Mobile marketers across the globe recognize the massive importance of ad spend measurement. The ability to effectively collect ad spend data from media providers directly affects a marketer’s success on mobile.
But various events can skew your ad spend data as it travels from your ad networks into your analytics, distorting metrics, destroying the ability to target your most profitable audiences, and interfering with vital activities like creative analytics. As a result, collection of accurate and detailed spend data from ad partners is a non-trivial task that trips many marketing teams up.
It is a problem that Singular set out to solve for marketers more than seven years ago. In that time we’ve pioneered numerous technologies to automate the collection of accurate and detailed ad spend data directly from media providers in just about any form imaginable: API, export, PDF, screen-scraping, and more.
As the industry matures, and other analytics platforms start to recognize the importance of ad spend and ROI analysis, the time feels right to review the various spend collection methods being utilized in the mobile marketing industry and highlight the advantages as well as the limitations of each method.
In doing so, we hope to advance the growing dialogue on ad spend collection in the analytics ecosystem and continue pushing the industry to improve the handoff of marketing data from media providers to advertisers.
Overview of spend collection methods
Currently there are three main types of methods for collecting ad spend:
Direct: platform integrations
Semi-direct: exports and reports
Indirect: passing spend data in tracking link parameters (i.e. cost “macros”)
Indirect: passing spend data in server-to-server postbacks
Platform Integrations
In this method, media providers such as mobile ad networks report rich metadata and performance information through some form of programmatic data reporting, commonly a reporting API. In many cases, networks have multiple API endpoints that may serve different granularities, breakdowns, formats, or audiences.
Advantages
Platform integrations give marketers the ability to accurately match the media provider numbers, including cases in which data changes retroactively
Platform integrations give marketers access to a wealth of information beyond ad spend, such as additional performance metrics, creative data, targeting options and more
Platform integrations are the only way to integrate with the self-attributing networks (SANs): Facebook, Google, Twitter, Pinterest, Apple Search Ads and others
Platform integrations pass sensitive data is securely, server-to-server
Platform integrations provide data as quickly as it is available, and therefore quicker than any other method
Limitations
Platform integrations are harder to build and maintain
Platform integrations must map media provider identifiers to user data, requiring coordination between tracking links and data collected
Platform integrations can limit data update frequency – while some networks offer near real-time updates, others offer hourly or daily updates
Semi-direct
There are also cases where networks send data in email reports to complement some form of reporting that the API lacks. There are other cases in which dashboards and various types of exports (e.g., CSV via Amazon S3) complement reporting where an API is not available.
Advantages
Semi-direct at least gives you data … always a good thing
Semi-direct data is right from the ad network, so it should be accurate
Limitations
Semi-direct data may not be timely
Semi-direct data for one time period could be different in a later export as more data from extended attribution windows becomes accurate
Semi-direct methods can be brittle
Passing Spend Data in Tracking Link Parameters
With this method, marketers attach a few additional macros for cost data to the tracking links they create in their attribution platform (e.g., cost={...}&cost_model={...}). These links are built such that additional cost information is appended on top of every ad click (and ad impression, when view tags are supported).
While most larger networks support passing spend data through tracking links, many networks do not support this method. In addition, we’ve found that relying solely on tracking links to transmit cost data frequently leads to inaccuracies, which is why we recommend marketers complement data from tracking links with data from Platforms integrations, side-by-side, to ensure 100% accuracy and consistency.
Advantages
Tracking link parameters deliver a built-in capability to attach cost to individual user data
Tracking link parameters update data in near real-time
Tracking link parameters are simpler technology and relatively easy to maintain
Limitations
Tracking link parameters have inherent discrepancies with media providers – tracking links don’t ensure a 100% match with the network’s spend figures, and spend could differ from the actual invoices marketers receive
Tracking link parameters make it difficult to support cost reconciliations, retroactive data updates and discounts
Tracking link parameters are not applicable for self-attributing networks (like Facebook, Google, Twitter, Snap and others) as tracking links aren’t supported in these networks
Tracking link parameters make it challenging to support CPM & CPA campaigns:
CPM requires impression tags, which aren’t globally support yet, and due to sheer volume/inaccuracies will only increase discrepancies.
CPA is harder to support as cost is determined by a downstream metric or a set of downstream metrics, and there isn’t a clear way to define that at the link level
Passing spend data in postbacks
This method is similar to the tracking link method, however, instead of using tracking link parameters, media providers can send cost data through postbacks directly to the attribution provider. While we expect postbacks to deliver improvements over the tracking link method, other challenges (listed below) still remain unresolved.
