Digital Marketing Intelligence Software
Imagine a marketer.
She has a left hand and a right hand.
In the left hand are all the things she is doing: campaigns, ads, emails, offers, websites, apps, and more. In the right hand are all the results those activities generate: sales, sign-ups, app installs, awareness, engagement, sharing, and more.
Q. How can she bring those hands together?
A. Via a marketing intelligence platform.
The primary function of a marketing intelligence platform is to connect effort with outcome at aggregate and granular levels. This lays the foundation for three critical things:
Complete visibility of aggregated marketing impact.
Full knowledge of granular ROI and customer acquisition cost (CAC) for each activity, campaign, creative.
Deep predictive insights into profitable future growth.
Those strategic marketing insights include customer journey flows across devices and platforms, of course. More critically, that knowledge includes visibility into what products, offers, and creatives converted which customers and cohorts, regardless of device or platform.
In effect, a MIP gives you deep insight into your customers as well as your marketing efficacy. Now that’s business intelligence.
Enabling technologies for a true marketing intelligence platform (MIP) are many: unification of marketing data, including spend and attribution data. Simple interoperability with CDPs, BI systems, and other data platforms. Fraud detection, creative reporting segmentation, cohorting, automation, and more.
Now the left and the right hand meet.
Together, they unlock scientific growth in a new world of marketing.
It's a new world
Every marketer knows that we are living in a new market, one that is increasingly driven by intelligence and connection. Digital marketing is now the norm. If the transition to mobile was earth-shattering, there’s 10X more disruption coming. And marketers need every competitive advantage they can get.
Every minute we watch 4 million videos on YouTube, send half a million tweets, and post 50,000 photos to Instagram. That’s a lot of social media. Every second, Google processes 40,000 searches. The global datasphere is growing from 33 zettabytes (one ZB is a trillion GBs) in 2018 to 175 ZB by 2025. This is an avalanche of data: new people, new devices, new means of commerce, and (of course) new ways of advertising, as brands are continually looking for competitive advantage.
Mobile and web
By 2025 we’ll have 5.9 billion mobile subscribers, with mobile service reaching 71% of the world’s population. Europe and North America will be saturated with 80% of the population enjoying mobile access. U.S. adults already spend an average of 3 hours and 35 minutes each day on mobile devices in 2018, and this will continue to grow. That’s why mobile ad spend hit $76 billion in 2018, more than TV, and by 2022, mobile ad spending will be nearly half of all ad spend: $141 billion.
Meanwhile, the WWW continues to grow, however people access it: desktops, laptops, tablets, and phones. 4.39 billion people access the web today, up almost 400 million from 2018. Internet users are increasing at a rate of 11/second, and 52% of them are primarily mobile. Digital marketing, clearly, is increasingly a big data business.
Wearables & IoT
Global shipments of wearable devices — smart watches, wristbands, clothing — reached 125.3 million in 2018. That’ll jump to 189.9 million in 2022. At the same time, the number of IoT connections will increase more than threefold, reaching 25 billion in 2025. Each of these smart devices is another node in the network — another step in a customer journey. And another opportunity to engage strategic marketing and competitive intelligence.
OTT, digital TV, and smart TV
We’re watching video everywhere now, but we still love the big screen. At times, Netflix sucks up 15% of total global internet traffic. 70% of TVs shipped in 2018 are smart TVs with apps, app stores, and voice assistants — and in many cases ad tracking — and most of them run Android, Tizen, or WebOS. And while people now spend more time on the Internet than watching TV, it’s still almost three hours a day on average globally. That’s an old market being rejuvenated into a new market, and it requires new strategic marketing business decisions.
AR + VR + MR = XR
AR went mainstream with Pokemon Go. Now we have augmented reality filters in Facebook and Instagram. And every day, Snapchatters spend over 500 years with Snap’s AR camera. Total XR revenues will grow from $3.7 billion in 2017 to $56 billion in 2022, and all of it represents engagement, interaction, and opportunity for connection … plus new marketing efforts and new marketing campaigns.
Smart speakers and voice assistants
Google, Amazon, and others sold 86 million smart speakers in 2018, and billions of phones and tablets give voice-first AI assistants virtually complete penetration in most nations. Meanwhile, the number of Alexa skills doubled last year, and now sits at over 70,000, allowing you to activate everything from your car to your toaster to your TV with just your voice. What competitive advantage can you can you engage here for your digital marketing campaigns?
