what we mean by a 360-degree customer view, what it enables, and how we went about doing this at Blueshift. This post is the first in a multi-part series that looks at key innovations in the Blueshift platform.

A 360-degree View of the Customer – Finding Marketing Zen

In this post, we’ll explain what we mean by a 360-degree customer view, what it enables, and how we went about doing this at Blueshift. This post is the first in a multi-part series that looks at key innovations in the Blueshift platform.


The 90/20 Reality of Marketing and the Single Customer View

David Raab of the CDP Institute quotes a recent survey that shows that 90% of marketers think that a unified multi-channel customer view is important, yet only 20% of them have such a view. Other studies, such as one performed by Gartner, find that even FEWER brands (10%) have a 360-Degree customer view.

For those of us on the “technical side” of the food chain (building software for marketers), this is not surprising — and more than likely, much lower than the stats suggest. Creating a Single Customer View is a hard problem to solve — people have been trying for a while, and often promising more than they can deliver. The fundamental goal is to provide customers with a unified and relevant experience across all channels. To achieve this, you need to have a 360-degree view of your customers in real time.

Traditional approaches to solving this problem, such as data warehouses and, later, data lakes, have come up short because they have either not been able to (a) collect the data or (b) organize it effectively in real-time.


What is a 360-degree view of the customer?

The term 360-degree view of the customer is a catchy phrase. And the problem with catchy phrases is they are used as buzzwords, and once that happens, you really have to look carefully under the covers and beyond the hype.

Figure 1: 360-degree view of a customer

Sometimes referred to as a Single Customer View (SCV), a true 360-degree view of the customer is built on having several important types of information about customers/prospects for use in real-time:

Customer Submitted Data (typically captured in a CRM)

  • Customer attributes & demographics such as Name, Gender, Location, Birthday, etc. The data may be submitted by the customer using online forms or collected through other requests for information.
  • Opt-in and other communication choices.
  • Preference Centers built for a user to indicate preferences for brands, colors, categories, genres, and more.

Customer Transactions

  • Transactional data including purchase records, course completions, and lead submissions along with changes in transactions such as cancellations.
  • Subscription data such as enrollment, upgrades, downgrades, and cancellations.
  • Customer service data including trouble tickets submitted, resolved, and still outstanding.

Product Interactions and Behavioral Data (Observed data, gleaned by collecting the customer’s behavior)

  • Web and mobile behavioral data including page views, swipes, clicks, likes, and “add-to-list” actions.
  • Marketing interactions such as opens or clicks of emails or push notifications, and views and responses to ads from multiple channels.

Derived Information (gathered by analyzing the “metadata”/patterns of customer interactions across channels)

  • “Identity” of anonymous visitors to websites or apps inferred using web cookies or device IDs, combined with login or opt-in.
  • Location information inferred by mapping IP address or latitude/longitude data.
    User affinity towards a category or brand that is inferred through browsing and buying behaviors (beyond stated preferences).
  • Stage in customer journey derived from customer activity.
  • Lifetime attributes such as orders, visits, sessions etc.
  • The propensity to convert based on recent and lifetime activity.

It’s important to note that in today’s online world, the real value of this 360-degree view can only be realized if all these data types are indexed and query-able for use in real-time. The data must be usable.


Why does this matter?

Paraphrasing another quote from David Raab, quality data, and more specifically an accurate 360-degree view of the customer, is the fuel that drives effective marketing and provides customers with the best experiences. And for organizations today, it provides the foundation for all customer-facing activities.

Figure 2: Foundational benefits of a 360-degree view of a customer

There are six essential benefits of having an accurate 360-degree view of the customer:

  1. Single Source of Truth
    Providing data access and integrity is fundamental to any organization’s success because it gives a single source of truth about your customers.
  2. Personalization and Segmentation
    Enabling dynamic personalization and segmentation of campaigns using multiple behavioral attributes collected in real-time makes campaigns more effective and relevant.
  3. Data-Driven Triggers
    With data-driven triggered events, companies automatically interact with customers in real-time to influence their decisions.
  4. Cross-Channel Engagement
    Simplifying the orchestration of cross-channel campaigns across multiple systems yields consistent and relevant engagement across all marketing channels.
  5. Compliance and Security
    By having a single source of truth, supporting compliance with rapidly changing regulations and practices around personally identifiable information and the protection of this information through directives like GDPR becomes much easier.
  6. Accurate Reporting
    Facilitating a consistent and accurate reports of activities and results.


Imperatives to building our 360-degree customer view

Even before Blueshift started building our 360-degree view, we stipulated the following key principles that were necessary for our view of the data to solve problems for the marketer:

Our customer view has to be updated almost instantaneously after any new interaction. We stipulated that this had to happen in near real-time because many marketing activities, such as campaign journeys are triggered based on customer activities, and personalization is far more effective in the context of recent activity

Unified cross-channel identity
The data has to be query-able with various forms of identity ranging from customer ids, email addresses and Facebook IDs to mobile device tokens and cookies.

Open data schema
We recognized that every business has a different way of looking at data, and we needed an open schema to more easily ingest and work with multiple forms of data coming from multiple sources.

