Industry Deep Dive

The 4 Best Growth Marketing Campaigns That Delight Travelers

This series of blogs goes into detailed campaigns that growth marketers can run for specific industries. These campaigns are tailored towards goals and revenue that growth marketers are responsible for. Our third industry deep dive takes a look at the digital travel booking industry and campaigns specifically tailored for growth marketers to move users along the buying cycle fast and keep them coming back for more purchases.

The digital travel industry has come a long way in the past decade. What started from a handful of booking sites has grown into thousands of websites all fighting for attention through price comparisons, user experience, loyalty benefits, convenience, etc. Everyone is working hard to differentiate themselves from their competition. What they all have in common is thousands of people coming to their site everyday, ever changing inventory and prices, and millions of unique searches of what people are looking for. This creates the perfect recipe for growth marketers to cook up something new in digital engagement campaigns.

Below are 4 personalized email and notification campaigns growth marketers at digital travel companies launch to reduce churn. 

Abandoned Search

For your known users who make a search on your site and do not make a purchase, you can recommend fares based on their recent search with the dates and location from the search. This has to be sent out 1, 3, & 7 days after the search since it is a time sensitive search.

Add a Hotel/Car

A great up-sell campaign for customers who have recently booked a flight on your site is a personalized offer to add a hotel or car to their booking on those same dates based on the flight location/dates. This has to be executed immediately or between 1 and 3 days of the customer booking the flight.

Trending Getaway Deals

This is a great evergreen campaign for all your users to send them the latest and trending weekend getaway deals personalized based on their specific location. It can be sent on a weekly or monthly recurring basis. below is an example using a visitors location to deliver weekend getaways within relatively close distance to them.

Location based recommendations



Screen Shot 2017-03-01 at 12.04.36 PM

Promotional Sale

Another great evergreen campaign that requires little work on the marketers part is a promotions campaign. Airlines and hotels put out promotional offers every now and then and those can be used to send personalized offers of deals from nearby airports/locations based on the user’s location to your active customers on a weekly or monthly basis.






Watch out for more posts about growth marketing, and check out our comprehensive guide here for everything you need to know about the subject.


Urban Ladder

4x Conversion Lift: Urban Ladder Finds The Secret Sauce to Reach Online Furniture Shoppers


Urban Ladder is a leading online furniture and home decor company that provides a curated shopping destination for your home. Their modern designs and uniquely styled products attract millions of customers and has propelled them to be the #1 source for furniture in India. With millions of customers coming to their site via multiple channels and interacting with their catalogue of over 4,000 products across 50 different categories, they found it hard to market to all their customers while staying true to their promise of a personalized experience.

“With Blueshift, we have launched very personalized triggered campaigns on email & mobile app push notifications. We are seeing significant improvements in conversion rates on these marketing campaigns which are highly targeted and relevant for the users.”

Ashish Goel, CEO of Urban Ladder


Urban Ladder turns to Blueshift to help address its issues

Urban Ladder’s unique design at the heart of every marketing message

Urban Ladder’s unique design at the heart of every marketing message

Urban Ladder has a distinctive brand and look which had to come across in every channel they market across. With a web based store and a mobile app, they had a hard time tying in multiple data sources into a unified customer profile in real-time. They needed a robust recommendation engine for their 4,000+ product catalogue consistent with each person’s browsing and purchase behavior. Handle personalization to varied sales cycles, like furniture which tends to have long consideration cycles rather than home decor, which can be impulsive.


Blueshift’s Solution


Blueshift provided the ability to unify each user’s behavior data across mobile and email for a complete 360-degree view of the customer. It enabled Urban Ladder to deliver a consistent user experience across all channels that represented their brand along with powerful recommendations and simplified paths to purchase.

After a quick integration Urban Ladder was able to launch cross-channel triggered campaigns for welcome series, abandonment, post purchase, complete-the-look cross sells, and product recommendations based on user behavior in just a few days.




Urban Ladder using the “back in stock” product alert in their newsletters powered by Blueshift.

Urban Ladder using the “back in stock” product alert in their newsletters powered by Blueshift.



