A complete guide
Why Advanced Machine Learning Matters in Personalization
Machine Learning (ML) is no longer a technology limited to big players like Netflix, Facebook, Tesla, etc. Today it’s available and increasingly adopted by many marketers and merchandisers.
But the need for advanced ML has become more apparent with growing digital targets, heightening competition, discerning shoppers, and (since Covid), a sudden interest from the C-suite in all things digital.
To exceed digital targets and meet the expectations of consumers and internal stakeholders, marketers and merchandisers need the ability to deliver 1-to-1 personalized experiences at scale. A recent report from McKinsey found that companies that capture more value from personalization grow faster. Those with above-average revenue growth see 40% more revenue from personalized marketing actions or tactics.
1-to-1 personalization is key to succeeding in today’s digital landscape, but without advanced ML, 1-to-1 personalization is not possible.
We know the power of personalization; marketers have seen a steady flow of stats streaming in from analysts and solutions for years. For a glimpse at the impact at a more granular and tangible level, Monetate’s research shows that customers exposed to three or more personalized pages convert at two times the rate. Similarly, visitors exposed to 1-to-1 personalization (even just once) converted 110% more than those not exposed to Monetate ML. That is huge.
The goal is clear: to personalize as many elements of the customer journey as possible to every visitor. However, it is impossible without advanced ML as you can’t scale. With an ML solution with fully integrated testing & personalization capabilities, you can scale on two axes.
