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Data visualizations can help us to see things in ways we otherwise wouldn’t, and to make connections we might not see without the correlation mapped out. In a beta visualization that draws from script dialogue and other sources, Wind and Words explores, analyzes, and visualizes Game of Thrones characters’ vocabularies, direct interactions, and attitudes during those interactions. In other words, they use sentiment data.

Character sentiment data visualization is particularly interesting in the context of B2B applications of buyer intent data. Buyer intent signals can correlate company / user behavior with a likelihood of making a purchase, and the correlations are based off many data points: which topics are actively researched, which content is consumed, historical purchase and tech install information, and at which point in the buyer’s journey a user is.

In the Wind and Words beta, conceived and built by Impossible Bureau, characters’ vocabularies, instances of dialogue, and their sentiment is tracked over time, letting the user predict whether a given character will succeed or fail in the Game of Thrones.

Hint: You’ll want to use the directional arrows on your keyboard to navigate through the Wind and Words visualization, and click with your mouse.

Similarly, to predict whether a prospect will ultimately result in a win or loss, Aberdeen tracks buyer intent over time, using machine learning and natural language processing to parse 12 billion web pages and 480,000 actively researched and targeted keywords.

Whereas the Wind and Words beta tracks all the dialogue, all the din (check out the data tabs for each season; In season 1, each word averaged a 7.555 Scrabble score, there were 3,679 spoken determinative words, and there were an average of 24.5 profanities per episode!), all the vocabulary, and all the sentiment behind each spoken line (can you believe Ned Stark had only one positive sentiment the entire first season?), Aberdeen’s massive sets of buyer intent data include only the active research within actual buyer journeys, and intent signals are captured from within the noise of normal Internet activity.

This creates the most accurate way to predict which signals are coming from someone who’s in-market for a purchase.

And while Aberdeen’s machine learning-powered, intent data-capturing tech cannot accurately tell us who will win and who will die in the Game of Thrones, it can offer up to 91% accuracy in predicting purchase intent, as shown in blind tests run by clients.

Do you know which specific companies are currently in-market to buy your product?

Wouldn’t it be easier to sell to them if you already knew who they were, what they thought of you, and what they thought of your competitors?

Good news – It is now possible to know this, with up to 91% accuracy. Check out Aberdeen’s comprehensive report Demystifying B2B Purchase Intent Data to learn more.  

The post Words are Wind: Sentiment Data Visualization appeared first on Aberdeen.

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“I can give you all the information you need to take action, but if you don’t know where you’re going or what your goal is, then it’s pretty useless.”

On this week’s episode of The Intelligent Business Show podcast — your one-stop shop where business leaders, experienced practitioners, and noted experts offer their sage wisdom so you can make your business more intelligent — our host Matthew Grant discusses what goes into developing a prominent data strategy with Aaron Vidas, Founder of Strategybox. 

Aaron Vidas is the Founder of Strategybox, a Vancouver based company that uses AI & machine learning to perform marketing, sales and operations analysis for their clients. Aaron is also an advocate for mental health in entrepreneurship and speaks regularly on the topic. He holds a degree in Business & Society, specializing in History & Sociology from York University.

You can also find us on SpotifyiTunes and YouTube, or have the latest episode delivered directly to your inbox by subscribing here.

Do you know which specific companies are currently in-market to buy your product?

Wouldn’t it be easier to sell to them if you already knew who they were, what they thought of you, and what they thought of your competitors?

Good news – It is now possible to know this, with up to 91% accuracy. Check out Aberdeen’s comprehensive report Demystifying B2B Purchase Intent Data to learn more.  

The post The Intelligent Business Show: EP14 – Your Data Strategy Starts from Within appeared first on Aberdeen.

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Recent years have seen the rise of automated marketing operations, insights, and data analysis. Marketing and Sales organizations are using data from new sources in innovative ways to holistically inform and improve their performance and conversion rates.

These first- and third-party data sources are largely captured and categorized by algorithms augmented by artificial intelligence, natural-language processing, and machine learning.

And contrary to popular fears, the robots haven’t taken over yet, which highlights the reality that there have been business analysts, data scientists, and marketers making sense of these new data streams and intelligence.

In a recent article on martechadvisor.com, author Daniel Raskin predicted that real-time data analysis will become so central to the marketing function that the marketing data scientist profession will emerge and become cemented in the marketing function.

Marketing Data Scientists Abound

Some practitioners consider the marketing data scientist profession to be beyond nascent.

Michael Lock, Aberdeen’s senior vice president of research, argues that the vast majority of an organization’s data processing and analysis is done for the purpose of marketing.

