Of course, that’s where the challenge lies. And doing it is not nearly as easy as saying it. But, as we’ve seen in our Scaling Mobile Growth report, top marketers get more and spend less, helping their companies achieve breakthrough growth.
A great product is a necessity, a great team helps, and a great offer is important, but great marketers know that to maximize their results, they also have have to successfully manipulate four key levers: creative, media sources, bids, and budgets.
Get them right consistently, and you win.
Screw up any one of them, and you risk blowing budget, wasting time, and killing credibility.
Brendan Lyall
Brendan Lyall knows more than most about being a top 10% marketer. He was a growth marketer at RockYou! and Storm8, then built businesses in mobile marketing: Grow Mobile, which was acquired Perion Networks, and Downstream.ai.
He’s currently helping Snap build out its ad solutions for marketers.
I spent some time with him recently to talk about marketing, growth, and moving beyond what is safe and known in order to achieve outsized results.
Essentially: how to become a top 10% marketer.
Koetsier: There’s a comfort level for digital marketers in using ad partners they’ve always worked with before. But what’s the risk in that?
Lyall: I’ve been a marketer and I’ve been on the ad network side too.
It is always risky for marketers to get too complacent or comfortable with their current ad vendors. The ad ecosystem is a constantly changing landscape of ad partners and that also includes ever-changing performance of ad partner inventory quality and install value.
There are also inward changing variables that app marketers need to keep in mind to keep they their UA campaigns running efficiently. As many mobile apps evolve, so do their user bases, features, monetization strategies and a countless number of other variables, and this can directly impact the app’s ad campaign performance.
One common scenario is, your ad partner breakdown at one stage in your mobile app lifecycle might be a great fit and provide excellent yield, making the ad partner choice and reliability appropriate, but as the app transitions to another lifecycle phase this can result in certain ad partners being no longer effective or the best choice for a marketer’s ad partner stack. Active and savvy marketers should always consider and test new partners and track the performance fluctuations in correlation to the changes to the app.
It’s important to never allow ad partners to run unsupervised with no or minimal optimization. A marketer’s comfort can lead to complacency and while we all know building a efficient and ROAS-rich strategy is not accomplished overnight. Always stay on top of your marketing campaigns and constantly validate and iterate your ad partner prioritization.
All of the quality ad partners in the mobile ecosystem have evolving products, ad units, optimization algorithms and publishers, so continue to stay up to date on your ad partner’s product offerings and how they benefit your marketing strategy. Marketers should test new channels continually and optimize their campaigns to ensure that their ad partners remain relevant throughout their app’s lifecycle.
Koetsier: The same applies to old versus new ad formats. What have you seen happen when marketers try new ad formats?
Lyall: This is something that has been a constant struggle for a lot of marketers. New formats are really exciting, but can be challenging if you don’t have the resources. Creative and ad formats are often the one of the most crucial part of a performance marketing campaign and can often get overlooked.
Quantitative optimization often takes the front seat for obvious campaign changes since they can be most closely A/B tested and correlated to certain results. Creative and ad formats are the hardest to optimize effectively and to quantify accurately.
One thing that Snap has been very conscious of is user experience and how different brands mesh with that quality standard. As mobile users we are constantly finding ways to subliminally block out ads, this brings up the importance for both marketers and ad partners to constantly iterate on ad units and formats. Testing new creatives is something we encourage our marketers to do frequently. Snap ads are a full-screen experience and this is a unique and immersive way to interact with an app or a brand, and when people spend time with them, they demonstrate high levels of intent.
It’s no surprise that ad solutions that allow for strong customization for natural and native experiences perform the best. For marketers who do not have the luxury to do high production ad formats, it’s important to iterate and test the formats that are within reasonable scale to your budget and resources.
We often see marketers test Snapchat Ads for the first time get impressive results. With thoughtful optimization and iteration within our Snap Publisher Tool, they have been really pleased with the new segment of users coming from Snapchat through the immersive ad units. User experience is very important to Snapchat which is why the ads team has continually iterated on new ad formats that resonate best with Snapchat’s audience of users while also presenting relevant ads for them to interact with.
Ultimately, new kinds of ad types have created new opportunities for stronger adoption and better performance.
Koetsier: What’s working best on Snap right now? What kinds of campaigns for what kinds of brands?
Lyall: From a performance perspective, we see a lot of scale when it comes to gaming and commerce. We’ve also had very significant scale in dating and travel.
Many of these are very performance-driven campaigns, and we see a lot of advertisers who have very specific downstream metrics and post-install events being able to scale very well with Snap. In commerce, deeplinking has been very successful for specific sales, either to another app or an external website.
Koetsier: What are the common characteristics of the best marketers — top 10% marketers — that you’ve seen?
Lyall: The Snapchat Ads self serve ads manager tool is used by a wide range of marketers and advertisers based on budget size, business category and needs.
To speak more towards the relevancy of this blog post, app advertisers have shown really significant adoption and success in the ads manager’s short history. Our most successful UA managers who generate app installs from Snapchat exemplify a heavy quantitative and non-biased approach towards their ad partners and constantly iterate and test on a wide range of campaign variables.
