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By Michael Lock    

Among the many uses of web-based intent data is its ability to profile the buyer’s journey for B2B technology.

Consider today’s most talked about technology – AI and Machine Learning. If you were interested in deploying this technology in your organization, how would you go about that process? Would you look for analogous examples of the technology being used in organizations similar to yours? Would you plan out a resource roadmap for how you might staff up to support such an endeavor? Would you explore vendors and solution providers that you might partner with?

Aberdeen’s intent data tracks buyer behavior and content consumption across millions of websites to connect web-based search activity to discernible buying intent signals. Not only does the data provide intelligence about topics and concepts of high interest to B2B buyers, it will provide URL-level detail to demonstrate the exact content being consumed, as well as visibility into the number of devices (i.e. individuals) consuming it.

Given the current level of hype surrounding AI in today’s business and consumer environments, it would be reasonable to assume a high degree of browsing based on news-worthy human-interest stories, and Aberdeen’s data confirms this. However, limiting the data to present-day, business-relevant content, the findings reveal an interesting breakdown of topics. Measured by the number of unique device IDs interacting with content of three main types, Aberdeen’s breakdown of the top 50 most visited websites is as follows (Figure 1).

Figure 1: What Are Buyers Reading? Top AI / Machine Learning Web Activity

Each category depicted above is described in more detail below.

Vendor-related. Content that discusses vendors active in the AI / Machine Learning sector. From Amazon and IBM Watson to smaller start-ups, this includes companies actively solving business problems with AI & Machine Learning technology. Uses include: early stage vendor exploration, technology consideration.

General education. Content discussing definitions of terms and technologies within the AI / Machine Learning sector. Also includes content providing present-day news, statistics, and general information about the business landscape for AI / Machine Learning technology. Uses include: hiring and talent acquisition, skill building.

Practical use case. Content exploring different ways to utilize AI / Machine learning in a live business environment – by industry, job role, or functional area. Uses include: business problem solving, evaluating organizational fit.

In the face of these findings and the strong slant in favor of vendor-related content, one might be compelled to ask – is this what people want to read, or is this simply what’s available? It’s been said that content consumed doesn’t necessarily equal content desired, and Aberdeen’s additional research in the Analytics and Big Data space shows that there might be a greater desire for practical examples bubbling up within our workforces. According to a recent survey, more than 80% of companies see value in AI that ranges from future potential to present-day strategic priority (Figure 2).

Figure 2: A Strong Outlook for AI in the Enterprise

The buyer’s journey is not a cookie-cutter process from company to company, but there are certain elements that remain common experiences. Findings from Aberdeen’s large pool of intent data demonstrate a slant toward vendor-related content, but additional research findings validate the need for more educational content that can guide buyers along a more savvy path toward implementation.

By Michael Lock    

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