Michael Lock

Whether an organization approaches data science as an art or a discipline, the output is “inextricably linked to marketing,” Lock said.

The business analyst role has existed for decades. Many organizations already structure that position within the marketing function because business analysts (who Lock figures are branding themselves as data scientists these days) are analyzing data to open up new markets, determine which products are worth developing, and predict how products will perform and which features will resonate with users.

The work business analysts and data scientists do now is “inherently marketing-driven the vast majority of the time,” Lock said.

The idea of the emerging marketing data scientist role isn’t so much a question of “is there a future in it?” he said, but instead, an argument of “how much of the present is already taken up by data science applied toward marketing purposes — and my sense is that a lot of it is.”

Data Science vs. Marketing Data Science

As for the future of the data scientist role, there will be times when the job isn’t tied into marketing.

At organizations such as manufacturers who deliver unfinished or partially finished goods to other manufacturers, their marketing efforts are not high on the list of organizational priorities. At such an outfit, data scientists will continue to focus on optimizing the product supply chain and new product development process.

At high-tech giants like Oracle, IBM, SAP, or Microsoft, the ability to identify new customers and sell to them is core to the business model, and that responsibility falls under the purview of marketing. So, the majority of their data science efforts are going to be tied to marketing.

“Those companies are gigantic marketing machines,” Lock said. “For any organization that has a significant portion of their budget tied to marketing, you can bet that a large element of data science is going to be applied to those marketing efforts.”

Smaller organizations that lack a current budget allocation for data science need not fret, because user-friendly business intelligence and analytics solutions have proliferated the market.

Rather than trying to hire an expensive data science resource, small- and mid-sized businesses can instead invest in tech that can give “citizen data scientists” the ability to search and identify correlations and buyer intent in their data, Lock said.

The Future of the Marketing Data Scientist

According to Lock, the way that data science is evolving is aligning it to be geared towards marketing, to a large extent.

“With the exception of companies that are purely operational, or almost entirely operational (where data science is going to be applied towards the optimization of supply chain management or whatever it might be), it’s going to have a high degree of relevance in marketing. The connection between the two isn’t going anywhere,” Lock said.

The future of the marketing data scientist may be nothing more than a more accurate title.

The post The Marketing Data Scientist Exists, Just not in Title appeared first on Aberdeen.

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Aberdeen research underscores that migrating to the Cloud is critical for manufacturers in their digital transformation journey to Industry 4.0 (Figure 1).

Figure 1: Benefits of Manufacturing’s Move to the Cloud

Top-performing companies understand the importance of the Cloud in supporting the digital transformation journey to Industry 4.0. Cloud-based systems or hybrid architectures (a combination of Cloud and on-premises), particularly in a geographically dispersed environment, can have the effect of unifying disparate systems and teams, thus accelerating digital transformation.

According to Aberdeen research, the Best-in-Class are migrating to the Cloud faster than All Others because they see many benefits, including those shown in Figure 1. For example, they identify faster implementation speed, better efficiency in total lifecycle costs, scalability, and easier deployment / upgrades as the top benefits pulling them to the Cloud. Ultimately, the Cloud is an important enabler for delivering “product-as-a-service.”

By getting to value faster with cloud capabilities, the Best-in-Class can capitalize on emerging industry 4.0 use cases. Because the Cloud accelerates the digital transformation process, the Best-in-Class are considering SaaS, managed services, and similar technologies as a critical part of their business strategy.

Do you know which specific companies are currently in-market to buy your product?

Wouldn’t it be easier to sell to them if you already knew who they were, what they thought of you, and what they thought of your competitors?

Good news – It is now possible to know this, with up to 91% accuracy. Check out Aberdeen’s comprehensive report Demystifying B2B Purchase Intent Data to learn more.  

The post The Cloud Accelerates Digital Transformation to Industry 4.0 appeared first on Aberdeen.

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This week’s roundup of intent data industry news and features spans market updates, using AI in B2B marketing, Google’s proposed foray into determining the intent of search-based web activity, and a sales intelligence industry report.

5 AI Approaches to B2B Marketing

In this article from the MIT Sloan Management Review, Brian Kardon explores five AI solutions transforming B2B marketing.

Kardon introduces Megan Wharton, an AI sales program who qualifies leads. He also explores the lead scoring use case for AI, which uses B2B prospects’ intent signals and stage of the buyer’s journey to determine the quality of the lead and likelihood of a purchase. He discusses automated email programs which engage inbound leads in conversation, AI’s deep levels of personalization, and intelligent content creation.