Advantages
Postbacks deliver a built-in capability to attach cost to individual user data
Postbacks deliver data in near real-time
Postbacks offer support for all campaign types (as opposed to tracking link parameters)
Limitations
Postbacks suffer from inherent discrepancies with media providers – this method doesn’t ensure a 100% match with the network’s spend figures, and spend could differ from the actual invoices marketers receive
Postbacks make it difficult to support cost reconciliations, retroactive data updates, and discounts
Postbacks are not applicable for self-attributing networks like Facebook, Google, Twitter, Snap and others
Postbacks require development from the network, and not all networks have the resources, ability, or desire to change their ad server to fit these requirements, and as a result, coverage is still limited
Summary
As pioneers in this field, we are excited to see the increased awareness of the problem of marketing data collection. This is a problem we have been solving for our customers for over four years, and along the way we have seen the impact of our work: better collection techniques, new interfaces with media providers, and overall increases in granularity, speed and accuracy.
Our fundamental belief is that the best solution to the problem is the most comprehensive one: one that combines all available methods of ad spend and marketing data collection into a hybrid approach. Singular’s customers are some of the largest marketers in the world, and as such, we are held to the highest standards of delivery for accuracy, coverage, speed, and granularity.
Our promise to our customers and our ecosystem is to keep innovating, and tackling the problems to come. In fact, we have some groundbreaking innovations we are excited to share with the world in the upcoming months, and we can’t wait to tell you more about them.
To learn how Singular can solve for marketing data collection in your business, request a demo now.
Cohort reporting is not limited to a month, three months, or even six months any more. Singular now supports a full year: 365-day cohort reporting periods.
In other words, if you’re doing cohort tracking or cohort analyses in verticals that need more data than a D30 retention rate report, you’re in luck. And if your marketing campaign time period is three to nine months, you can now get extra margin and extra insight in your cohort statistics.
Web-based marketers recognize cohort reporting from Google Analytics, where you can see your retention rate and the impact of your marketing efforts on the web. Mobile marketers need the same — in fact more detail — analytics in their mobile marketing reports.
A class of students is a cohort
More data makes you smarter. More data means your marketing campaigns bring in more revenue.
In mobile marketing, more data tells you invaluable information such as your customer life cycle. Your average revenue per group of acquired users. Your average sessions per user, by cohort. This goes far beyond vanity metrics and gets to the most important behavioral analytics that define user lifetime value (LTV).
That’s why cohort analysis tools are so vital, and how marketers looking at a cohort table can see trends and create opportunities that, like product improvements, can have cumulative benefits.
I recently took some time to talk about the change with Singular VP of Product Alon Nafta.
John Koetsier: Singular recently updated its cohort lengths to 365 days. But before we talk about why, let’s talk about cohorts. What are the primary reasons to do cohort analysis, and what do marketers learn from them?
Nafta: When we say cohorts it’s important to define first what these are in the context of marketing and user acquisition.
By definition a cohort is a group with shared characteristics. In the context of user acquisition, a cohort often refers to users acquired in similar manner. At the most basic level this could be the date, but it can extend to the marketing channel, the campaign, and even the creative. (Imagine what knowing which creative a cohort responded to could tell you about that group of people.)
A cohort report takes these groups of users, and looks at how they behave over time, say after one day, one week, one month and so on.
By “behave” we often refer to retention or any other important KPI you want to measure your product by. That gives you a very clean view since for example different dates can correspond to different marketing activities or product releases. And different channels, campaigns, or publishers may result in an acquired user profile that can be dramatically different from each other.
So a cohort analysis also helps me establish my baseline — for example, how my organic users are behaving over time — and benchmark acquired users for different campaigns to these profiles.
As a marketer, that can ultimately teach me if my paid acquisition is exhibiting the right results, and point me on what should I focus on when trying to improve. Equally important, it also gives me insight into how fast I’m earning back my acquisition costs, which ultimately is one of the most important things for effective paid marketing.
There’s just so much you can do with cohort analysis. It really is a fundamental tool for the mobile marketer.
John Koetsier: What are the primary ways to define a cohort, and when would you use each? Time of acquisition is of course one … what else is interesting?
Nafta: Interestingly enough, even time of acquisition is not a fully strict definition since acquisition is not just one singular point in time.