They also are able to understand that while the quantitative optimization is one piece of the puzzle, the creatives and ad formats are much more difficult to quantify and take a scientific approach to how they evaluate creatives and ad units. Overall, a tireless effort from UA advertisers who are willing to get their hands dirty and optimize campaigns and constantly iterate and test.
Koetsier: Thank you for your time!
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To learn more about becoming a top 10% marketer, get a copy of our Scaling Mobile Growth report. We analyzed over $10B in ad spend and a trillion ad impressions to learn what the best marketers are doing, and are sharing the insights with you.
Singular is pleased to announce that it has established a new office and business entity in Seoul, Korea, which will help Singular serve both Korea and the broader Asian market.
Seven of the top 10 mobile gaming publishers in Korea are Singular customers, and Singular’s team in Korea has grown 10X over the past year.
Leading Singular Korea and wider Asia Pacific operations is General Manager Alvin Kim. A mobile veteran, Kim has had executive positions with Opera (AdColony), TUNE, and AppDisco after founding his own mobile marketing company.
Singular Korea general manager Alvin Kim – Kim has a Master of Business Administration from Sogang University, and is well-known and respected in the industry.
“The mobile marketing market is changing rapidly and marketing risks are increasing due to unpredictable variables,” says Kim. “With this complex and intricate marketing performance analysis, Singular is helping marketers run faster and more sophisticated marketing. South Korea is also one of the fastest growing markets in the world and has important implications for entering Asia. Singular Korea will support local marketers and will play an important role as a beachhead for Japan and China.”
Korea is pivotal for Singular.
The country is one of the most highly penetrated mobile markets on the planet.
“Korea is an incredible market,” says Singular CEO Gadi Eliashiv. “The marketers here are extremely savvy, their scale is big, and their challenges are real. That makes this a perfect fit for Singular’s product and technology. In the past year we have built a massive infrastructure here, and have already started taking top market share in key verticals.”
Singular powers profitable customer acquisition by combining upper-funnel campaign and spend data with bottom funnel conversion, attribution, and customer behavior data in one unified view. Deep integrations with more than 2,000 global marketing and advertising partners including official measurement partner status with Google, Facebook, Twitter, Apple, Pinterest, and Quora enable unprecedented data completeness and integrity.
The result is total marketing intelligence: big picture and ground truth. Aggregation, and granularity.
The new office in Seoul will help Singular serve local customers better, and help Singular expand in both China and Japan.
How much do you really know about the effectiveness of where, when and how your marketing dollars are being spent?
“…there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns — the ones we don’t know we don’t know.”
– Donald Rumsfeld
Former U.S. Secretary of Defense Donald Rumsfeld spoke these words at a press briefing in 2002 in response to a question about weapons of mass destruction in Iraq. While he didn’t invent this concept of known knowns, it became his most famous line. The title of his memoir is “Known and Unknown,” and Errol Morris made a documentary film about him titled “Unknown Known.”
The “unknown known” is a fourth concept that Rumsfeld defines as “the things that you think you know that it turns out you did not.” How can these four concepts of known and unknown that Rumsfeld applied to national security intelligence also apply to marketing intelligence?
Let’s take a look.
Known knowns
The first step to marketing intelligence is knowing what you have. On the campaign side, you know your impression, click, and spend data for each campaign, ad set, and ad on each media source. On the attribution side you know how many installs, in-app events, leads, or purchases you’re getting.
While these facts can help you gauge some level of marketing effectiveness, there may be issues getting the data frequently enough to make meaningful decisions. Still, these are the facts you know you know.
Known unknowns
Knowing what you don’t have is important so that you can embark on a journey to find it. You may have an estimation of ROI, but a true picture of ROI is one of the most common known unknowns for marketers. It requires linking campaign data with attribution data in a marketing intelligence platform like Singular.
An example of a known unknown might be understanding ROI by media source for a subscription-based business. Without unified data, it’s difficlut to know ROI over a long lifetime. Finding LTV over both web and mobile is equally challenging. These are known unknowns that can be solved with the right tool.
Unknown unknowns
The unknown unknowns of marketing intelligence are the benefits you could see by understanding ROI at a more granular level. They are the questions you may not be thinking about off the top of your head, but can answer with the right tool.
Consider these questions:
How effective is my creative? If you knew when your creative fatigues on a given source, you could achieve greater ROI simply by swapping old ads for new.
Are my bids optimal on this media source? Granular ROI insights would allow you to set bids higher or lower depending on the source.
Am I setting the optimal budget to maximize delivery? Setting optimal budgets and pacing spend provide additional pockets of ROI.
In addition, creating custom dimensions such as CPA, ROAS, eCPI, ARPU and LTV by combining upper funnel and lower funnel data can yield unknown benefits. If you don’t already have this data, the metrics are unknown, as well as the benefits of knowing those metrics and taking action on them. The most advanced marketers run A/B tests and experiments to uncover more and more revenue-boosting results.