Urban Ladder Realizes a 4x lift in conversions and a rapid time to value outperforming all other vendors
Urban Ladder now delivers a delightful user experience across mobile & email by combining their in-house creative team and Blueshift technology. Using the 360-degree customer profile powered by Blueshift as the foundation of their customer data and utilizing deep segmentation capabilities of Blueshift, Urban Ladder has seen 4x higher conversion rates over previous tactics.


Poor Historical View

Personalization Pitfall #4: Poor Historical View of the Customer

In this series, we cover the common pitfalls all marketers face at some point when scaling personalization in their triggered marketing. From emails to mobile push notifications to SMS to display retargeting, the common platforms used today to market across channels begin to lose efficacy when organizations try to personalize their communications to an ever more complex and growing customer base.

Poor Historical View of the Customer

Watch this video to learn more about this subject from Brian Monahan, former CMO of 

Lifecycle marketing is a highly engaging way companies can re-activate or re-engage old customers. Using past interaction and transactions online, companies surface relevant products and promotions through different channels to influence a purchase. Sounds simple enough right? On the contrary having a 360 degree view of your customers over a long period of time and in real-time is very tricky for most businesses and our pitfall number 4.

Out with the old…

An old approach to this strategy has been to remarket to customers based on each item they browsed without taking their historical behavior into consideration. If a customer is browsing patio chairs, hammocks, and outdoor umbrellas, they are probably looking to furnish their backyard. Offering them 5 options of patio chairs might not be the best way to influence a sale.




Overcome Amnesia of Your Customers

Your product recommendation engine has to be smart enough to suggest “next best products” or “complete-the-look products” or a product in the same category or brand. Only personalized, smart product placement and recommendations can work to win back customers in the highly competitive market of today.

The key to re-marketing the right way is to connect every piece of user behavior and past purchase in real-time with a deep knowledge of the company’s catalog. Using a holistic customer view, marketers can provide a hyper-personalized story relevant to each user’s context.

Subscribe Now to this series
To learn more about all the common personalization pitfalls covered in this series, watch this VentureBeat Webinar that provides real world examples and fixes you can start using now.

  • Each update sent directly to you with extra tips NOT included in the blog posts
  • Access to the VentureBeat Webinar with former head of marketing at
  • Receive an audit of your current triggered activities with a marketing consultant

Message Overload

Personalization Pitfall 3: Customer Message Overload

In this series, we cover the common pitfalls all marketers face at some point when scaling personalization in their triggered marketing. From emails to mobile push notifications to SMS to display retargeting, the common platforms used today to market across channels begin to lose efficacy when organizations try to personalize their communications to an ever more complex and growing customer base.

Overcoming the Message Overload Pitfall

Watch this video to learn more about this subject from Brian Monahan, former CMO of 

When you build out your company’s personalized marketing landscape you soon find your volume of messages increasing exponentially. As you set up re-engagement campaigns along the customer journey, the volume of messages across all channels can quickly add up to 10 or 15 different messages. Of course, that doesn’t mean you send them all 10 of these messages in one day or even a week. Customers feel overwhelmed if their inbox is flooded with one particular company emailing them again and again. Message overload is a sure way of ending up in your customer’s spam folder or worse, unsubscribing from all your communications. This rapid deluge of communications to your customers is our pitfall #3.

Don’t Be Annoying…

Message Overload across all channels is a personalization pitfall

Finding the balance between quality and quantity will save marketers from those dreaded mistakes of sending a customer too many messages in a day. But how do you make sure you aren’t sending to many messages across all your channels?

The way to achieve message zen is by smart segmentation of customers who fit a certain criteria based on their attributes and behavior on site. Behavior-based marketing resonates better than single trigger marketing because it tends to be more accurate rather than an in-the-moment action or even sloppy demographic focused bucketing. Grouping together customers who have shown similar behavior and sending a set of targeted messages that are personalized to their persona is a controlled way of using triggers on your site.