And there's new marketing
The world has changed. So has the marketing.
The complexity of modern marketing almost can’t be compared to pre-digital. From newspaper, radio, TV, mail, and outdoor, marketers have added dozens of digital channels: social, search, email, push messaging, in-app, video, web, influencer, content, display … and the list goes on.
In 2012 there was one platform that connected over a billion people. Today there are seven, mostly social media, with more than one billion users: YouTube, WeChat, Tiktok, Facebook, Messenger, WhatsApp, and Instagram. (In fact, it’s eight if you count iMessage.) In addition, LinkedIn, Snapchat, Twitter, Reddit, QQ, Viber, Pinterest, and Qzone each have hundreds of millions of users. Each has its own methodologies for marketing, interfaces for advertising, and different tactics for connecting with potential customers.
More tools: 7X in 4 years
As both the number and consumer adoption of digital channels and platforms have gone through the roof, the number of tools marketing technology companies provide for marketers has exploded too. In fact, it’s jumped from under a thousand just five years ago to almost 7,000 today, according to Scott Brinker’s martech landscape. Those companies fill an astonishing 48 categories in marketing technology, especially in marketing analytics.
More channels and more tools equals more data: big data. As the global datasphere grows to 175 zettabytes by 2025, more and more of that has become addressable by marketers with the right tools and decision-making process.
Organic marketing data such as social signals, mobile app usage, web traffic, email opens, and more joins paid marketing signals such as impressions, engagements, and conversions. Increasingly, marketers are getting location data from mobile devices and biometric product intelligence: data from wearable devices. Plus preference data from social media. In addition, enriched personal data from first-party sources such as account and purchase history is added to third-party demographic, habit, and consumer information.
Even a short decade ago, competition was largely localized. Today, Chinese entrepreneurs sell clothing on Facebook to American consumers, shipping it across the Pacific. European and Korean game publishers compete on global App Store and Google Play leaderboards. Direct to consumer brands compete with major fashion labels. And the pace of innovation is increasing, and you need more competitor intelligence.
Add it all up, and marketers are drowning in data. The challenge is seeing the signal through the noise: generating usable business intelligence insights.
In a study of 200 chief marketing officers in January 2019, we found that their biggest challenge was too much disconnected data. Data, which is supposed to be digital marketers’ salvation by revealing what impact both halves of their advertising have accomplished, has turned into an enemy.
In fact, the majority (76%) of CMOs report the complexity of measuring marketing performance and sales attribution severely impairs their ability to grow, make informed growth decisions, and take appropriate investment risks.
So there are new marketers
The data, channel, and tool explosion has fundamentally changed marketing.
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 nothing to being one of the fastest-growing jobs in just a decade.
But today is the age of the marketing scientist.
Most performance and growth marketers are millennials who grew up with computers. They understand intelligence data. They speak code. They can interact with smart systems in intelligent ways, and think of marketing as science as well as art.
“As a scientist, the only thing I believe in is experimentation. I look at my current system, perturb it through different creative and different levels of spend, and different bids, and new channels, and new partners, and I look what the effect is on the metric I care about.”
Alok Gupta, Head of Marketing Science, Lyft
Marketing scientists use tools. They use data. But they are not defined by their tools or their data.
Instead, they are defined by their mindset. Marketing scientists operate as scientists.
- They form hypotheses
- They run experiments
- They measure results
- They optimize based on their source of truth: accurate ROI for every marketing activity
This isn’t about setting it and forgetting it: there are always new channels, new creative, new ads, new offers, and new initiatives to test and optimize. And the cycle of experimenting, measuring, optimizing never ends.
So there's a new need: a marketing intelligence platform
At the very start of this guide, we asked you to envision a marketer with a left hand and a right hand. In her left hand were all her many actions and activities and spend. In her right hand were all her varied results and conversions and sales.
(There’s so much in each hand, some have said the better metaphor is an octopus!)
Marketing intelligence brings those two hands together, we said.
But how? Let’s dig deeper.
What is marketing intelligence?