Unknown knowns
An unknown known (data you think you know but actually don’t) sounds an awful lot like fraud. How can you actually know how much you are wasting on fraud? How many of those new clicks, installs, and conversions are actually fake?
Do you have real users with stolen attribution crediting?
Classic metrics may indicate you’re doing well when you really have wasted spending on faked clicks. There’s nothing worse than optimizing based on metrics that are being manipulated. Singular’s fraud detection and prevention features help marketers uncover activity from even the most wily fraudsters. With the help of our fraud engine and in-house fraud expertise, those unknown knowns will start to become known.
Intelligence is exciting
There’s a big difference between working in national security intelligence and marketing intelligence. But successful intelligence workers share a common trait: they are excited about the unknown.
They know that things they thought to be true are often not. They can approach every day without assumptions and make new discoveries. Eventually, the unknowns become known, and there are deeper, more interesting unknowns to unpack. Not only is marketing intelligence an adventure, it’s a lot less risky than searching for weapons of mass destruction.
To learn more about how marketing intelligence can help you venture into the unknown unknown without the tricky language of a career politician, contact us for a demo today.
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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Looking for a platform to unify your marketing data and derive smart insights on how to grow?
The very best mobile marketers get more while spending less than average marketers. We’ve seen it in the data.
But questions remain.
How do they achieve outsized results? Are they just smarter? Do they pick better ad networks? Did they choose the right agency that just happened to massively over-deliver?
None of the above. Instead, what our research shows is that super-successful marketers who outperform their competitors have a number of unfair advantages. To put it simply, they use the right tools.
For one thing, marketers generally recognize that working with more ad partners increases your chances of success. Research indicates that, Singular’s data proves it, and marketers instinctually recognize it.
So why aren’t marketers doing it? Perhaps the most important reason: they lack the right tools to manage multiple ad networks at scale.
Here are the three tools they need:
Essential martech tools: measurement
Without the right tools to measure, manage, and optimize your marketing spend, marketers have to deal with too much incompatible data, too many reports, too many dashboards, and too many incomplete perspectives on their overall picture.
Marketers need a way to see the big picture: all their data normalized, standardized, and visible in one place.
Essential martech tools: optimization
Once marketers’ data is assembled and accessible, it becomes a gold mine of valuable insights that the right platform can reveal. That means marketers don’t have to guess where they’re getting more value.
They know.
In addition, growth marketers don’t have to wonder how different creatives are performing: they know. They can compare ad units and creative across all campaigns and all platforms, understanding which images, text, and playables resonate with which audiences across all their ad partners.
Essential martech tools: management
When they add new networks, marketers also open themselves up to increased risk. They need a way to assess the relative quality of traffic, clicks, conversions, and installs from each ad network, and ensure they’re not paying for non-converting users.
In short, marketers need a way to maximize ROI and control fraud.
None of this is easy
Digital marketers generally know two or three “safe” sources of traffic, clicks, app installs, and conversions. The big two, Google and Facebook, are usually in that picture. After that, Amazon is getting some play — although mostly in consumer goods — and Apple Search Ads is growing as well.
But beyond these names many mobile marketers simply aren’t sure where they should go, which networks are trustworthy, and who they should try.
“Scaling mobile partners is hard,” says Barbara Mighdoll, Senior Director of Marketing for Singular. “It requires more effort, and without the right tools, you take more risks on fraud and traffic quality.”
Scaling is challenging, but without scaling, marketers are left in the same boat as all the others: mediocre results at high cost. And without the right tools, it’s almost impossible to scale ad partners safely.
I was recently speaking at a mobile marketing conference in San Francisco and saw a competitor’s booth.
In the booth, the competitor showed the relative market share of the various mobile attribution providers. Predictably, theirs was highest. Other players didn’t show very well, and Singular was one of them.
I loved it. Because they don’t understand what we do.
Playing a different game
Mobile Attribution is a very critical piece in a much larger puzzle.
That’s why we acquired an MMP, re-architected it as part of a holistic solution instead of a point solution, and that’s why we are winning over a massive number of tier one customers.
In fact, Singular has more customers, bar none, in the top 100 grossing apps on Android and the top 100 grossing apps on iOS than any of our competitors. 46% of the top 100 grossing iOS apps are Singular customers (and 50% of the top 50) and 46% of the top 100 grossing Android apps are Singular customers (and 50% of the top 50).
That’s because we offer something different.
Something bigger.
Singular is a marketing intelligence platform. Our mission is to provide actionable insights to our customers, the best scientific marketers in the world.
We do that by solving the massive problem of data explosion and fragmentation in the marketing ecosystem across mobile, web, TV, offline, as well as paid, email, push, organic and any other form of marketing. We go beyond the confines of mobile advertising and mobile attribution, and are the only single pane of glass for all your marketing activity.
Every company in the world needs this.
Looking to the future
Today, we unify the biggest spectrum of more than 2,000 marketing technologies. And it’s just the beginning.
