New Forrester Total Economic Impact™ Study Shows Blueshift Drives 781% ROI for Customers

Imagine what would happen if you could scale 1:1 campaigns and deliver better cross-channel experiences to all your [millions of] customers.

Or, don’t. Instead, hear about the exact returns four of our customers saw after using Blueshift in this commissioned study from Forrester Consulting, including:

  • 781% in total ROI
  • $128 million increase in incremental revenue over 3 years
  • 60% increase in click-through rate
  • 20% increase in cross-channel campaign revenue

The Total Economic Impact™ (TEI) Study

If you’re unfamiliar with Forrester’s TEI study, it’s essentially a commissioned, independent examination of the ROI customers have experienced with the tool or offering in question.

For us, the findings are based on in-depth interviews with 4 customers (two eCommerce, consumer finance, and online learning) and a robust financial analysis using Forrester’s industry best-practice methodology.

Today the results are available for everyone, and we couldn’t be prouder. Forrester has surfaced some really incredible numbers, such as:

  • $81M incremental revenue from AI-powered targeting and real-time interactions
  • $35M incremental revenue from streamlined work and automation
  • $12M incremental revenue from improved cross-channel customer engagement
  • $1.8M Avoided costs from previous solutions and additional resources

The senior director of CRM at a consumer finance platform told Forrester: “Getting all the customer signals in one place and being quick and responsive to those signals to manage customer touchpoints across channels was key to our success. We could not have gotten where we are without Blueshift. We wouldn’t have the revenue numbers we achieved. It’s exciting to be able to really use the data to drive positive customer responses and to drive revenue.”

Bonus Benefits

Forrester also took a look at the unquantified benefits our customers have experienced, which can’t be overstated.

  • Elevated marketing teams: marketers were able to deliver higher revenues and contribute more to the bottom line.
  • Innovation and cross-team collaboration: with Blueshift, teams were able to test new creative approaches to improve customer experiences and extended the platform beyond marketing
  • Scalability and customization: the ability to finally stretch and grow with ease allowed marketing teams to embrace change and meet quickly changing customer demands.

In addition to the stellar findings, this report offers the level of transparency we value, and we’re excited to be able to share it with you. By offering you a behind-the-scenes look at real success, we hope to help create a demonstrable business case that you can easily communicate with key business stakeholders. Get started by downloading the full study

3 Tips to Avoid #Personalization #Fail Part 2

The benefits of personalization are no mystery. Companies that manage to execute personalization well, in tandem with a solid martech stack, provide engaging and relevant content that keeps customers coming back for more. But what happens when marketers don’t have a solid martech stack? What happens when data remains siloed and teams end up executing against outdated or incorrect insights?

Check out a few examples of #personalization #fails and some tips on how to avoid them.

1. Use a solution that understands your catalog, and when 1 really is enough  


Sometimes a helpful nudge towards products can be a time-saver for busy browsers, but there’s an art to good recommendations. It’s key to find a platform that’s able to digest a wide array of catalog data — not just a SKU number. This will allow it to understand the difference between occasional purchases and everyday needs.

2. Understand triggered events in real time against transactional data


The data customers leave behind is invaluable. It can power everything from recommendations to upsell, but only if it can be understood against transactional data. If your data systems are disparate, you run the risk of making embarrassing suggestions like the ones above. Traditional systems can struggle to keep up with real-time activities across siloed data sources, but computing for the 21st century is here: check out our guide to the CDAP, and how it’s revolutionizing the way marketers utilize their data.

3. Personalize to unique browsing patterns, not what’s hot


Today’s city-dwelling consumer is overwhelmed by upwards of 5,000 ads per day, making batch-and-blast marketing a thing of the past. Segmentation based on continually updated data left behind during browsing sessions, rather than stagnant information like age, gender, and location, presents an exciting opportunity for marketers. With this information readily at hand, segmentation can be as granular as one-to-one, and your customers can be continually delighted with timely and relevant content based on their unique needs and affinities. 

Ready to see how Blueshift helps customers avoid these common pitfalls? Connect with us today, or check out a few more tips on avoiding personalization failures here.