Flexibility in modeling the data
Each piece of data may have something to tell us about how the customer interacted with the brand, and our system needed to model this data into the 360-degree view. For instance, for a client in the hospitality industry, a customer might have multiple “events” corresponding to the same booking ( book, check-in, check-out, complete a survey), and additional events relating to other bookings. Our 360-degree view had to capture and store all of these events in the same context. Similarly, in a Media business, customers might interact with content in different categories or from different authors. Here we had to model all of these interactions relative to the “catalog” of content or products for the media business.

Our technical challenges in building Blueshift’s 360-degree view while adhering to these core principles were in these four important areas:

  • Gathering all the different pieces of disparate data about an individual from dozens of input sources and hundreds of events in each session.
  • Resolving Identity and stitching together all this loosely structured data in order to get an accurate view of the behavior of each individual
  • Building a single customer view from 1 & 2 in “real-time” so that customer behavior can drive personalization and interactions across multiple channels
  • Maintaining data integrity and consistency across different systems(search, user store, data warehouse, data science, analytics)



The unified 360-degree view of a customer is a key foundational element needed to more effectively market to and interact with customers using artificial intelligence (AI) techniques. In our next post in this series, we will discuss how we went about building this single customer view in the Blueshift platform and the challenges we encountered.

For More Information
Read more about AI-powered marketing in our resources section.

This post was made possible through joint collaboration with Atri Chatterjee, Anuraj Pandey, and Cibin George.

Watch this rare webinar with Analysts from Forrester Research and VentureBeat hosted by Blueshift about getting the most out of your customer data with AI

Blueshift's AI-powered marketing platform solves for The 3 Is of AI in Marketing: More Than Just Intelligence

The 3 “I’s” of AI in Marketing: More Than Just Intelligence

Watch this rare webinar with Analysts from Forrester Research and VentureBeat hosted by Blueshift about getting the most out of your customer data with AI

This is the third and last post of our 3-part series on the topics covered in our joint webinar with Forrester Research and VentureBeat on AI-Powered Marketing. In part 1, we focused on the primary problem facing many marketers: the explosive amount of customer data being generated and an inability for marketers to use much of it. This has led many to a search for new solutions and technologies, such as AI, to help process this marketing data in real-time to create and execute more effective campaigns. However, Rusty Warner of Forrester Research, advises us in part 2 to not rush into a decision without first considering three crucial elements – strategy, organization and technology – when planning the adoption of AI. We now end the series by summarizing the webinar discussion and Q&A session on what one should look for in an AI technology for marketing.


Don’t Lose Sight of the Problem to be Solved

In any technology evaluation, it’s important to always keep in mind the problems you are trying to solve and how best the technology will solve those problems. Our webinar attendees answered several questions that indicated that they had three major overarching problems that they are expecting an AI system to solve:

  1. Generating copious amounts of behavioral data about their customers, but using very little of it.
  2. Unable to unify the customer data and therefore unable to gain insights from this data and effectively use it for marketing campaigns.
  3. Long delays in getting insights from their customer data, and even then, these insights have very little information about marketing channels


Data Stats

Marketers have a data problem


Any AI technology has to solve these three overarching problems that revolve around organizing, accessing and acting on your customer data in real-time.


Identity, Insights & Intelligent Orchestration

Any AI technology for marketing can only be effective if it solves the three problems around identity, insights, and intelligent orchestration.


In order to effectively use customer data to inform our marketing campaigns, we have to first gather, organize and tie this data to the identity of specific individuals. An AI system must have the ability to track a customer or prospect’s interactions across multiple channels and stitch together behavioral data gathered from browser cookies, device identifiers, email addresses and customer IDs to develop a comprehensive view of the activities of a specific individual.

This is analogous to a person knowing things about another individual and committing that to memory for use in future interactions.

360-degree user profiles

360-degree Customer View


Once individuals are identified and their behavioral data organized, the system should also be able to take this behavioral information and combine it with information about product attributes, historical data and other external data such as location and time to develop insights that can drive marketing activities to that individual. These insights could take the form of predictive scores that determine the likelihood of a person purchasing a particular product or a churn score that indicates the likelihood of a customer ceasing to use your product or service. These scores are developed using a variety of signals such as decrease in product use, frequency of interaction, type of interaction, changes in patterns of interaction, etc.

This is analogous to the cognitive powers of the human brain to combine the information it has about an individual in its memory with external data to develop insights and opinions about that individual.

Churn Score img

Churn Score for a customer

Intelligent Orchestration

After developing insights into each customer based on all the factors discussed above, the AI system should also enable the marketer to develop segments of customers and execute multi-channel campaigns that act upon these insights to segments of customers. In our example above, customers with a high churn score could be invited to speak with a customer service representative to try to better understand their change in behavior.

A typical multi-channel campaign should execute across email, web, social media (such as Facebook) mobile messaging and notifications, and direct mail.

And in today’s world, all these three activities need to happen continuously and in real-time.

Multi-channel customer journey orchestration with Blueshift's AI-powered marketing platform

Multi-channel journey orchestration



The three blog posts in this series (Part 1 here and Part 2 here) give you a more complete view of the challenges facing business-to-consumer marketers today and the potential for AI to help solve some of these issues, a blueprint for developing a strategy to incorporate AI in your marketing, and key attributes to look for when evaluating AI technologies for marketing.

For More Information

Read more about AI powered marketing in our resources section.

