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We’ve seen how AI can replicate and automate intelligent human analysis at scale, in our last blog.
Let’s start with results. A 2017 eMarketer report shows that marketing leaders apply AI to marketing’s sweet-spots digital transformations: personalisation (54%); customer understanding (54%); identifying and reorganising customers across channels (48%); and targeting and acquiring prospects (41%).
AI is delivering. Companies report: greater marketing efficiency and effectiveness (86%); better data analysis and new insights (79%); boosted creativity (74%); and improved decision-making (71%). With metrics like these, it’s hardly surprising that Gartner predicts global AI spending to jump from $450 million in 2016 to over $28 billion by 2021
“Potential is one thing, but how can the modern marketeer apply it ─ and what does it deliver?”
Technological convergence means that companies can now start doing much more powerful marketing: From lead management and scoring, to insight gathering and customer database management, marketers can harness the power of technology to deliver more powerful messages to their customers.
Lead scoring can now be driven by machine learning, with campaigns now delivering up to four times the pipeline than accounts nominated using traditional processes.
We’re seeing other practical applications for customers: a healthcare customer using AI to optimise messaging saw a 200% increase in click-through rates. AI can automate and standardise processes that were once “activity sinks”, allowing marketers to focus on what they do best.
The lessons for the smart marketing manager are to: start small; get the data right; match the right technology to the right challenge (initially where intelligent automation can make a big difference); test and measure; and enhance and report.
Finally ─ and critically ─ find the right technology partner. AI needs data from, and has the power to transform, the whole business ─ so scalable, flexible and interoperable solutions will always bring the best returns.