Think Beyond the Inbox…

“don’t simply focus on the amount of messages you send per channel, look at the aggregate of ALL of your messages sent through all of your channels”

Another way of working around the message overload problem is to build and monitor multi channel campaigns. Marketers constantly compete for inbox space along with numerous other brands. When’s the last time you looked at your inbox and didn’t feel like you were being yelled at by dozens of brands? A quick reminder to complete your purchase and checkout can easily be done via text message or push notification – abandoned cart campaigns are not simply just an email tactic. Dividing your messages across different channels can keep your brand name top of mind and limit annoying your customers. And remember, don’t simply focus on the amount of messages you send per channel, look at the aggregate of ALL of your messages sent through all of your channels. Otherwise, you still run the likely risk of annoying your customers with message overload.

Subscribe Now to this series
To learn more about all the common personalization pitfalls covered in this series, watch this VentureBeat Webinar that provides real world examples and fixes you can start using now.

  • Each update sent directly to you with extra tips NOT included in the blog posts
  • Access to the VentureBeat Webinar with former head of marketing at
  • Receive an audit of your current triggered activities with a marketing consultant

What is Programmatic CRM? [Infographic]

What is Programmatic CRM? [Infographic]

Programmatic CRM is a technology that enables marketers to be customer-centric and leverage real-time behavioral data to reach every customer on an individual level throughout all of your marketing channels. Bringing Programmatic CRM into your marketing stack enables marketers to finally automate the delivery of consistent and delightful user experiences on every channel with true scalability and greater results.

Reach the perpetually connected consumer across all channels at the moment they are most inclined to engage with you brand.

The building blocks of Programmatic CRM

Programmatic CRM is built up a number of key components that work together to Engage with Segments-of-One:

  • Real-Time Triggers to engage customers based on their actions
  • Cross-Channel Reach to be customer-centric, not channel-centric
  • Personalized Recommendations to tailor recommendations to user behavior
  • Dynamic Audiences for segments that update with every customer interaction
  • Measurability to deliver end-to-end reporting on engagements and conversions


Isn’t it time you stop marketing to stale databases built of attributes and demographics?


Real-Time Segment

Behind-the-Scenes: Real-time segments with Blueshift

(Here is a behind the scenes look at the segmentation engine that powers Programmatic CRM.)

Real-time segmentation matters: Customers expect messages based on their most recent activity. Customers do not want reminders for products they may have already purchased or messages based on transient past behaviors that are no longer relevant.

However, real-time segmentation is hard: it requires processing large amounts of behavioral data quickly. This requires a technology stack that can:

  • Process event & user attributes immediately, as they occur on your website or mobile apps
  • Track 360-degree customer profiles and deal with data fragmentation challenges
  • Scale underlying data stores to process billions of customer actions and support high write and read throughput.
  • Avoid time consuming steps of data modeling that require human curation and slows down on-boarding

Marketers use Blueshift to reach each customer as a segment-of-one, and deliver highly personalized messages across every marketing channel using Blueshift’s Programmatic CRM capabilities. Unlike previous generation CRM platforms, Segments in Blueshift are always fresh and updated in real-time, enabling marketers to respond to the perpetually connected customer in a timely manner. Marketers use the intuitive and easy to use segmentation builder to define their own custom segments by mixing and matching filters across numerous dimensions including: event behavioral data, demographic attributes, predictive scores, lifetime aggregates, catalog interactions, CRM attributes, channel engagement metrics among others.


Segments support complex filter conditions across numerous dimensions

Segments support complex filter conditions across numerous dimensions

Behind the scenes, Blueshift builds a continually changing graph of users and items in the catalog. The edges in the graph come from user’s behavior (or implied behavior), we call this the “Interaction graph”. The “interaction graph” is further enriched by machine-learning models that add predicted edges and scores to the graph (if you liked item X, you may also like item Y) and also expand user attributes through 3rd party data sources (example: given the firstname “John”, with reasonable confidence we can infer gender is male).