Blueshift Customer Stories: Skillshare Boosts Enrollment 89% with AI-Powered Personalization

When it comes to picking online classes, abundance of choice can often slow the process, or sometimes even halt it completely — especially when it comes to a class catalogue as broad as Skillshare’s. As their online learning community’s base expanded and the class catalog grew even larger, the team realized that if they wanted to engage students, they’d have to find a way to quickly serve each individual with classes tailored to their unique goals and interests.

Building vs. Buying Personalization

The Skillshare team initially tried to build their own solution, spending both significant time and resources on the initiative. But an in-house solution built by in-house engineers ultimately proved to be inaccessible to the marketing team, and needed a manual update for each campaign.

They went back to the drawing board, listed their requirements for an external solution, and chose Blueshift for its out-of-the-box algorithms, ability to tailor course recommendations, scale personalized campaigns, and continuously test and optimize without any heavy lifting from the marketing team — or any team. In no time, they were able to accomplish more than they’d ever done before.

“AI marketing helps Skillshare’s marketing team appear larger than it is,” said Brooke Young, Senior Marketing Manager. “We are a lean team and having Blueshift helps us present ourselves and speak to our customers in a more sophisticated way like companies 10X our size.”

AI = Increased Enrollment and Engagement in 90 Days

The Skillshare team started seeing ROI within 90 days of enlisting the power of AI, all without having to rely on their engineering team. Results like an 89% increase in enrollment rates, a 33% increase in engagement rates, and a slew of all-new campaigns that only took 10 days to launch.

“We’ve never had a shortage of good ideas,” added Brooke. “There were just too few hours in the day and too much reliance on other teams to execute them. Blueshift has allowed our lean marketing team to be nimble and self-sufficient. We now have the power to spin up any advanced campaign idea quickly and test it on the fly.”

Discovering New Opportunities

By automating so much of the process, Blueshift gave Skillshare’s marketing team the opportunity to explore additional projects. They started by testing all the new ideas on their shortlist, including complex campaign strategies with multiple segments and different content versions for each.

To learn more about how Skillshare is achieving results like these, download the full case study. Or, if you’re ready to start investigating how AI might help grow your own marketing efforts, you can request a demo.

#MarTechConf: 5 Expert Tips and Tricks on Architecting your MarTech Stack

Moving beyond customer data management to customer data activation is what today’s brands are doing in order to gain a competitive edge, but, the process is easier said than done. For starters, you need the right data and technology foundation.

Whether you’re just starting to think about how to build out your martech stack, rethinking its existing components, or somewhere in between, this conversation between our co-founder and CEO, Vijay Chittoor, and Alexei Yukna, Director of Marketing Technology and Research at 14 West (the Agora Companies) with 20 years of experience and a love for guitar in his background, can offer some helpful pointers.

Quick Tip 1: Ditch the monolithic approach

“You have to be nimble,” said Alexei. “One of the biggest challenges is — and it’s not just for companies of our scale, it’s companies of all sizes — if you’re tied to monolithic systems…you are not agile by definition. We strive [to] deconstruct the monolith. You really have to find ways to work with many different vendors to build a stack.”

Quick Tip 2: Truly unify data to understand the customer

“The biggest issue is we have as marketers is that there’s information we’re gathering across channels, [but] we’re not getting a true view of who our customer is, what our customer is doing and I think, more importantly, what they are not doing in those spaces,” said Alexei. “So the challenge becomes how do we pull together all of these different data points across different avenues, across different channels, especially as channels emerge…how do you have a view of customers that makes the most sense, how are you able to pull in real and meaningful data and do actual, strategic execution against that data.”

“Customers are moving between different forms of identity,” added Vijay. “We may know a little bit about them, they may be anonymous or they may seem anonymous at some point in time, they might become known customers at other points in time, they might become mobile-addressable customers…so, managing that transition has to be part of the full circle view.”

Quick Tip 3: Trust AI

“The [AI] layer was unachievable 5, 6, 7 years ago,” says Alexei, “but now it’s available. Sometimes [it’s] inherent in some platforms, or leasable, and it can be utilized. It used to be a scary thing to have AI make decisions for you, but it really does cut down the toil and make those intelligent activation decisions.

“You can always go to a data scientist and say, go build me a model to do XYZ,” added Vijay. “But then, often times, they don’t have the full circle view of the customer so it takes them a month to stitch together data from disparate sources, a month to normalize the data, another month to extract features [all] before they even start building these models. And once they’re built out, they’re still not informing a customer experience.”