For mobile marketing, a common way is to look at the time (or rather, date) of install. But you can also define the starting point of a cohort by the timestamp of the attributed click or impression (AKA critical touchpoint), which tends to be the case for web marketers.
Some marketers, especially in the digital commerce space, may want to look at a different event such as registration or first purchase. They may consider that a much more meaningful and significant starting point in defining a cohort.
Once you define how the cohort is calculated, a good cohort analysis tool or report should give you as many breakdowns as possible to differentiate between important characteristics of these groups of users.
That includes data in a table or in visualizations around:
How they were acquired
Key properties of the acquisition campaign … the customer journey
Whether they were new users or retargeted former users
Acquisition costs
What types of post-acquisition activities are they engaging in, including the active user rate
Conversion rate to purchase or other value-creation activity
And more, depending on your app, your vertical, and your KPIs
Lastly, you also define how many time units — commonly days — you’re looking at after the defined start time of the cohort, and if you’re measuring accumulatively.
Note, this may differ between different types of activities. For example a 30-day retention cohort would commonly mean how many users came back on the thirtieth day after install (or re-install). But a 30-day purchase sum cohort can either refer to the total number of purchases made in the first thirty days (which is more common), or just the sum on the thirtieth day.
Both are applicable. Which you’re using needs to be determined to understand what are you looking at, and avoid confusion with coworkers.
John Koetsier: Does Singular make cohorts available based on re-engaged and re-attributed users?
Nafta: Absolutely.
Looking at cohorts for re-engaged or re-attributed users just as important as it is for newly acquired users. In fact for some verticals, acquired users in the sense of users who have just installed the app is almost not interesting at all for paid marketing, since almost everything focuses on re-engagement.
John Koetsier: OK, let’s get to the big story. Singular updated cohort periods to enable 365-day cohorts. Why?
Nafta: Well, as explained earlier, you’re trying to look at how different groups of users behave over time and draw conclusions accordingly.
In some cases you can draw conclusions or make some predictions based on a relatively short timeframe. For example, you might be able to predict the life-time value (LTV) of a user in a mobile game based on the first seven days of in-app purchases.
However, for some companies and products, the window of interesting activity that is important for prediction may take a much longer time.
For example, if I do a monthly subscription for my fitness app, I’ll probably need to review at least three to six months to understand how users are engaging, retaining, and upgrading their subscriptions. Or, if it’s a digital commerce product where customers are buying more expensive items that you typically don’t buy daily or weekly, marketers likely need to look at several months, half a year, or even a full year of data to be able to produce high-quality conclusions and predictions.
These numbers are extremely important for data science teams, who are often tasked with modeling LTV as well as LTV prediction. More time allows them to improve their models, test them better against reality, and iterate accordingly.
John Koetsier: What business types or app verticals typically benefit most from longer cohort periods?
Nafta: It really varies but I think it ultimately comes down to the expected activity profile beyond retention for your product. If users are taking actions on a monthly or longer basis, one-year cohorts — and even longer — are extremely important.
We see this for subscription services, digital commerce, fintech, and even gaming (with varying impact from hyper-casual to mid-core to hard-core games). It also depends on the level of sophistication and effort companies can invest.
Longer cohort periods produce more data and can allow better models, if you have the resources in place to take advantage of it.
John Koetsier: Often we look at cohorts individually over time to see return on ad spend (ROAS) for a group of acquired users. What else can you learn by tracking individual cohorts?
Nafta: ROAS is only one metric. It’s very meaningful of course to marketers who are working on paid sources. But cohorts are also meaningful to product managers, since by looking at retention against product release dates I can learn quite a bit about how my releases affect retention and adoption.
It’s also important for understanding seasonality, and many more insights.
A different creative asset, for example, which shows a different item to be purchased, uses a different coupon or offer, or just uses different design — such as more straightforward versus more artful — can say a lot about the types of users you’ve just acquired.
This is an important data collection for mobile marketers, and an analysis report with insights here often leads straight to increased revenue.
John Koetsier: As you’ve said, cohort analysis is pretty important for LTV analyses. Does Singular automatically surface LTV and ROI for cohorts?
Nafta: Yes. By default we surface three important KPIs in our cohort report: LTV, ROI, and CPE (cost per event) for every event a marketer has defined as interesting.
Of course, a lot can be customized to meet individual needs. And specifically for retention we have a designated retention report which shows the same cohorts.
