Blueshift raises $30 million in Series C funding round to scale its SmartHub customer data platform

Marketing and customer experience (CX) are increasingly intertwined in today’s connected world, and marketers are being tasked with understanding customers through the lens of CX data to craft personalised experiences. However, traditional marketing platforms focus only on marketing response data (like clicks), and are unable to leverage CX data from across the customer journey. The first generation of customer data platforms (CDPs) attempted to solve this challenge by focusing only on data integration, but lacked any intelligent decisioning, and were not built for marketers or CX professionals.

Blueshift’s SmartHub CDP platform combines the data fidelity of a CDP with the intelligence needed for marketers to make real-time decisions. This enables them to not only unify 360-degree CX data (CDP), but also to make AI-powered decisions from large volumes of data (Smart), and distribute the decisioning to every touchpoint in the customer journey (Hub). The Blueshift SmartHub CDP platform uses patented AI technology to unify, inform, and activate the fullness of customer data across all channels and applications. Put simply, it gives brands the tools they need to deliver 1:1 experiences in real-time across the customer journey.

Blueshift’s SmartHub CDP platform has been adopted by global brands including LendingTree, Discovery Inc., Udacity and BBC, and has been shown to deliver 781% RoI in a study conducted by Forrester Research. “With the increased urgency towards digital transformation, we have seen an increased demand for a SmartHub CDP, that can not only unify silo-ed data, but also unify silo-ed experiences,” said Vijay Chittoor, co-founder and CEO of Blueshift.

Recently, Blueshift announced a $30 million Series C funding round, bringing the total amount raised to $65 million. This funding round was led by Fort Ross Ventures, along with Avatar Growth Capital. Existing investors including Softbank Ventures Asia, Storm Ventures, Conductive Ventures and Nexus Venture Partners also participated in the round.

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Most of the big data analytics we perform centers around what we expect it to be

“Why is the product unsuccessful? Did we not plan it after going through big data analytics?” asked a company executive. This is not the first company or the first time such confusion has happened. If we take a close look around, most of the data analytics we perform centers around the concept we expect them to be, leading to massive setbacks. Yes, it is true. Even though we praise big data for being the accelerator of every decision, we can’t deny the fact that it can be misleading at times.

Big data is more than just structured and unstructured data. It is seen as a base ingredient of all decision-making processes. For the past two decades, ever since mobile phones came into existence and technology evolved exponentially, It became a critical part of every business operation. Big data in business is a very common substance that executives and employees used to get insights into their market performance. Technology experts are all praises about data, with many touting it as the best thing that has happened to humankind. But the truth is a little twisted. When data is used correctly, it opens the door to double or triple-fold the revenue in minimum time. Unfortunately, it can also be misleading, draining the company’s efforts to go down the gutter. A report by Blazent, an IT intelligence company unravels many findings of big data disadvantages. It shows that around 42% of executives state that misuse of data can impair revenues and 39% said this can be deteriorating for correct decision-making. Henceforth, this article takes you through how big data is misused and what can be done to patch the gap.

Drawing an example from political endeavor

Political circle, especially, presidential elections were heavily relying on big data outcomes. Of course, curiosity didn’t let us be silent. It almost became a custom to know the result through pre-poll analysis. But if we look back at the records, they were not always right. Most recently, the 2016 election that gave Hillary Clinton a 90% chance of victory ended up making Donald Trump the President of the United States. This could either be because of a crack in the data or the data itself was faulty. The big data analytics clearly depicts the fact that human nature as of yet, cannot be reduced to a series of ones and zeros.

Moving on to big data in business and its disadvantages 

Businesses are increasingly relying on big data today. Starting from making simple decisions on marketing and promotion to big ones like where to invest and how to gain more revenues, literally, everything revolves around data. Unfortunately, business executives are unaware that technological innovation is a double-edged sword. If it is not used for good intent, It can wreak havoc.

Datasets are huge and are spread across many disparate locations and diverse forms. Henceforth, business organizations are unaware of whether the data is clean, accurate, manageable, and usable. Besides, some of the data are also manually entered into the system, prompting human errors. While such mismatched data are processed together, it leads to serious negatives and misleading outcomes. However, companies, unaware of the datasets condition take the result as everything and proceed with it.

Businesses are increasingly relying on algorithms to sort company issues. Brian Bergstein of MIT Technology Review suggests that the growing reliance on big data in business is creating a corporate bubble of overconfidence. But why are algorithms unreliable? Even though algorithms are computer-based, they have their own form of risk since they are ‘created by people and they contain interferences and assumptions coded in.’ These coded-in values shape the outputs like computer-generated predictions, recommendations, and simulations.

