Blueshift commits to supporting the Open Data Initiative

Blueshift is excited to announce support for Open Data Initiative sponsored by Microsoft, Adobe & SAP. The Open Data Initiative ensures data flows efficiently within organizations, between relevant roles and systems to build a unified single customer view that powers all customer journeys. A Single Customer View builds the foundation that powers personalized customer journeys using AI and predictive analytics.

The Open Data Initiative is based on three guiding principles:

  • Control: Organizations own and maintains complete, direct control of all their data.
  • Intelligence: Customers can enable AI-driven business processes to derive insights and intelligence from unified behavioral and operational data.
  • Open: A broad partner ecosystem should be able to easily leverage an open and extensible data model to extend the solution.

Blueshift’s mission is to empower marketers with an AI-first customer data platform to power cross-channel marketing. Customer profiles within Blueshift are updated in real-time by unifying cross-channel identities and behaviors to personalize user journeys. Blueshift’s industry leading AI powered customer journey helps marketers:

  • Audience: Determine “who to target?” with predictive audience scoring
  • Content: Compute “what to say to each user?” with ML powered recommendations
  • Cadence: And control the right timing and channel for message delivery with “when and where to message?”

Blueshift is committed to build support for Experience Data Model (XDM), the common format to exchange customer information including CRM, behavior, transactional and more between systems. Blueshift is built on noSQL technology that adapts to customer specified data models and natively supports JSON formats. With support for the Open Data Initiative, Blueshift customers will be able to import CRM, behavior & transactional data from Microsoft Dynamics, Adobe Experience Cloud & SAP, while quickly enhancing user journeys in these systems with Blueshift’s Customer Data Platform computed predictive scores and 1:1 computed personalizations.

Latest independent research commissioned by Blueshift finds Marketers see the huge potential of AI, but the data remains a challenge

Marketers see the huge potential of AI, but the data remains a challenge


Download Blueshift's report on the state of AI, Marketing, and Customer Data - lots of marketing stats

Since the advent of the personal computer, no technology has captivated businesses in the way artificial intelligence (AI) has. Its influence is expected to span industries and job functions — and marketing is right in the thick of it.  

To learn and quantify what marketers are doing with AI today, understand their challenges, and hear their aspirations, we teamed up with TechValidate (a SurveyMonkey company) to study the use of AI in marketing. We surveyed 200 marketing executives and practitioners from 198 business-to-consumer companies to understand their current experiences with AI technologies and their plans for the future. The results are in our report: Activating Customer Data for AI Powered Marketing. Here are some highlights.

The use of AI in marketing remains rudimentary, but interest is high

Though more than 80% of marketers are using some form of AI, few have deployed advanced capabilities. Today’s AI use is largely focused on audience expansion and targeting using techniques like “lookalike expansion” — finding new audiences by targeting those with traits similar to existing audiences — on the large advertising networks.

AI in Marketing

Use of AI in Marketing Today

However, only 6% of all respondents were using collaborative filtering (automated predictions about user interests) and predictive modeling techniques (forecasting outcomes) and only 16% used advanced segmentation technologies like predictive affinities on their own data to market more effectively and precisely to customers.

Advanced AI Techniques

Use of advanced AI techniques

Of all marketers surveyed, 64% want to expand their use of AI in both experimental trials as well as production campaigns within the next 12 months.

Marketer-controlled access to data is key to unlocking AI potential

We were surprised to learn that marketer-controlled advanced access to data plays an important role in the sophisticated use of AI. We found that marketers who control their own data access, without having to go through IT, activate and use more of their customer data to drive campaigns. Those who had such access and control were 1.6 times more likely to be using a majority of their customer data in their AI-driven campaigns.   

Proportion of marketers who are using most of their customer data to run AI-driven campaigns

In addition, with advanced access to more of their customer data, marketers were two to three times more likely to use sophisticated AI techniques like collaborative filtering and predictive modeling.  

Most marketers are still struggling with customer data 

Effectively using a company’s own customer data (first-party data) for AI-driven marketing campaigns is perhaps the biggest hurdle facing marketers. Almost all respondents (92%) in the study identified one or more of three factors — access, unification, or analysis — as a major challenge, resulting in a majority of marketers using less than 50 percent of their own customer data.

Top challenges hindering data use

Activation of customer data correlates with company revenue

We asked respondents about their companies’ revenue performance in the most recent fiscal year and looked at patterns in their marketing as it related to the use of AI. We were surprised to see that companies in which marketers activated and used more than 75% of their customer data were 1.4 times more likely to have exceeded revenue targets than those that had not.

Respondents that exceeded revenue goals

The results of the study are enlightening. They show that AI is inextricably tied to the activation of data, which itself is connected to several other aspects of data in an enterprise. The report’s data and insights are organized into seven key findings along with four recommendations that every business-to-consumer marketer looking to use AI should read.

Download Blueshift's report on the state of AI, Marketing, and Customer Data - lots of marketing stats

Get the full report here

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 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

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.