For us what matters is having the best North Star. And that is the top customers. In every market, the top companies are a constant source of envy and imitation by the up-and-comers and smaller companies.
Since our launch in 2014, and up until this very moment of me typing this, these top customers are the strongest source of influence on our roadmap. That, combined with our vision, is helping us move forward.
We’ve got a lot in the kitchen. You’re going to start hearing more about it in January. Our vision is huge, and we’re well capitalized to make it happen.
For our amazing Singular customers, our sole mission is to be a great, innovative partner that will always put you two steps ahead of the competition. Accept nothing but relentless drive to serve, topped with the whipping cream of world-class innovation.
The cold hard reality of mobile marketing is that the rich get richer and the smart get smarter. That sounds unfair, but there is a sunny side up: nearly every mobile marketer has a shot at success.
But achieve breakout mobile growth isn’t easy.
Among other things, it requires prioritizing what already know you should be doing, but aren’t.
Cold hard data on mobile growth: what we’re seeing
Over the past year our customers used Singular to optimize more than $10 billion in annual ad spend. That includes over a trillion ad impressions, billions of conversion events, and hundreds of millions of app installs.
The average mobile marketer achieving average mobile growth uses just a few ad partners: typically ones whose names your parents would know. That’s not a bad thing: those massive media sources are used by billions of people every single day. They have unparalleled audience and reach, which every mobile marketer will likely need.
But it’s definitely suboptimal to only dance with the big boys and girls of advertising. When you look at the average cost to onboard new mobile users, for example, mobile marketers using five or fewer ad partners pay $3.58. Marketers using six or more average just $2.24.
That’s a big difference. And it means that for the same $100,000 ad spend, top mobile marketers achieve 44,643 app installs … while others get only 27,933. Over a year’s worth of marketing, that’s well over half a million potential new users in your app versus just 335,000.
That’s massive competitive advantage. And at an example $10 LTV, it’s over $2 million in extra revenue that top mobile growth experts bring in.
Which, of course, is additional fuel for even more growth.
Most marketers know what they need to do
The results above are based on hard data … actual data on spend and performance and conversions. But hard data like this doesn’t tell us something very important: why marketers are doing what they’re doing.
Or, of course, why they’re not.
So we surveyed over 900 marketers who run ad campaigns, and what we found is that marketers who fail to create mobile growth don’t fail because they they don’t know what to do — at least at the macro level. Instead, they fail because they don’t know how to accomplish what they need to do effectively, at scale, while avoiding fraud.
Most marketers — 60% of them — understand that in order to access significant growth, they need to add media sources, or ad partners. The problem is that scaling is tough. In particular, scaling beyond known safe channels is dangerous.
Marketers know and trust just a few name-brand media sources. Going beyond those entails serious risk: from complexity, fraud, management, knowledge/skills gap, and more.
As a result, most marketers simply try harder with partners they already know. Even though they believe that the best way to grow is by adding more ad networks, they turn to optimizing with existing, known networks instead of experimentation with new, unproven options.
Optimization is not bad. In fact, it’s a critical part of success.
But when marketers are optimizing on only a very limited subset of possible partners, they’re reducing their chances of bigger-picture success. Getting small incremental wins is great, but opening up entirely new veins of fast growth is better.
Most of this problem is simply due to lack of needed tools for scaling growth safely and profitably via marketing intelligence. Essentially, the price growth marketers pay for the lack of marketing intelligence is sub-optimal growth.
How to unlock breakthrough mobile growth
We’ve talked to the top echelon of mobile marketers who are achieving outsized growth. And we know how they’re doing it.
4 critical levers that top growth marketers optimize
3 key ways top marketers achieve smart insights on growth opportunities
3 toolsets top growth marketers use to run their campaigns
7 ad partners who are delivering outsized returns
Testing and trying more marketing options improves results. It lowers costs, and it increases conversions. It’s what all marketers instinctively know, but it’s also hard.
And it does come with more risk.
With the right tool, however, marketers can understand what’s happening. Measure it. Analyze it for results. And use strategies and insights that allow them to beat the market … achieving significantly great results for less cost.
That translates directly to competitive benefit. And, ultimately, to faster growth.
We know, it’s sad. You missed our mobile attribution webinar last week. We missed you too!
But we have a solution. Two of them, in fact.
First, if you missed our “No BS Mobile Attribution Webinar” last week, it is still available on-demand. We had fun doing the webinar, and we think you’ll enjoy listening to it as well. But second, if you don’t have 30 minutes to spare, it might be faster to read the answers we provided here.
First, a quick recap: content & speakers
Mobile attribution can be confusing, and it can seem pretty detailed and technical sometimes. That’s why we hosted the attribution webinar with friends from Vungle and Liftoff. And we had three experts, who are also providing the answers you see here …
Barbara Mighdoll
Senior Director of Marketing
Singular
David Bennett
Sales Engineer
Liftoff
Rina Matsumoto
Performance Optimization Lead, US
Vungle
OK. The mobile attribution webinar questions (and answers)
1) What is mobile attribution?
