Mobile Marketing Tech Stack Foundations
Why Do You Need a Marketing Tech Stack?
Most modern marketers don’t dispute the need for a tech stack, but for those that do, or who don’t know what a tech stack is, let’s start by defining the term.
Here’s an example of a well-rounded tech stack, courtesy of Amplitude:
There are varying opinions on the necessity of each stack solution, and whether it is more prudent to build certain solutions internally, choose a free technology service or pay for more advanced solutions. Regardless of the path you choose, the main benefits of MarTech generally boil down to efficiency, efficacy, and scalability.
Some examples of how these benefits play out include:
- Integrating disparate systems (e.g. data storage, partner integrations, data enrichment for cross-channel analytics, building audiences, postbacks, etc.)
- Activation and campaign orchestration (e.g. audience and messaging segmentation, link/creative deployment, A/B testing, email, push, etc.)
- Advanced insights to improve decision making and ROI (e.g. data visualization, cohort analysis, mobile attribution, etc.)
- User flow optimization (e.g. A/B testing, deep linking, web-to-app banners, etc.)
Indeed, these benefits are clearly reflected in Ascend2’s March 2019 survey, which ranked efficiency as the #1 objective for marketing tech utilization:
The same survey also found that 63% of marketers plan to moderately increase their total MarTech budget and 24% plan to significantly increase their MarTech budget.
This speaks volumes to the value of MarTech. In many cases, the benefits of paying for more advanced solutions outweigh the costs.
Foundations: How to Start Building a Mobile Marketing Tech Stack
Whether you’re building your mobile marketing tech stack from the ground up or extending your web-first stack to accommodate a new mobile app, the first step to building your tech stack is goal mapping.
Consider questions such as:
- What are your growth goals (to inform data volume and scalability needs)?
- What does your customer journey look like?
- What are the primary KPIs for each step of the journey?
- What additional events would be helpful to measure in order to measure and optimize the full customer journey?
- What channels and functionalities will you need to acquire and retain users?
- What media partners are you working with (or plan to work with)?
- What infrastructure will you need to store and manage data?
- Who owns your data, and what security protocols should you consider to ensure your data is safe?
- How will you visualize marketing and product performance?
- Is it necessary for you to have a full-funnel view of marketing/product activity across channels and platforms?
These questions will allow you to not only categorize “must-have” vs. “nice-to-have” tools, but they’ll also guide your partner evaluation process as you compare factors such as feature capabilities, cost, partner integrations, support and scalability. Although the first tools you adopt may be contingent on budget, you will be better off in the long run if you design your tech stack for scalability.
So as you go through the exercise of goal mapping, prioritization, and budgeting, you should also think about future implications to help you build the case for new tools and guide your strategy for MarTech testing and expansion.
Customer data platform Segment recently released a data study that illustrates various ways this strategy can play out. For example, some “companies have a steady ‘graduation’ across tools—they start with a handful of use cases, and steadily shift those out over time […] These users add a few tools every month. As they discover new parts of their business to unlock, and new best-in-breed tools, they migrate their business to use the cutting edge.”
The following graphic illustrates an example of this strategy, in which similar colors correspond to similar categories of tools.
Here’s another example of a graduate who steadily expanded their tools across analytics, messaging, advertising and attribution over a 1.5 year period:
While this approach may be useful for early-stage companies who are gradually building the funds and knowledge to add new tools over time, it requires thoughtful planning to avoid resulting in a “franken-stack”—meaning a convoluted assortment of tools that are haphazardly connected into a stack. Taking a more serious upfront approach usually results in more clean, clear and unified tool collections.
Segment provides a more organized example of an upfront approach called “The Bulk Buy” in which companies “make big stack decisions in bulk. Users don’t just enable tools one at a time. They often enable new destinations in groups of 3 to 5.”
Which strategy makes the most sense for your company?
As you start to evaluate partners within each solution category, think about the trade-offs in terms of opportunity costs as well as financial costs—and from there, what impact your strategy will have on your ability to effectively manage and scale your marketing operations over time.
Some trade-offs to consider include:
- Starting with free/basic vs. paid/advanced solutions
- Buying vs. building your own solution
- Legacy “all-in-one” tools vs. modern “best-in-breed” architecture
We’ll dive deeper into each of these topics in later chapters—but first, let’s start by defining the core components of a mobile marketing technology stack.
Fundamentals: Mobile MarTech Hierarchy of Needs
As an attribution company, it goes without saying that we believe attribution is core to running effective marketing. Although attribution might not be the first thing you think about as you build and launch your first app on a budget, the reality is attribution and deep linking tools are mission-critical to any marketing team that seeks to build a world-class growth and user acquisition function. Growth is the name of the game among high-velocity startups and even Fortune 500 companies that are now expanding their digital footprint—and ultimately, growth starts and ends with being able to attribute and track the sources of your traffic across web and mobile.
Still, there are many factors which can feed into the decision-making process and determine whether mobile attribution is a must-have in your marketing stack or a nice-to-have. These factors can include company stage and size, marketing team technical skillset, budget, legacy tech interoperability, and personal preference among others.
The following graphic illustrates a mobile marketer’s typical hierarchy of needs throughout the product lifecycle vs. each solution’s role across the user funnel. The order across lifecycle stages indicates the categorical priority from a must-have standpoint, but does not necessarily reflect the most logical order for sequential integration. For example, BI tools such as CDPs, data visualization and cloud storage tools could be considered the foundational core for a connected tech stack. Although you don’t need all of them to get started, if you’re designing for scale you might want to consider integrating some of these solutions sooner than later.