Industry News

G2 Crowd has announced a partnership with LinkedIn this week. This move will enable customers of LinkedIn Sales Navigator and G2 Crowd to combine LinkedIn’s sales intelligence solution with G2 Crowd’s buyer intent data to better connect their Sales organizations with the in-market buyers on G2 Crowd’s business marketplace.

Exploring Search Intent

This article on searchenginejournal.com explores the Google patent “Optimized web domains classification based on progressive crawling with clustering,” which contains a proposal for processing and classifying URLs via web crawlers.

When it comes to intent data sources, there’s a big difference between online search data and active research data. Exploring online search data reveals which terms are searched for most frequently. Active research data reveals which URLs users visited (regardless of what search terms they might be using).

This Google patent proposes a URL processing mechanism that crawls pages, clusters them into social, informational, navigational, and geographic clusters, and then tries to understand whether the cluster can answer the original search query.

The goal is for Google to be able to tell whether a website is an appropriate response to a search query by determining the intent of the pages a user lands on.

Sales Intelligence Software Market 2019

Report Consultant has announced the publishing of the Global Sales Intelligence Software Market 2019 report.

The report explores the sales intelligence marketspace, market dynamics, and a look at the major players within the sales intelligence software space.

Do you know which specific companies are currently in-market to buy your product?

Wouldn’t it be easier to sell to them if you already knew who they were, what they thought of you, and what they thought of your competitors?

Good news – It is now possible to know this, with up to 91% accuracy. Check out Aberdeen’s comprehensive report Demystifying B2B Purchase Intent Data to learn more.  

The post AI in B2B Marketing, Search-Intent Data: Headline Roundup appeared first on Aberdeen.

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Can we see obvious differences in the content consumption habits of people conducting buyer research in a particular space?

That’s the question we asked ourselves. To answer it, we took at look at the online content consumption activity in the ERP space undertaken by 26,265 unique users, representing 26 different countries, during the week of 1/22/19-1/29/19.

These users came from 1,620 unique user domains, visited 129 unique publishers, and consumed roughly 7,100 unique articles/pages.

As a result of our analysis, we were able to determine the top ten articles these users looked at over the course of the week:

We then looked at these users in terms of their overall profile. Specifically, we identified all those users who seemed to be actively conducting buyer research. (We do that by setting an historical baseline for each account, and then seeing which accounts have increased activity above that baseline).

As you can readily see, buyers in an active research phase gravitated to a different set of articles:

While the relative sample size is small, there are some interesting lessons to learn here, both in terms of buyer research around ERP as well as in terms of buyer research more broadly.

First of all, we notice a more concentrated focus on emerging technical innovation in the ERP space. The top six articles include two on machine learning, one on blockchain, and one on AI.

Second, we see an absence of high-level interest in specific solutions. The articles on Uber Freight and Oracle appear on the broader list but disappear from the “researching” list.

Finally, the general interest story about Chinese New Year, the most popular by a factor of two on the broad list, drops to 10th on the focused list.

A Few Takeaways

While I wouldn’t bet the farm on one quick example like this, it does confirm some of my basic assumptions about buyer research and how marketers should think about targeting active research.

  1. At the beginning of the buyer’s journey, people want to understand what’s possible. As a result, they want to know how the latest solutions are incorporating the technologies that currently have the most buzz (machine learning, blockchain, etc.).
  2. Buyer’s want to know what’s next. No one wants to make a major investment in technology only to discover they bought yesterday’s paper. This explains all the interest in coming trends and what executives can expect.
  3. The latest news is a distraction for buyers conducting research. They have practical questions to sort out, and breaking news or articles of current interest won’t generally help them do that.
Do you know which specific companies are currently in-market to buy your product?

Wouldn’t it be easier to sell to them if you already knew who they were, what they thought of you, and what they thought of your competitors?

Good news – It is now possible to know this, with up to 91% accuracy. Check out Aberdeen’s comprehensive report Demystifying B2B Purchase Intent Data to learn more.  

The post How Buyer Research Behavior Impacts Content Consumption appeared first on Aberdeen.

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A story with a happy ending is something most folks enjoy reading. A story about a tech company improving their customer acquisition metrics through the strategic use of data is a happy ending that B2B marketers and sales reps, and probably not many other folks, enjoy reading.

I saw a data-driven success story posted on SalesTechStar and wanted to share it with the readers of Aberdeen’s blog, because we can all celebrate when someone impressively boosts their performance via data strategy.

Informatica, a software development firm from California, offers cloud data management and integration solutions. According to this data-driven success story, they wanted to shake up their account-based marketing strategy by imbuing it with buyer intent data.