Finally, one of the biggest setbacks of big data analytics is people’s perceptions. While company executives have a perception on certain products or product developments, the consumers’ viewpoint might vary. But this goes unnoticed when companies focus on delivering their viewpoint to customers without addressing their concerns. Organizations design questions that they want to ask. It is solely on the executive’s perception of what clients needed to answer. They weren’t reflecting on what clients wanted to express. As a result, business takes the wrong path in the name of following big data insights.

What can be done?

Listen to customers. It is the only option to keep away misconceptions. Even though engaging with customers and having a face-to-face or virtual conversation may not be as exciting as compiling big data answers, they reflect on people’s thoughts. When we ask random questions and let them talk, they talk their hearts out and say things that might build the stairs for the organization’s success. For example, Toyota and Adobe are two such companies that go for people’s view than big data decision-making.

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AI may determine whether a company makes it to a human evaluation at all, according to Gartner’s latest study

More than 75% of venture capital (VC) and early-stage investor executive reviews will be informed using artificial intelligence (AI) and data analytics by 2025, according a recent industry study.

According to Gartner, by 2025, the AI- and data-science-equipped VC or PE investor will become commonplace. In addition, increased advanced analytics capabilities are rapidly shifting the early-stage venture investing strategy away from gut feel and qualitative decision making to a more modern platform-based quantitative process.

“Successful investors are purported to have a good ‘gut feel’ — the ability to make sound financial decisions from mostly qualitative information alongside the quantitative data provided by the technology company,” said Patrick Stakenas, senior research director at Gartner.

“However, this ‘impossible to quantify inner voice’ grown from personal experience is decreasingly playing a role in investment decision making. The traditional pitch experience will significantly shift by 2025 as VC and private equity (PE) investors turn to leveraging AI and data science insights for due diligence.”

The Gartner study also noted that information gathered from sources such as LinkedIn, PitchBook, Crunchbase and Owler, along with third-party data marketplaces, can be leveraged alongside diverse past and current investments.

“This data is increasingly being used to build sophisticated models that can better determine the viability, strategy and potential outcome of an investment in a short amount of time. Questions such as when to invest, where to invest and how much to invest are becoming almost automated,” said Stakenas.

Current AI technology is already capable of providing insights into customer desires and predicting future behaviour. Unique profiles can be built with little to no human input, which can be further developed via natural language processing AI that can determine qualities about an individual from real-time or audio recordings. 

While this technology is currently used primarily for marketing and sales purposes, by 2025, investment organisations will be leveraging it to determine which leadership teams are most likely to succeed.

“The personality traits and work patterns required for success will be quantified in the same manner that the product and its use in the market, market size and financial details are currently measured,” said Stakenas. “AI tools will be used to determine how likely a leadership team is to succeed based on employment history, field expertise and previous business success.”

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Turns out, “calling it from the gut,” may become a strategy of the past as data increasingly drives decision-making. But how will these data-driven approaches change investment teams?

In the age of digital transformation, artificial intelligence and data science are allowing companies to offer new products and services. Rather than relying on human-based intuition or instincts, these capabilities provide organizations with droves of data to make more informed business decisions.

Turns out, “calling it from the gut,” as the adage goes, may become an approach of the past as data increasingly drives investment decisions. A new Gartner report predicts that AI and data science to drive investment decisions rather than “gut feel” by mid-decade.

“Successful investors are purported to have a good ‘gut feel’—the ability to make sound financial decisions from mostly qualitative information alongside the quantitative data provided by the technology company,” said Patrick Stakenas, senior research director at Gartner in a blog post. “However, this ‘impossible to quantify inner voice’ grown from personal experience is decreasingly playing a role in investment decision making.”

Instead, AI and data analytics will inform more than three-quarters of “venture capital and early-stage investor executive reviews,” according to a Gartner report published earlier this month.

“The traditional pitch experience will significantly shift by 2025, and tech CEOs will need to face investors with AI-enabled models and simulations as traditional pitch decks and financials will be insufficient,” Stakenas said.

Alongside data science and AI, crowdsourcing will also help play a role in “advanced risk models, capital asset pricing models and advanced simulations evaluating prospective success,” per Gartner. While the company expects this data-driven approach as opposed to an intuitive approach to become the norm for investors by mid-decade, the report also notes a specific use-case highlighting using these methods.