Rina: Mobile attribution is the way mobile marketers understand from which marketing channels their app users are acquired.
It’s incredibly important to know which traffic sources are bringing not only users but high LTV users into your app. This will allow you to invest your marketing budget in the right sources.
2) From Andrew at Flipboard: “Can you please touch on challenges and capabilities for tracking attribution from a mobile app?”
Barbara: Well, this is a fairly broad question that could be taken in so many directions, and since we are just starting the discussion I’ll keep this high-level.
Mobile attribution at the core is the bridging together of advertising and mobile technologies. The challenge to attribution is being able to keep up with this constantly evolving technology, and I’ll also add the constantly evolving ecosystem threats like fraud. However, when done right, the insights from mobile attribution allow marketers to execute and evaluate their mobile marketing campaigns with proper app conversion metrics.
2) What are tracking links? How do they work?
David: There are multiple types of tracking links, impression tracking links and click tracking links. These links are used to gather data around what partners are driving impressions and clicks for you. They also allow us to track what users are downloading your app after seeing an ad.
This helps you assign attribution.
The tracking links also help us route users to the App Store, the Google Play store, or other app marketplaces. In the case of re-engagement or retargeting campaigns they can also be setup to route users directly to your app. In general, they make data collection for digital marketing possible.
As for how they work, they send information to your MMP when impressions are shown or when ads are clicked. The information that they send contains device data as well as a few other key pieces of information. Since they contain device data it allows you to track when users are installing your app because of your advertising efforts and what actions they are taking in your app because of your advertising efforts.
3) What is deep-linking? Why does it matter?
David: Deep-linking is a technology that allows you to link to your app directly from your ads [editor’s note: whether in an app or on the mobile web]. For re-engagement campaigns this means a smoother user experience.
This is important because it allows you to minimize the number of steps that your users have to complete in order to reach the desired event. This usually leads to better performance and increased ROI.
Barbara: Just to add a quick comment here, I think this technology has become a pretty standard part of an attribution stack, and because of that most users now expect when they click on an ad with a particular CTA, the app will open in the correct location.
3) What are postbacks? Should I be getting them?
Rina: Postbacks are the way networks receive in-app data from clients, whether that’s installs or post-install events like in-app purchase or tutorial completion.
These postbacks will be key depending on your network’s buying model or optimization methodology. So it’s important to consult your network partners on what postbacks they’ll be needing.
David: This enables you to share user behavior with your advertising partners.
4) What is a SAN?
David: Self attributing networks such as Facebook, Google, and Twitter inform your attribution partners which installs and actions they drove.
5) What is granularity? Why do marketers need granularity?
Barbara: Granularity describes how deep a marketer is able to analyze their data.
For example, basic granularity usually includes drilling down to the app & source level, while sophisticated marketers are able to go deeper into the campaign, publisher, keyword and even creative levels. With this level of detail, marketers can decide when they should shift budget. They also can better inform how to spend their time optimizing – and know exactly where to optimize.
Advanced marketers who have been able to achieve scale and see massive growth are the ones who are able to optimize at deep levels of granularity. For example, as part of our Marketing Intelligence Platform we offer creative reporting where we are able to pull in your ad creative so you can easily match your data to your ads.
One of our customers who started utilizing these creative level insights saw ROI increase 40% within 2 months.
Rina: I agree with Barbara. Granularity helps you understand what types of users were acquired and how they were acquired. Are these users from iOS 11? Were they acquired from a specific type of creative?
It’ll also help in investigating any issues with discrepancies and potential campaign or fraud issues, by being able to drill down to specific parameters.
6) Why do marketers need to combine customer-level mobile attribution data and campaign-level marketing data?
Barbara: This is a great question, and one that we address frequently because the complexity of this is often misunderstood.
Before I jump into the why marketers need to combine this data, I first want to touch on why combining it is even a challenge.
Marketing data is only available in aggregate like ad spend, while attribution data is available at the user-level like app installs. By nature, aggregate and user-level data do not fit together – it’s like trying to assemble a puzzle with pieces from different sets.
This means that marketer’s datasets are often left incomplete and inaccurate. Left this way, marketers do not have the ability to dig into granular levels of insights. And this is a core problem Singular solves – we redefined how attribution data matches campaign data with the experience we’ve acquired over 4 years of mapping this ecosystem.
So to answer why marketers need to combine these two datasets, the answer is pretty simple: to unlock ROI at granular levels like the campaign, publisher, keyword and creative-levels.
Rina: User level data are data points like device type, OS version, and country. Campaign level data are data points like publisher and creative information. Only once you marry this data do you have a full understanding of your marketing campaigns.
7) Can I see where ad networks are running my ads? If so, how?
Rina: At Vungle, we try to provide as much transparency to our advertisers as possible. We share publisher site names with all of our clients to give full transparency into their campaigns.
This transparency allows advertisers to better understand their user base and buy more intelligently on our platform.
8) What are the most critical reporting needs in mobile attribution?