John Koetsier: Anything else?
Nafta: At Singular, cohort reporting is at the core. Our philosophy is to ensure that everything can be reported in a cohorted manner.
While reporting by date — which we sometimes refer to as actuals — can give you insight especially in real time, reporting against cohorts is what truly uncovers the outcome of your marketing. This includes being able to attach the cost of acquisition, the type of campaign, ad set and ad, the bid, the strategy, and the type of campaign.
And of course … what all of these are generating in terms of business results.
Every day, we’re bombarded with stories about data breaches, successful hacks, and privacy violations. The world of advertising attribution is not immune to any of those.
Just in 2019, there have been 63 breaches exposing 100 million records in the fintech sector alone, according to the Identity Theft Center. Plus 363 in the medical field and 59 in government/military. Add in all the other sectors, and there have been over 1,000 breaches exposing data on over 146 million records … just this year.
Short version: the problem is real.
So the question is: What is Singular doing about security and privacy? And, how are we enabling both while still providing best-in-class marketing analytics and mobile attribution?
Securing and advertising attribution
Every attribution provider has to maintain a critical set of design principles and methodologies for building, testing and auditing every single part of their platform to maximize data security, and that’s exactly what Singular has done.
It’s true: there is no silver bullet in security.
But prioritizing security when building products, together with comprehensive security knowledge and organization-wide awareness will minimize the chances for a breach.
Serving the world’s top advertisers, and with our engineering team composed of cyber security veterans, we are more than equipped to secure your most sensitive data. To date, Singular has never had a security breach.
That’s good news, but we’re not resting on our laurels.
In fact, under GDPR regulations, attribution providers now have the regulatory requirement to report on any security incident within 72 hours from the time of breach.
Authentication is also of vital importance: your attribution provider must support two-factor authentication, single-sign on, and strong passwords to ensure your marketing data stays private.
Audits and pen testing
Security experts agree that periodic audits and penetration testing by respectable parties is another great tool to evaluate how secure your provider is with handling your data.
You have the right to see these proofs, and an honest vendor will be happy to show them to you. (So yes, you can ask us!)
Advertising attribution and privacy
Privacy, although often coupled with security, is a requirement on its own. It may not be something that most people think about when they think of an advertising attribution provider, but it is something we think about at Singular.
There are a number of important factors to consider around privacy:
Regulatory compliance:
Everyone says they comply, but Singular goes the extra mile. We comply with GDPR, CCPA, COPPA, and other standards. And we enable privacy-related requests such as Right of Erasure and Right of Access programmatically through a set of API endpoints. That’s scalable privacy.
Respecting your users privacy is critical
You need to protect your users’ privacy at all costs. That includes SDK-based methods to cease tracking for under-age users or users who did not consent. It also includes never, ever mixing one customer’s user data with another customer’s dataset.
Hint: if your attribution provider is touting people-based attribution as a core feature, you might want to ensure that no generally-available device or user graph is being enriched at your expense. And, maybe more importantly, your users’ expense.
And, respecting your users’ privacy means working on methods of accurately attributing marketing results and advertising impact even if device IDs like the IDFA go away.
Over the past decade we’ve seen the rise of the marketing technologist, who has one foot in the marketing department and another in engineering. And we’ve seen the data scientist role jump from almost nonexistent to being one of the fastest-growing jobs in just a decade.
Increasingly, as marketing is changing, technology is central to how marketers perform. Growth is now a key unifying function in brands and enterprise, and we’re also seeing the rise of the Chief Growth Officer.
We’re releasing a report on that in about a month.
But … our CEO Gadi Eliashiv gave a sneak peak at some of the results recently at Mobile Apps Unlocked in Las Vegas.
The rise of chief growth officers
Ultimately, the way chief growth officers lead their organizations is by using data-driven insights. Some of the most successful leaders drive those insights via marketing intelligence platforms like Singular.
The primary function of a marketing intelligence platform?
To provide insights for growth by connecting effort with outcome at granular and aggregate levels.
Ultimately, that’s how CGOs and other growth leaders get the score. Understand if they’re winning or losing. And know at both as high level and as granular as they want: how successful are our marketing, our campaigns, our ads, our creative.
Knowing that — and getting smart insights for optimization — powers breakthrough improvement in conversions and ROI. And that’s exactly what most brands, enterprises, and companies need.
Without complete and accurate ad monetization data, app marketers can’t optimize growth decisions. Today, that means they need to include ad monetization data as well as in-app purchase data in their ROI calculations.
