Four Ways AI Can Improve Revenue Operations


Four Ways AI Can Improve Revenue Operations

When I first began advising businesses some years ago, I noticed what I thought was a “data gap.” Marketing, sales, customer success, and product teams were not aligned, and they definitely weren’t sharing data across the customer lifecycle to make decisions. Granted, marketing didn’t want to spend too much engaging with prospects who would never buy, but alignment among departments tended to be sporadic and haphazard at best.

This problem, I’ve learned, is more ubiquitous than I realized at the time, and it’s too important to be labeled a “data gap.” Instead, the issue is fragmentation, and it affects organizations of every type. In too many companies, the customer lifecycle is fragmented, with strategies and tactics driven by individual departments’ goals. This results in conflicting metrics of success, inefficiencies, and often enormous missed opportunities to create and capture value.

Enter the field of revenue operations, or RevOps. RevOps is a relatively new framework that seeks to ensure that marketing, sales, and customer success teams are all rowing in the same direction. By creating alignment around the customer, RevOps also has deep implications for product management, design and strategy.  

How can a business leader make sure that RevOps doesn’t become merely a buzzword? How can a business leader implement and maintain alignment that drives actual value, profit and growth? The answer lies in a strong foundation of advanced analytics and AI.  

Here are four ways that advanced analytics and AI ensure success with RevOps:

1. Creation of common metrics – At the heart of any good analytics strategy is the measurement of important outcomes. Business leaders need to ask themselves, “What metric, if changed, provides evidence of value created?” If marketing doubles the number of prospects that it sends to sales, but sales don’t close any more deals, then marketing hasn’t created value. If the sales team closes 10% more deals, but these new customers have low lifetime value, then sales haven’t created 10% more value. RevOps requires precise and agreed-upon metrics and thus, works hand-in-hand with advanced analytics to ensure the right things are measured.  

2. Foundation of evidence – Too often, sales and marketing leaders argue over why a prospect fizzled out or a deal was lost: a lead wasn’t qualified well, the handoff was botched, or the follow-up was too slow. AI eliminates this post-hoc – and often fruitless – debate by providing a foundation of rigorous evidence of what worked, what didn’t, and where the real gaps were. By eliminating guesswork, AI leads to real fixes in real time because leaders can no longer hide behind siloed data and misaligned goals.

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Four Ways AI Can Improve Revenue Operations