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 Leadership Spotlight: Josh Francia, Chief Growth Officer

My path to Blueshift started way back in 2011 when I was leading the CRM efforts for an online travel company. After several years of double-digit YoY growth, our external technology systems started to break. We went through three ESPs in six years before realizing they were never architected to scale to our innovative aspirations. The solutions on the market at that time simply couldn’t handle the amount of data we were piping in, or what we ideally wanted to do with it. With no suitable options, building our ideal solution was the only approach we had left to try.

With help from my team and our incredibly supportive manager, we identified our needs for real growth and set out to build a system that could accomplish five key goals.

  1. A single profile view for all customers that contained every touchpoint and would stitch together anonymous and identifiable sessions.
  2. Algorithms to run against these profiles to predict future behaviors.
  3. Enough storage for product data from 200k+ hotels, rental cars, and airline deals.
  4. The ability to recommend personalized deals through a lightweight but powerful templating language that supported things like looping and in-memory variable storage etc.
  5. Speed. We wanted to be lightning fast. Like, send 2 million 100% personalized emails out in 1 hour fast.

We got to work and in roughly six months had a system up and running. It was challenging (to say the least) working through petabytes of data and never-ending legacy systems. In addition, it seemed that everyone who knew or built those systems had left the company years ago, leaving us to piece together a jigsaw puzzle of customer data loose ends. Luckily, we had the support of the senior team to fix the system and fix it fast. The resulting platform, and I use that term generously, was rough around the edges and only worked through command line prompts, but it worked.  

To say that taking a risk on a new way of thinking about data and how to use it across our marketing was rewarding would be a gross understatement. We somehow managed to patch together the makings of a CDAP before the industry was even close to defining it. And with this innovative build, the company was able to process 10+ million records, score them, and provide real-time product recommendations every single day.

Ultimately, this meant high double-digit YoY revenue growth for the next four years that I was there.

Building vs. Buying Customer Data Activation

Fast forward to December 2016. I had just joined LendingTree and on my first day, then-CMO asked me to “fix” the CRM system. They too had outgrown their infrastructure and needed a replacement. It all sounded eerily familiar. Because of my previous experience, I knew exactly what they needed, but this time was hoping someone had built it. My previous experience taught me that internal product builds that live outside the core product offering are almost always short-sighted and quickly become a maintenance nightmare.

I started my research and found 30 — yes, 30 — companies that claimed they could help us achieve those same 5 goals I identified at my previous company. After dozens of sales calls, demos, and sandbox accounts, it became clear that 29 out of the 30 either couldn’t or couldn’t do it at the scale we needed. But one company stood head and shoulders above the rest. A startup out of San Francisco called Blueshift, that was founded in 2014 by former Walmart and Groupon marketing and tech guys who’d faced challenges similar to mine.

I was impressed with Blueshift’s technology from day one. It reminded me a lot of the system my team built at previous company but with a UI, production level code, and support. It was built to scale infinitely, which is hard to find in the SaaS space. We signed with Blueshift and in about 60 days were up and running. All our customer data and events were loading in real-time and we were ready to go live.  

Not everyone was excited to bring in a new, unheard of, and untested system. Two executives told me “Blueshift better work” and that I should have considered some of the big marketing cloud players. I told them I had, in fact, researched some of them, but realized their technology wouldn’t scale or allow us to do what we needed to do to drive significant growth. Big cloud players become big through bolt-on acquisitions, not core engineering. It was painfully evident that the bolt-on product offerings were nothing more than a re-brand of the archaic and obsolete architecture that I had broken so many times before. I said, “You’re just going to have to trust me on this one.”

LendingTree launched Blueshift in June 2017 and it was an instant success. We drove more revenue through Blueshift from June 2017 through Dec 2017 than we did the entire year before.

Everyone on the team quickly became a believer and we continued to iterate and evolve. We added predictive modeling, journey flows, and audience syncing with Facebook and Google for our paid marketing campaigns. The results continued to impress with record-breaking YoY revenue growth each year.