Quick Tip 4: Don’t forget Decisioning

“The biggest change that’s come in our world is what was crazy expensive a few years [ago] — storing mass data to create these views — that cost has come down,” noted Alexei. “We used to be in the batch and blast world; it was all push and we had no way to understand what right screen, right time, right audience meant. Now we’re able to get that feedback through these systems. The ability to have all that all crunched data-wise and have data stored for us has been the big revelation in the space. Without that, we’re not going to have any…decisioning.

“It’s a lot of computational power, but it’s an exceptionally large piece of this puzzle to understand what decisioning means,” he added. “It means saving time — marketers can do more work, they can do more marketing, they can be more strategic. It’s a piece that I think is missing in the conversation’s stack with a lot of firms. It has to be part of that discussion.”

Quick Tip 5: Make the Shift

“The paradigm shift is customer-centric,” said Vijay. “The old way of doing things was buying an email system and a paid media system, and they would all have their own data and decisioning. What’s happened now is… architecture allows you to put your customer at the center.”

I love the phrase paradigm shift in this context,” Alexei added. “It’s both ways…much in the way social media marketing exploded on the scene as…a conversation, that’s the way to look at this type of exercise. It’s information we’re getting from a customer about their preferences…simply as they interact with our offerings. For us organizationally, it’s looking from the top down so we can make data-driven decisions.”

Ready to get started? Alexei’s advice is:

“If you want to start somewhere, start with having an opinion about where your customer data lives and how you’re looking at that customer data. I would caution to say it doesn’t mean a CRM. A CRM is storing objects that have happened; customer data, customer data platforms, customer data activation platforms are storing data you can work in real time. If I was to sit down and carve out a budget, that’s where I would look.”

This talk took place at this year’s MarTech Conference in San Jose. If you missed it, or are looking for a deeper dive into this expert advice, watch the full recording.

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.

Introducing the First-Ever Customer Data Activation Platform, Powered by Blueshift

We live in an always-on world where customers are increasingly interacting with brands across multiple touchpoints and “channels”. Not only have newer channels of engagement emerged, but more of the traditional broadcast channels have become “people addressable”. Ten years ago, we could not have imagined that banks would be engaging with customers over chatbots, or that TV advertising could be segmented and personalized. At the same time, traditional channels like email, SMS, direct mail, telesales and more have continued to stay relevant.

The Challenge of Intelligent Customer Engagement

Having so many people-addressable channels at their disposal gives brands the opportunity to drive true 1:1 Customer Engagement at scale. However, brands must overcome the challenge of delivering relevant and consistent messages on every touchpoint. This can be challenging with an ever-expanding set of tools and apps for each channel. Each of these channels and tools has a limited view of the customer, and that makes it challenging for marketers to intelligently engage customers. Common challenges include:

  • One-to-One: How can I personalize at a one-to-one level on each of these tools, merchandising the best offer or content for each customer at the right time?
  • Real-Time: How do I understand my customers and respond to them in real-time?
  • Cross-Channel: How do I deliver a consistent experience across touchpoints?

Without a complete view of customer data to aid these decisions, marketers often resort to simple batch-and-blast techniques that lead to irrelevance and incoherence across channels. How can brands escape a fragmented customer experience, or worse still, an experience that lacks any relevance to customers?

Activated Customer Data is the Glue

The problem lies in putting channels at the center of our processes rather than people. Marketers regularly build programs for paid media, email, and so on. But that approach means we’re always following (and attempting to catch up to) the customer. When we flip the order to focus on the customer first, the output becomes much more valuable. Customer data contains clues to customers’ intents and interests, but the data often lies dormant and disconnected from most channels. Activating this dormant data can help brands move from channel-centric to customer-centric.

In an always-on world, relevant customer engagement involves making millions of intelligent decisions in real-time, across multiple channels. Intelligent decisions that are made with a consistent view of customer data can help brands deliver a highly relevant and consistent experience for each customer. In other words, activated Customer Data is the glue that can make customer engagement relevant again. That is why I am excited about Blueshift’s launch of the Customer Data Activation Platform (CDAP) today.