Watch this rare webinar with Analysts from Forrester Research and VentureBeat hosted by Blueshift about getting the most out of your customer data with AI

3 Crucial Elements Marketers Must Consider When Implementing AI Strategy, Organization, and Technology

3 Crucial Elements Marketers Must Consider When Implementing AI

Watch this rare webinar with Analysts from Forrester Research and VentureBeat hosted by Blueshift about getting the most out of your customer data with AI
This blog is the second installment of a 2-part series on how artificial intelligence (AI) can help marketers put their customer data to work discussed in a rare joint webinar with Forrester Research and VentureBeat.

In the first part of this series, we explained how marketers can win their customers’ moments if they fully harness all their customer data — which is easier said than done since most marketers admit to only using 10% of their customer data.

“50% of Marketers State Data Unification as their Greatest Challenge to making the most of their customer data.”

Having existing data silos is one of the main reasons why marketers cannot fully leverage all their customer data. Customer data often sits in disparate and geographically dispersed systems. “Marrying” all this data can be extremely difficult. At a recent Forrester webinar, half of polled marketers said that data unification is their greatest challenge when they want to make the most of their customer data.

half of polled marketers said that data unification is their greatest challenge when they want to make the most of their customer data.

Armed with artificial intelligence (AI), however, marketers can successfully break data silos and effectively generate and orchestrate customer insights and actionable intelligence quickly.

But hold on…don’t end up like countless other marketers who have battle scars from trying to bring AI into their organization. I have personally spoken with dozens of marketers and product leads who have “battle scars” when trying to implement AI into their marketing stack in the past. Before you implement AI, first consider these three crucial dimensions (Strategy, Process, Technology) in order to make the most of your AI investments and avoid costly mistakes.



It’s fairly straight-forward… don’t jump to a tool before you know what you want. Start with a strategy for fully maximizing the potential of AI. Determine what the marketing team –– or, better yet, the organization as a whole (more about this in the next section) –– is trying to accomplish and how, using AI, it can deliver business results. A key question to ask  here is, “How will AI help me scale my results?”

The ultimate goal should be to become customer-led and data-driven because customer experience is the new battlefield. According to Forrester, many organizations aspire to become customer-centric and data-driven (70%), yet few can turn data into profitable actions (29%).

The ultimate goal should be to become customer-led and data-driven because customer experience is the new battlefield. According to Forrester, many organizations aspire to become customer-centric and data-driven (70%), yet few can turn data into profitable actions (29%). AI can bridge this gap and enable marketers to become customer-led, insights-driven, fast, and connected. AI will give them greater visibility into customer behavior, make appropriate and contextual offers, and deliver personalized and unified experiences across all channels. And based on the insights generated, AI empowers marketers to further optimize their offerings and overall strategy.

TIP 1: Think about how you will measure the success of your efforts (KPIs).

TIP 2: Your strategy must include a “Crawl, Walk, Run” approach when rolling out AI that has clear KPIs at each step.



AI adoption impacts not just marketing processes but the entire business. Determine the organizational gaps that must be closed and address the factors and misconceptions that may hinder the organization from implementing AI.

It's about people and processes... Determine the organizational gaps that must be closed and address the factors and misconceptions that may hinder the organization from implementing AI.

TIP 3: Draw out the customer journey (physically draw it out) and include all steps in the process beyond just what happens in marketing. Understand where the bottlenecks are and address the key pieces that you wish to have AI help you solve. (this could be data unification, post-sale tracking, behavior tracking, multi-channel engagement, customer experience)

To evaluate the organization’s readiness to adopt AI, look at its people and processes:

People: Lack of AI skills is the primary reason why companies hesitate to implement AI. In fact, only one-third of surveyed marketers said that they have the right skills and capabilities to adopt AI. It is also a challenge to recruit people with the right blend of business and technology skills who can easily adapt to a customer-centric culture.

Process: Marketers should study how AI will impact existing processes. For example, how will AI allow for greater transparency? How will it enable siloed departments to obtain better visibility into what others are doing? How will it help me scale what I am doing?

Additionally, it is important to ensure that everyone in the organization has the right understanding of AI. For instance, they should be aware that AI alone cannot externalize knowledge. It requires both people and technology. Which brings us to the next point.



AI is not merely a plug-and-play component in your marketing stack. Look at all the components in your marketing stack to understand how AI will interact with your data, your team, and your campaign execution.

AI is not merely a plug-and-play component in your marketing stack. Marketers should know how to deploy it successfully and set the right controls and monitoring systems. Should they turn on a model and let it generate results for people to review? Or, should they embed it into an application that automates processes such as personalizing content and optimizing email campaigns? (Ideally, you would have a system that would do both. Automation is key to getting the most out of AI, otherwise, you are stuck with insights and no action…and who wants that in this market?) How will they put the right controls and monitoring to ensure that models are working properly and delivering results?

TIP 4: Don’t jump right in to look for technology partners UNTIL you have a clear strategy and understanding of your organizations real needs.

Most of the “battle scars” I referred to earlier stemmed from prematurely jumping into a new platform without proper internal preparation.