Correlation Ventures uses information gleaned from a VC financing and outcomes database to “build a predictive data science model,” according to Gartner, allowing the fund to increase both the total number of investments and the investment process timeline “compared with traditional venture investing.”

“This data is increasingly being used to build sophisticated models that can better determine the viability, strategy and potential outcome of an investment in a short amount of time. Questions such as when to invest, where to invest and how much to invest are becoming almost automated,” Stakenas said.

A portion of the report delves into the myriad ways these shifts in investment strategy and decision making could alter the skills venture capital  companies seek and transform the traditional roles of investment managers. For example, Gartner predicts that a team of investors “familiar with analytical algorithms and data analysis” will augment investment managers.

These new investors—who are “capable of running terabytes of signals through complex models to determine whether a deal is right for them”—will apply this information to enhance “decision making for each investment opportunity,” according to the report.

The report also includes a series of recommendations for tech CEOs to develop in the next half-decade. This includes correcting or updating quantitative metrics listed on social media platforms and company websites for accuracy. Additionally, to increase a tech CEO’s “chances of making it to an in-person pitch” they should consider adapting leadership teams and ensure “online data showcases diverse management experience and unique skills,” the report said.

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Researchers say that machine learning could help guide healthcare workers’ treatment decisions for lung cancer patients after developing a model that is 71% more accurate at predicting survival expectancy of patients.

A team of Penn State Great Valley researchers conducted a study in which they developed a deep learning model that is more than 71% accurate in predicting survival expectancy of lung cancer patients, which is significantly better than traditional machine learning models that the team tested which have around a 61% accuracy rate.

Deep learning is a type of machine learning that is based on artificial neural networks, which are generally modelled on how the human brain’s own neural network functions.

Informing patient care

The team say that the information on a patient’s survival expectancy could help guide doctors and caregivers in making better decisions on using medicines, allocating resources, and determining the intensity of care for patients. The machine learning model is able to analyse vast amounts of data and can include information such as types of cancer, tumour size, the speed of tumour growth, and demographic data.

Youakim Badr, associate professor of data analytics, said: “This is a high-performance system that is highly accurate and is aimed at helping doctors make these important decisions about providing care to their patients. Of course, this tool can’t be used as a substitute for a doctor in making decisions on lung cancer treatments.”

According to the researchers this deep learning method may be uniquely suited to tackle lung cancer prognosis because the model can provide the robust analysis necessary in cancer research.

Badr said: “Deep learning is a machine-learning algorithm that makes associations between the data, itself, and the labels that we use to describe the data examples. By making these associations, it learns from the data.”

Robin Qiu, professor of information science and engineering and an affiliate of the Institute for Computational and Data Sciences added that deep learning also offers several advantages for many data science tasks, especially when confronted with data sets that have a large number of records, in this case patients, as well as a large number of features.

“In deep learning we can go deeper, which is why they call it that. In traditional machine learning, you have a simple structure of layers of neural networks. In each layer, you have a group of cells,” he said. “In deep learning, there are many layers of these cells that can be architected into a sophisticated structure to perform better feature transformation and extraction, which gives you the ability to further improve the accuracy of any model.”

In the future, the researchers would like to improve the model and test its ability to analyse other types of cancers and medical conditions.

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A deep learning algorithm could help providers predict survival expectancy in patients with lung cancer, which could help guide treatment decisions.

A deep learning tool was able to accurately predict survival expectancy in patients with lung cancer, potentially leading to more informed care decisions by providers, according to a study published in the International Journal of Medical Informatics.

The study showed that in certain conditions, the deep learning model was more than 71 percent accurate in predicting survival expectancy of lung cancer patients, compared to other machine learning models that performed with about 61 percent accuracy.

The tool can analyze a large amount of data that describe the patients and the disease to understand how a combination of factors affect lung cancer survival periods. These factors include information like types of cancer, size of tumors, speed of tumor growth, and demographic data.

“This is a high-performance system that is highly accurate and is aimed at helping doctors make these important decisions about providing care to their patients,” said Youakim Badr, associate professor of data analytics at Penn State Great Valley. “Of course, this tool can’t be used as a substitute for a doctor in making decisions on lung cancer treatments.”

Information on a patient’s survival expectancy could help guide providers and caregivers make improved decisions on using medicines, allocating resources, and determining the intensity of care for patients.

Researchers noted that deep learning techniques are uniquely suited to address lung cancer prognosis because the technology can provide the comprehensive analysis needed in cancer research.