App marketers need to measure ad revenue on two distinct levels: aggregate and granular.
Granular ad revenue
Mobile app marketers need the ability to see user-level ad revenue so that they can accurately understand their return on ad spend for user acquisition. Without granularity you won’t know which users are ad whales … and which are ad duds.
Plus, you’re not sure where they came from. Or where to get more of them.
“Granularity is critical in mobile ad monetization,” says Singular CEO Gadi Eliashiv. “Understanding the relative value of their ad impressions helps mobile publishers optimize their apps for maximum revenue. It also helps them improve user experience by making decisions that can minimize irrelevant and wasted ads.”
Aggregate ad revenue analytics
You also need aggregated data so that you know exactly where you stand with ad-based revenue across all partners, plus of course IAP and product/service purchase data. Only then can you get a full picture of your growth efforts.
You also need to understand ad requests and fill rates.
“Smart companies check the fill rate all the time to optimize their waterfall,” says Singular CEO Gadi Eliashiv.
MoPub’s new impression-level revenue data
Singular has been providing ad monetization services for almost a year with a variety of partners, including IronSource. Now, MoPub is providing revenue information to mobile app publishers for every single ad impression. Not only that, MoPub is also surfacing what supported demand source was able to fill the ad slot and what country the user is in.
This is extremely powerful.
With this data, you can understand ad-based life-time value of your users. That’s increasingly important, because just 2% of mobile app users are converting to paying customers via in-app purchases.
Ads are one of the key drivers of monetizing the other 98% of your users, so it’s no shock that ad monetization is increasingly critical for app marketers. In fact, 60% more apps are monetizing through ads in 2019 compared to last year, and ad revenue now represents more than half of many app publishers’ total revenue.
Available today
Starting today, Singular supports MoPub’s impression-level revenue data product, enabling marketers to measure granular ad revenue.
The result is better data precision, more accurate and complete LTV models, superior user acquisition and monetization strategies, and ultimately, the potential to earn more revenue.
And all of it, of course, right inside your Singular dashboard, providing a single pane of glass to understand your cost and revenue.
In the simplest possible terms, a chief marketing officer’s role is to implement strategy that ultimately increases sales. A chief growth officer’s role is even simpler and more explicit: grow the company.
But how?
And what tools do they need to achieve those goals?
Singular is privileged to work with growth marketers at companies like Lyft, LinkedIn, Rovio, Wish, AirBnB, DraftKings, StitchFix, plus many more. We’ve seen what the best growth marketers the planet do, and we know what technology they use.
“Marketers are drowning in data,’ says Jo Ann Sanders, a VP at Optimizely.
That’s the problem.
“With the exponential growth of data over the past decade … it’s becoming harder daily to turn information into action,” says SurveyMonkey CMO Leela Srinivasan.
Marketers are drowning in data thanks to the unprecedented data exhaust of our digital lives.
We browse the web, we install apps, we watch four million videos on YouTube every minute, we search on Google 40,000 times a second. The world will soon have almost six billion mobile subscribers, and American adults now spend more than 3.5 hours a day on their phones in branded apps, sponsored media, and ad-supported sites.
At the same time, marketers are dealing with an exponential rise in tech tools, more digital channels than ever before, and more billion-user platforms every year.
Add in global competition, and 76% of CMOs say they can’t measure marketing performance accurately enough to make truly informed decisions.
Marketing intelligence platform
What marketers need most is actionable insights for growth. So CMOs’ (and CGOs’) biggest challenge is simply mining nuggets of gold from all that data. That requires real-time measurement and analysis at scale across potentially hundreds of platforms, partners, and channels.
That’s why Singular built what we call a Marketing Intelligence Platform.
The new marketers are different. They speak data and write code. They form hypotheses and run experiments; then measure results and optimize. These new marketers are marketing scientists, and they need tools of their trade.
With a Marketing Intelligence Platform, marketers achieve three critical things:
Unprecedented visibility at scale
On-demand flexible reporting
Full customer journey insights
That’s seeing not just your data, but your ROI on every activity. It’s slicing and dicing not just by campaign, but getting CAC per creative asset. And it’s measuring not just conversions, but cross-device and cross-platform journeys that led to customer action.
This requires at least nine components, combined into a single platform, grouped in three sections. We’ll take a very brief look at each. For a full in-depth overview, however, check out our complete Marketing Intelligence Platform report.
