In previous articles, I have explored the practical strategies for analysing user behaviour derived from website interactions and A/B testing methodologies. These techniques are particularly valuable when aiming to identify high-performing user segments and craft targeted digital experiences. However, this process often reveals unexpected insights, especially when initial testing does not yield favourable results.
Consider a scenario in which a newly introduced test design, despite its promising conceptual appeal, fails to deliver the desired impact. Quantitative results may be unequivocally negative, with every relevant key performance indicator (KPI) registering a decline. In such circumstances, the data suggest that the variation, such as Recipe B, is not simply underperforming; it has failed across the board. This presents two immediate paths forward: one may either accept the outcome, analyse the failure, learn from it, and iterate on future design efforts—or choose to segment the data to investigate underlying causes.
This latter approach can become a complex and treacherous endeavour, resembling a descent into a highly intricate and potentially misleading analytical rabbit hole. Sorry, Alice!
For example:
An initial segmentation comparing new versus returning visitors may reveal that returning users did not react as negatively as first-time users.
A further segmentation might then show that returning users accessing the site via mobile devices performed slightly better than their desktop counterparts.
Digging even deeper, one might uncover that returning mobile users interested in higher-priced products exhibited improved engagement or conversion metrics.
While these findings may appear promising, they come with a significant trade-off. After applying successive layers of filtering, the relevant audience is reduced from an initial 50% of total traffic to just 4.3%. This raises a critical question: is it worth allocating substantial resources to tailor a unique experience for such a narrow segment?
For certain brands, particularly high-end luxury retailers such as Rolls Royce or Beaverbrooks, personalised attention may be both viable and beneficial. However, for businesses operating in more commoditised sectors, such as discount pet supplies, the return on investment for micro-targeting may be far less compelling.
This same dilemma applies when designing personalisation campaigns. As I have highlighted previously, two major challenges confront marketers:
Acquiring and interpreting high-quality, granular data.
Producing and managing sufficient content to serve these differentiated segments.
Assuming these hurdles are overcome to a satisfactory degree, one must still exercise caution in the granularity of targeting. Excessive segmentation risks over-engineering the user experience, while minimal segmentation may fail to resonate at all.
Let us consider a common digital merchandising challenge: What content should be displayed in the homepage hero banner? What recommendations should populate the "We think you'll like this..." carousel? Should your virtual storefront attempt to predict and proactively suggest specific products based on past user behaviour?
For instance, one might propose that a returning visitor be presented with a Lego set—unpromoted, without a discount—on the basis of prior browsing behaviour. This raises the question: is your targeting sophistication high enough to ensure that this suggestion will genuinely resonate?
Many practitioners point to brands such as Netflix and Amazon as exemplars in personalised recommendation systems. Statements like "Because you watched Star Trek: Deep Space Nine" provide a transparent rationale for the curated content that follows. These systems succeed because they offer breadth—providing up to 42 scrollable options—ensuring that even if the primary recommendation does not engage, several others might.
This model can be effectively emulated in retail contexts. For instance, a virtual toy store could present a curated range of 42 Lego models and invite user exploration. Such an approach is far more engaging than presenting a single product, especially when stock limitations may lead to user frustration if the highlighted item is unavailable.
A broader tactic, such as "Would you like to explore our Lego collection?" may prompt greater engagement than asserting, "We believe you want this specific model." The former understands more about user independence and improves interaction metrics (which are often paramount KPIs in digital strategy, right?).
Compare the following messages:
“Welcome to our toy shop! These are our favourite toys!”
“We think you’re interested in construction toys.”
The first message represents a generic push strategy, commonly found in homepage banners that prioritise brand objectives over user intent ('we' are more important than 'you' - the long-standing tension between marketing and customer needs). The second, although still broad, reflects an attempt to connect with presumed customer interests. In this context, even minimal targeting is advantageous—and likely more effective than overly narrow personalisation.
Let us assume predictive modelling yields:
A 71% likelihood a visitor is interested in Lego products.
A 23% likelihood they are seeking Lego Technic.
A 7% probability they want the Lego Technic Excavator
Rather than presenting the most specific item, a better approach would be to guide users toward the Lego Technic category and enable autonomous navigation from that point. This ensures relevancy while allowing for user discovery and choice. Move users forwards one step down the funnel (or the rabbit hole), not two. Maximise your chances of being correct, and let users navigate from there.
While the hypothetical website may sell any range of products, the Lego example is a globally recognisable, visually compelling use case. Naturally, any product category could be substituted in this framework. If I'd been analysing your browsing history, I might have tailored this piece to your specific interests.
Until next time!
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