AI shouldn’t be something you fight with. The technology must be something you work with to really scale your efforts. Picking the right technology isn’t easy, but there is plenty of help…


Get expert help

AI gives marketers profound competitive advantages. For one, it enables predictive scoring to determine important indicators such as purchase intent, customer engagement, customer retention, and customer churn. Using cutting-edge tools like Blueshift, marketers can generate and manage results such as these in an intuitive dashboard, putting insights at their fingertips so they can quickly make profitable frontline actions.

Enabling AI-powered marketing can result in an optimized customer journey, greater efficiency, smarter decisions, increased speed, and continuous performance improvement. Implementing AI, however, can be a complex initiative. Don’t rush it. Commit to it and by looking at strategy, organization, and technology before implementing AI, marketers can ensure they get the most out of their investment and avoid being burned by AI.

Watch this rare webinar with Analysts from Forrester Research and VentureBeat hosted by Blueshift about getting the most out of your customer data with AI

Further Reading and Referenced Sources:

  1. Forrester Research, VentureBeat, and Blueshift discuss AI, customer data, and cross-channel marketing in this webinar: AI-Powered Marketing: Put Your Customer Data to Work.
  2. Two-thirds of businesses do not have skills to adopt AI” via ComputerWeekly.com. Despite the growth of artificial intelligence (AI), only a third of businesses say they have the necessary skills to adopt the technology.
  3. Gartner Report: “Customer Experience Is the New Competitive Battlefield” that discusses the evolving strategy of building better digital and offline customer experiences to better define your brand.



AI-Powered Marketing – It’s About the Data “Cupid!”

Marketing is a fertile petri dish for the use of artificial intelligence (AI).  In fact, a recent study by Forrester Research found that 46% of respondents said that marketing and sales were the leading groups inside their companies evaluating the investment and adoption in AI.  In a recent webinar with analyst Rusty Warner from Forrester Research, Stewart Rogers of VentureBeat and Vijay Chittoor of Blueshift, we discussed these and other developments in the world of AI as they relate to marketing.  This is part 1 of a 3-part series about the topics discussed.

Customer data is exploding and marketers need help

Anyone who’s been in the trenches of online marketing in the last few years has seen the explosion of data first-hand. We’ve gone from having to work with just demographic data to the plethora of online data including online browsing activity tracked through cookies, opens and clicks of emails, mobile app activities, social media engagement, intent to purchase and purchase data and so much more. There is a treasure trove of information in all this data, but marketers are overwhelmed. 85% of them are unable to extract value from their data according to a study by Econsultancy and 59% of those attending our webinar told us that they are using less than 25% of their marketing data.

Not surprising to most marketers, there is a growing rift between the amount of customer data being generated and the capacity for traditional marketing techniques –largely powered by human analysis– to process this data. Rusty Warner refers to this phenomenon as “exceeding the human cognitive capacity” because of the complexity, volume and velocity of information.

Effective use of AI can narrow this human cognitive challenge by processing these large volumes of data quickly and use machine learning to recognize patterns in the data and predict what to do next based on the past behavior of similar audiences. While this sounds pretty logical and straightforward, it’s not as easy as buying a black-box AI system and dropping it in.  

The decision to deploy an AI system to improve marketing performance is a big one that should to be given the same level of planning and preparation that was given to deploying a CRM system (your system of record) or your marketing automation system (your system of engagement). The AI powered system will become your system of intelligence that must work closely with these other systems to improve results.

In the next part of this series, we will outline Forrester’s recommendations for planning, organizing, and deploying AI for your marketing.

webinar ai powered marketing and put your customer data to work with blueshift featuring forrester and venturebeat

Making sense of AI and customer data and getting executive buy in

Making Sense of AI and Customer Data and Getting Executive Buy-In

“Brands who fail to leverage more of their customer data also fail to retain relevance and loyalty with their customers.”

Forrester, VentureBeat, and Blueshift joined together in a webinar titled “AI-Powered Marketing: Put Your Customer Data to Work” to discuss how brands can make the most of all their customer data across all marketing channels. The crux of the conversation revolved around using Artificial Intelligence to make the most of your customer data and the inherent problems and myths marketers today face when trying to cut through the hype and really make AI work for them.

webinar ai powered marketing and put your customer data to work with blueshift featuring forrester and venturebeat

In this article, I will address three hotly discussed questions that our viewers asked on the topic of AI and Customer Data. For AI, we’ve moved from a state of novelty to a state of necessity, and these questions revolve around how to get Artificial Intelligence into an organization and how long should it take. So you don’t have to read all of it, I summarized the responses from analysts at Forrester, VentureBeat, and our own co-founders.

What is the best way to “sell” the need for an AI strategy or investing in AI to the C-level?
TL;DR: To get the most use of your customer data, you need AI (there’s just too much of it) — and getting buy-in for AI is a complex process, but when approached with a solid measure for success and a “crawl, walk, run” approach, it gets very doable.

How do you differentiate Machine Learning and AI (deep learning, etc.)? A lot of content presented today has more to do with machine learning than AI, don’t you think?
TL;DR: they’re not exclusive topics…but it’s time to cut through the AI-hype-cycle, and understand what AI and Machine Learning really are and how they help you.

Once you sign the dotted line for an AI platform, how long would one allocate toward implementation and planning and executing campaigns?
TL;DR: Implementing an AI platform gets better with time and shouldn’t be a “one-click solution”, however you can start seeing results in weeks. It takes partners who will guide you through the process and help you plan/organize/execute on the data and systems you have.