*Logos shown within each category are not an endorsement by AppsFlyer, but rather a sample set of example partners.
With this hierarchy in mind, let’s start to define the individual components:
- Product Analytics: Focused on mobile app experience, product analytics companies specialize in UX testing and persona categorization. Through behavioral and predictive analytics, they provide an easy structure for product optimization in addition to advanced reporting on retention metrics, user funnels and cohort analysis. For new apps, it’s obviously crucial to have a measurement plan in place for UX testing prior to launch. However, we generally see app start-ups begin with basic measurement from free tools such as Firebase and/or in-app analytics provided by mobile attribution and marketing automation providers, prior to investing in product analytics tools with more advanced mobile data such as Amplitude and Mixpanel. Likewise, web-first companies expanding to build their first app often lean on web-first analytics tools such as Adobe Analytics early on in their app lifecycle.
- Attribution Provider: The core functionality of partners like AppsFlyer is mobile attribution—measuring and attributing every app install and in-app engagement to the marketing campaign and media source that drove it. Although some might not consider attribution providers to be “necessary” until paid media begins, it’s important to remember that most marketing automation providers do not provide tools for universal deep linking. For example, OneLink by AppsFlyer allows marketers to create a single link that can automatically detect each user’s platform and app state (installed or not) in order to send them to the optimal web or app page. Deep linking is crucial not just for paid media, but also for owned media such as email, webpages, and SMS. In addition to mobile attribution and deep linking, attribution providers also provide 1st party audience segmentation, fraud protection, advanced analytics and depending on the provider, people-based attribution to connect customer touchpoints across web, app, and TV.
- Marketing Automation: Marketing automation partners such as Braze and Leanplum focus on re-engagement with existing users via CRM channels and the core product. Customer engagement can be paired with audience segmentation and A/B testing through push messaging, email marketing, in-app communications or SMS.
- Media Partners: Media partners such as Google, Facebook, and Amazon provide advertising inventory across mobile app, web, and TV to drive awareness, acquire new users and re-engage existing users. Media partners can be used to serve ads via various channels and formats—including social, display, video, native and more. You can read more about media partner subcategories in our Getting Started with Mobile Attribution guide.
- User Acquisition Platform: While not included in the infographic above, in some ways UA platforms can be considered an extension of the media partner category. Also referred to as Campaign Management Platforms, UA platforms enable marketers to manage, optimize, and analyze their activity across multiple channels using a unified interface, often with at least some level of automation. In addition to automating processes such as creative testing and bid management, UA platforms also have the added benefit of cross-source insights. As UA platforms tend to be more valuable for performance marketers who manage complex media campaigns across many partners, they tend to be adopted later in the product lifecycle.
- Location Services: The King of Cannes 2019 was none other than Burger King, and in their award-winning marketing use case, they demonstrated the power of combining private location preferences data with marketing automation and user acquisition. Location Services effectively give the ability to track users against distinct geofences in an easy, interoperable, and flexible way. This means being able to set up geofences on the fly and ensure that marketing data makes its way to your CDP, attribution and marketing automation tools. Similarly, it incorporates the ability to tie competitor and other places of interest into your user segments for targeting—all without compromising user security and privacy data, as The New York Times recently reported.
- Customer Data Platform (CDP): An emerging player in the marketing tech world, CDPs such as mParticle and Segment collect, unify, segment, and activate user data from various SDKs. CDPs automate and enrich the assignment of customer data segments between all other systems in an advertiser’s tech stack in real-time. This not only makes it easier to set up new SDKs but also maintains consistency for aggregated raw reports downloaded from data warehouses and visualization products.
- Cloud Storage: Cloud storage providers and data warehouses maintain, manage, and remotely backup raw data across all of your tech stack systems. Providers such as AWS, Microsoft Azure, Google Cloud Storage, and Snowflake provide a secure and scalable way to store and access massive amounts of raw data across your organization.
- Data Visualization: The simplest data visualization is typically done via spreadsheets (i.e. CSV, Excel, Google Sheets), but this format may become too manual or unscalable as your business grows. For this reason, more advanced teams often work with products such as Looker and Tableau to create online dashboards, statistical models and automated reports.
- Data Management Platform (DMP): DMPs are used to collect audience data across platforms, devices, and channels. Unlike MMPs, DMPs not only collect 1st party data but also 3rd party data to map demographic, psychographic and firmographic segments. Most media partners are able to access 3rd party DMP data for you (charging a mark-up for audience targeting), but some advertisers choose to work with their own dedicated DMP to facilitate standardization across 1st/3rd party audience management.
- Marketing Cloud: Perhaps the broadest partner category, marketing cloud companies offer services across the entire spectrum of MarTech, AdTech and BI Tools (in spite of their categorization under marketing automation above). Marketing clouds such as Adobe, Oracle and Salesforce provide customer identity management, audience tools, campaign orchestration and analytics across a full suite of channels including mobile, web, TV and offline CRM. Some cloud providers also offer additional products such as CDPs, data management platforms (DMPs) and demand-side platforms (DSPs).
Now that we’ve covered definitions, let’s dive deeper into the specific features and use cases for some of the top tools for digital marketing: attribution, marketing automation, product analytics, CDPs, and UA platforms.