Barbara: First of all, discrepancy and transparency are critical. No matter how your attribution provider is getting install and cost data (i.e. via API or tracking links), there are bound to be discrepancies between your provider and your ad networks. Being able to analyze these discrepancies is extremely valuable to avoid making decisions based on incorrect data.
Shameless plug:
One of the advantages of using Singular, is we allow you to compare data sets side by side without having to toggle between dashboards. And using our transparency feature, marketers can select their preferred source for each metric, then easily locate discrepancies in their data, while even setting-up alerts when discrepancies exceed a threshold.
In addition, ROI (return on investment) is the single most important metric for mobile marketers. However, most attribution providers are only able to provide ROI insights at the source level because they are unable to reliably match cost and campaign data with user level data. True ROI data empowers you to optimize your advertising by the quality of users it’s driving, instead of just install and revenue data. It’s also a must-have if you want to scale your programs while maintaining or even improving efficiencies.
David: In my experience at Liftoff, when there are some discrepancies in between different reports the first two places that we would look are fraud and tracking issues. If the discrepancy is due to fraud we revamp what we are doing and work hard to protect our customers.
If the discrepancy is caused by tracking issues we work with our customers and their attribution providers to get tracking functioning as expected.
9) What kinds of ad fraud are most common? How can I avoid them?
Barbara: Today there are two main forms of fraud: fake users and attribution manipulation. Fake users involves bots, malware and install farms to emulate clicks, installs and in-app events, causing advertisers to pay for activity that is not completed by a real user.
Attribution manipulation is an especially dangerous form of fraud since it not only costs marketers their spend but also corrupts performance data, causing marketers to make misguided acquisition decisions. The two most common types are click injection and click spamming.
David: Click fraud is a major form of fraud that we are seeing right now. It can be anything from click farming to click spamming to click injection to ad stacking. These types of fraud are meant to drive a high number of clicks, reduce the CPC of a campaign and possibly steal attribution from users that could convert organically.
Another example of fraud would be install-fraud through something like install farming or click spamming to steal install credit. These types of fraud are done to drive a higher number of installs to reduce the CPIs of a campaign. In order to combat both click-fraud and install-fraud Liftoff recommends focusing campaigns on actions that users perform through CPA goals or KPIs or through setting ROAS goals or KPIs.
Other ways that we help our customers avoid fraud are blacklisting suspicious traffic, blacklisting traffic from suspicious sources, we even go so far as to reject anonymous traffic, or traffic that doesn’t have advertising IDs or IP addresses associated with the devices.
10) How can I avoid ad fraud?
Rina: Attribution partners and ad networks will have their own technology to prevent and detect fraud.
Something that you can do as an advertiser is take a look at ROAS data, which can be useful to spot install fraud or fake users. However, click fraud or attribution manipulation will typically snipe organic users that usually have high LTV.
At Vungle, we recommend marketers take a closer look at their CTR/CVR and click to install time distributions to find any anomalies. Any abnormally high CTR or low CVR can signal that the clicks aren’t real. A click-to-install time distribution that is skewed beyond the one hour mark is also an indicator that most users didn’t download after a real click that redirected them to the store.
11) Should I pay extra for fraud protection?
Barbara: The biggest mistake marketers can make is to think that fraud is a “nice to have” feature, or that they can “block fraud manually”. Even traffic that looks great i.e. good retention, high ROI can actually be fraud due to attribution manipulation. That’s why we at Singular offer fraud prevention for free.
Also be careful of the actual type of fraud prevention your provider has. With fraud costs so high and growing every year, you need to ensure that your attribution platform not only detects fraud but proactively prevents fraud in real-time.
And by this I mean some attribution providers do not offer actual prevention, but only detection. That means they offer “alerts.” where you then have to manually look at the data and fix it in retrospect. Be on the lookout for prevention types including IP blacklists, geographic outliers, hyper engagement, install validation, and time to install analysis – and the more included the better.
12) How can I ensure brand safety in my mobile advertising?
David: We have customers that worry about brand safety and focus on targeting specific verticals and avoiding others. This is done by setting up either blacklisting or whitelisting for specific types of apps. An example of this would be to blacklist violent apps.
13) Getting app installs is great, but it’s just the first step. What are the most important post-install events to measure?
David: App marketers need to determine which post-install events are the best indicators for future conversions and revenue. Once these events have been determined, these become the events that should be tracked and used to set goals for your campaigns.
These events might be adding an item to your cart or reaching level ten in a game. The idea is that these events indicate a high LTV.
Rina: Understanding short-term metrics as a proxy to determine long-term LTV is the key for performance marketing.
Often times ROAS in the short term is strong indicator of high LTV.
If users often monetize later in their user lifetime, looking at other benchmarks like level completions or retention could be the solution for campaign optimizations.
14) Data is critical to mobile marketing success. Why do I need API access to my attribution partner’s datastream? What kinds of data should I have access to?
Barbara: One of the critical elements to pay attention to if you are in the search for a new attribution provider is data accessibility. After all, your data is only valuable if it’s readily available and in a usable format. This is especially important for marketing organizations with centralized internal reporting.