The primary function of a marketing intelligence platform is to provide insights for growth by connecting effort with outcome at granular and aggregate levels.
When marketers achieve this, they achieve a number of incredibly important things.
First, unprecedented visibility at scale.
With a MIP, decision makers achieve full knowledge of the return on investment for all their marketing efforts at a granular level. That means understanding both the full costs of each marketing activity, whether paid or organic, and the specific results those activities achieved. When marketers have this at a granular level, they have data on their results by overall campaign, by ad sets/groupings, and even by individual pieces of creative, or grouped sets of creative around similar themes. All of that helps in seeing market trends and making smart business decisions.
Second, on-demand prismatic reporting.
With a MIP, marketers can slice and dice their big data like never before — like calculating CAC by creative asset type. Using a marketing intelligence platform allows brands to connect data that otherwise would never connect: dimensions that might exist just in your internal customer segmentation models. For example, a prospect might convert into a customer via a campaign targeted at luxury buyers, but actually buy a product focused more on utility. Knowing which customers respond to which messages helps brands communicate in smarter ways to customers in order to maximize profitability.
Third, customer journey insights.
With a MIP, marketers have a global perspective on their customers’ journey across devices and platforms. Marketers can see when customers who onboarded via a Google search ad re-engaged via a Facebook ad. And they can see when a customer begins with purchases online and moves to buying in-app. Google Analytics won’t reveal this, but a MIP will. This is critical for business growth metrics such as ROI and true CAC. Absent that data, marketers don’t have accurate insights, and may make incorrect future budget allocation decisions.
So … what’s required for marketing intelligence? In broad strokes, there are three major components, each of which is composed of multiple engines.
Most of this is invisible to everyday users: they spend their time in the reporting and visualization modules and it just works, delivering the results they need. But without all of these components, a MIP cannot adequately perform its tasks.
- Unified marketing data
- Data governance
- Data ingestion
- Data processing
- Dimensional data combining/synthesis
- Intelligent insights
- Reporting and visualization
- Actionable insights
- Data transport
- Intelligent automation
One: Data governance
Unifying marketing data starts with clean marketing data.
Creative and campaign names can easily proliferate to chaos. High-volume modern marketing organizations work with dozens of marketing technology vendors, multiple marketing agencies and/or partners, and easily upwards of thirty paid marketing channels. A critical step is getting campaign and creative names correct and consistent from the very beginning. Deciding on a common link structure and taxonomy and sticking to it is essential.
Data governance maintains nomenclature sanity, which aids in data unification, normalization, and combining, as well as in certain types of attribution. And it doesn’t just happen at marketing data’s final destination, but rather at its source.
Getting this right enables maximal granularity of data and trackability of marketing impact.
This can’t be accomplished reliably at scale via manual processes. Automating naming and link taxonomy and monitoring of usage ensure this becomes standard operating procedure.
Two: Data ingestion
Marketing runs on data, but data in dozens or hundreds of different siloes is not usable. So automated ingestion of data into a single location for marketing intelligence is essential.
That starts with sources: email, web, mobile, app stores, and e-commerce systems, just to name a few.
But it also includes paid advertising channels such as ad networks: collecting setup details automatically such as targeting criteria, creative data, bids, budgets, and more. Delivery data such as impressions, clicks/taps, and cost is critical, of course, as is conversion data: app installs, purchases, custom events, revenue, and more.
Ingestion is complex because the world is complex. A MIP ingests data via web scraping, PDF reports, mobile SDKs, web tags, tracking links, Amazon S3 files, APIs, email, and server-to-server integrations.
Three: Data processing
Getting data is one thing. Making it usable and relatable so you can see trends and insights is another entirely.
So a marketing intelligence platform has to run data quality assurance, cleaning and organizing the data. Fraud prevention is a major aspect here: what data do we trust is legitimate human activity, and what data is automated, bot-driven, fraudulent, or otherwise unwanted?
But QA isn’t enough.
Different marketing systems and platforms have different terms and varying methods of measuring prospect engagement and activity, so normalizing and standardizing data is an essential component of unification.