The decision makers at Informatica didn’t want to spin their wheels preparing to integrate intent data into Sales and Marketing operations, so they started their journey by letting new partner, Bombora, identify signals of content consumption of targeted topics and competitors’ companies. Informatica prioritized those signals for sales outreach.

That simple first step toward fueling their ABM with intent data added more than $500,000 to their pipeline, according to SalesTechStar.

Informatica didn’t stop at a weekly targeted list; the company introduced more granularity into their identified prospects, and devised four distinct categorizations for buyers who emitted signals indicating they were in-market for a purchase.

The next step was to augment their intent data sets with insights from Lattice Engines, who verified and provided the context needed to ascertain whether prospects were the right fit for Informatica’s offerings. (After all, intent data (plus context) can indicate that a B2B buyer is in need of a service or product, but if that prospective buyer isn’t in need of the kind of service or product your company offers, then they are a poor fit for your pipeline and a definitive waste of resources.)

Like the data-driven ABM pros they were becoming, folks at Informatica then used the untapped wealth of first-party data they always had — their own website traffic data. By informing their ABM strategy with information captured via tags embedded within their website, Informatica was able to further qualify and contextualize their in-market prospects.

The final step in Informatica’s journey to intent-enlightened ABM was to loop Marketing in on the successful integration of Sales and intent insights. By ultimately aligning Marketing and Sales in their evolving intent data-infused approach to engaging in-market prospects, the firm not only improved customer acquisition rates, but did so at a pace that ensured they got it right at each phase of implementation.

Do you have a similarly exciting intent data-driven success story? Let me know.

Do you know which specific companies are currently in-market to buy your product?

Wouldn’t it be easier to sell to them if you already knew who they were, what they thought of you, and what they thought of your competitors?

Good news – It is now possible to know this, with up to 91% accuracy. Check out Aberdeen’s comprehensive report Demystifying B2B Purchase Intent Data to learn more.  

The post Data-Driven Success: Informatica Ups Customer Acquisition appeared first on Aberdeen.

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Data visualizations can map out data in ways we might not notice otherwise. For example, the shape of the lifespan of news stories. They can also illustrate correlations we might not have made without seeing them depicted.

“The Lifespan of News Stories,” a mesmerizing and interactive multi-part data visualization illustrates how news enters and exits the public consciousness.

The various modules of the visualization explore news story lifespans from three perspectives:

  • geographical (where interest originates and how it spreads)
  • time in the spotlight (some stories stay in public view and some fade away)
  • by shape (the shapes of graph plots can speak to the nature of the attention a topic gets)
The Information Behind the Visualization

The visualizations reminded me of my days in SEO when I tracked the performance of stories in Google Analytics, and the term “attention economy” also made me think about the data science and methodology of tracking buyer intent signals, content consumption, and active research being conducted on the web.

(You’ll have to head over to the The Lifespan of News Stories website to view their huge, complex, interactive data visualizations.)

Though the following visualization is far less complex, the methodology is similar.

Figure 1: Average Weekly Research Activity by Topic (# of Companies)


 
This visualization depicts the average weekly research activity by topic and maps it against the number of companies in-market for a purchase. Using site-level cookies and tags, and bidstream metadata, Aberdeen tracks active research. This research is conducted by folks at some stage of a buying journey across hundreds of industries.

The folks behind the Lifespan of News Stories visualization relied on search metrics, unique search terms, the Google Trends API, and geographical context to collect and synthesize their data. Aberdeen’s algorithm that tracks active research similarly uses other contextual pieces of information to make connections. Namely, what is being researched, by individuals at which company location, and when they are doing so.

Aberdeen’s purposes, of course, are to determine which research conducted by in-market companies indicates actual intent to purchase, and the Lifespan of News Stories project sought to determine, or at least illustrate, the lifespan of online news stories.

The nature of news, of course, differs from nature of the content a B2B buyer researches before making a tech purchase — but can understanding the way news content persists or fades in the public memory lend insight into the staying power of B2B content aimed at users in various stages of their buying journeys? Let me know what you think.

Do you know which specific companies are currently in-market to buy your product?

Wouldn’t it be easier to sell to them if you already knew who they were, what they thought of you, and what they thought of your competitors?

Good news – It is now possible to know this, with up to 91% accuracy. Check out Aberdeen’s comprehensive report Demystifying B2B Purchase Intent Data to learn more.  

The post Comparing News Consumption and Active Research appeared first on Aberdeen.

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“You can’t standardize the way people operate the best.”

