Platforms with the flexibility needed to sustain the demands of scaling marketing programs will demonstrate a first-class approach to data management.

Such platforms will prioritize how they compile, exchange, and mobilize data that add to engagement-rich campaigns.

Integration

1. What should we expect during the data migration process?

• How will the solution optimize our user data structure?
• How is product catalog or inventory data managed?
• How are business-critical campaigns migrated and launched?

2. How readily does the platform integrate with other tools and data sources?

• What else does the platform integrate natively with?
• How robust are the platform’s APIs?
• What data transfer options does the platform support?

3. How does the platform ingest and store data?

• Which technologies are used for data warehousing?
• How flexible is the platform’s data storage schema?
• How does the platform store and recall metadata?

4. Are there limitations concerning data?

• Does the platform have data storage limitations?
• Are there constraints pertaining to data expiration?
• What associated data or API fees should be expected?

Activation

1. What levels of personalization are possible?

• What are the platform’s real-time capabilities?
• How does the platform support dynamic content?
• What consumer messaging channels are supported?

2. What segmentation capabilities currently exist?

• How granularly can audiences be segmented?
• Does the platform support dynamic segmentation?
• How quickly can the platform query audience segments?

3. What is the availability of customer profile data?

• What is the process for querying customer data?
• How readily is new data integrated into campaigns?
• How are user profile and event updates handled?

4. How does the tool handle personalization?

• How does the platform support the advancement of each customer’s journey?
• To what degree can the platform accommodate behavior-based decision making?
• How does the platform incorporate data from our servers to personalize?

What can I do within the platform?

What it means:

Design speaks to the platform’s campaign management capabilities and the degrees to which your team can elevate messaging efficacy when efficiently creating and launching compelling cross-channel campaigns.

What to look for:

Evaluate design by determining how effectively each platform facilitates campaign creation, deployment, and results. As you demo different platforms, envision how integrating each one fundamentally changes your campaign design process.

Think about how the platform can automate specific functions (like templates) or manual processes associated with your campaign creation strategy. Similarly, ask your team members how each platform presents new ways to evolve current crosschannel campaign deliverables. The right martech solution will present new real-time personalization and message-triggering opportunities that aren’t possible with legacy systems.

Additionally, automation is a necessity to foster brand innovation, so seek out AI-powered solutions with capabilities to assist with copy creation, audience segmentation, campaign optimization, and more.

Why this matters:

Much of the highly engaging marketing we encounter today is rooted deeply in personalization—delivering the right content at the right time on the right channel is only possible when it consists of a much larger customer data “story.”

Understanding how each platform manages your data-rich ecosystem first through native integrations, webhooks, APIs, data feeds, or partnerships is key to knowing where your team gets access to insights.

But then, a platform that properly facilitates campaign creation and management will help you separate fact from fiction when delivering 1:1 personalization across your entire customer database. If your new customer communication platform hinders you from doing more with the resources you already pay for (such as a poor UI, limited automation, or clunky integrations), it probably won’t be the upgrade in usability your team needs.

$1 of every $6 spent by CMOs is invested in innovation, despite doubts about the skills and capabilities available to support these programs (source: Gartner)

Creation

1. How simple or complex is the campaign creation interface?

• How functional is the drag-and-drop editor?
• Are both WYSIWYG and HTML editing supported?
• How easily can cross-channel journeys be created?

2. How quickly can you build and send a campaign?

• How do users build audience segments?
• How intuitive is the template creation process?
• Does the platform support proofing and device previews?

3. What level of detail can be built into campaigns?

• What types of data are stored inside customer profiles?
• How are product catalogs incorporated into the messages?
• How can behavioral events be incorporated into campaigns?

4. How will the tool reduce the time needed to build campaigns?

• How easily are channel-specific campaigns created?
• Does the platform support template and content replication?
• How easily can campaigns be personalized?

5. How easy is it to automate a journey?

• How sophisticated is the journey mapping tool?
• Can I build journeys myself without technical resources?
• What kinds of triggers are possible?

6. How do you tailor a journey to different audience segments at scale?

• How easy is it to build segments yourself?
• What kind of coding skills or technical resources are required?
• How dynamic are the segments or lists?

7. How do I get started with building journeys?

• Are there templates?
• Can you clone journeys for future use?

8. How do I account for different geographies, regions, languages, etc?

• Are there marketer-friendly localization tools?

Does the platform enable a cohesive customer journey across channels?

What it means:

Delivery speaks to the ability to effectively convey personalized messages to consumers across various channels—like email, SMS, and mobile push notifications—in a cohesive and coordinated manner.

What to look for:

Orchestrating a cohesive customer journey isn’t possible when data is siloed in disparate systems. Streamlining the martech stack involves consolidating multiple single-channel tools into one platform. It’s important to assess the breadth of channels that a platform natively offers and the availability of webhooks to integrate nonnative channels. Additionally, a platform’s future roadmap should include plans for innovation that encompass new and emerging messaging channels.

Why this matters:

The primary goal of proper delivery is to create a cohesive customer journey. For marketers specifically, consolidating to a single cross-channel platform means greater efficiency as they no longer have to switch between different tools, removing chances to lose sight of a customer’s stage in the lifecycle. This integration allows for the sharing of assets, avoiding repetitive, manual tasks, which, ultimately, leads to a faster time-tomarket for campaigns. Crucially, it results in a better customer experience, ensuring audiences receive personalized information when and where they’re most likely to engage.