Full, more thought provoking answers below…

And if you would like to watch the full webinar, you can check it out here.

What is the best way to “sell” the need for an AI strategy or investing in AI to the C-level?

Manyam Mallela, Chief AI Officer at Blueshift
Crawl, walk, run: Start with a specific use case to show results and build from there.
“The best way to get executive buy-in for newer artificial intelligence technology platforms is to show how AI improves immediate KPIs. Start by targeting and building personalization in one or two campaigns using segment-of-1 marketing, and do this without having to hire more data analysts/engineers/scientists,” says Manyam Mallela “Once a specific KPI improves, it’s easier to make the case for a wider roll out of AI in the organization.”


Rusty Warner, Principal Analyst at Forrester Research
Focus on how it will improve the customer experience in a specific way and test it.
“There are two levels to selling the need for AI into the C-level. First, start with the benefits AI will bring to the organization as a whole. Focus on what it will do to improve the customer experience (or, digital experience), then look for a specific use case so you can prove the value of the technology.” says Rusty Warner “Second, many of the barriers have been lowering as executives have begun to understand more what it takes to bring in AI and the the value it can surface.”

Before you touch your marketing platforms, understand what your KPIs are (Marketing 101) – what are you trying to accomplish and how will you measure this. Have a plan! In addition, marketers must be able to easily activate customer data quickly in their campaigns across all channels. Selecting a “single-point solution” that only solves one issue backs you into the current situation most marketers face today of a “frankenstack” of band-aided technologies with overlap in functionality and disconnection between their data sets.


How do you differentiate Machine Learning and AI (deep learning, etc.)? A lot of content presented today has more to do with machine learning than AI, don’t you think?

Vijay Chittoor, CEO of Blueshift
For marketers, it’s the difference between manual, “driver assisted” automation, and truly autonomous “self-driving” automation
“When looking at the difference in Artificial Intelligence and Machine Learning, think of the evolution of “smart cars”/driverless cars.” says Vijay Chittoor “Machine learning would be similar to “driver-assisted” cars where the machine learning is in the passenger seat. It’s helping in the driving but not actually doing any of the driving for you. For instance when backing up, you might get an alert of nearby objects from machine learning – you, as the diver, must still make the decision of what to do. Contrary to “driver assist” (machine learning), a “self-driving” car would process all the incoming data, assess the situation, and decide on the the best action to take…autonomously. This is what Artificial Intelligence is — the “self-driving car” that ingests, analyzes, and takes action on its own using predictive scoring/models that would have the best outcome.”


Stewart Rogers, Analyst of VentureBeat
It’s the difference between finding and applying a pattern, or, taking a pattern and actually improving upon it
“Machine learning is finding patterns in the data, and by finding patterns in the data, we can apply that pattern to other data. This is useful for predictive analytics, for example.” says Stewart Rogers “With Artificial Intelligence (AI), on the other hand, we have a system that takes the pattern and then makes the best version of it order to optimize or improve upon the data so you get a better result.”

Most legacy marketing platforms use a form of marketing automation that’s more driver-assisted where it’s a simple triggered response to an event being fired or an action being taken, like a yes/no logic in a workflow or a simple “if/then” logic that must be manually built for every outcome by a marketer. There’s no “thinking” involved, no decisions based on continuous learning. Sure, it’s automated, but it’s time consuming, very manual, full of list pulls and exports, and has no real intelligence behind once it is executed. You’re just applying a pattern to a problem and letting it run.

New platforms use the “self-driving” model, where artificial intelligence runs the show — essentially making the marketer smarter and able to really scale their efforts by making informed decisions at remarkable scale across channels. Modern technologies continuously learn and optimize for better performance.


Once you sign the dotted line for an AI platform, how long would one allocate toward implementation and planning and executing campaigns?

Manyam Mallela, Chief AI Officer at Blueshift

It’s not something you get by just pushing a button, however you can see value and results in weeks, not months
“We have seen over 90 percent of our clients go live with high impact campaigns in less than 4 weeks with a continuous rollout after that based on their priorities.” Manyam Mallela, Chief AI Officer at Blueshift “Blueshift’s unique architecture allows faster on boarding by not having rigid data schemas or limits on size and types of data.”

Activating and getting value out of AI doesn’t come from just a push of a button… But, at the same time, seeing value from AI shouldn’t be a year-long effort. Modern platforms built from AI help marketing and product departments initiate complex campaigns with ever-changing data quickly. And most of all, these campaigns get better. Older, bolt-on solutions introduce lag at every step, and anyone who has the battle-scars from trying to bolt AI onto their “Frankenstack” knows the struggle and countless months spent just trying to get a single campaign out.

The more an organization thinks intelligently about their customer data (from how it’s structured, to where it’s stored, and how it is used), the better prepared they are to truly put their customer data to work using AI. No need to fret though, even if your customer data isn’t all housed in a massive data lake or in one system, there are platforms that will help you unify your data, create a true Single Customer View, and then give marketers the autonomy to actually run campaigns quickly and with the most up to date data.

webinar ai powered marketing and put your customer data to work with blueshift featuring forrester and venturebeat

A.I. to reach your audience

Facebook Custom Audiences Just Got Smarter with Blueshift’s AI

In the rapidly evolving world of mobile devices, social media and the always-connected consumer, marketers want to get their messages out and amplify them quickly. Now more than ever, Lester Wunderman’s* principle of “communicating with each customer as an audience of one” takes on a new level of importance. And this means getting the right message, to the right person, at the right time on the right channel.

