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JJ Kardwell is a featured contributor to Predictive Analytics Deconstructed: A Super-Simple Recipe for Marketing Success report.

Here's the complete contribution:

Recently, we've seen a number of significant transactions in the B2B data and predictive marketing space: DiscoverOrg acquired RainKing, ZoomInfo was acquired by private equity firm Great Hill Partners, and ESW Capital acquired Infer. This spate of M&A is not unexpected – it’s a harbinger of things to come. Quite simply, companies in the predictive analytics space need to be more data-centric and offer more assurances that their view of prospects is accurate.

Predictive analytics and applied artificial intelligence promises to make marketing and sales better, smarter, faster and, most importantly, more personalized. The technology is moving from easing execution to now supporting go-to-market decision making. So sales and marketing teams will be looking for not only “predictive” and “AI-assisted” capabilities but also looking for tech providers to help them seamlessly execute strategy across their go-to-market teams.

Insights that power the selection of target accounts are also valuable for demand generation, sales development and sales team members to help them craft relevant, personalized outreach needed for emails, calls and meetings (saving hours of prep).

The most important element is that the data that goes into anything predictive or AI-driven is accurate, in-depth and up to date. That’s hard to do, and companies that get it right will win. Successful companies have to be able to execute at all levels of the data, data science, and application stack. Success requires both big-picture vision and disciplined execution. Companies must build the application, design with intuitive UI/UX, harness a torrent of current and robust data, and have automated data science so that it stays that way, while also designing and managing a big data architecture to deliver it all at scale. Without all four, predictive companies and applications won’t be successful.

The final piece is agility: Look for more intuitive, self-serve platforms, plugged into or replacing legacy elements of the tech stack, and seamlessly integrated. Customers will demand this — they can’t afford to wait weeks or months for tech teams or providers to crunch the answers. They need to be able to move quickly and see immediately verifiable results, and the pace will continue to accelerate because of the intelligence and learning loop that AI makes possible.

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