Blueshift interaction graph

Blueshift interaction graph

The segment service can run complex queries against the “interaction graph” like: “Female users that viewed ‘Handbags’ over $500 in last 90 days, with lifetime purchases over $1,000 and not using mobile apps recently and having a high churn probability” and return those users within a few seconds to a couple of minutes.

360-degree user profiles

For every user on your site/mobile app, Blueshift creates a user profile that tracks anonymous user behavior and merges it with their logged-in activities across devices. These rich user profiles combine CRM data, aggregate lifetime statistics, catalog-related activity, predictive attributes, campaign & channel activity and website / mobile app activity. The unified user profiles form the basis for segmentation. A segment query matches these 360 degree user profiles against the segment definition to identify the target set of users.

360-degree user profiles

360-degree user profiles in Blueshift

Multiple data stores (no one store to rule them all)
The segmentation engine is powered by several different data stores. A given user action or attribute that hits the event API is replicated across these data stores including: timeseries stores for events, relational database for metadata, in-memory stores for aggregated data & counters, key-value stores for user lookups, as well as a reverse index to search across any event or user attributes quickly. The segmentation engine is tuned for fast retrieval of complex segment definitions compared to a general purpose SQL-style database where joins across tables could take hours to return results. The segmentation engine leverages data across all these data stores to pull the right set of target users that match the segment definition.

Real-time event processing

Website & mobile apps send data to Blueshift’s event APIs via SDKs and tag managers. The events are received by API end-points and written to in-memory queues. The event queues are processed continuously in-order, and updates are made across multiple data stores (as described above). The user profiles and event attributes are updated continuously with respect to the incoming event stream. Campaigns pull the audience data just-in-time for messaging, which result in segments that are continuously updated and always fresh. Marketers do not have to worry about out of date segment definitions and avoid the “list pull hell” with data-warehouse style segmentation.

Dynamic attribute binding

The segmentation engine further simplifies onboarding user or event attributes by removing the need to model (or declare) attribute types ahead of time. The segmentation engine dynamically assesses the type of each new attribute based on sample usage in real-time. For instance, an attribute called “loyalty_points” with a value of “450”, would be interpreted as a number (and show related numeric operators for segmentation), while an attribute like “membership_level” with a value of “gold” would be dynamically interpreted as a string (and show related string comparison operators for segmentation), or an attribute like “redemption_at” with a value like “2016-09-23” will be interpreted as a timestamp (and show relative time operators).

Several Blueshift customers have thousands of CRM & event attributes, and are able to use these attributes without any data modeling or declaring their data upfront, saving them numerous days of implementing data schemas in SQL-based implementations.

The combination of 360-degree user profiles, real-time event processing, multiple specialized data stores and dynamic attribute binding, empowers marketers to create always fresh and continuously updated segments.

Verbs not Nouns

Market to Verbs, not Nouns

Marketers have always believed in targeted marketing. In the past, targeting has meant building a database of customers and their attributes, especially demographic attributes like first & last name, gender, location, and more. In this notion of database marketing, the databases describe nouns, like customers and products, and attributes of these nouns.

I wrote an article today in CMS Wire on how marketers should market to verbs, not nouns. Today’s leading edge marketers are finding that targeting based on nouns is outdated in the world of “Perpetually Connected Customers”, who are accessing information every second on web & mobile. The Perpetually Connected Customer’s actions & behavior are an indicator of their needs and wants desires, and marketers are confirming something that we have always suspected: that customers are multi-dimensional, and behave differently at different times.


Why are verbs more important than nouns in targeted & personalized marketing? And why is their importance increasing over time? The two primary drivers are related to how customers have changed over the recent years, and how media has changed:

  • Perpetuals are not the same consumer from moment to moment: Perpetuals’ willingness to consume, changes depending on what they are doing. When describing people or customers, you could choose to describe them using static attributes like location or gender, that don’t change over time. However, people are multi-dimensional, and their interests and desires shift over time. Understanding the customer’s stream of actions is the only way to react to the changing desires of the perpetually connected customer.
  • The death of mass-media and the drive towards 1:1 personalization: Customer attention spans are shifting away from mass-targeted media (like broadcast TV) towards truly personal mediums where people consume content on their own terms. Correspondingly, marketers and advertisers need to shift their framework away from describing people in ways that are hangovers from the mass-media world –using attributes like gender, location, education etc. Instead, marketers need to concentrate on understanding the set of actions that truly set every individual apart, as no two customers rarely ever follow the same sequence of interactions with the same items.