The three things that CGOs and CMOs need to drive and accelerate growth are …
One: Unified marketing data
You can’t get the golden nuggets of actionable insights without mining your data, and that starts by unifying it.
Unifying marketing data includes:
Data governance
Data ingestion
Data processing
Attribution
Dimensional data combining/synthesis
Data governance ensures clean data from every source, and enables processing, enriching, and combining later on.
Ingestion is getting all your relevant data from every source, and it’s not easy. Processing is essential to standardize and normalize it, at which point you can conversion outputs to marketing inputs. Combining and synthesizing top-funnel and low-funnel data reveals deeper trends and granular results.
Two: Intelligent insights at scale
At a high level, marketers need to know the score: across all their campaigns, are they winning or losing? At more granular levels, they need to know if a specific campaign, partner, publisher, or creative is performing.
Generating intelligence insights includes:
Reporting and visualization
Actionable insights
Reporting and visualization shows marketers what’s happening, and actionable insights provide clues for future profitable growth. Some of those insights are pull, but some need to be push: alerts about out-of-scope campaigns, click-through rate drops, poorly performing ad partners, and so on.
Three: Automation
The volume of data flooding marketers’ dashboards, reports, and spreadsheets cannot be handled manually at scale. Automation is required, and it includes:
Data transport
Alerts, fraud, audiences
And much more
It is not useful to have a system that only ingests data. Marketing data needs to move from systems of deployment to systems of analysis to systems of engagement, and sometimes in multiple directions. So building in the ability to do that via API, exports, or S3 to internal BI systems and hundreds if not thousands of external partner systems is critical.
And while modern scientific marketing is not a set-it-and-forget-it activity, marketers increasingly need to be able to automate actions within set parameters.
That includes automated creation and distribution of audiences for retargeting, look-alike campaigns, or suppression lists. It also includes built-in on-by-default configurable mitigation of fraud, along with both whitelisting and blacklisting of sources and publishers in paid media campaigns.
And at higher levels, it includes automation of bids and buys for ad campaigns at scale.
Results: what a marketing intelligence platform delivers
What does a marketing intelligence platform deliver?
Find out soon in part two of this blog post, coming next week.
I’m guessing very, very, very few people can say yes to that question. Maybe fewer than 1,000 on the entire planet. So if you’re trying to grow — and grow aggressively — it makes a lot of sense to listen to someone who can answer with a yes.
Last week our CEO Gadi Eliashiv shared twoarticles by UserAcquisition.com’s Dave Riggs in one of Singular’s Slack channels. In them, Riggs talks from personal experience about the tech marketers need when they start scaling user acquisition for hyper-growth.
The most important point?
World-class measurement: something that our very best clients (and the best marketers in the world) whole-heartedly agree with.
The key differentiator between okay UA, good UA, and great UA comes down to data and measurement. Invest in it. The very best UA teams have sophisticated technology setups that allow them to slice and dice any data by any segment imaginable.
– Dave Riggs
That starts with an MMP, Riggs says: a mobile measurement platform. But it extends far beyond just an MMP. To do a world-class job of scaling user acquisition, you need five critical components in your marketing technology stack, he says.
Attribution (an MMP)
A cost data solution (could be same as #1)
A database/data warehouse
A BI platform
A real-time visualization tool
Obviously, Singular plays in both slot #1 and slot #2. And Riggs has some high praise for Singular:
If you want a provider that offers both cost and attribution tracking, I recommend Singular. There’s nothing better.
– Dave Riggs
That’s pretty exciting for us to see here at Singular. But even more exciting are the reasons Riggs provides:
Extreme accuracy
“Far more network integrations than competitors”
All your tracking in one place
No gaps in reporting
Those are great reasons. Even greater, however, is how scaling user acquisition successfully at such extremes feels when you have the right tools: safe.
Let me repeat that: safe.
This is extremely important. After all, you’ve gone from $100/day, or $36,500/year to an almost unimaginable $250,000/day. If you kept your foot on the gas pedal all year at that rate — unlikely unless you’re achieving the most rarified heights of mobile success — that’s an astounding $91.25 million/year.
Some of Singular’s clients spend twice that. And more.
Feeling safe at such extremes of spending is almost more important than words can convey. (And actually being safe is even more critical.) Mistakes at this kind of spend velocity run into the millions of dollars very quickly.
So how do you scale your user acquisition spend 2,500 times? With the right technology … including Singular.