Regarding what kinds of data you should have access to, there are two types:
Aggregate
This includes LTV, retention, or other in-app KPIs grouped by any number of segments (app, media source, campaign, ad ID, etc).
User-level/device-level
Why do you want this? Just one example: you may need to join that device-level data with offline or proprietary data and perform internal analysis on that combined dataset.
15) Do I have to use one attribution solution across all my apps?
David: The short answer is no … but the long answer is a lot more complicated but really comes down to how many tools you want to worry about integrating and how many tools you want your employees to have to learn.
The more attribution solutions you use across your portfolio the more complexity you add to your portfolio.
Barbara: Yes, complexity is the issue. Do you want to have multiple dashboards? Different workflows?
16) Measuring installs is great, but we do have attrition. How important is uninstall measurement?
Barbara: Uninstall measurement is a useful metric when it comes to understanding your users.
Uninstall data by itself is interesting, but its best used in conjunction with other lower-funnel events to understand the behavior of your users and of your marketing activities.
Aside from the insights, uninstall data can be provided to partners to be used in campaigns for retargeting audiences.
17) Can I use attribution to know how much ad revenue I’m generating from each mobile app user? Or from each network?
Rina: Analytics providers are starting to develop features to ingest ad revenue data to be able to track true LTV of acquired users. As ad revenue on the user level data becomes more readily available, I expect this feature will be widely used by developers.
Barbara: The short answer is yes. It’s a developing technology that we have some customers using right now. The best thing I can say is … talk to us!
Next steps: mobile attribution master class
Quick-witted readers may be wondering: How did 27 questions turn into 17? The answer: via the magic of multiple queries within each one.
This simple question is the billion-dollar puzzle that drives the adtech industry. For marketers, finding the answer unlocks the door to optimizing growth.
– who you send your ads to matters
– where people see them matters
– how often people see your ads matter
– the brand attached to them matters
But creative outweighs them all. And not by a little. Combined.
Why creative is so overwhelmingly important
All of these things are important, naturally. But your advertising effectiveness is mostly determined by one critical quality: the creative.
According to Nielsen, the quality, messaging, and context of your creative is responsible for as much as 49% of all sales lift. How many people see your ads is just 22%. Targeting to the right kinds of people? Only 9%.
Why?
Creative is emotionally powerful.
In fact, a study published in the Journal of Advertising found that ad creativity impacts 13 key variables in five separate stages of the ad experience, from brand awareness to liking, accepting/rejecting claims, and future brand intentions. For 12 of those 13 variables, great creative drives positive impact, and poor creative gets ignored.
According to Ipsos, a massive 75% of an ad’s ability to make a brand impression is due to creative. Creative is so important that ads that win awards, Ipsos says, generate a full eleven times more share growth.
It’s hard to overstate the importance of this finding.
If you succeed at everything else in advertising, but fail in creative, you are leaving almost 50% of your results on the table according to Nielsen, and 75% of your potential results on the table according to Ipsos.
That is why you need creative reporting
Singular offers creative reporting because it’s so critical. It’s something you can get in many places, of course, in silos. Facebook, for instance, offers creative reporting that tells you what images performed well on its platform.
That’s great.
But what top marketers need is an understanding of how their creative is performing across all their ad partners.
One major Singular customer, for instance, works with 20+ different channels including Facebook, Snapchat, Instagram, Twitter, Pinterest, and Google at any given time. In each, this client is using between 15 and 30 different creative units.
Yep.
That’s between 350 and 600 different combinations of platform and creative at any given moment.
That’s not just mildly challenging to measure as a marketer. It’s basically impossible without automated help. The problem is that you don’t know which creative might resonate with which audience.
But you absolutely need to.
Some images will work well in one context and bomb in another. Some videos will resonate with the unique demographic slice that ad partner A accesses, and achieve a collective yawn from ad partners B’s audience. And a playable ad that hits one ad network audience’s behavior graph may not touch another.
Creative reporting is the solution.
“Singular’s Creative Reporting determines asset level ROI across more media sources than any other provider,” says Singular senior product marketing manager Saadi Muslu. “With it, you can quickly identify creative performance against any dimension and metric, group similar creatives regardless of minor copy or compression differences, or group creatives by keyword using tags based on any dimension.”
Why we sometimes ignore creative: piping and wiring
There is a lot of infrastructure in the modern marketing department.
The data explosion and almost 7,000 marketing technology tools haven’t helped with this, and the large numbers of ad networks and partners we work with add to it. In fact — and it’s something we’ll be releasing data on next month — Singular data indicates that top-performing marketers work with a much wider variety of ad partners than average or poor-performing marketers.
(Watch for that report soon!)
But there’s another challenge to all this martech/adtech piping and wiring.
Sometimes it’s easier to focus on the pipes and the wires than on what they’re actually carrying. The world of marketing technology seems concrete, observable, and controllable. If we create a drip email flow, we can set it up, schedule it, and press save. If we’re initiating an ad campaign, we set parameters, initiate buys, and monitor performance.