To read more, head over to CMS Wire.




Need For Speed

Need for Speed: Why Marketing needs to Adapt to High Velocity Data

This holiday retailing season, as customers shopping preferences shift, will you still be marketing to them using stale data?


With the rise of mobile devices, and the “always-on” user, the amount of time spent on the

internet has nearly tripled over the last 5 years: 450 billion minutes per month in 2010 to more than 1200 billion minutes per month now. The velocity of data is going to continue unabated growing into the future, with some projections pointing to another 10X increase in data velocity by 2020. Interestingly, the increase in the overall amount of time spent on the internet has also translated in users spending more and more time with the same apps or brands.

All of this additional user time is generating behavioral clickstream data for companies at speeds faster than ever before. At large omnichannel retailers, the volume of clickstream data generated in one day now rivals one year of PoS data: or, in other words, there are 300-1000 pieces of unstructured clickstream data for each purchase.

While marketers have long understood the importance of “RFM” (recency, frequency, and monetary value), with the increase in volume of data every day, “recency” has become ever more important. Without near real-time usage of behavioral clickstream data, the value of the data decays, making it meaningless for targeting. For instance, during the holiday season, which is typically the biggest season for retailers, many users are shopping for gifts, and their purchase behavior deviates significantly from the norm. Businesses that can develop processes to understand and react to such data quickly can earn superior engagement and profits.

Despite the rise of big data technologies, most CMOs are increasingly feeling underprepared for “data explosion”. What are the top initiatives that can help CMOs get ready for this new age of high velocity data? Here are our top 3 recommendations:

  • Process streaming data, and store everything: When dealing with low velocity data, you would first model the data to develop a data-warehouse schema; data that’s not modeled would be discarded. With high velocity data, however, you need to complement your data warehouse strategy with schema-less big data infrastructure that can store all data. The idea is to give analysts the ability to play with data to discover insights that can then be modeled. A good example is from Orbitz, which started collecting unstructured data around trip planning from users, and went from 30TB of data storage to 750TB, revolutionizing their hotel sort.
  • Understand “identity” across platforms and channels: Users are increasingly adept at switching between devices and it’s not uncommon for an user to use 3-4 devices, often times in single day, while they shop around. To understand each user, you have to develop infrastructure that ties together seemingly disparate points of data from desktop & mobile into one unified profile. In addition to using well structured identity information like email addresses or customer ID, smart marketers are also looking at fingerprinting technology to fill the gaps in their knowledge.
  • Get machines to help humans with analysis: Once you have the ability to process and store streams of real-time data, the next step is to have your analytics keep pace with the speed of data collection. Purely manual process can impose delays of weeks, and CMOs need to provide machine learning tools to assist their teams of analysts in uncovering insights. For example, creating lookalike audiences with machine learning on real-time data, can help marketers acquire more high value customers. During the holiday season, and other times when user behavior changes significantly, machine learning will always be a step ahead of human modeling. Machine learnt models can then be refined more by humans who can layer in additional business logic.
  • Reduce the time to action with automation: Not only do you need to process data in real-time, you need to be able to act on your analysis faster. This requires a high degree of automation. In the old world of slow moving data, you might have tolerated a 24 hour delay for ETL processes to load the data in your data warehouse, as well as several weeks of delays imposed by manual analytics processes to leverage the data, and batch processes to act on the data.

amazon_emailHowever, in the high velocity world, actions need to be automated to respond to various behaviors, in a personalized manner. Simple automations like browse abandonment emails, or “related products”, can go a long way, as this example from Amazon shows.

The best consumer marketers of tomorrow will be the ones who embrace the challenges of high velocity data. Need for speed, and higher degrees of automation, will become critical capabilities for marketing organizations in this new world.