Whether it’s buying a new piece of clothing from your favorite brand, ordering your favorite food or planning your dream vacation, we all want to get what we want when we want it — and brands live and die by how well they deliver on those expectations. They must know enough about their customers to deliver what customers need and only what they need. “Knowing enough” requires capturing, storing and accessing often very complex data. For most brands, that‘s a struggle.

The Problem of Complex Data

Brands need to better ensure they’re collecting the right customer information and storing it correctly. The required information is often a complex data set, such as all items currently in the customer’s cart or all the upcoming flights they’ve booked. With all the different touchpoints a brand has with customers  — messaging engagement data, web and app usage data, social media cues and qualitative feedback like surveys, reviews and customer service interactions — it can be challenging to identify and organize all that data into a usable customer profile.  

Historically, brands have devised clever but unwieldy ways to house complex data sets in various tables or user properties with complex naming conventions. These practices often led to issues with updating and parsing multiple tables or challenging lookup processes to pull the correct information together. The result: frequently delayed outbound campaigns, forced limitations in the scope of marketing efforts, frustration with tools, and, ultimately, a less-than-ideal customer experience.

JSON Attributes Redefines Complex Data Storage and Access

JSON Attributes is the latest addition to the robust set of segmentation and personalization tools Airship provides today. With JSON Attributes, you can now store complex detailed information without having to use outdated and slow table lookup methods or be limited by the number of data points you can identify. JSON, or JavaScript Object Notation, is a semi-structured data format, so it doesn’t have a rigid structure like tabular formats. It’s flexible in size and structure, which gives it advantages over tabular data like CSVs. However, it’s still easily readable and usable due to its use of key/value pairs, such as Name:Sam or Date:2024-03-26. 

To understand this better, let’s look at a simple table that tracks events.

This table works well as long as the data we’re storing is always in this format and has only one property. The moment we need to track additional properties, the number of columns will need to expand to store that data. Additionally, some events may not have some of the properties, so you’ll end up with blank or null values in the table. Also, if you need to track a new property, the entire table must be modified to incorporate it. 

With Airship’s JSON Attributes, you can store data using a flexible and easy-to-understand structure with as many or few properties as needed — for example, a user’s entire shopping cart with details for each item in the cart:

Or each of the products that they’ve subscribed to with current offers available:

Or all of the itinerary data for travel they’ve booked with details for each flight:

Using data stored within JSON Attributes, you can use Airship’s advanced segmentation tools to segment users and then easily use the same data to personalize your messaging to each user. JSON Attributes joins the already robust set of tools Airship provides and can be used in conjunction with Tags, Events, Preferences and others to create a complete, real-time user profile: 

Using JSON Attributes, you can deliver the experience your customers want when they want it — and do so with ease. Airship includes JSON Attributes as part of the AXP platform unlike other vendors that charge data points, including invisible-to-you array updates. More of the right data is key to delivering better customer experiences and accelerating your activation, engagement and monetization strategies. We’re thrilled to introduce JSON Attribute capabilities and look forward to seeing how you employ them to solve your most complex use cases. Check out our documentation: https://docs.airship.com/whats-new/2024-02-26-json-attributes/

For help with your personalization and zero-party data strategies, or to learn about Airship’s preference center capabilities, please reach out to your Airship Account Manager. We also have Strategic Services available to help guide you in developing better mobile app experiences for your customers and your brand.

Written on Apple Vision Pro

The post Your Users Are Complex. Engaging with Them Doesn’t Have to Be. appeared first on Airship.

While experimentation is widely understood as a way to help craft experiences that appeal to audiences, marketers can also test the audiences themselves. What’s the best audience segment to receive certain content or offers?

Why Segmentation Is Important

Every marketing organization is constantly on the hunt for the best and biggest audience for their products or services. They want to find people who fit the ideal profile as easily and affordably as possible.

For owned channels — where you know the individual users or addresses you’re speaking to — you need to sub-segment your user base into discrete groups based on channel engagement, purchase history, propensity models from your data science teams, and other criteria, depending on your goals. The identification of and marketing to these groups is what we mean by segmentation. 

The ultimate goal is to send to these specific audiences marketing communications that encourage further engagement with your brand — the right message, at the right time, to the right audience on the right channel. This is the holy grail of owned channel marketing. 

A segment should be dynamic, where users can flow into and out of that segment in real time based on changes in their behavior (or lack thereof) with your brand. Good segmentation drives higher engagement, which drives better business results.

Why Segment Experimentation Is Necessary

One of the keys to segmentation is experimenting with what makes an effective segment. You’ll likely have a hypothesis about what makes a logical collection of users based on the information you have about them and what kind of messaging you think they’ll find interesting. You’ll put all the pieces together to create an audience, craft a message and then send it out hoping the audience finds the message engaging. Often this works exceptionally well based on leading indicators like opens, reads and clicks, but you won’t always know if the message is driving improvement in your core metrics.

You need to be able to test your hypothesis. We’ve already talked about a few variables in a message that you can test, such as audience, content, time and the channel. To measure results and know what’s working we need to be able to test these and other variables individually. Otherwise, if we get a great result (or a bad one) we won’t know which variable had the biggest impact. 

Testing each attribute individually to deliver better results can seem like a daunting task. It doesn’t have to be if you have the right tools — and the organizational motivation to do so.

It Helps to Have a Culture of Experimentation in Your Organization

I’ve lost track of the number of times I’ve heard someone in a marketing role say, “We didn’t have time to test it … we just needed to get it live.” 

Establishing a culture of experimentation in an organization requires that everyone slow down, think about the pieces that make up a marketing campaign, messaging series or product update, and ask the obvious questions: Why are we sending this out at noon on a Tuesday? Why do we have two buttons instead of just one? Why are/aren’t we including emoji? Why are we sending an SMS first instead of an email or an app push? Ideally, these conversations include analytics teams, marketers and even product teams to make sure they account for all the variables that could affect who gets your message and how likely they are to engage with it.