And how does that make you feel?
Picture it.
An expanse of open road appears before you. You accelerate and feel yourself pulled deeper into the plush leather. Your heart beats faster. Meanwhile, you appreciate the sweet complexity and design that holds you in perfect equilibrium, while the world around you flies past at breakneck speed. Thanks to world-class engineering, you know you’re perfectly safe, even as you accelerate. You’re blaring your horn and laughing like Cruella DeVil, as you ride up on the shoulder, leaving all the basic and intermediate fools in your dust.
– Dave Riggs
If you need that feeling today, let’s talk. Contact us, or request a demo. You deserve to feel safe. And if you need more info, here are the links to Dave’s articles again.
Some of the larger marketing organizations we talk to in EMEA think about building aggregated campaign analytics and ROI insights themselves. They generally don’t see the full difficulty and continuous maintenance this project involves. In this article, I explore the challenges of building and why a solution like Singular meets and exceeds these needs. This is part one; part two will arrive in a month.
EMEA is a hub of marketers big and small representing every type of app developer and web-centric marketer you can think of. The data explosion has affected each one. It has made actionable insights, which make all the difference in this competitive landscape, the holy grail of every growth marketing team.
Build vs buy
One question that is a serious challenge for them all: should we build an in-house mobile attribution solution or buy it from a third party?
Our customers are smart and between them own over 50% of the top 100 grossing apps. So it’s no surprise that they employ intelligent engineers and data-savvy growth teams who already have the knowledge of how to achieve aggregated campaign analytics and could have a good shot at the greater challenge of getting ROI in an accurate, timely manner … although getting ROI at the most granular levels would be a massive challenge.
Therefore, it’s not a question of whether they can do it, but rather should they do it. We found that when addressing this question, the same considerations led even the largest enterprises out there to outsource this crucial work to a marketing intelligence platform like Singular.
The first thing to take into account is the cost of undertaking such a huge project and the time to completion.
Engineering time is not cheap and a company can rack up several hundred thousand dollars to build the required infrastructure even before considering the ongoing cost of maintenance. Not to mention that a project of this size and complexity will take months to complete and in such a fast-paced industry, this is long enough to start falling behind the competition.
Cost is not just measured in currency
However, the cost of this is not just monetary.
Valuable technical resources likely need to be diverted from core product projects, which impedes innovation and custom developments that address the specific needs of the business, allowing even more breathing room for competitors.
Getting the foundations right is no easy feat: you have to get a framework for your BI system, make sure that your MMP matches that framework, and then map your cost APIs into it correctly to get full aggregated campaign analytics. Furthermore, if your marketing efforts extend beyond Google and Facebook, you will have to set up multiple APIs with all the different networks you run with and for any new networks you want to test in the future.
If engineering time is limited, as it often is, and new networks are not integrated – what is the impact of the inability to test on the business? The cost of passing on new inventory and networks with new targeting and ad format capabilities cannot be underestimated.
Once you have your APIs connected, additional work is required to configure the internal dashboards to display the new data. It’s a manual process that is prone to human error which can easily render datasets inaccurate and therefore unsuitable for optimization purposes. If you’re going down the build route, you’ll need to put in place time and resources for checking accuracy before you even start thinking about which data visualization platform you’ll use to make sense of it all.
From aggregated campaign analytics to marketing intelligence platform
That’s another reason why our customers choose Singular, a marketing intelligence platform built with the modern growth marketer in mind, addressing their requirements of instant access to reliable data for granular optimization.
Even if all the above is accomplished so that data is flowing in and is accurate which we’ve seen can be done, the issue of combining it with internal data sets poses a true challenge.
Filling in the gaps and delivering the insights requires a complex infrastructure with strong identifiers for combining purposes to enrich campaign and publisher granularity, which almost certainly still leaves creative level combining — and therefore creative ROI — beyond reach.
All this means a lot of data and heavy queries that slow down the internal systems.
Our research and customer feedback reveals that the above challenges, opportunity cost, and continuous and expensive maintenance of self-built infrastructure are what drives small and big enterprises alike to a conclusion that a third party is a better solution for this essential need.
What you actually buy from Singular
Here at Singular, we understand these challenges well — after all, we went through the pain of building it ourselves.
Our product is our bread and butter and we’ve gone far beyond the basics to build a true marketing intelligence platform that frees up engineering time of our clients to build marvelous things that uniquely aid their goals while giving growth marketers the tools that they need.
