Defining the Customer Data Activation Platform (CDAP)

There are some things about data that we can say with absolute certainty. Things like, it’s complex, its key to decision-making, it’s increasing at an unprecedented rate. And, for most of us marketers: we’re not utilizing data to its full potential. We’re not even close.

While we gather loads of information about our customers and prospects from across all the channels through which they engage with our brands, that data is disconnected and dormant. Most of us are far from having a true view of who customers are, their interests, or their intentions. And using data to glean actionable insights in a timely manner to better engage customers? That’s for marketing teams with endless resources.

The problem lies in current martech stacks and processes that place channels at the center, rather than people. We build campaigns for paid media, email, SMS, and so on, in the hopes of catching interest, but because every channel has only a partial view of our customer, the experience falls flat. But when we flip the order to focus on the customer and use deep, actionable intelligence to create cross-channel experiences around them, the output becomes much more valuable.

Marketers are starting to realize that current data systems―DMPs, CRMs, CDIs, MDMs etc.―can’t achieve this switch. To make sense of all the essential data that’s available and use it to deliver intelligent customer engagement across channels, marketers need a Customer Data Activation Platform.

What’s a CDAP (“SEE-dap”)?

In simple words, the CDAP helps brands deliver intelligent engagement on every touchpoint on the tools they already use, by activating a Full Circle View of their customers. It’s what makes 1:1 marketing at scale possible.

For example, let’s say you have 100 customers. Breaking that amount of people out into segments and marketing to them in a relevant and personal way is doable. You’d know each and every one of them by name, and you’d know their preferences by heart. But what happens when you have a few million? And what happens when those few million live across time zones and oceans? How can you market to millions of people effectively, 24/7, in a way that continues to feel as personal and relevant as it would if you only had 100 customers? Or, better yet, just one.

With a CDAP, no matter how many customers or touchpoints you have, you can be sure that each interaction is based on real-time customer insights, consists of the right content, and is happening at the exact right moment in the channel it’s most likely to drive action.

Data in, ROI out

In other words, the CDAP is a bridge between your siloed data and your execution channels. It’s the complete package of customer profiling, predictive intelligence, and automated decisioning.

The CDAP’s “secret sauce” isn’t much of a secret. It’s just really, really good, deeply integrated AI. Here’s a breakdown of how it works:

Level 1: Full Circle View

The first step to customer data activation is piecing together Full Circle, single customer profiles. These profiles capture the complete histories and real-time behaviors of each customer from their interactions across channels, devices, and systems. The CDAP creates these comprehensive profiles for each identifiable and anonymous customer and reconciles cross-device identities.

This level of customer profiling within the CDAP is a continuous, ongoing process that automatically updates each individual profile as interactions happen, meaning you always have the most up-to-date customer understanding.

Level 2: Predictive Intelligence

When you have millions of customers, you can’t look at each individual Full Circle profile to determine the next best actions. Instead, you need to automate the process of gaining insight into what each customer is likely to do, want, and respond to so that you can tailor marketing actions accordingly.

Predictive Intelligence taps into the auto-updated Full Circle View to derive actionable intelligence and inform marketing touchpoints for each customer. What’s more, its self-learning abilities continue to optimize marketing actions with feedback from each campaign activity. The CDAP’s pre-built―yet easily customizable―predictive modeling surfaces which customers are at key points in their customer journey with your brand and what will drive them to take the desired actions through the following:

“The WHO”: Predictive Segmentation connects you with customers at key decision points
“The WHAT”: Predictive Recommendations delivers content, products, and offers tailored to in-the-moment interests
“The WHEN”: Predictive Engage Time reaches customers when they’re most responsive
“The WHERE”: Predictive Channel-of-Choice finds them where they’re most receptive

Level 3: Automated Decisioning

Full Circle View and actionable intelligence create a great foundation, but that foundation can’t actually provide value until you do something with it. That’s why the last level of the CDAP, automated decisioning, is essential. This is what activates the real-time predictive intelligence from the Full Circle View and uses it to guide campaign execution and customer interactions across channels based on how customers are interacting with your brand.

Automated decisioning is also what lets marketers infinitely scale their campaigns by automating the millions of decisions that need to be made about which customers to engage and how to best engage them. And because the Full Circle View is constantly updating, so is the decisioning―meaning it doesn’t matter how often your customers change their tastes or their behavior; the CDAP continuously adapts the next best action accordingly. With the following capabilities, marketers can deliver the exact marketing actions that will drive each customer down the path to conversion:

Journey Flows: Create individualized, cross-channel, multi-stage, adaptive customer experiences.
Live Personalization: Personalize every customer interaction for in-the-moment relevance.
Audience Sync: Improve targeting effectiveness by messaging only the right customers across channels.

