Equinix CIO uses AI technology to explore potential partners for global data infrastructure companies

Multinational data infrastructure company Equinix has been leveraging machine learning technology since 2018. They embarked on an initiative to use machine learning probabilistic models to predict the likelihood of potential customers purchasing Equinix products. Since the program's inception, Equinix has grown its reliance on channel partners to accelerate customer acquisition and expansion. It is also increasing. So in 2021, Equinix took a fresh look at its leads platform and took it even further by adding a data-driven approach to sales leads. It also uses artificial intelligence to identify those partners best suited to help businesses drive new sales globally, as well as in specific regions and countries. In certain regions, sectors and industries, Equinix’s channel partners have unique advantages. , can meet customers' needs for unbiased guidance, integrated solutions and advanced services. A prime example is in the federal business sector, where it is critical to identify partners with the necessary licenses and established relationships. Milind Wagle, the company’s chief information officer, said: “Unlocking the huge potential of artificial intelligence for our Delivering tangible impact to the business is a top priority for our IT organization. A perfect example of this is building an innovative, intelligent, AI-powered opportunity engine for our channel program, which allows us to combine AI The innovative power of technology creates competitive market differentiation for the company and helps improve the experience for our customers and our channel partners."

This program is called "AI-driven partner opportunities" and it clearly Prospects best served through Equinix direct sales, indirect partners or channel sales. The program has two goals: identify partners with the greatest potential to drive new customer acquisition and prioritize those expected to generate the highest subscription value. Ted Dangson, senior director of AI strategy and analytics at Equinix "This allows Equinix to focus investments and resources on the partners best suited to co-sell and resell," said , showing how data science can help IT leverage artificial intelligence and machine learning to better target sales targets and increase revenue

The power of prediction

Ram Bala, senior principal data scientist at Equinix, introduces us His current job assignment

Bala said: "Equinix has unique needs in terms of opportunity identification and partner prioritization. More than 1,300 technology vendors and service providers globally go through a rigorous vetting process to become Equinix partners, and they have closed over 9,000 deals with Equinix in the past three years. There are many opportunities and a large number of RFPs in the United States alone, so identifying Equinix-related RFPs and co-sales partners is a must."

By applying appropriate data management, trend analysis, machine learning and business intelligence tools, Jackson said his team discovered in 2021 that Equinix was able to analyze data from channel partners and end customers to determine which Customers are best served directly through Equinix and which are best served through partners and resellers. Additionally, they are able to connect end-user needs with partner service affinity and surface insights to help all parties accelerate revenue growth

Dangson’s team works closely with Equinix partners and federal sales and marketing teams , looking for opportunities. They first looked for vendors who might have out-of-the-box solutions that could cover their use cases, but ultimately decided to work with Equinix's IT, data science and engineering teams to build a custom AI model in-house.

Bala and his team Our team of data scientists conducted extensive internal and third-party data analysis on this project to determine which data sets were critical to developing an effective partner-first data science strategy.

We leveraged Company statistical and technical data attributes related to prospects and partners, relying on historical government contract and award data from the open source federal database, in addition to our comprehensive access to text documents and PDFs that provide information about upcoming opportunities and RFPs Extensive information. We also identified historical relationships between similar customers and partners from Equinix internal data sets."

Next, the team began to build a machine learning model to use this data to:

• Develop global and national ratings and recommendations for enterprise partner priorities
• Identify government-initiated digital transformation projects related to Equinix and develop national ratings for federal agency partner priorities and agency-level ratings and recommendations
• Scientifically validate existing partners and prioritize new partners
• Identify the best end prospects through direct or indirect sales
• Re-adjust channel sales goals to allow partners to activate potential customers and launch data-driven sales efforts.

Dangson said Equinix’s partner opportunity platform uses natural language processing algorithms to extract relevant excerpts from RFP documents and attaches a relevance score for each opportunity. He noted that the algorithms also provide supporting rationale for their recommendations. Equinix's partner opportunity platform uses natural language processing algorithms to extract relevant excerpts from RFP documents and attaches a relevance score for each opportunity, Donson said. He noted that the algorithms also provided supporting justification for their recommendations. He said these additional details revolutionized how end users interpret and utilize model predictions, leading to a gradual increase in adoption and gains. An overall success, Barra said experience has shown that the project's biggest challenges have been insufficient annotation of the data and poorly labeled samples needed to train machine learning models. A lack of annotated data makes it difficult to build highly accurate and computationally efficient models to identify Equinix-related RFPs from government agencies, and inaccurately labeled samples make it difficult to train machine learning models to prioritize partners for enterprise sales

To address these issues, we use technology from various academic and corporate research institutions. We spent nearly four months developing a minimum viable product and five months developing a scalable, integrable end-to-end solution

Delivery and Innovation

After deploying the solution, Equinix reports that end users consider it a key tool that makes their jobs easier, faster, and more accurate. According to the second quarter 2023 earnings report, Equinix's channel program accounted for 40% of subscriptions and acquired almost 60% of new customers

Bala believes that in the process of driving digital transformation, through AI The key to the success of driven partner opportunities is to strike a balance between delivery and innovation

Our goal is to create an environment full of innovation and development, so that we can deliver measurable business value and maximize Return on investment goals. As we spread this culture of innovation throughout our organization, we begin to see transformative initiatives slowly gain interest and gain traction. In the process, we not only foster creativity and positively impact team morale, but we also create an environment where failure is viewed as a valuable learning experience

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