Type: Reflective Essay (Kolb’s Experiential Learning Cycle) | Subject: Business | Level: Undergraduate | Word Count: ~1600 words
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Using Kolb’s (1984) Experiential Learning Cycle, write a 1,600-word reflective essay on a single episode from your placement or internship, working through concrete experience, reflective observation, abstract conceptualisation, and active experimentation.
This essay applies Kolb’s (1984) four-stage Experiential Learning Cycle – concrete experience, reflective observation, abstract conceptualisation, and active experimentation – to a specific episode from my ten-week marketing internship with a mid-sized UK homeware retailer. The cycle was chosen because, unlike more emotion-led models, it foregrounds the movement from a specific event through generalised theory back into changed future behaviour, which suits a placement built around discrete, repeatable tasks such as campaign planning, and because Kolb (1984) explicitly frames learning as a continuous cycle rather than a single, closed episode, which fits how I returned to and refined this same skill repeatedly across the placement. The company and individuals involved have been anonymised in line with my university’s placement confidentiality guidance, and figures have been rounded to protect commercially sensitive detail while preserving the substance of what happened.
In week six of my internship, I was given lead responsibility, under supervision, for a paid social media campaign promoting a new autumn homeware range across Instagram and Facebook, with a budget of £800 and a two-week run. Having researched the brand’s previous campaigns over the preceding fortnight as part of my induction, I chose to centre the creative around lifestyle imagery of the products in styled room settings, believing this matched the aspirational tone the brand had used successfully before and that had, in my reading of the case studies, generated the strongest engagement historically. I set the campaign live with broad UK-wide targeting on adults aged 25 to 54, reasoning that homeware purchasing was not a strongly age-specific behaviour and that a wider audience would maximise reach for the budget available, a decision I made largely independently before showing my supervisor the final settings for sign-off. After the first four days, the click-through rate was running at roughly a third of the account’s typical benchmark, and cost-per-click was correspondingly high, which meant the campaign was on track to under-deliver against its sales target well before the two weeks were up, and my supervisor flagged the dashboard figures to me during our regular Thursday check-in. We paused the campaign together, examined the analytics dashboard in detail, and identified that engagement was concentrated overwhelmingly among women aged 35 to 54, with almost no meaningful engagement from the younger and male segments I had included in the targeting, a pattern that became obvious only once we filtered the data by age and gender rather than looking at the headline figures alone. This was the first campaign of the internship where I had been given genuine ownership rather than a supporting role drafting captions or scheduling posts under close direction, which is part of why the moment stayed with me; it was also the first time a decision I had made independently was shown, in real time and with real numbers, to have been the wrong call.
Looking back on the decision-making that led to the underperforming targeting, I notice that I treated the previous campaigns I had researched as evidence for creative direction but did not actually examine the audience-level performance data sitting behind them, even though that data was available to me in the same reporting dashboard I later used to diagnose the problem; I had, in effect, looked at the images and headlines of past campaigns without opening the analytics tab that sat one click away. I think I assumed, without fully testing the assumption, that broader targeting was inherently safer than narrower targeting, because it felt lower-risk to reach more people rather than fewer, and because narrowing the audience felt, at the time, like a decision that could be criticised more easily if it turned out wrong. In hindsight, this reflects a slightly cautious, risk-averse instinct on my part as someone new to spending a real client budget, when the more analytically sound approach would have been to accept the higher apparent risk of narrower targeting because it was actually better supported by the evidence already sitting in front of me, rather than avoiding it purely to protect myself from visible responsibility for a bold call. I also notice that I did not think to run even a very small, low-budget test of two or three audience segments before committing the full budget to one broad segment, despite this being a standard practice I had read about during my second-year digital marketing module but not yet internalised as something to apply myself, rather than simply recognise and describe correctly in an exam answer. A further, related observation is that I did not ask my supervisor to review the targeting settings before launch, even though sign-off was available to me had I sought it out; I treated the setup stage as purely mine to own, when in practice a two-minute conversation before going live would have caught the same issue at almost no cost in time or budget.
This experience connects clearly to established digital marketing theory on audience segmentation and testing. Segmentation theory holds that campaigns perform better when messaging and targeting are matched closely to a defined segment’s needs and behaviours, rather than spread thinly across a heterogeneous broad audience, because relevance drives engagement and conversion more reliably than raw reach alone (Kotler and Armstrong, 2021). The underperformance I observed is consistent with this: by targeting a broad age and gender range, I diluted the campaign’s relevance to the narrower group, women aged 35 to 54, who the historic data suggested were actually the strongest converters for this product category, effectively spending a large share of the budget reaching people who were statistically unlikely to respond. The episode also illustrates the value of structured testing in digital campaign management; Chaffey and Ellis-Chadwick (2022) argue that even small-scale, low-budget test phases before full launch allow marketers to validate targeting and creative assumptions against real data rather than informed guesswork, substantially reducing the risk of the kind of underperformance I encountered, and Ryan (2021) makes a similar point specifically in relation to paid social campaigns, where audience settings can be adjusted quickly and cheaply once early signal is available. Kolb’s (1984) own theory is itself relevant here at a meta level: my initial approach relied on what he would term abstract conceptualisation drawn from past cases I had merely observed, without having yet had the concrete, hands-on experience needed to test and correct that understanding, which is precisely why the cycle emphasises that learning is only complete once experience and concept have each informed the other, rather than treating either one alone as sufficient preparation for practice. Wright and Ashill’s (2019) work on decision-making biases among early-career marketers adds a further layer, arguing that inexperienced practitioners often over-weight recent, vivid examples, such as the single successful past campaign I had focused on, over the fuller, less memorable dataset that would give a more reliable picture; this closely mirrors my own reliance on a handful of remembered case studies rather than the complete performance history sitting in the dashboard.
Following this episode, my supervisor and I relaunched the remaining budget with narrower targeting focused on women aged 35 to 54 in the regions where the retailer had physical stores, and the revised campaign’s click-through rate recovered to slightly above the account’s historic benchmark within three days, which supported the conclusion that the original targeting, not the creative itself, had been the primary problem, since the same images and copy performed well once shown to a better-matched audience. For the remainder of my internship, I adopted a personal rule of building a small test phase, typically 10 to 15 percent of any campaign budget, into every new campaign plan I proposed, and I began routinely reviewing audience-level performance data from previous campaigns before finalising targeting decisions rather than relying on creative impressions or headline reach figures alone. I also started keeping a short weekly log of campaign decisions and their outcomes, partly so that patterns like this one would become visible to me sooner rather than being noticed only once a supervisor pointed them out. Looking ahead to my final-year dissertation and future employment, I intend to apply this same test-then-scale principle more broadly, treating marketing assumptions, including my own, as hypotheses to be checked against data rather than conclusions to be trusted by default, and I plan to seek out further practical experience with campaign analytics tools such as Meta Ads Manager and Google Analytics so that this becomes a comfortable, default working habit rather than a lesson I only apply when specifically prompted to by a supervisor’s intervention. I have since raised this episode, briefly, in a peer supervision session with two other placement students, partly to normalise talking about mistakes openly rather than only presenting polished successes, and partly because one of them was about to run their own first paid campaign and found the specific example of testing before scaling more useful than the general advice we had both been given in lectures.
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