Put your data to work — not to sleep

One final thing that we can say with certainty is that when data is activated, it works for us. Plain and simple. When it’s properly utilized, marketers can connect to their growing customer base on a personal level and stay relevant. We can build brands that feel like trusted friends rather than impersonal salespeople. We can scale intelligent customer engagement and create marketing campaigns that break through the noise. And isn’t that everything we’ve been asking for?

But putting data to work is no simple feat. It requires 3 elements ― customer profiling, predictive intelligence, and automated decisioning ― working together, in unison, in real-time. That’s the CDAP.

The Rise of Customer Data Platforms: How CDPs Move Beyond Data Warehouses, DMPs and CRMs

We all know data is the key to better customer engagement; the question is how to best cultivate it?

Knowledge is power. That’s why businesses today revolve around data. For marketers who have been challenged with delivering increasingly real-time, customized, elaborate customer experiences, data and having a deep customer understanding is essential. Data is now the heart of the whole marketing system.

But data can only deepen customer insight and enable more precise marketing when it’s harnessed effectively. However, the ever-increasing speed, volume, and variety in which data is generated creates a real challenge: data perpetually changes, which means insights have a brief shelf-life.

How do marketers make sense of all their data and use it drive growth?

DATA MANAGEMENT SYSTEMS 101

The right data foundation makes or breaks marketing strategies. Over the years, marketers have relied on a number of data platforms to manage and analyze their data. But because these systems were built for or managed by IT, marketers have been limited with how they could use their data to drive marketing execution.

Three commonly used data management systems are Data Warehouses, CRMs and DMPs.

Data Warehouses

Data Warehouses are vast, central, enterprise-wide storehouses of business data created to help organizations make better business decisions. These powerful repositories provide business analysts and data scientists with quick access to business data for use in their analytics applications and business intelligence (BI) tools.

  • Primary Purpose: Data Warehouses support business intelligence and analysis. Their key benefit is faster access to a variety of business data.
  • Data Types Supported: Data Warehouses aggregate data, including historic data, from various systems across marketing, finance, sales, and other functions. These include CRMs, ERPs, billing systems, supply chain systems, and other internal and external applications. Data Warehouses process and organize data into schemas to make it quickly accessible for analysis. They work best when data is consistent and well-defined.
  • Intended Users: IT specialists and dedicated enterprise data management professionals with the technical background to manage the granular data and processes.
  • Limitations for Marketers: Data Warehouses can’t support real-time customer engagement because they don’t provide real-time insight into cross-channel identities. Data Warehouses can’t process raw, unstructured, or complex data formats in which customer engagement data is generated. They don’t provide customer data at an individual level. They also tend to have static, latent data that’s updated weekly at most. Most importantly, Data Warehouses were created for offline use and weren’t meant to funnel data directly into marketing applications. Additional, manual data manipulation is required to get data into a usable state for marketing systems.

CRMs

Customer Relationship Management systems, or CRMs, centralize all customer interaction and transaction information. Originally built for B2B sales and customer facing teams, CRMs have been adapted to support B2C use cases.

  • Primary Purpose: CRM systems support direct customer engagement by managing and connecting customer accounts, attributes, and touchpoints at the user level.
  • Intended Users: CRM systems are best utilized by sales, customer service, and direct marketing teams.
  • Data Types Supported: CRM systems capture all historic customer engagement and transaction data for known customers generated across customer interactions. This includes data from brand touchpoints such as their website, email, mobile, offline channels, social media, order management systems, and payment data.
  • Limitations for Marketers: CRM systems were designed to store customer engagement information, but not to activate it. They can’t make data available in real-time because they can’t rapidly ingest large volumes of data, process that data, or unify different data types, especially when that data has different identifiers. They also offer minimal system access and control to marketers. Furthermore, CRM systems only work with known users, typically those that have an email address and customer ID. In today’s cross-channel landscape, marketers need cross-devicel identity resolution that includes anonymous identifiers. By not capturing cross-channel identities and related channel data, CRM systems miss customers on key channels and can’t engage anonymous customers who are further up the funnel.