Facebook’s recent policy changes to prioritize content from friends over those from businesses puts even more pressure on us marketers because our content is going to be de-emphasized. This means we need to do more to get the same value from our ad impressions. And while Facebook has given us the basics of targeting in their Ad network with techniques like Custom Audiences, in today’s world, this is not enough.

Custom Audiences just got a little brighter

Custom Audiences is a targeting technique that enables you to upload a list of customers and prospects with their email addresses that Facebook then uses to find and serve your ads to them on its network of properties. Facebook can also use its data about a Custom Audience profile to find similar audiences. A nice feature that improves targeting and reach of episodic Ad campaigns of the “Wunderman” era but falls short in today’s fast changing environment. At Blueshift, we’ve taken this idea to the next level. We use our Artificial Intelligence (AI) techniques to enable you to create dynamic segments that we call Predictive Audiences and use them as Facebook Custom Audiences for your Ad campaigns on Facebook. You immediately benefit in three important ways:

  1. You get highly relevant Predictive Audience segments that are created using AI and machine learning techniques that evaluate hundreds of variables over large data sets to create highly targeted dynamic segments. You can now create ads that directly speak to these groups.
  2. Your Blueshift Predictive Audiences can be synced with your Facebook Custom Audiences in real-time ensuring that as people move through the buyer’s journey, your ads on Facebook remain relevant to the stage they are in. For example, a visitor to your site who has browsed your catalog should see different ads from a prospect that is yet to look at your catalog. But once the casual browser becomes more interested, your message should change accordingly.
  3. You can run similar multi-channel campaigns simultaneously across email, mobile, website and Facebook using the same Predictive Audience segments to drive engagement. This way you have a relevant message that reinforces your brand across channels

Facebook Custom Audiences Just Got Smarter with Blueshift’s AI. Using Blueshift's AI, brands can now optimize their RoI on Facebook, and drive 1:1 customer experiences. The latest release extends Blueshift's Cross-Channel Platform that already supports channels like Email, Mobile Push notifications, SMS and Websites.

With declining organic reach and increasing Ad prices, marketers need to keep innovating

As the Facebook network keeps growing and their policies shift towards favoring friends over organizations, business reach will continue to decline. And just as with property in Manhattan, San Francisco and Mumbai, when supply drops and demand increases, prices go up. This is clearly reflected in ad prices on Facebook. A recent study by Adstage reports that Facebook CPMs (cost of impressions) increased 171% and CPCs (cost per click) by 136% while CTRs (click through rates) remained unchanged in 2017.

Source: Adstage (https://blog.adstage.io/2017/09/18/facebook-cpms-increase-2017)

The only way for marketers to beat this trend is to out-think and out-execute the next guy. At Blueshift, we’re here to help you do both.

See Also
Blueshift is committed to being ready for the GDPR well before the May 25th timeline. This is a continuation of our previous and current efforts to handle EU data in a way that complies with the current regulations

GDPR Compliance with Blueshift

The European Union’s General Data Protection Regulation (GDPR) is slated to go into effect starting on May 25, 2018. This new regulation builds on previous EU efforts to strengthen the security and protection of the personal data of EU residents, and is billed as the “the most important change in data privacy regulation in 20 years”. If you are a EU based business, or even a global business with consumer footprint in the EU, you need to take steps to be compliant with the new regulation.

Blueshift is committed to being ready for the GDPR well before the May 25th timeline. This is a continuation of our previous and current efforts to handle EU data in a way that complies with the current regulations (including our participation in the Privacy Shield Framework).

Not only will Blueshift be ready for GDPR, we are also making tools available for you to comply with GDPR. Specifically, we will support our customers in two ways:

  • We will provide an updated Data Processing Agreement (DPA) that reflects the requirements of the GDPR and ensures compliant data transfer with storage outside the EU
  • We will offer new product capabilities to help you be compliant when your end-customers plan to exercise their rights around accessing the data and to be “forgotten”.

Understanding GDPR

Compared to previous regulations, GDPR imposes more stringent requirements on businesses. For instance, under GDPR, end-customers (“data subjects”) have the following rights:

  • Right to Access: Provide end-customers (“data subjects”) the right to review & correct their data.
  • Right to be forgotten: Enable customers the ability to request your business to erase all (or some) of their data.
  • Data Portability: Enable customers to take their own data elsewhere, by providing a copy in a commonly used and machine readable format.

A more detailed list of rights can be found here.

GDPR imposes a set of requirements on Data Controllers (i.e. entities that track or monitor EU residents and decide why and how data is collected and processed), as well as on Data Processors (entities that process data on behalf of Data Controllers).

Failure to meet the requirements can result in penalties of up to 4% of annual global turnover or €20 Million (whichever is greater). Further, the regulations apply to all companies processing the personal data of EU subjects, regardless of the company’s location.

How We Plan to be GDPR Compliant

As our customer, you are likely to be a Data Controller, and one of your requirements is to only work with compliant Data Processors.

