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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Although many marketers rely on simplistic or outdated attribution models, improvements are being made.

We forecast that 58.3% of US companies will use multichannel attribution for their digital marketing efforts in 2019, up from 48.6% in 2017.

Multichannel attribution assigns marketing credit to more than one marketing channel or touchpoint. It also provides more complete data on a customer’s path to purchase than does last-click attribution, which credits a purchase or conversion to the last ad clicked by a customer.

While companies can better prove the value of their marketing efforts with multichannel attribution, they have a long way to go in gaining a deeper understanding of their campaign metrics.

For many, bridging traditional media measurement methods—like marketing mix modeling with multichannel attribution—remains a challenge. Even fewer are capable of combining the two and accounting for critical nonmarketing touchpoints, such as call centers, sales teams and the in-store experience.

While there is no panacea for marketers’ attribution challenges, data scientists may be part of the solution. Marketing firms should view their data science hires as a complement to their traditional marketing analytics, not as a substitute, according to Jim Spaeth, partner at media consultancy Sequent Partners.

“Without data science, traditional marketing analysts can’t cope with big data sets,” Spaeth said. “Without traditional marketing analytics, data science overlooks decades of knowledge about consumer behavior and advertising dynamics, and then mistakes data for answers.”

Many marketers agree. In a Salesforce poll of 4,101 marketing leaders worldwide, 42% of respondents said they are using methods driven by data science to measure their marketing success. A similar percentage said they measure their marketing success with attribution modelling, which included marketing mix modelling and multitouch attribution.

“In my experience, successful brands invest a lot of time and resources in making sure that data science and marketing collaborate,” said Nancy Hua, CTO of A/B testing platform Apptimize. “Marketers want to make decisions quickly, while data scientists want to take the time to collect and analyze the results for the most accurate answer possible. At companies that really succeed in digital, there’s a healthy tension there.”

Data scientists are in demand at marketing firms, and they don’t come cheap. A Burtch Works study found that mid-level data scientists have a median salary of about $130,000.

“Building attribution solutions internally by hiring data scientists will be possible only for companies that have had success building large-scale data management and science teams, and are able to invest a lot more in the attribution solution than what they would have to pay if they go with an external vendor,” said Brian Baumgart, CEO of marketing analytics company Conversion Logic.

But that’s not to suggest that firms aren’t making room for these high-value roles. Andreas Reiffen, CEO of retail tech firm Crealytics, noted that thanks to advancements in artificial intelligence (AI), some companies are choosing to automate campaign execution. From there, budget is being reallocated to data science.

“You used to need a lot of head count to run your campaigns,” Reiffen said. “With AI, this has started to shift, because you can fully automate lots of these long-running repetitive tasks. That leads us to situations today where we need highly skilled, numbers-driven people—data scientists—who are able to perform these tasks.”

For more on how companies are shifting their internal structures and even their organizational cultures to make way for more data-driven, holistic attribution efforts, stay tuned for eMarketer’s upcoming report series on attribution.

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Many marketers have taken the AI plunge. In fact, according to a survey by Salesforce, marketers expect AI will have a substantial impact on their business in the next five years in the areas of hyper-personalization of content (61 percent), dynamic landing pages and websites (61 percent) and delivering the right message, on the right channel at the right time (61 percent). They also feel it will help increase marketers’ productivity (59 percent), campaign analytics (59 percent) and digital asset management (59 percent).However, analysts and experts warn, to crawl before you walk. Or in this case, know your data before you implement AI.

Data is a Must

Marketers’ ability to successfully inject Artificial Intelligence (AI) into their marketing programs depends heavily on their organization’s data and analytics maturity, according to Martha Mathers, practice leader for marketing and marketing technology at Gartner. “It’s pretty critical that you look at the data that you have,” Mathers told CMSWire in an interview about AI use cases for marketing. “And do you know enough about your customers to actually be able to use the tool? We can get so distracted by that next shiny object that we don’t think about the fundamentals. A lot of the marketers we talk to today are still early in their data and analytics maturity.”

So just how are marketers leveraging AI into their programs? Experts shared with CMSWire the latest trends for AI in marketing in the areas such as content, lead scoring and customer profiling.

AI Boosts Content Searchability

Mark Gross, president of Data Conversion Laboratory, said organizations are leveraging AI to better search for relevant content. For instance, if a marketer wants to discover material in a scientific journal without the manual headache, they can turn to AI engines that help surface the relevant metadata and content. “I think you have to be very specific and you have to know what problem you’re trying to solve,” Gross said of leveraging AI for these types of searches. “And you have to have enough examples that you can teach the machine literally what to do.”