This needs to happen on both the activity/spend/campaign side (the “left hand”) and the attribution/results/conversions (the “right hand”) side. Both input data – effort that marketers are exerting such as campaigns they’re creating or emails they’re sending – and output data – the results that marketers see due to their efforts such as opens, clicks, conversions, and sales – need to be unified and normalized so decision makers can make smart marketing decisions.
Point solutions that simply try to aggregate the left hand or attribute the right hand invariably cannot provide the granular insights marketers need to optimize. Knowing your results is good. Knowing precisely how you achieved them is gold.
Other key components of the data processing step include data enrichment and identity stitching.
Once you have all the cleansed and normalized data, accurate attribution can happen. Now we’re connecting inputs — marketing efforts — with outputs.
That includes standard last-click or last-touch as well as view-through attribution, plus viewability of assists in a multi-touch attribution model. Re-attribution for lapsed and renewed customers and/or users happens here, as does event attribution: what caused this existing customer to purchase a particular item at a particular time?
In the mobile app world, uninstall tracking and reinstall tracking happens in the attribution engine as well.
Five: Dimensional data combining/synthesis
Data ingestion is just the first step.
Once you’ve unified and normalized your marketing inputs as well as your marketing outputs, and attributed causation of those results, you need to combine the data intelligently to derive accurate data on conversion, cost of customer acquisition, and ultimately return on investment. That can happen via a unique identifier on a customer, via a deterministic attributed conversion, or via a probabilistic attributed conversion.
But it’s not just about getting simple campaign ROI.
Dimensionality is a key criterion to enabling deeper insights and optimization.
A MIP can ingest and relate data points that are different in granularity and that therefore cannot easily be matched one for one.
One simple example: aggregated marketing campaign data and customer/user-level data. Another: creative assets that are different in size, format, or even technological execution, but that share similar tone, messaging, and belong to the same campaign. Yet another: cost data connected to digital ad campaigns that is not visible in ad network reporting, such as agency fees, registration fees, or processing fees.
With dimensionality, marketers can understand campaign cross-over: prospects who received a luxury offer, for instance, who converted to an economy line. They can also get CAC and ROI per creative assets that are similar in tone and message, not just individual iterations of those assets.
Six: Reporting and visualization
Data needs to be accessible and understandable to be of any value. Reporting and visualization summarize data and tell multiple teams what is happening, and provide insights for business decisions.
The first customer is the marketing team, of course. But the business intelligence team, the finance team, the C-suite, and others also need to know — at varying levels of aggregation and granularity — what’s going on.
That includes tables, charts, and dashboards, of course, but also customizable reports, pivoting, cohorts, plus extensible reporting frameworks for high-level customers. Including, where applicable, competitive intelligence.
Seven: Actionable insights
Unifying data is a means, not an end. It powers actionable intelligence for profitable growth. In fact, that is precisely CMO’s top priority for their data in 2019:
Unified and combined “left and right hand” data in a marketing intelligence platform unlocks actual ROI and true CAC across all marketing activities. That’s exactly what unleashes marketers to find pockets of profitable growth: understanding which activities will unlock the highest potential ROI.
Actionable insights include benchmarking both against your own past performance and current broader market conditions, trend analysis, and forecasting for key performance indicators like ROI, LTV, budgets, and CAC.
Higher-level insights come from simulations and testing, and a marketing intelligence platform also delivers recommendations based on both first-party data as well as third-party data. These include lift metrics, media mix modeling recommendations, and fraud analysis.
Eight: Data transport layer
A system that only ingests data is not useful.
Marketers might need integrations with other measurement systems as well as marketing action platforms like email and push notifications. Marketing intelligence platforms also send data to internal BI systems via either API or S3 data dumps for further processing, analysis, or integration.
And, of course, partner mediation in terms of postbacks on installs, events, or purchases is critical.
A marketing intelligence platform cannot unify marketing data without speaking to dozens of different kinds of systems, and thousands of individual platforms. And that communication is often bi-directional, with enrichment happening from multiple sources at multiple stages.
Nine: Intelligent automation
Even though 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 begins with customizable alerts for when campaigns fail to hit or exceed parameters.
It 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 use of machine learning and other forms of artificial intelligence where applicable.
An intelligence platform must find and minimize fraud, since it simultaneously wastes spending, decreases ROI, increases CAC, and skews analytics. Fraud management starts with detection but includes automated abatement. Plus, it gives marketers the ability to drill down to the sources of fraud in order to take further action.