Creative doesn’t work that way (although AI is getting better at helping).
There’s no button to press for great copy, compelling images, or a funny video. It’s not linear, doesn’t follow a defined process, and can’t be switched on and off.
Magic: melding art and science, data and creativity
That’s where the magic enters, however.
When we pair marketing designers’ and writers’ creativity with insights from marketing data — like those in Singular’s Creative Reporting — we can set creativity free to try dozens of different things, and let data decide which resonate, which penetrate, and which generate productive results.
Nothing could be simpler, even at scale.
Another of Singular’s clients builds an astonishing 50 videos each and every week. Pairing that level of creativity with the data that indicates which ones work would be a tough task, manually. But letting machines do what machines do well makes it possible.
And, tells marketers once and for all: what makes their ads work.
Peanut butter is just peanut butter. And chocolate is just chocolate. But if you have the creativity and insight to combine them, you create a magical mystery confection that makes your mouth cry out for joy and high-five your stomach. You get, perhaps, dual integration.
Imagine the peanut butter is marketing campaign data.
Imagine the chocolate is attribution.
Put them together, and the result is not magical and not mysterious: it’s marketing science that unlocks ever-increasing but previously hidden value. And that’s just one of the secrets revealed in our No Bullsh!t Guide to Mobile Attribution.
But what exactly is dual integration? And how does it work?
Dual integration technology
“Simplistically, dual integration technology is connecting marketing data with outcome data,” says Singular VP of Client Services Victor Savath. “On the marketing side, we’re talking about information on campaigns, publisher, creative, and sub-campaigns. On the outcome or attribution side, we’re talking about user or customer install and event data.”
But … at as granular a level as you implement your marketing spend.
That means every outcome, or attribution, is enriched with campaign information. Now you know not only that you acquired a new customer, or user, from Ad Partner XYZ. You also know what campaign it was from. Where the campaign and the customer intersected. And what specific creative cued the conversion.
When you combine these two datasets, you get true granular ROI, says Savath.
“It’s not about whether or not a network performs, it’s what is performing within a particular network,” Savath told me yesterday. “Sometimes we see that marketers are quick to dismiss performance marketing, or a particular ad network, because the results are all blended. But granularity highlights the pockets of value. For example, in one network … one specific set of creative might work very, very well, while another does not. With granularity, you know.”
Alternatively, some publishers or traffic sources that an ad network uses for your campaigns might be horrible: poor quality or even fraudulent. But other traffic sources are amazing. Seeing this close up means that marketers can optimize for the best-performing publishers within an ad network. That unlocks potential pockets of profitable growth.
The problem?
Most marketers aren’t able to get to that point.
Missing out on magic (or marketing science)
There are many different types of granularity: creative, publisher, network, campaign, region, with metrics from both the network and attribution side. But what matters the most is ROI granularity … which is inherently matched to your ability to tie both sides of the equation together.
The problem is that most marketers don’t have a tool that connects and aligns all the data properly.
And that means they’re making future resource allocation decisions based on limited information.
“For example, if you’re just using vanilla attribution data, you might say that a certain publisher is generating revenue for you,” Savath says. “The problem is, you’re not exactly clear at what specific cost you’ve achieved this revenue.”
Dual integration might show you that A, B, and E campaigns are really working well with a certain ad network, while C and D are not: they’re complete duds. That insight may mean the difference between writing off an ad network as a total loss versus optimizing your efforts with that partner.
And, of course, achieving much better results.
The big aggregated campaign picture alone has its own challenges, of course.
“Alternatively, if you’re just using spend data, you don’t understand your outcomes at all,” says Savath.
Magic isn’t hard. It’s science
The best part is that with Singular, dual integration isn’t any integration at all. At least, not on your part.
Singular does it for you. And it’s not a back-end thing, it’s a built-in thing.
Most attribution solutions provide tools to create tracking links, or make them in bulk, or allow marketers to import them. The problem is that most marketing managers build tracking links in a vacuum, without knowledge of how a partner will report spend back to you. With Singular, there’s no manual link building … Singular removes the whole element of manual creation of tracking URLs from the measurement workflow.
“Instead, Singular creates the links for you and automatically embeds campaign, creative, publisher, ad network, and other data into your tracking links,” says Savath. “Since our marketing data is informing what the link structure should be, you have automatic alignment between marketing data and attribution data. And thanks to Singular’s deep integrations to thousands of ad networks and marketing partners, your URLs will always have the right parameters and the right values.”
ROI versus IOR
Thanks to the performance-based nature of much of modern mobile marketing, marketers are not so much calculating return on investment as investment on return. In other words, they get the attributed results of their marketing and determine how spend and marketing activity relates to those results.
While there’s definitely a big place in performance marketing for spending based on results, only being able to look at marketing data this way creates serious challenges.
One of the biggest: data reconciliation problems.
“Singular’s approach is matching conversions to spend versus matching spend to conversions,” says Savath.
Get the full Guide for much more
The full No BullSh!t Guide to Attribution contains much more insight on how to do attribution right, focusing on seven core topics:
