Lilla Hortoványi

Strategy Without Templates

Adaptation in Digital Environments

9.7 Illustration: Learning Under Platform-Mediated Feedback

To make the mechanisms developed in this chapter more concrete, consider the case of a small e-commerce firm selling kitchen appliances on a large digital marketplace. The firm has operated profitably for three years, but in recent months its flagship product – a mid-priced coffee grinder – has experienced declining visibility and sales. The case illustrates how mediated feedback shapes organizational learning in practice.

Transformation of feedback conditions. The firm’s situation reflects a broader shift in how feedback operates under platform conditions. The account manager does not face a direct relation between action and outcome, but a mediated relation structured by the platform. What can be observed is partial, what can be explained is uncertain, and what can be controlled is indirect. This transformation provides the context within which the following mechanisms unfold.

Selective visibility: The firm’s account manager can access detailed performance dashboards showing daily impressions, click-through rates, conversion rates, and review scores. Yet this data reveals only a partial picture. The manager observes that product impressions have dropped by 40% over eight weeks but cannot observe why. The platform does not disclose the weighting logic behind ranking, nor does it reveal how competing products are prioritized or how relevance is calculated. The observed decline in visibility, therefore, appears as an outcome without a visible decision rule. What is available to the firm is not the full process but a filtered representation of it.

Causal opacity: Multiple explanations can account for the observed decline. A competitor may have introduced a lower-priced substitute. The platform may have adjusted its ranking criteria to favor faster delivery. Recent reviews may have triggered a relevance penalty. Search term weighting may have shifted. Each explanation is consistent with the observed pattern, yet none can be confirmed. The firm confronts a situation in which outcomes are visible, but the generating mechanism remains indeterminate. The problem is not the absence of data, but the inability to isolate causal structure within it.

Demand construction: The firm initially interprets the decline as a shift in customer demand. However, closer examination suggests that the composition of demand has changed. The product had previously been positioned alongside premium espresso machines, attracting quality-oriented buyers. More recently, it appears in comparison sets with lower-priced alternatives, attracting price-sensitive customers. Conversion rates decline and return rates increase. The observed demand signal is, therefore, not independent of the platform’s recommendation logic. It reflects how the product is positioned within a curated interaction environment rather than purely external market preference.

Inferential learning: In the absence of clear causal attribution, the firm shifts toward experimental adjustment. Product images are modified, titles are reformulated, prices are reduced, and advertising intensity is increased. Some changes appear to improve performance, others do not. Over repeated iterations, certain patterns stabilize – for example, images depicting the product in use within a kitchen environment consistently outperform isolated product shots. These patterns are retained not because their underlying mechanism is understood, but because they produce reliable improvements. Learning proceeds through repeated testing and pattern recognition rather than through verified causal explanation.

Knowledge brokerage and calibration: Recognizing the limits of internal interpretation, the firm engages external expertise in the form of a marketplace specialist. The specialist interprets performance indicators and translates them into actionable suggestions, such as adjusting fulfillment methods or repositioning the product within specific price bands. In parallel, the firm draws on shared observations circulating among platform participants, where informal rules emerge from aggregated experience. These inputs do not eliminate uncertainty, but they render signals more interpretable. Once such interpretations are formed, the organization shifts toward continuous calibration: adjustments to pricing, visibility settings, and product presentation are made iteratively in response to evolving signals rather than according to a fixed plan. Strategy becomes a process of ongoing alignment rather than episodic decision-making.

The case illustrates the chapter’s central claim that firms continue to learn and adapt, but not through transparent feedback loops. In digital environments, firms operate within a system in which signals are filtered, causality is obscured, and demand is partly constructed by the platform itself. Learning depends on interpretation, provisional inference, and iterative adjustment rather than on stable causal understanding. In this way, the mechanisms identified in this chapter operate jointly, translating mediated feedback into ongoing organizational adaptation without restoring full visibility or control.

Tartalomjegyzék

Kiadó: Akadémiai Kiadó

Online megjelenés éve: 2026

ISBN: 978 963 664 204 4

Gazdaság

What happens when understanding comes only after action has already begun?

Traditional strategy rests on the assumption that organizations can understand their environment before deciding how to act. Yet the conditions that once allowed organizations to rely on benchmarking, best practices, and proven strategic templates can no longer be taken for granted. Today, organizations increasingly face situations for which no clear roadmap exists. Established assumptions become less reliable, familiar reference points lose their clarity, and strategic decisions must be made before their consequences can be fully understood.

Strategy Without Templates explores how organizations learn, adapt, and navigate environments in which uncertainty is pervasive and established templates are absent or no longer sufficient. Instead of treating strategy as a process of prediction and planning, the book explores how strategic paths take shape through action, experimentation, adjustment, and learning.

A central insight in the book is that temporary solutions are often necessary. What begins as a practical response to an immediate challenge may gradually shape future possibilities in unexpected ways. Some solutions create new opportunities and sources of advantage. Others become constraints that are difficult to overcome.

Hivatkozás: https://mersz.hu/hortovanyi-strategy-without-templates//

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