Finally, at higher levels of functionality, a MIP automates bids and buys for ad campaigns at scale, enabling marketers to make high-level allocation decisions and market entry decisions that are automatically optimized via intelligent, learning systems. Successful campaigns continue and grow; campaigns where CAC increases unsustainably shrink or get shut down entirely.
Now marketers are achieving new results
Any investment of resources requires consideration of the projected outcome. The investment in a marketing intelligence platform such as Singular can generate a huge payoff in time savings, efficiency, growth, and increased ROI.
Enabling massive global growth at scale
Modern marketing moves quickly. Billions of digitally-connected consumers around the world means that a brand with the right product and the right marketing can grow exponentially with targeted marketing regional and/or globally when driven by marketing intelligence.
Two recent examples are DGN Games and Yelp, both Singular customers.
DGN leveraged Singular to become the fastest growing social casino company, growing a massive 85% YoY. And Yelp used Singular’s granular data in strategic planning to target growth by market segment, critical for such a local-based service.
“Singular allows us to expose data that enables faster and smarter optimizations on our ad campaigns. The reliability and granularity of the data we receive through Singular as well as the flexibility of Singular’s platform to break out performance by geography and other custom dimensions makes it an invaluable resource for our User Acquisition teams.”
– Eyal Grundstein (GSN, Yelp, Amazon Web Services)
Modern marketers who are managing multiple geographies, each with multiple targeted locations and multiple channels, advertising partners, and marketing campaigns need intelligent and automated help to manage everything.
Expanding ROI through multiple ad channels
Marketers limit growth potential and improved ROI by only utilizing a few top ad channels – like Google or Facebook. They’re going to be critical to success, naturally, given their massive reach and exceptional quality, but finding more available growth requires more.
The most effective marketers use a marketing intelligence platform as part of their strategy to reach many more new customers at lower cost. For example, we found that marketers who advertised on more than five ad networks had a 37% lower CPI and 60% higher mobile app installs with the same exact ad spend.
The critical requirement: a marketing intelligence platform that provides accurate and timely insights on ROI and other key performance indicators across all your channels … and controls for fraud at the same time. This helps you understand the strengths and weaknesses of each channel and partner.
“Singular will make it really easy to test and optimize new channels and see an apples-to-apples comparison between channels all in one place…when I set up a new advertising channel, I can go into Singular and select which events I want to map to that channel on my own, without engineering help.”
– Eliza Jonathan (LoseIt)
Improving conversion rates
Putting your product, brand, or app in front of new potential customers is a great first step, but getting them to engage, convert, and purchase is even more critical. That’s why the granular analysis that marketing intelligence software enables is so important: it allows marketers to uncover exactly where and how and why attention is turning into conversions.
What we’ve seen: marketers who leverage Singular to uncover ROI at the deepest levels see an average 100% improvement in conversion rates.
Optimizing efforts, assets, and creative
Creative is responsible for half of all your ad-driven sales lift. It’s also where your marketing team spends a huge amount of its time. For both reasons, it’s critical to ensure that your creative converts. Granular creative-level ROI is the answer, and it’s another important capability of a marketing intelligence system.
One multinational game publisher leveraged Singular’s Creative Reporting to dictate the direction of asset creation and saw a 40% increase in ROI.
“Singular’s creative analytics has been a helpful tool in analyzing creative performance. Prior to Singular we were going through each of the ad networks piecing together as much of this information as we possibly could to figure out creative performance.”
– Patrick Whitham (Product Madness, Postmates, JP Morgan)
Ensuring accurate data
Without regular data pulls, marketers simply don’t have accurate data in either “hand.” It sounds simple: get all the data.
But in reality, it’s incredibly challenging to get accurate data: both your own and competitive intelligence.
First, the data needs to be complete with metadata like campaign information including changes, creative details along with creative effectiveness, plus delivery details for paid sources like cost, impressions, and clicks. For organic sources, that might be open rates for emails and clicks, web traffic, and other data points. Many data sources require multiple data pulls to get full granularity.
