Performance marketers today face a conundrum. On the one hand, you’re tasked with creating marketing campaigns that appeal to wide—and sometimes very different—groups of prospects. On the other, those campaigns and efforts must deliver results.
Why is it a conundrum? Because a single strategy, no matter how refined and researched, cannot optimally appeal to all of your visitors. Let us make a distinction here, segmenting by its very definition means lumping people together, a segment. Personalisation is unique to the individual, so beware of anyone who tells you otherwise as they could have a hidden agenda. Perhaps they have outdated software to sell and are still tasked with marketing it.
Performance marketers long since turned to A/B testing to identify the single best solution for all visitors. While A/B testing has helped marketers better quantify the impact of their ideas, trying to find the single “one size fits all” solution to show all of your visitors leaves money on the table and wastes time and effort.
Let’s explore an example to illustrate this. Say you are responsible for optimising the conversion rate of a website and the current messaging that performs best (which we’ll call the baseline) converts 3% of your visitors. You decide you want to see if different messages can perform better, so you create two new test variations. In this example, you have three different, equally sized audience segments visiting your site: existing customers, prospective customers and competitors’ customers.
Conversion Rates for 3 Different Variations to 3 Audience Segments (with equal traffic)
| Variations | ||||
| Baseline | 1 | 2 | ||
| Audience Segments | A: Existing Customers | 3% | 2% | 1% |
| B: Prospective Customers | 2% | 4% | 1% | |
| C: Competitors’ Customers | 1% | 3% | 5% | |
| Average conversion rate | 2.0% | 3.0% | 2.3% |
Pick one to show to all: 3% conversion rate overall
Personalise to each segment: 4% conversion rate overall (33% lift)
If you could only show a single variation to every site visitor, you would select Variation 1 because it delivers the best overall conversion rate across all customers (3%). However, if you could personalise the experience for each group and show each group the variation that performs best for them, you would show Baseline to existing customers (3%), Variation 1 to prospective customers (4%) and Variation 2 to competitors’ customers (5%). This personalised approach would result in a 4% conversion rate from the same ideas and the same group of visitors — a 33% improvement in performance.
Hyper-personalisation outperforms “one size fits all” approaches.
While rules-based personalisation is better than a “one size fits all” approach, you are required to set up static rules to deliver specific experiences to predefined segments of your audience. However, AI hyper-personalisation uses machine learning to present the best experiences to each visitor to your site, and more significantly the highest return in marketing ROI that now exists.
Additionally, both A/B testing and rules-based personalisation optimise for a point in time, irrespective of how your visitors’ behaviour changes or how your marketing efforts change in the future. Hyper-personalisation software, working wholly autonomously, adjusts to changes in visitor behaviour over time, shifting traffic to your best-performing experiences. With email hyper-personalisation you have the single greatest exponent of the practice, which is send-time optimisation, delivering what people want, when they want it. Or to be specific when their data suggests is the optimum time to meet their needs and most relevance. You no longer have to wait for them to come to your site.
There are three advantages hyper-personalisation offers marketers:
– Faster Results: Hyper-personalisation begins optimising without waiting weeks or months for statistical significance, unlike A/B testing. It also allows you to test more experiences and variations at the same time than A/B testing so you can see results across more ideas sooner. With the advent of AI, it uses the data being captured in nano-seconds, the perpetually improve each customer’s product selection.
– Better Results: Hyper-personalisation outperforms “one size fits all” approaches by serving the best experiences to each user. Additionally, as visitor behaviour changes over time, hyper-personalisation adjusts accordingly to deliver the best-performing experience perpetually. A/B testing, on the other hand, picks a winner once and does not adjust again.
– Less Work: A/B testing requires your ongoing attention, monitoring experiments, deciding when to call winners, and managing potentially large matrices of separate test cells. Hyper-personalisation doesn’t need human beings with all the inherent traits of errors and omissions. It doesn’t stop for evenings, weekends or holidays. As well as significantly enhancing the ROI for your investment, it frees marketers to spend more of their time understanding prospects and developing new ideas to drive conversion.
Hyper-personalisation software is distinctly different from the top 30 vendors. It identifies the consumer’s future behaviour, and it then ranks every SKU for each consumer, by their greatest propensity to purchase from all your SKUs.
In other words, the ones they love best. CLV soars and RoR is all but eliminated. It outperforms segmenting many-fold. But the art to it isn’t choosing one over the other, the seasoned marketer runs them both in tandem, to achieve maximum effect, the effect of a 20x higher overall return, according to Forrester and McKinsey, a huge hike in profits!
Any performance marketer who has a frustratingly long backlog of ideas to test and wants to see results quickly should be investigating hyper-personalisation. A/B testing gave us a way to understand the best single experience for all visitors at one moment in time, and hyper-personalisation now gives us a way to deliver the best experiences for every visitor as visitor behaviour changes.