Joining a Winning Team  

I’ve been a professional marketer for long enough to confidently say that I know the direction the industry is moving in. It’s not just the companies I’ve been a part of; organizations of all types are finding that their existing systems just aren’t enough, that in-house builds are too demanding, and that big cloud players aren’t all they claim to be. But understanding why they’re not enough and exactly what’s needed to solve today’s and tomorrow’s issues is another story.  

In late 2018, I reached out to Vijay Chittoor, Co-Founder and CEO of Blueshift and I said, “You guys have built something amazing, but the problem is that it’s so far ahead of what most marketers think they need that you first have to educate the market and the marketer. It’s like building a rocket ship when people were just getting used to the automobile.” Vijay asked me to join the team and help them craft that story.   

So that’s what I did. I joined Blueshift as their Chief Growth Officer in March of this year to help other B2C marketers and businesses experience sustained step-change growth year after year.  Combining innovative thinking with AI-Powered scalable technology unlocks the key to unlimited 1:1 personalization at scale. It is, without a doubt, the only way to exceed customer expectations and leapfrog the competition. I’m excited to help your business grow. Let’s get started!

#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.

Blueshift Raises $15M Series-B Investment

We have some exciting news to share today! We raised a $15 million Series-B round led by SoftBank Ventures Asia. SoftBank deeply believes in AI’s ability to transform how we live, work and play, and we’re thrilled to partner with them. We have a long way to go in our mission to put AI in the hands of every marketer, but we have some concrete plans on how to get there, fueled by this new funding.

The Paradigm Shift in Customer Engagement

We live in an exciting time to be a consumer marketer. Today’s always-on customer is leaving behind large amounts of dynamic data about their intents and interests.  At the same time, as more and more digital channels emerge, and more of them become “people-addressable”, brands have more avenues to put this data to work and intelligently engage with every customer as a segment-of-one.

Adapting to this always-on world requires a paradigm shift in marketing. Brands and marketers need to make the shift from channel-centric approaches to a truly customer-centric one, intelligently orchestrating customer engagement across every touchpoint.

Marketing cloud solutions, built 20 or more years ago for a single-channel world with largely static data, have proven to be insufficient for engaging today’s always-on customer. Customer Data Infrastructure companies have emerged to solve some of the growing pains around data, helping technology teams manage new forms of data. However, simply “managing” the data is not enough, and brands need to make the shift from towards putting the data to work on every channel, driving intelligent customer engagement.

The Shift So Far: AI in the Hands of Marketers

In this first phase of our journey, we are proud to have worked with cutting edge marketers, who have embraced the power of AI to unlock their customer data intelligently on every channel. Blueshift has helped marketers scale their storytelling and personalization, and drive revenue and ROI in the process. Using Blueshift’s AI-Powered Cross-Channel Journeys, Predictive Audience Syndications, and Live Personalization, marketers have been able to transform their customer engagement on email, SMS, mobile push notifications, websites and paid media channels like Facebook Custom Audiences and Google Customer Match.

In short, marketers using Blueshift’s AI have made the shift from being button-pushers to becoming strategic and creative brand storytellers, scaling their stories to millions of customers and creating AI-Powered Brands in the process.

The Next Shift: AI in Every Customer Facing Application, Everywhere

As we enter the next phase of our growth, we’re excited to extend the power of Blueshift’s AI to every customer-facing application. When we started Blueshift, we knew that customer engagement would be multi-channel, but even we couldn’t have imagined that banks would be engaging their customers over chatbots, or that TV advertising would start becoming “people-addressable” so quickly. Our newly announced app framework and Customer Data Activation Platform (CDAP) will help brands make every customer engagement application, including proprietary in-house applications, smarter with Blueshift’s AI.

We have been very fortunate to have a global base of customers from our early days. With the new funding, we will continue to invest in our global presence to support our customers everywhere. In addition to our San Francisco headquarters, Blueshift will be expanding in North Carolina, UK and India.

A big thank you to all our customers, employees, partners, and investors. We couldn’t have made it so far without your support at every step. We are excited about the future, and look forward to working with all of you in the next phase of our journey.

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.