DMPs

Data Management Platforms, or DMPs, help advertisers and publishers buy, sell, and manage digital advertising and optimize media spend by improving audience targeting. DMPs unify, store, and manage anonymous audience and campaign performance data, create rule-based audience segments, and connect audience segments to ad platforms.

  • Primary Purpose: DMPs support digital advertising by providing high-value audience segments to target at the cookie-level and optimizing media spend based on segment performance.
  • Intended Users: DMPs are utilized by advertisers, agencies, and publishers for awareness and customer acquisition use cases.
  • Data Types Supported: DMPs consist mostly of third-party, anonymous audience and campaign performance data generated from digital interactions. They aggregate anonymous web-browser cookies and device IDs that contain audience information such as demographics, past browsing behaviors, interests, location, devices, and limited purchase information. They also enrich audience data with data from external data vendors. DMPs never store PII (personally identifiable information). While DMPs can onboard offline data, that data must first be anonymized.
  • Limitations for Marketers: DMP use cases are limited to top-of-funnel display advertising and measurement. Because DMPs focus on broad, anonymous, cookie or device-level segments, they can’t support customer engagement and retention strategies. Furthermore, DMPs only store data for a limited time period, which hinders targeting precision.

 

MARKETERS’ GROWING NEED FOR CUSTOMER DATA PLATFORMS

With all the existing data platforms, why is there now growing interest in customer data platforms? Because marketers are still frustrated with their level of data access and activation.

Marketers don’t need yet another system to store customer data. They need a system that makes their data actionable and connected across their channels, and makes their marketing smarter and more effective. Legacy data platforms haven’t provided marketers with the control, flexibility, speed, insight, and connectivity they require. To support the 1:1 personalization that’s required to succeed in today’s fast-changing, digitally-connected landscape, marketers need their data platform to provide:

  1. A single, comprehensive, persistent view of each known and anonymous customer that captures all customer interactions across every channel
  2. Seamless, real-time data integration into marketing platforms and supporting applications
  3. Real-time intelligence and marketing decisioning
  4. A marketer-friendly interface

 

WHAT IS A CUSTOMER DATA PLATFORM?

Customer Data Platforms, or CDPs, were purpose-built to provide marketers with data access, activation, control, and speed. Their sole focus is helping marketers deliver more relevant, responsive customer experiences by activating customer data in real-time while cutting out the time-consuming, manual work typically required.

While the industry is still debating the definition, CDPs focus on 3 key capabilities:

  1. Profile Unification: CDPs provide marketers with a comprehensive, unified, persistent view of each known and anonymous customer. They can ingest any data typeーregardless of structure and complexityーprocess it, and make it immediately available for use across all systems. CDPs stitch together all historic and real-time customer data, including customer profile, behavioral, transactional, and brand interaction data.
  2. Intelligence: CDPs provide a marketer-accessible interface to do advanced segmentation, create customized recommendations, orchestrate customer experiences, and more.
  3. Decisioning: Most importantly, CDPs derive actionable insights from the single customer view and use it for marketing decisioning and guiding real-time marketing interactions across all their channels.

CDPs aren’t intended to replace existing data systems, but rather to unlock value from marketers’ siloed data and use it to drive marketing effectiveness and ROI. With CDPs, marketers can finally scale data-driven marketing and use their data to deliver the rich, real-time customer experiences they’ve been aspiring to for years.

Getting Started with AI in Marketing

AI Marketing in Action: Best Practices for Getting Started with AI

After being heralded as “the next big thing” for years, AI has arrived, bringing with it the power to add game-changing leverage, focus and control to your marketing efforts. Of course, as with any new technology, the move to AI entails a learning curve, and its perception as a “black box” has left many unsure how to begin applying AI to their marketing strategies.

For those ready to embrace this truly transformational approach, AI puts the tools in marketers’ hands to harness a complete and constantly updating customer profile to engage with customers with incredible precision, relevance, effectiveness and speed. At the same time, it helps marketers be more efficient, productive and effective by simplifying, automating, and accelerating marketing workflows. In previous posts we explored how AI helps determine the right marketing actions for each individual customer at every step by continuously optimizing:

  • WHO are the right customers to target at any moment by using Predictive Audiences to determine customers who are ready to purchase, at risk of churning, ripe for upsell and more.
  • WHAT content will most likely influence each customer by using Predictive Recommendations to surface the most relevant offers, products or articles for each individual customer based on what they seek at that moment.
  • WHEN each customer will be most responsive by using Predictive Engage Time to optimize send times for when each customer is most likely to actually engage with your brand, not simply open a message.
  • WHERE is the channel they’ll be most receptive by using Predictive Channel-of-Choice  to adapt which channel to deliver the message based on their preferred channel in the current context.