We plan to be compliant with GDPR by taking the following measures:

  • Updated Data Processing Agreement (DPA): We plan to roll out an updated DPA for our customers, reflecting the additional requirements of GDPR
  • Secure data transfer and storage outside the EU: Transfers of personal data outside the European Economic Area (EEA) are permitted if certain safeguards are in place. Our new DPA contains the EU Model Clauses, which are industry standard for data safety. This means that Blueshift agrees to protect any data originating from the EEA in line with European data protection standards. 
  • Technical and organizational security measuresBlueshift takes a holistic approach to security, including measures built into our product as well as organizational measures. Some of the measures we take include securing your data in transit and at rest, restricting and securing data access, providing continuous incident monitoring, performing regular vulnerability testing, and conducting regular security training.  We also participate in Truste’s Privacy Certification Program.
  • Processing the data in accordance with Data Controller instructions: As has always been the case, we only process personal data according to instructions from the controller (our customers). 
  • Prompt breach notificationsIn line with our current policies, Blueshift will promptly inform you of any incidents involving your users’ personal data. 

Helping you achieve compliance

In addition to ensuring that Blueshift is compliant with GDPR, we are also rolling out new capabilities that help you achieve compliance as a Data Controller. Some of the rights available to EU Residents as Data Subjects are hard for Data Controllers to manage, due to the limitations of certain systems. Specifically, it is hard for most businesses to implement processes that ensure the right to erasure (the right to be forgotten), the right to object, and the right to restrict processing.

Our customers use Blueshift to unify their customer data (along with deriving customer insights on the data, and finally activating the data and insights through cross-channel campaigns). Because of this, we are in a unique position to help you achieve compliance with GDPR.

We plan to roll out the following enhancement in our product before May 25 that are geared towards helping you a Data Controller:

  • Deletion and automatic suppression: We are adding a /delete endpoint to our existing user API. Issuing this call for a given userId ensures that all personal data related to the userId is deleted from the index of customer data that Blueshift maintains for you, and any future data related to the userId is also suppressed from the index. As a result, the data will not make its way to any marketing action in Blueshift.

Additionally, the following existing capabilities of Blueshift enable you to comply with accessdata portability, and rectification rights:

  • CSV Export of End-Customer Data: You can use the segment export functionality in Blueshift to download user data in CSV format. Under the GDPR, EU residents have a right to access their personal data and are entitled to obtain their personal data in a commonly used format, such as a CSV file.
  • Update (Rectify) End-Customer Data using API or CSV: The GDPR also empowers individuals to correct any personal data that is deemed inaccurate or incomplete. You can rectify user data in 2 ways:
    • Identify Event:  You can fire an “Identify” event to update the data
    • CSV upload: You can also rectify the data using the user upload functionality.

Closing Thoughts

Regulations like GDPR are an important step in making sure that businesses treat customers with respect. GDPR’s guidelines around consent will force every brand to start valuing first party customer data, where the customer has explicitly opted in not only to the collection of the data, but also to the use of the data in marketing. Respectful use of customer data will be critical to delivering delightful brand experiences, and building trust with consumers.  

How recommendations help you stay relevant in content overload

How Recommendations Help You Stay Relevant in the Era of Content Overload

One thing we can guarantee about the future: we’re never going to run out of content.

Take TV for example. Where once we had a handful of channels broadcasting one program at a time, we now have multiple streaming platforms, countless cable channels, on demand, and DVRs.

Or music: You’re not limited by your carefully curated CD collection anymore. You can choose from almost any song ever recorded on Spotify.

For the content consumer, it’s an embarrassment of riches. For businesses that rely on advertising or subscription revenue, it’s a challenge.

Attention spans are shrinking. With endless options, consumers will move on in matters of seconds if what they see or hear doesn’t capture their interest.

To stay relevant in the media industry — bringing targeted audiences, charging top dollar for your ads and maintaining a healthy growing subscriber base — your content needs to be relevant.

And, of course, every consumer’s tastes are different. The key to relevance is personalization recommendations.

For example, after revamping its mobile website to deliver a personalized, Facebook-like experience, USA Today saw a 75-percent increase in time spent per article.

Recommendation Models Used By Successful Advertising and Subscription Businesses

As content executive Paul Lentz points out, publishers have been using data to target specific content at specific audiences since the print era.

In today’s digital era, a few successful media companies have developed recommendation techniques to engage and retain users with almost supernatural precision.

  • After experimenting with content-based and collaborative filtering, the New York Times settled on a best-of-both-worlds approach that models the content and adjusts it according to viewing signals from readers, models reader preferences, and uses the resulting data to make recommendations.
  • Netflix’s recommendation engine divides users up into “a couple thousand” taste groups. Netflix claims the engine is worth $1 billion a year and is responsible for more than 80 percent of the shows users choose.
  • Spotify’s Discover Weekly playlists have become a favorite feature among users for introducing them to new songs and reminding them of old favorites. The “magic” of the algorithm, the man behind the playlist says, comes from comparing your listening habits to those with similar taste and “filling in the blanks.”

What does each of these approaches have in common? Each media company leveraged a massive database of user data to make comparisons among users, identify trends in their preferences, and anticipate their behavior.