In deep learning, developers apply a sophisticated structure of multiple layers of artificial neurons. The learning aspect of deep learning comes from how the system learns from connections between data and labels, the team noted.

“Deep learning is a machine-learning algorithm that makes associations between the data, itself, and the labels that we use to describe the data examples,” said Badr. “By making these associations, it learns from the data.”

The structure of deep learning also offers several advantages for many data science tasks, especially in cases involving large datasets.

“It improves performance tremendously. In deep learning we can go deeper, which is why they call it that. In traditional machine learning, you have a simple structure of layers of neural networks. In each layer, you have a group of cells,” said Robin G. Qiu, professor of information science and engineering and an affiliate of the Institute for Computational and Data Sciences.

“In deep learning, there are many layers of these cells that can be architected into a sophisticated structure to perform better feature transformation and extraction, which gives you the ability to further improve the accuracy of any model.”

Researchers analyzed data from the Surveillance, Epidemiology, and End Results (SEER) program. The SEER dataset is one of the biggest and most comprehensive databases on the early diagnosis information for cancer patients in the US. The program’s cancer registries cover almost 35 percent of US cancer patients.

“One of the really good things about this data is that it covers a large section of the population and it’s really diverse,” said Shreyesh Doppalapudi, a graduate-student research assistant and first author of the paper.

“Another good thing is that it covers a lot of different features, which you can use for many different purposes. This becomes very valuable, especially when using machine learning approaches.”

When the team compared several deep learning approaches to traditional machine learning models, the deep learning approaches performed much better than traditional machine learning methods.

The deep learning technique enabled researchers to find associations in the SEER dataset, which includes about 800,000 to 900,000 entries – something that would have been incredibly challenging without the assistance of AI.

“If it were only three fields I would say it would be impossible — and we had about 150 fields,” said Doppalapudi. “Understanding all of those different fields and then reading and learning from that information, would be impossible.”

Going forward, researchers will aim to improve the model and test its ability to analyze other types of cancers and medical conditions.

“The accuracy rate is good, but it’s not perfect, so part of our future work is to improve the model,” said Qiu.

Researchers also plan to connect with domain experts on specific cancers and medical conditions.

“In a lot of cases, we might not know a lot of features that should go into the model,” said Qiu. “But, by collaborating with domain experts, they could help us collect important features about patients that we might not be aware of and that would further improve the model.”

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It’s often assumed that as the complexity of an AI system increases, it becomes invariably less interpretable. But researchers have begun to challenge that notion with libraries like Facebook’s Captum, which explains decisions made by neural networks with the deep learning framework PyTorch, as well as IBM’s AI Explainability 360 toolkit and Microsoft’s InterpretML. In a bid to render AI’s decision-making even more transparent, a team hailing from Google and Stanford recently explored a machine learning model — Automated Concept-based Explanation (ACE) — that automatically extracts the “human-meaningful” visual concepts informing a model’s predictions.

As the researchers explain in a paper detailing their work, most machine learning explanation methods alter individual features (e.g., pixels, super-pixels, word-vectors) to approximate the importance of each to the target model. This is an imperfect approach, because it’s vulnerable to even the smallest shifts in the input.

By contrast, ACE identifies higher-level concepts by taking a trained classifier and a set of images within a class as input before extracting the concepts and sussing out each’s importance. Specifically, ACE segments images with multiple resolutions to capture several levels of texture, object parts, and objects before grouping similar segments as examples of the same concept and returning the most important concepts.

To test ACE’s robustness, the team tapped Google’s Inception-V3 image classifier model trained on the popular ImageNet data set and selected a subset of 100 classes out of the 1,000 classes in the data set to apply ACE. They note that the concepts flagged as important tended to followed human intuition — for instance, that a law enforcement logo was more important for detecting a police van than the asphalt on the ground. This wasn’t always so, however. In a less obvious example, the most important concept for predicting basketball images turned out to be players’ jerseys rather than the basketball. And when it came to the classification of carousels, the rides’ lights had greater sway than its seats and poles.

The researchers concede that ACE is by no means perfect — it struggles to meaningfully extract exceptionally complex or difficult concepts. But they believe the insights it provides into models’ learned correlations might promote safer use of machine learning.

“We verified the meaningfulness and coherency through human experiments and further validated that they indeed carry salient signals for prediction. [Our] method … automatically groups input features into high-level concepts; meaningful concepts that appear as coherent examples and are important for correct prediction of the images they are present in,” wrote the researchers. “The discovered concepts reveal insights into potentially surprising correlations that the model has learned.”

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