And the numbers don’t lie; as our recent benchmark study found, compared with traditional marketing efforts AI-powered campaigns:

  • Drive up to 7X greater customer engagement and 3X revenue
  • Deliver 2X greater impact on engagement for mobile push compared to email
  • Achieve 50% lift as AI continues to accelerate engagement and impact and learning from customer interactions

 

PUTTING AI INTO ACTION: CONSIDERATIONS FOR SUCCESS

So how do we put AI into action? Like any successful initiative, getting results with AI begins with being clear about what you aim to accomplish and making sure you have the right people, processes, platform and implementation plan in place. Accept that there will be a learning curve, and begin by laying the foundation before you begin to scale.

 

  1. Be Clear About Your Desired Outcomes

It seems obvious in hindsight, but to succeed you need to be clear about what “success” actually means. AI doesn’t determine your strategy for you, but it can applied to any variety of marketing strategies. The end goal will determine how to incorporate AI across marketing activities, and which of the many metrics it delivers you’ll optimize to meet your desired outcome.

 

  1. Assess How AI Will Integrate With Existing Technologies and Workflows

AI can only accelerate your marketing efforts when it fits seamlessly into your existing technologies and processes. Look for AI partners that tightly integrate into your tech stack and ecosystem. Then, ensure AI is core of your efforts, not simply added at the margins. Many marketers treat AI as an “add-on” to their existing workflow, but that’s a recipe for mediocre ROI.

AI also needs to integrate into existing workflows. Begin by determining how AI will fit into your business processes, and identify the organizational gaps that must be addressed.

 

  1. Approach AI as a Cross-Channel Strategy

AI isn’t limited to a single channel or platform. Because AI connects data across channels, it allows you to turn your focus to delivering a holistic and highly focused cross-channel customer experience. Use AI as an opportunity to unify your cross-channel strategies and multiply their impact.

 

  1. Ensure AI and People Are a Partnership

AI’s purpose is to accelerate and amplify marketing activities. To do that, AI requires human guidance to make the right decisions; people bring context and that essential human touch. Outline the interplay required between your team members and AI, detailing the direction and input they’ll need to provide, and you’ll be on a much stronger footing to make the most of this transformational tool.

 

  1. Understand AI Is a Process

Every new tool and approach has a learning curve, and AI is a long-term play in which you must first crawl, then walk, before you are ready to run. If you begin with initially applying AI to a single, more basic marketing program and then ramp up gradually, you’ll have a solid groundwork to build upon. Because of AI’s self-learning, continuously improving capabilities, the sooner you start, the quicker you’ll reap the benefits of AI.   

 

  1. Think Big: AI Can Help Achieve Transformational Impact

AI enables you to orchestrate customer experiences with nuance and scale not possible with existing tools. While AI has the potential to truly revolutionize your marketing, it demands that you scale your imagination and your ambition accordingly. It’s an invitation to think outside of the bounds of your comfort zone, and to search out partners that can scale with your needs and help guide your own AI evolution.

 

THE TAKEAWAY

AI-powered marketing is the only way today’s marketers can effectively engage their large, diverse, and rapidly evolving customer bases. And now is the time to embrace it, as AI will quickly move from being a competitive advantage to being an essential, table-stakes tool. While the process of fully integrating AI into your strategy and workflow can seem daunting, the longer you put off AI, the longer it will take you to catch up with your competitors adopting AI today.

And know that there is no one-size-fits-all strategy for AI. Start small with a single-use case, and then scale up. You’ll quickly see that AI will not only cut down your manual tasks, infuse customer insights across your marketing activities and help you launch faster, but it will free you up to get back to the heart of marketing: Employing your creativity and your strategic intuition to craft next-level customer experiences.

For the full set of findings as well as real examples of marketers who have used AI to drive revenue by making better, faster decisions around the “Who, What, When & Where” of cross-channel marketing, download The ROI of AI in Marketing: 4 Levers for Cross-Channel Success.

 


Download ROI of AI Marketing: 4 Levers for Cross-Channel Success


 

ROI of AI Marketing - Artificial Intelligence helps marketers know the right channel and the right time to send messages to each individual

AI Marketing in Action: Delivering Perfectly Timed Content on the Right Channel Improves Engagement 4X

Consumer attention is getting scarcer. We are all increasingly connected across multiple devices on which we are bombarded with near-constant messages, notifications and ads. In this hyper-saturated environment, how do you stand out in customers’ inboxes and across channels?