Not only do marketers need to capture all levels of granularity from every marketing data source, they also need to normalize and standardize the data, rationalizing different terms and attributes from each of them. In addition, without deep combining and synthesis of campaign data with attribution data, marketing teams connect properly connect effort and outcome.
Ultimately, very few marketing teams, if any, have the capability to repeatedly generate accurate data at scale.
Human errors, delays, processing issues, data pull inconsistencies, API outages, and unrealized normalization problems simply make this incredibly challenging. This is a challenge that a marketing intelligence platform such as Singular deploys huge amounts of technology and large numbers of dedicated data scientists to solve.
Saving time and resources
Even when they try to solve these challenges on their own, marketing teams can easily spend 15-25 hours each week manually aggregating and normalizing campaign analytics data from multiple media sources into a single view for side by side analysis. And that’s before they spend any time on competitor analysis … not a great strategy.
Reporting bottlenecks slow down analysis, while reliance on in-house engineering to update code slows down implementation of new initiatives. Many media sources force marketers to combine several ingestion methods and/or API calls to achieve maximal granularity. And after you have standardized campaign analytics data, combining it with attribution data is even trickier.
A marketing intelligence platform eliminates or streamlines that work, freeing up analysts and engineers for more strategic deployment, as well as helping them find solutions for more advanced problems.
“There are significant efficiencies gained by having a partner like Singular that does this [aggregate spend data] full-time as their specialization and is constantly investing resources into making it better.”
Malachi Rose (Zynga, Digit)
Increasing spend efficiency
Marketers find more opportunities for optimization when data is updated in real time. And they find them quickly, adjusting or eliminating underperforming channels before wasting time and dollars with inefficient spending.
Surfacing fraudulent activity in real time also has a major impact in eliminating wasted spending.
In a data-driven hyper-competitive market, the edge belongs to those marketers who can successfully synthesize and integrate more distinct data-sets into their daily data-driven decision-making process.
Those who are missing data-sets — or cannot integrate them into intelligible insight and coherent strategy — are looking at the complete picture and can only perform limited, local optimization.
Aligning all departments via data transparency
It’s not helpful for Marketing to have the right numbers but Finance to have incorrect information. A marketing intelligence system recognizes that a critical part of company success is a single source of truth not just for marketing but for every team in a company.
When all team members can access reliable, standardized data within a single platform, teams are empowered to make the decisions that truly benefit the business. And talking apples to apples enables better communication between marketing and finance, agency and brand, and channel managers.
“Every day at least one person on our 25-person marketing team is using Singular. We relay all of this information to different teams: Data, Finance, Product, and Creative all benefit from the insight the User Acquisition team gathers from Singular.”
Jason Conger (Backflip, Wooga)
Marketing intelligence: connecting all the dots
Imagine a marketer.
Her right hand and her left hand are connected. She knows the ROI of every campaign and activity she engages in. She understands exactly how her various creatives are performing. Seeking ultimate customer satisfaction, she knows why people become customers or users, what channels they prefer, and she sees when members of one segment are actually better suited in another.
She has unparalleled market understanding because she knows in real-time what marketing activities are generating ROI, and she knows what publishers inside an ad campaign managed by a partner are worth sticking with, and which are a waste of time. She can align spend to results daily or hourly or even more frequently, if she chooses. She can run experiments and tests, and reliably apply the results to future market segmentation or marketing allocation decisions. She gets actionable insights from quantitative data, and applies them to optimize her marketing daily.
And she can sleep at night, because her marketing intelligence platform isn’t only filtering out fraud and low-quality traffic, it’s also alerting her to any anomalies or out-of-parameter spending, ROI levels, click-through rates, conversion rates, and more.
In short, she is a scientific marketer.
One of our customers put it like this, referencing the original Wizard of Oz, which plays in black and white until the moment Dorothy leaves Kansas in a hot air balloon for the magical land of Oz. He’s comparing the period before having Singular to what marketing felt like while employing Singular’s marketing intelligence platform:
“It was like that scene in The Wizard of Oz when everything turned to color”
– Patrick Kenney (Warner Bros., Yelp, Vivino)
Isn’t it time you were seeing in color?
The best marketers in the world use marketing intelligence — which we define a little differently than some — in general and Singular’s marketing intelligence platform in specific to out-perform their competition and grow their companies faster.
Are you ready to take a look?