Announcing the Customer Data Activation Platform (CDAP; “SEE-dap”)

The CDAP is a vision we have been working towards with our previous release, including AI-Powered Cross-Channel Journeys, Predictive Audience Syndications, and Live Personalization. Today, we released our App Framework that completes our vision. In simple words, the CDAP helps brands deliver intelligent customer engagement on every touchpoint on the tools they already use, by activating a full circle view of their Customer Data.

It’s not hard to imagine when and where a platform like this would be useful, but some key use cases include:

  • Personalize messages across engagement channels. Increase the relevance of messaging across online, offline, or proprietary custom built apps including email, mobile, direct mail, and chat
  • Increase paid marketing return on ad spend (ROAS). Identify high-intent customers through predictive modeling and keep them continuously updated in Google, Facebook, Twitter, Taboola, Linkedin, etc. through Blueshift Audience Sync.
  • Coordinate and optimize customer experience across channels. Trigger real-time, behavior-based messaging across customer touchpoints. Self-learning models optimize the journey flow.
  • Power CRM, analytics, and other systems with the latest user behaviors and dynamic segments. Automatically update and keep all your BI tools, analytics systems, CRM systems, and other technologies synced.
  • Streamline the customer experience across the full customer journey. Have customer service teams and messaging applications access a complete customer understanding and trigger real-time actions.

You can read the full press release here.

Winning through Data Activation

For years, brands have been investing in projects that help them “manage” data. While data management is a worthy goal, successful brands are more often defined by whether they can activate that data to drive intelligent customer experiences. Winners move beyond working for their data, and put their customer data to work for them.

Activating your customer data leads to a virtuous cycle for brands:

  • Customers interact with your brand and leave behind some first party data
  • Activating this data helps deliver personalized experiences
  • Personalization leads to trust with customers
  • The increased trust leads customers to interact more with the brand and share more data

This virtuous cycle of “data -> personalization -> trust -> more data” is the key to winning with today’s customers. In fact, a recent study by Blueshift and Techvalidate showed that the brands that activate more of their customer data also drive revenue success.

With the Customer Data Activation Platform, brands finally have the full set of tools they need for activating their customer data on every touchpoint with their customers.

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?


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.


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.


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.



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



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.

Blueshift Customer Stories: Hospitality Brand, Suiteness, Grows Bookings 2x with AI

When it comes to travel accommodations, suites are the equivalent of flying first class. There’s a ton of space, comfort levels are high, and the amenities are endless.

Also like first class? The price tag. But a company called Suiteness aims to make this exclusive experience more accessible and wallet-friendly. Groups of four or more can use the service to book a luxury connected suites online for less, complete with a personal concierge for help with exclusive restaurant and club reservations, unique experiences — all the bells and whistles.

When their no-brainer offering hit the hospitality scene, Suiteness’ lean marketing team needed a way to deliver rich, timely, personalized end-to-end travel experiences as it grew and diversified its user base, partners, and markets. They turned to Blueshift.

Personalize, Automate, and Scale with AI

With the Blueshift platform, the Suiteness marketing team quickly built out core activation, conversion, and retention campaigns, including a Welcome Series, Abandoned Browse and Cart, and Member Reactivation. In other words, the entire customer engagement flow was re-imagined and executed successfully — all within a month.

The results? Outstanding.

“Thanks to Blueshift’s AI-backed platform, our content is more relevant to the consumer – this is evident in the 170% growth in conversions from email,” said Divya Mulanjur, Head of Email & Content Marketing at Suiteness. “I’m amazed how many customer programs we’ve been able to automate, optimize, and scale in a short time without needed to pull in other teams. The intelligence and recommendations have allowed us to understand our customers and speak to them on an individual level.”

Next Steps

With all their campaigns on autopilot, the Suiteness team was free to start re-focusing their collective brainpower on ideating and optimizing new experiences. Next on the list: using Blueshift’s other capabilities, such as geolocation, to recommend suites in nearby locations, and adding triggers and touchpoints.

Meanwhile, the company has been enjoying some well-deserved recognition for their Blueshift-powered email series, including:

Take an in-depth look at how Suiteness achieved success by downloading the full case study, or, if you’re ready to start investigating how AI might help grow your own marketing efforts, you can request a demo.

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



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 mak