While you invest significant resources to create rich content and experiences tailored to specific customers, if your message is delivered at the wrong moment or context, it’s a wasted opportunity. And in today’s lightning-fast marketplace, opportunities are the last thing you can afford to waste. Timing and channel can’t be an afterthought.

But as engagement and sales are no longer limited by time of day or to any one device or channel, how can marketers determine the optimal time and channel on which to engage each customer as they switch fluidly between a growing number of devices and channels throughout the day?

Optimizing send times has long been standard practice for marketers. Traditional send time optimization requires manually analyzing historic trends and running A/B test campaigns at various times of day to determine optimal send times. But there are problems with this approach. For one, it treats your customer base as a uniform group and neglects the dynamics of today’s always-on, mobile, multi-device consumer. More importantly, it focuses on optimizing for opens, rather than downstream actions that drive revenue, such as website engagement and conversions.

If you’re not learning from customers’ behavior patterns as they use different devices, apps, and channels for different purposes at different times of the day, you’re taking a guess as to how to best engage with them. Can you afford to do that?

Fortunately, AI is here to help.

AI empowers marketers with incredible leverage, helping them better engage customers across every channel. By generating insights from every aspect of customers’ behavior and previous engagement with the brand, it makes precise recommendations as to how to engage with them depending upon your desired outcome.

It optimizes WHO marketers should be targeting, selects WHAT content they engage with, WHEN to engage with them and WHERE is the best channel. This “AI Marketing in Action” series explores AI’s impact on these 4 Levers of cross-channel marketing and quantifies its impact on each lever. Our findings are based on a recent benchmark study that analyzed 3.8B marketing interactions from campaigns across various channels and verticals.

In our last posts, we explored how AI helps you determine WHO are the right customers to target and WHAT is the best content to bring customers at any moment for each of your customer strategies. Now, we’ll explore how AI helps determine WHEN and WHERE customers see that content.



AI-POWERED PREDICTIVE ENGAGE TIME AND PREDICTIVE CHANNEL-OF-CHOICE

Predictive Engage Time and Predictive Channel-of-Choice work in close partnership to ensure that your messages get noticed and acted upon.

Today’s perpetually connected customers are much more likely to have many frequent bursts of activity around the clock than a recurring habit of opening their emails at certain times of day or clicking onto sites or apps at specific hours. Unlike traditional Send Time Optimization, Predictive Engage Time Optimization uses AI to examines campaign clicks, website browsing behavior, total engagement time, engagement depth and transactions to determine windows of time in which each user is most likely to engage. From analyzing the full downstream activities, AI identifies and optimizes send times to when each customer is most likely to engage with your brand, not simply open a message.

Predictive Channel-of-Choice steps in to determine the best channel to engage your customers. It similarly uses AI to learn on which channel each individual customer is most likely to engage at a given moment by performing an ongoing, deep analysis of customer engagement and transaction behaviors. As campaigns run, it automatically adapts the channel on which each customer receives your message accordingly.

SHOW ME THE FACTS

Of course, these optimizations aren’t of much use if they don’t provide real-world ROI. Our recent benchmark study found that Predictive Engage Time drives 3X engagement over email and nearly 5X over mobile push. Why the significant lift? Because research shows users prefer to engage deeply at certain hours of the day, while casually browsing throughout others. Adapting individual send times to when users are most likely to engage in downstream activity pays off in real-world results.

Beyond that, combining Predictive Audiences with Predictive Engage Time drives up to 3.8X over email and 7.2 over mobile push.

 

THE BOTTOM LINE

With Predictive Engage Time and Predictive Channel-of-Choice, marketers can ensure that their messages capture customers’ attention without their constant monitoring. Their messages are continuously optimized to reach each individual customer at the moment and channel where they are most likely to take action. And that translates directly into engagemnt and revenue.

 

For the full set of findings, as well as real examples of marketers who have used AI to drive revenue by making better, quicker decisions about the “Who, What, When & Where” of cross-channel marketing, download The ROI of AI in Marketing: 4 Levers for Cross-Channel Success.

 


Download ROI of AI Marketing: 4 Levers for Cross-Channel Success


 

The ROI of AI - understand the 4 levers to create ROI from AI in Marketing. This blog post talks about WHAT to send using predictive recommendations.