The Data Conversion Laboratory released a survey last month that backed the struggle with content management. It found that most organizations (64.6 percent) cited their search capabilities needing improvement as their biggest shortcoming for content management. Another 48.6 percent cited another content shortcoming, “We have so much content that customers cannot find the correct information to help them be successful.”

AI for Personalization and Volume Gains

Gartner’s Mathers sees content production and personalization as big growth areas for the use of AI in marketing — companies pursuing personalization and trying to target and tailor messaging. He cited a company in the medical space that essentially built a capability to personalize content using AI. “They’ll take a core asset and use AI to actually accomplish some of the tailoring. AI can boost your volumes pretty quickly. Our clients have gotten excited about the ability to produce content at much higher clips than they would be if they were just relying on their content marketing team,” Mathers said.

Marketers Leveraging AI for Offer Design

Brands always want to be ahead of the game on next best offers, and AI is helping, according to Mathers, who called it “offer design.” This area, Mathers shared, has a lot of people in the experimentation phase. “We see companies in the hospitality space or telecommunication space doing some pretty cool things here. They’re suggesting different offers or next best offers.”

AI’s helping marketers create these best offers for a certain segment of their customers. That, according to Mathers, is another area where he is seeing ongoing focus and innovation, as people are able to tie real-time results and a more predictive ability to send that follow-up message.

AI’s Impact on B2B Buying Habits, Predictive Lead Scoring

Chandar Pattabhiram, chief marketing officer of Coupa, said his team leverages machine learning technologies for everything from predictive lead scoring and customer pulse across social channels to determine propensity to buy based on buyer behavior to helping orchestrate multi-channel campaigns. They also use it for predictive content that automatically offers the right asset to the right buyer at the right stage of the buying cycle. “The impact of machine learning in our marketing strategy is substantial,” Pattabhiram said. “Because we now have the ability to better understand our buyer based on real data, we are able to develop a stronger relationship and open dialogue with sales. We can focus on being more creative in our work while leveraging knowledge gathered by our technology to point us in the right direction, so we don’t waste time on projects that are destined to fail before they begin. It enables all of us to be true data-driven marketers.”

Building an ‘Ideal Customer Profile’

One of Pattabhiram’s focus today is building and refining his team’s “Ideal Customer Profile.” They leverage predictive modeling for this to enable sales and marketing to collectively target the same accounts in their Account-Based Marketing (ABM) strategies. “We call this the ‘Allbound’ approach vs. the traditional inbound and outbound approaches to demand generation,” Pattabhiram said. “We also are focused on automating the delivery of the right content to the right buyer at the right time.”

Through the “Ideal Customer project,” Coupa marketers identify the accounts with the highest propensity to buy across. For example, 5 percent of Coupa accounts may have a 6X propensity to buy based on multivariate factors that machine learning algorithms use including location, industry, average sales price, win rates, sales cycle length, buyer personas and more. “This project has also enabled us to identify sub-verticals within target industries,” Pattabhiram said.

“We have moved to an ‘Allbound’ model where sales and marketing collectively target these ICP accounts. We will also use AI-based technologies to determine customer pulse and the best time to upsell and cross sell products at the right stage of the customer journey.”

Adding the Human Touch to AI

Marketers like Pattabhiram that are fusing AI into their marketing processes are also ensuring it comes with its share of human, emotion-based logic. Without that human touch, it can be dangerous for marketers, Pattabhiram said, because you could drown in too much data. “Sometimes the efficiencies created or data identified doesn’t tell the whole story,” Pattabhiram said. “You still need to tell technology what to do and determine how to optimize based on results. Some of this can be automated, but not all of it.”

Marketing, he added, is still a storytelling function. Although machines can automate processes and analyze large data sets, they cannot create great stories and true “Authentic Interactions.” Pattabhiram called this “the other AI.” “Whether you are a B2C or B2B marketer, your buyer is still human, not a machine, and humans crave authentic understanding of their pain points and aspirations,” he said. “As modern marketers, we need to leverage machine learning as superhuman extensions of ourselves, but never forget to think and analyze what the data is telling us both from a quantitative and qualitative perspective, and then determine the right stories to tell to capture the emotional side of our buyers.”

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