy. Apple has clearly positioned itself as a top privacy-conscious company and will continue to hold this stance as data privacy becomes more top-of-mind in the industry.