AI Marketing in Action: Delivering More Relevant Content with Predictive Recommendations Increases Engagement Up To 5.7X

We’ve all experienced the power of uniquely tailored, relevant content and recommendations. Top-tier platforms such as Netflix, Amazon and Spotify utilize powerful content and product recommendation engines to create the kind of seamless, delightful user experience that keeps customers coming back again and again.

Most of us, however, don’t have access to tools as sophisticated as those of Amazon, Netflix or Spotify. Yet, we still want to provide our customers with useful, relevant, actionable information that simplifies their purchase decisions. But exactly WHAT that information is constantly changes throughout the customer journey. This makes selecting the content, products, and offers which will “break through” a very difficult and fast-moving target. Multiply that by your number of customers and potential touchpoints, and you’ll see that choosing content for each customer interaction quickly becomes next to impossible.

Of course, there are tools to help personalize content. Recommendation systems and content optimizers have existed for years now, but they are typically based on manually entered rules and inflexible templates. They are also driven largely by marketers’ hypotheses about what would resonate with their customers rather than what customers’ behaviors, interests and lifecycle stages reveal. Consequently, they don’t keep pace with today’s rapidly changing consumers.

And as we all know, irrelevant content is the #1 reason consumers disengage with brands. Can you really afford to base your revenue on guesswork?

That’s where AI is here to help.

AI helps marketers work smarter, faster, and more intuitively as they engage customers along their customer journey. It does so by optimizing WHO marketers should be targeting, selecting WHAT content they engage with, WHEN to engage with them, and WHERE is the best channel. This “AI Marketing in Action” series will explore AI’s impact on these 4 Levers of cross-channel marketing and quantify its impact on each lever. Our findings are based on a recent benchmark study that analyzed 3.8B marketing interactions from campaigns across various channels and verticals.

Our last post explained how AI helps you determine WHO are the best customers to target at any moment for each of your customer strategies. Now, we’ll explore how AI helps determine WHAT those customers see.



AI-POWERED PREDICTIVE RECOMMENDATIONS

Predictive recommendations surface the most relevant content—be it an offer, a product, an article—for each individual customer at a given moment. It ensures every message you deliver is unique and personalized to what each customer seeks at that stage of their journey with your brand.

It does so by analyzing—in real-time—a complete view of user profile, interest and behavior data in relation to your brand content. It then continuously tests itself, self-learning with every customer interaction and optimizing content for revenue-generating actions.

How much work is this for me? With Predictive Recommendations, you simply guide each message’s content by setting basic parameters and defining whether to base content on user actions, their affinities, other customers’ purchases and browsing behaviors, or relevant trending items.

The recommendation engine picks up from there, pulling together all user behavior event-streams from sites, apps and other sources as well as historic CRM data and your brand’s content and product information. AI then continuously analyzes the dynamic relationship between your users’ product interactions, historic brand engagement, and latest customer activity across multiple channels. As each campaign runs, AI selects the most relevant content for each customer based on their current context, crafting individualized messages for each of them without your having to lift a finger.

SHOW ME THE FACTS

Having an Amazon, Netflix, or Spotify-grade predictive recommendation engine to optimize your content is a real bonus, but let’s not forget where the rubber hits the road: ROI. And the numbers don’t lie: Our recent benchmark study found that Predictive Recommendations drive a 2.5X – 5.X lift in engagement. “

Predictive Recommendations also drive nearly 3X revenue relative to their use in the marketing mix.

THE BOTTOM LINE

With Predictive Recommendations, marketers can listen to customers’ needs, reply with the relevant, actionable content customers seek, and stop wasting critical opportunities to connect. More importantly, making your customer communications truly 1:1 not only improves your relationship with customers but also drives incremental ROI.

For the full set of findings, as well as real examples of marketers who have used AI to drive revenue by making better, quicker decisions about the “Who, What, When & Where” of cross-channel marketing, download The ROI of AI in Marketing: 4 Levers for Cross-Channel Success.

In upcoming blog posts we’ll explore AI’s impact the remaining two levers of marketing:

  • “The When” with Predictive Engage Time: Optimize the delivery of the campaigns to the times when each individual customer is most likely to engage
  • “The Where” with Predictive Channel-of-Choice: Deliver the campaign on each individual customer’s channel-of-choice

 


Download ROI of AI Marketing: 4 Levers for Cross-Channel Success