Getting the best results requires that you generate data and make data-driven decisions. To generate that data, you’ll need to create experiments that allow you to test your assumptions. Creating a control group of a small percentage of your audience will allow you to test the effectiveness of a particular channel. Creating an A/B test between different versions of the message will allow you to see if your users respond better to one type of message versus another. Creating two segments with slightly different audience criteria (for example people who spent up to $50 versus those who spent over $200) will allow you to see if your audience selection is too narrow or not narrow enough. Again, it’s important to test each hypothesis specifically to ensure you can differentiate the results. 

Once you’ve run your experiment, you should have a wealth of data you can use to understand business results and inform the next test, and so on. But how do you build an experiment and how would you analyze the data?

It Helps to Have the Right Tools 

It’s possible to build experiments manually. You can “cut a list” from your customer database of people who meet certain criteria, split that list to create A/B segments or control groups, and then import that list into your marketing tools. However, there are a few drawbacks to this approach:

• You can only target the customers who are in your database at the time you cut the list. You’ll be excluding anyone who’s signed up for your email program or SMS messaging, or installed your app since the list was cut. 
• Measuring results can be laborious depending on what tools you have at your disposal. Extracting data is time-consuming. Transforming it and loading it into your database/lake/warehouse for analysis can be very manual.
• Timely follow-up messaging based on customer engagement is not usually not an option when cutting lists. For example, if I wanted to send a better offer to the people who didn’t engage with my email, I’d have to first go back to my database and pull the users who meet those criteria. 

At Airship, we’re always working to make sure that creating audience segments, holdouts and A/B experiments is easy for our platform users. We believe that using the right tools is critical to helping you make decisions that benefit the customer and the brand. Last week, we announced a group of experimentation capabilities that enable brands to enhance the customer experience with more personal, more timely and overall, more delightful interactions. 

Airship’s segmentation engine makes it easy to create detailed cohorts of customers based on all the data you’ve collected in Airship, such as attributes, events and device/channel information. With our mission of delivering everything you need to capture customer value inside and outside the app, we’ve expanded the technology for testing to bring you next-level experimentation. 

Airship’s Experimentation Hub allows you to create, launch and evaluate experiments to give you a common understanding of how best to optimize your campaigns, end-to-end. You can easily build an A/B experiment to test segments, as well as variants of message copy or content.

You can also create global holdout groups to measure the overall effectiveness of your marketing campaigns in Airship against your defined goals and metrics. Turn customer actions into goals for each experiment and watch which version leads to more conversions. 

When it comes to getting useful data out of Airship, this task is made easy by our Real-Time Data Stream and Performance Analytics tools. You can add Campaign Categories to your messaging, which makes it much easier to match up the Airship data with the rest of your marketing stack by including your own campaign or experiment identifiers in the results. 

With our Fall Launch updates to Airship’s Performance Analytics tool, you can leverage visually dynamic reporting for Goals you create, and you can track success across each phase of the customer lifecycle with App Health Reporting, making it easier to optimize your campaigns. 

Want to learn more about using Airship’s experimentation capabilities? Register for our December 5th webinar where we’ll hear from brand leaders on how to use experimentation to systematically fine-tune everything and anything you share with customers. Register Now 

Navigator is Airship’s customer newsletter covering the latest mobile industry trends, product updates, use cases and best practices, and other learning resources. It’s yet another resource to help you deliver better mobile experiences and create greater value more quickly. If you’d like to receive our monthly Navigator newsletter please sign up here.

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Partnering to Increase Retention

At Airship, we’ve developed a robust segmentation solution that makes reaching the right person easier. Now with the Airship integration from AppsFlyer, a leading mobile attribution and marketing analytics solution, marketers are able to use the attribution data captured by AppsFlyer in Airship’s segmentation tools. Using AppsFlyer’s mobile marketing analytics and attribution platform, marketers can pinpoint their targeting, optimize their marketing campaigns and boost their ROI.

For example, a marketer can target all of the users who had first opened the app in the last seven days, and had installed the app from a particular campaign or media source (AppsFlyer data as Airship Attributes). This is also useful for tracking user’s app lifecycle. A marketer can track a user’s actions: tags added, events triggered, uninstalled, etc., and use this to help determine their most valuable marketing and user acquisition channels. Airship and AppsFlyer together provide the tools needed to spend marketing dollars wisely.

How It Works

The Airship Channel ID (Airship’s unique device identifier) is passed to AppsFlyer, via code in the app. AppsFlyer then passes the following attribution data to Airship via the API as attributes for every organic, and nonorganic install:

• Media Source
• Campaign name
• Campaign ID
• Attributed Ad ID
• Attributed Adgroup

These attributes can then be combined with other segmentation attributes in Airship’s platform to send timely, targeted messages to users as well as personalize the text of messages. This allows data from AppsFlyer to be combined with data from other sources (device, CRM, POS, etc.) without requiring complex nomenclature. 

The result is a cleaner data set, allowing for easier segmentation and more targeted messaging. Because this is done on a per-device basis, and because it uses Apple’s IDFV instead of IDFA, it maintains the utmost in consumer privacy and security.

Benefit from Richer Reporting

With Airship Performance Analytics, you can measure valuable engagement and retention metrics to measure “user quality.” You can also measure conversion over time to determine user lifetime value. This allows you to re-allocate your acquisition spend to the ad channels that perform best.

To start using AppsFlyer attributes to improve your messaging performance contact your account manager, or review AppsFlyer’s documentation. To learn more about AppsFlyer’s solutions visit their website or check out their blog.

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