On this week’s episode of The Intelligent Business Show podcast — your one-stop shop where business leaders, experienced practitioners, and noted experts offer their sage wisdom so you can make your business more intelligent — our host Matthew Grant discusses how data is changing the way organizations approach talent acquisition, talent management and talent development. Our featured guest is Aberdeen’s own HCM Research Analyst, Zach Chertok.

Zach Chertok is a Research Analyst at Aberdeen, specializing in Human Capital Management (HCM). He innovates and collaborates on the development of new research areas and measures to analyze incoming data. Zach holds a B.A. in Civil Engineering and Applied Mechanics Structural Engineering and Project Management from McGill University.

You can also find us on SpotifyiTunes and YouTube, or have the latest episode delivered directly to your inbox by subscribing here.

Do you know which specific companies are currently in-market to buy your product?

Wouldn’t it be easier to sell to them if you already knew who they were, what they thought of you, and what they thought of your competitors?

Good news – It is now possible to know this, with up to 91% accuracy. Check out Aberdeen’s comprehensive report Demystifying B2B Purchase Intent Data to learn more.  

The post The Intelligent Business Show: EP13 – People Can’t Be Quantized: Data and HCM appeared first on Aberdeen.

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This week’s roundup of headlines in the intent data space explores Bill Gates predictions from 1999, a merger and acquisition, the idea of the marketing data scientist, interactive content syndication, and an emerging security and development trend that has transformative potential.

 

You Heard It from Bill Gates First

This article from entrepreneur.com reflects on 15 Bill Gates predictions made in 1999 in his book Business @ the Speed of Thought. As it turns out, two of the predictions define what we now call intent data-driven strategies.

He predicted automated promotional offers, saying “Software knows when you’ve booked a trip and uses that information to suggest activities at the local destination.”

He also predicted smart advertising: “Devices … will know your purchasing trends and will display advertisements that are tailored toward your preferences.”

Both of those activities require historical purchase information, personalization, and targeting that can only be informed by contextual information that’s captured, synthesized, and executed by automated programs or tech powered by machine learning.

Industry News

In data-driven news, DiscoverOrg has acquired ZoomInfo (Zoom Information, Inc.). You might remember DiscoverOrg from our Who’s Who in Intent Data roundup, and you may know ZoomInfo as another provider of B2B data.

Both firms offer B2B intelligence platforms for Sales, Marketing, and Recruiting organizations. Those platforms will be available via a “light” integration for clients, and the databases will be combined into one B2B intelligence platform within the next year.

Henry Schuck, DiscoverOrg’s co-founder and CEO, will helm the merged company.

Interactive Content Syndication Networks

In a demandgenreport.com article, Elise Schoening explores the emerging B2B marketing trend of investing in interactive content and content syndication networks.

Content insights provide data on buyer accounts and purchase intent, but supposedly, interactive content and content syndication networks can maximize content reach and insights.

Static content has long been relied on to generate leads, determine buyer intent and interests, and deliver targeted information. According to Schoening, interactive content can provide unprecedented levels of insights due the nature of interaction; users engage with interactive content, answering questions and essentially defining their exact intent signals.

I See Science in Your Future, Marketer

In a martechadvisor.com article, Daniel Raskin says the world of marketing has have moved on from “point in time” analytics to continuous analytics of customer activities, historical purchase and win/loss information, using machine learning for predictive intelligence, and AI in marketing strategy and operations.

He predicts that data will become so central to the marketing function that real-time customer data will be real-time applied to campaigns for real-time optimization engagements. He posits, “As customers engage with the marketing platform, the customer profile [will be] constantly updating.”

And Raskin’s grandest idea, to me at least, is the emergence and rise of the Marketing Data Scientist.

Different Kind of Intent Data for DevSecOps

This article takes a sharp departure from the usual intent-based marketing covered in this weekly roundup: Intent-based security might be the next big thing for DevOps. According to this piece on securityboulevard.com, a new model for cyber security design has emerged.

Put incredibly simply, DevOps is a development methodology that uses software development and IT operations for rapid and high-quality product and services development.

With intent-based security, instead of developers having to wait for a security team to write and define the rules for products in rapid development, they will implement security themselves, following guidelines set by the security team. The guidelines are based on the intent of the product in development.

The author hypothesizes that machine learning will eventually dynamically determine the security intent and security rules for intent-based security.

Do you know which specific companies are currently in-market to buy your product?

Wouldn’t it be easier to sell to them if you already knew who they were, what they thought of you, and what they thought of your competitors?

Good news – It is now possible to know this, with up to 91% accuracy. Check out Aberdeen’s comprehensive report Demystifying B2B Purchase Intent Data to learn more.  

The post Bill Gates Predictions About Intent Data: Headline Roundup appeared first on Aberdeen.

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