Brands with consistent, effective cross-channel marketing have customer retention rates around 91% higher than those that don’t (source: Gitnux)

Platforms scoring high in the delivery category expedite the process to execute cross-channel campaigns by ensuring channels and data work harmoniously.

Look for features and functionality that facilitate proper compliance and deliverability of channels and ensure smooth execution of campaign triggers.

Execution

1. How is deliverability handled?

• What is the process for email authentication?
• Is there an IP warming process?
• How is sender reputation addressed and supported?

2. What level of engineering dependency does the tool require?

• How is ongoing technical support handled?
• Does the platform require knowledge of proprietary scripting languages?
• What are the ETL processing requirements?

3. What is the required setup for sending scheduled or triggered campaigns?

• What types of triggers exist for deploying campaigns?
• How are audiences segmented while campaigns run?
• Can the solution support transactional messages?

4. What level of involvement from technical resources is needed to launch a campaign?

• Is custom scripting required for campaign launches?
• Are campaigns constrained by SQL queries?
• Does the platform maintain data export/import requirements?

Channels

1. How can the tool help consolidate the marketing stack?

• What communication channels are natively supported inside the platform?
• How do I integrate channels that aren’t native to the platform so they become part of a cohesive journey?
• How does the platform process transactional messaging?

Does the platform continuously optimize campaigns to deliver value?

What it means:

Optimization in marketing refers to the process of fine-tuning campaigns to ensure they perform at their best and drive desired business outcomes. This process often involves AI-powered segmentation and recommendations, rigorous experimentation and A/B testing, and analyticsdriven insights.

What to look for:

When looking to achieve effective optimization, there are several key features to focus on. Campaign performance analytics, for example, are crucial for understanding the impact of different marketing strategies. Also consider A/B testing and experimentation capabilities, which, when run on a multitude of aspects of messaging and campaign-building, allow marketers to refine customer journeys.

Plus, the rapid advancement of artificial intelligence has enabled AI-powered personalization, which tailors the customer experience and enhances engagement. Predictive segments and AI-assisted improvements and recommendations can also help in forecasting trends and optimizing campaigns even further.

Why this matters:

Marketers face numerous variables when personalizing campaigns at scale, such as customer profile and behavioral data for segmentation, the content of campaign messages, subject lines, message frequency, contextual relevance, the timing of delivery, etc. Traditional batch-and-blast methods fall short of addressing these complexities.

Instead, a blend of high-quality data, advanced marketing technology, and AI assistance is necessary to realize the full potential of personalized marketing. This approach not only leads to a better understanding of your audience, but also increases productivity, makes marketing efforts more effective, boosts ROI, and dramatically improves customer retention and user experience.

88% of marketers find that AI software saves their company time and money (source: Capterra)

Platforms scoring high in the optimization category are focused on innovation and efficiency to allow your team—no matter the size—to do more at a higher success rate.

Look for features and functionality that analyze and provide insights with an optimal future in mind. These include AI, experimentation, and analytics tools that have a holistic view of your marketing.

Analysis and Experimentation

1. How can the tool support campaign optimization?

• How does the platform support testing initiatives?
• How is campaign effectiveness reported?

2. What level of reporting detail does it offer?

• How do users create reports?
• How does the platform integrate with BI tools?
• Can the platform measure different aspects of individual messages and channel-level results?

3. How does the platform handle experimentation?

• Are there any A/B testing capabilities?
• What aspects of the messages and campaigns can be tested?
• How does the platform determine and implement the optimal result?

AI

1. How does the platform leverage AI to improve marketers’ productivity?

• Does the platform provide any recommendations for the next best action?
• Does the platform assist with copy and campaign creation?

2. How does the platform improve campaign performance with AI?

• Can the platform automatically determine the best time to send a message?
• Can the right message frequency or cadence be identified for each recipient?
• How does the platform determine the preferred channels for each individual?

3. How does the platform use AI to find highperforming and lowperforming segments to personalize messages?

• Can the platform predict a customer’s likelihood to churn or purchase?
• Can segments be identified based on custom goals?
• Can the platform identify levels of loyalty or affinity based on customer behavior?

4. Does the AI in the platform require technical assistance or data science expertise to use?

• How easy is the platform for marketers to use?
• How transparent is the AI with what data it uses to draw conclusions or make predictions?
• How secure and trustworthy is the AI?

The road toward digital transformation is reluctantly paved with roadblocks, pitfalls, and challenges. Overcoming these challenges is the nature of growth and a necessary component of forward progress—this is the new call to action for modern marketers.

Exceeding the evolving demands of today’s consumers calls for insightful responsive marketing experiences that ebb and flow with an individual’s changing sentiment and behavior—and it starts with your marketing technology.

Before you select your next customer communication platform, carefully examine what each platform brings to the table from Data, Design, Delivery, and Optimization standpoints and how they empower AI-powered cross-channel experiences. Keeping a mindful eye on growth and enablement will insulate you and your team from making the wrong choice.

Our experts are here whether you are curious about Iterable’s capabilities or just unsure where to begin in finding the right technology for your business. Schedule a demo today to get started!