What you buy from Singular is beyond the aggregation and standardization you’d expect to build yourself: you buy a world-class solution that is focused on continued innovation and automation, to give you unrivaled insights and optimization capabilities.
You buy teams that build and support integrations, improve infrastructure and system performance, and constantly work to add new features. You buy a data science team that make it their business to spot discrepancies, a support team that handles data flow errors and API issues, and a stellar (if I may so so myself) customer success team that makes sure the platform is serving your business.
If you had engineering and BI time to spare — what would you build?
Next month we’ll hear from an EMEA customer about how Singular has enabled their business and aided their growth strategy. If you have ambitious goals and are thinking of buying or building, reach out to us about a demo to see what Singular could do for you.
How do you grow ROI while maintaining CPA and scale?
This is a question marketers face every day. And answering this question has become more complex as they advertise on more platforms across more devices than ever before. When conversions happen, it’s a struggle to connect the dots and understand what caused them.
Back when Singular was founded in 2014, we focused on solving this challenge first for the complex, highly fragmented, mobile ecosystem: providing a single solution that automatically collects and combines spend data and conversion data to expose mobile marketing performance, including ROI, at unrivaled levels of granularity.
That is powerful. And we quickly became the de facto solution for unifying campaign analytics and mobile attribution to expose ROI.
But in 2019, the game is different
Top brands advertise over a wide range of platforms to users on multiple devices. A customer may see an advertisement for a product on her desktop, and later buy that product on her mobile app. With today’s analytics, it’s hard to connect the two experiences and measure the customer journey accurately.
For mobile-first brands, this often leads to two separate teams, one web, one mobile app, using different tools, and even different metrics, to measure the customer journey. For web-first brands, it results in limited investment in mobile apps, preventing them from diversifying their marketing efforts to bring in incremental users, leaving untapped growth potential on the line.
Moreover, inaccurate measurement leads to misguided decision-making. Matter of fact, poor data quality costs brands an average of $15 million annually, according to Gartner. Making an investment and creative decisions with inaccurate and incomplete datasets is just plain costly.
In true Singular spirit, we sought to solve this new challenge for our customers so they can drive growth more effectively and efficiently in this multichannel world. And I’m happy to say that we have leveraged our vast experience in attribution and marketing analytics to do just that.
Cross-device, cross-platform attribution
Today, Singular is announcing the first-ever cross-platform and cross-device ROI analytics solution for growth marketers.
With the release of Cross-Device Attribution, Singular’s Marketing Intelligence Platform connects marketing spend data to conversion results across devices and platforms. First, we ingest granular spend and marketing data from thousands of sources. Then we connect it with attribution data from our easy-to-implement in-app and web SDKs as well as direct integrations with customer data platforms, analytics solutions, and internal BI systems, bringing the full customer journey into a single view. Finally, we match the two datasets.
The result is the most accurate cohort ROI and CPA metrics available to marketers, at the deepest levels of granularity including campaign, publisher and even creative.
That’s ground-breaking. It’s revolutionary.
But bringing cross-device and cross-platform ROI into Singular and measuring it accurately, at granular levels, is only the beginning to driving impactful growth.
Granular data for growth
Marketers can now access granular ROI cohort reporting that is more accurate than ever, as you can get clear, combined revenue for users across all devices. This is critical to achieving profitable growth and only possible with Singular – a complete platform that innovates beyond a single attribution solution.
Moreover, marketers can also utilize the wide set of capabilities that Singular’s Marketing Intelligence Platform offers to make smarter decisions and optimize their growth efforts with additional cross-device visibility; plus, they have more visibility into essential context such as the exact creative customers engaged with and the audience segments they belong to.
For example, you may find that a web channel’s impact is much higher than expected for specific types of customers. And now you can analyze the impact of the same creative across mobile and web.
In fact, we won’t be surprised if marketers start shifting investments with this new level of clarity. We are excited to see how growth strategists are going to rise above the crowd using this new solution to become part of the future wave of sophisticated marketers. Gone are the days of attribution feature wars – Marketing Intelligence has arrived.
Launching Cross-Device Attribution is just another step towards achieving our goal: to be every marketer’s indispensable tool in driving growth. We keep working not only to ensure that you can innovate your growth processes and have access to the highest data accuracy but also to ensure that we bring you the right insights at the right time to help you make timely strategic and operational decisions.
Are you ready to take part in the future of growth?
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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