Fast-Growing Real Estate Startup, Zumper, Scales Leads by 384% with Advanced, Predictive Recommendations

Finding a place to call home in a competitive rental landscape can feel like trying to find a needle in a haystack – except that haystack is also full of fake needles, spam, and a slew of competitors. So how can renters find their perfect fit, in their dream neighborhood, for the right price? 

Zumper’s mission is to answer such questions.  Their end-to-end rental platform aims to make renting a place to live as easy as booking a hotel. But, no two users have the same journey and as their user base grew, their marketing team struggled to deliver timely consumer centric marketing based on shifting user preferences and an ever growing catalog of listings. 

After legacy systems failed to meet their needs, and their engineering team exhausted their resources to build and maintain a platform in-house, they found Blueshift – it was a fast, flexible system that could keep up with millions of listings at any given time, and the team loved that it was AI-powered, rather than rule-based, because our recommendation engine that covered all their use cases. In a short time it became clear that Blueshift was the only platform that could check all their boxes and grow with Zumper simultaneously. 

Hyper-personalize and scale campaigns with advanced recommendations and triggers

Once the Zumper marketing team had made Blueshift their home, they set out to use our AI powered platform to revolutionize their user experience and put the power of customer data in the hands of their marketers. Blueshift’s ability to process Zumper’s vast amount of customer data in real time has surfaced some fantastic results for the fast growing real estate brand:

“Blueshift’s AI-powered recommendation engine allowed us to better serve our customers with targeted marketing and personalized campaigns at scale,” said Kristy Ng, Director of Lifecycle Marketing, “The platform enabled us to turn user behavior into experiences that capitalize on actionable insights that were critical to customer experience.”

The platform quickly provided value and became an integral part of the multi-channel marketing approach within two months of launch. Their lead submission grew by 384%, click-through rates skyrocketed, and message volumes expanded by 198% with no change to resources used by Zumper’s team – besides Blueshift, of course.

Next steps

After experiencing staggering growth with Blueshift onboard and scaling up 1:1 automated campaigns, the Zumper team plans to extend our personalization features onto new channels, including onsite content and paid campaigns on Facebook and Google, to continue to provide an engaging user experience – no matter where users navigate.

Take an in-depth look at how Zumper 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.


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!

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.

Going Beyond the CDP: An Iterable vs. Blueshift Showdown

In our Going Beyond the CDP series, we break down how we compare to other Customer Data Platforms (CDPs). In this edition, see how we stack up against Iterable.

The main difference between Iterable and Blueshift is in the architecture. While Iterable is a solid solution for email-dependent organizations, the platform wasn’t built to support today’s rapidly changing landscape of marketing channels. Blueshift, on the other hand, was created by veteran marketing technologists who have been building evolving, scalable database architectures for decades. Let’s dig into the details.

1: Adaptive Customer Journeys

The ability to set up if/then scenarios inside customer journey flows is a standard but important capability for today’s marketer. For example, if someone reserves a hotel room to check in on Tuesday the 1st, then they will receive a How is your stay? push notification from the hotel manager on Wednesday the 2nd. But what happens if that customer changes their reservation?

Well, with Iterable, you’d have to rebuild the segment. Blueshift, on the other hand, automatically adapts the How is your stay? email date based on the new reservation. And what if you want to send multiple messages at different times related to the hotel reservation? One day before check-in, one day after check-in, two days after check-out, 11 months after check-out, etc. With Iterable, you’d need to build multiple customer journeys for each scenario, which can become a lot to maintain.

With Blueshift, that same process is taken care of behind the scenes. Our platform enlists an individual’s historical data to trigger event-based campaigns without requiring the marketing team to activate a specific promotion. In this example, the individual might receive a message that says something like, “You loved last year’s trip; let us help you book another great vacation.” This feature, which we call transactional modeling, allows you to connect and segment lifecycle events/behaviors through a common identifier.


2: Cross-Channel Identification

Iterable was built to solve email personalization challenges, but consumer expectations have grown far beyond the inbox. In order to be successful, today’s marketer needs to be just as savvy when it comes to mobile, apps, social profiles, and more.

Blueshift’s customer record doesn’t have a single primary key, but can be created and executed against any of the following keys: email, phone, device UUID, customer_id and Blueshift’s anonymous_id. This means clients can execute personalized messages to all of their customers, both known and unknown. For example, in Blueshift you can send mobile push messages to anyone that has a device UUID — and that’s it. There’s no need for an email address or customer id. Meanwhile, Iterable’s current documentation states the inability to send mobile push notifications, SMS, web-push, or the like to anyone without an email, as email is a required field.

Blueshift also stores behavioral data related to anonymous profiles so you don’t lose valuable data insights just because someone hasn’t signed in yet. Once there’s an identifiable event (email, phone, customer_id, etc), Blueshift combines all the anonymous data into a single identifiable user profile.


Cross Channel Identification

3: Platform Scalability and Stability

No system is perfect. Even the best of the best will occasionally experience hiccups, but those that are designed from the ground up to be scalable will be far more stable than those that aren’t (over the last 18 months, Iterable has experienced 61 incidents which degraded the performance of the application compared to Blueshift’s 6).

The way Blueshift handles scale is unique because our platform uses micro-services. Each service is tied to a different backend data store including in-memory, key-value, time-series based, reverse index, OLAP, log-based, etc. This means the Blueshift infrastructure is optimized for speed and flexibility by always ensuring the application hits the right data store for speed. Iterable is optimized for storage, which limits the amount of data it can process and retain along with how fast it can respond.

Ready to make the shift? Let us help you start out on the right foot by giving you a test drive of what’s possible.

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.

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.