You can do the same with Blueshift’s AI-powered marketing platform. Blueshift can help you

Learn how to configure recommendations in a single click or bring your own algorithms to BlueShift Personalization Studio.

How Data AI and Automation Can Drive On-Demand Growth

How Data, AI, and Automation Can Drive On-Demand Growth

In the previous post, we outlined 5 tips for success in the on-demand economy. What each of these five tips comes down to is understanding what your users and service providers need, when they need it, and responding quickly and fluidly. In other words, anticipating what they want when they want it, and being there when they’re ready. This is how you build a community of enthusiastic, passionate users and service providers, eager to tell others about your brand.

How can you do all this? With the power of data.

We like to say that on-demand marketplaces are data-driven businesses. Every on-demand platform uses data in some way, and the successful companies are data-intensive operations.

But it’s not enough just to have the data about your customers and service providers. You need to derive useful insights from the data to provide immediate results and delight customers in real time. But with so much data coming in so quickly, our human brains can’t keep up. Artificial intelligence (AI) can.

Here are few ways successful on-demand service companies can use data and artificial intelligence to drive growth.

    1. Personalized onboarding. In the delivery business, once users sign onto a platform, 80 percent never leave. Artificial intelligence can pour through your customer data to personalize the onboarding process, ensuring customers feel cared-for even before they use your service.
    2. Personalized triggers and notifications. What users love about on-demand services is that they can find and track service providers effortlessly. AI platforms can automatically update customers and suppliers on delivery times, offer personalized choices based on where they are in their user journeys.
    3. Repeat transactions. Users who use the service again and again are the holy grail for on-demand businesses. With artificial intelligence, you can automatically target upsell and cross-sell opportunities for both buyers and service providers based on data. You can also segment them based on who is likely to re-engage or likely to churn, so you can target them with a tailored strategy.
    4. Curated content and offerings. Consumers demand relevant content, tailored to their interests and delivered to their device of choice. AI helps you personalize newsletters, web and in-app content, and recommendations based on user interests and affinities calculated from historic and up-to-the moment behavior
  1. Location-based personalization. As we said, most on-demand businesses start locally. With AI, you can segment your users based on where they are — but even more importantly, the context of why they’re there. You can provide location-specific offerings and recommendations across any channel, including the web, your mobile app, and SMS.

Explore How AI Can Transform Your On-Demand Service

On-demand businesses grow when they use data to understand the needs of their customers and service providers and connect them seamlessly. With a 360-degree view of your users and real-time insights powered by artificial intelligence, Blueshift can take the data crunching off of your hands so you can focus on strategy, growth, and retention.

To learn more about how Blueshift helps on-demand businesses grow, click here.

On-demand Economy

5 Tips for Success in the On-Demand Economy

How many times this year did you hear a business described as, “It’s like Uber, but for…”?

From groceries, to lodging, to — yes — transportation, a new crop of businesses has sprung up in recent years, providing their customers convenience at the push of a button.

And as the on-demand economy has risen, so have customer expectations that you should be able to get what you want, when you want it, with a minimum of fuss (and maybe even a bit of fun). Learn how data-driven marketing can help drive growth and sustainability in the long run.

The rise of the on-demand economy is intertwined with the sharing economy. Most on-demand service businesses aim to disrupt an existing industry by capitalizing on a ready-made workforce with a ready-made infrastructure. Uber and Lyft connect independent drivers with their own cars to passengers waiting for pickup. Postmates bridges the gap between hungry customers and the restaurants that surround them. Etsy helps artisans reach a nationwide marketplace.

The on-demand industry represents one of the best opportunities for business growth.

But some observers, such as Quartz, warn of an impending bubble collapse due to:

     Fickle customers.

     Expensive, sporadic services.

     A reliance on venture capital subsidies.

Despite these challenges, on-demand services can and have succeeded and gone on to become household names. How can you drive growth at your business?

5 Tips for Growth in the On-Demand Economy

Whether you’re starting out or trying to build momentum, here are a few tips to help your on-demand service business grow:

  1. Focus on passionate users first. One of the keys to getting an on-demand business off the ground is building up a critical mass of highly-engaged early adopters. The early adopters can be your first evangelists. If you can delight them with a seamless experience and a high-quality service, they’ll spread the word to new groups of users.
  2. Personalize customer experience. People are busy. Customers of on-demand services choose convenience over price. They expect you to know them, inform them and send them relevant information wherever they are in the lifecycle. Knowing and engaging your customers in real-time is essential to driving loyalty.
  3. Engage service providers/sellers. As an on-demand business in the sharing economy, you have to keep two very different groups of stakeholders engaged: users and service providers. Don’t neglect the service provider side of the equation. Ultimately, they’re the ones who will represent you to your customers. And whether or not they’re empowered to deliver a pleasing service can make or break your company
  4. Build a community. Etsy is the perfect example of turning a service into a community— by nurturing a two-sided marketplace. Forbes writes: “With tools to curate sellers and products, and opportunities to connect with other buyers and sellers, Etsy has turned into one of the strongest communities out there and is creating passionate users every day.”
  5. Practice location-based marketing. Most on-demand services are local. Location-based marketing strategies such as geo-fencing, running hyper-localized campaigns are key to on-demand success.

See how data, AI and automation can drive on-demand success.