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


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