The consumer journey and what happens after purchase

The AIDA model — Attention, Interest, Desire, Action — was proposed by E. St. Elmo Lewis in 1898 to describe a salesperson's job. A century of marketing literature has elaborated it into the familiar funnel:

The funnel is useful as an aggregate model — it lets analysts measure where prospects drop out and which stage to invest in. Its limits are well-known:

• It treats the journey as linear, ignoring loops and re-entry.
• It stops at purchase, even though for subscriptions, replacements, and habit-forming categories the post-purchase phase is where the lifetime value lives.
• It is silent on motivation — it describes the stages but not what drives the transition.

Modern frames (Court et al.'s 2009 McKinsey "loyalty loop", Edelman 2010) add post-purchase stages — experience, bond, advocate — and explicit short-cuts where loyal customers skip consideration entirely on the next purchase.

Of every product in a category, the buyer seriously evaluates only a handful — the consideration set. Empirical work (Hauser & Wernerfelt, 1990; Roberts & Lattin, 1991) puts the typical size at 3 to 7 items for moderately complex purchases.

Three facts about consideration sets that shape strategy:

• They form before evaluation begins. By the time a buyer is comparing specs, the set is already locked in. The question is not "how do we win the comparison?" — it's "how do we get into the comparison at all?"
• They are formed under cognitive constraint, not exhaustive search. Buyers use heuristics: brand familiarity, top-of-search-results, recommendations from a trusted source.
• They are sticky. The same buyer's set for the same category tends to overlap across years; new entrants displace existing members rarely.

The practical implication: above-the-fold visibility (brand awareness, search ranking, recommendation presence) does not win the sale, but it controls eligibility for the sale. A product invisible at the consideration stage cannot recover at the evaluation stage.

Consumers do not search exhaustively. Two patterns dominate the literature:

• Heuristic search — for routine, low-stakes, or familiar categories. Read the first review, check the price, buy. Cost of evaluation outweighs expected gain from more search.
• Systematic search — for high-stakes, infrequent, or unfamiliar categories. Compare specs, read multiple reviews, often consult an expert or community.

The transition is not symmetric. Buyers default to heuristic; they switch to systematic only when a trigger — a high price tag, a poor first impression, a credible warning from a trusted source — makes the heuristic feel inadequate.

This matters for the journey's shape. Heuristic-mode buyers move through the funnel quickly and forgive friction less. Systematic-mode buyers spend longer in consideration and evaluation and reward depth (specifications, comparisons, evidence). A category that triggers systematic search needs different content than one that stays heuristic — and pretending otherwise wastes both kinds of investment.

Festinger's 1957 cognitive dissonance theory predicts a specific post-purchase pattern: after committing to an expensive or hard-to-reverse decision, buyers actively seek information that confirms the choice and avoid information that contradicts it.

The canonical experimental finding (Ehrlich, Guttman, Schönbach & Mills, 1957): new car owners read ads for the model they bought significantly more than ads for models they considered but rejected. The ads they read after purchase no longer informed the decision; they reduced the dissonance of having committed to it.

Observable consequences in consumer behavior:

• Asymmetric review-reading. Many product reviews are read by people who already bought, looking for confirmation.
• Defensive elaboration. Buyers post-rationalize features they did not weight pre-purchase.
• Buyer's remorse asymmetry. Returns spike most for purchases where the dissonance was unresolvable — usually high-cost decisions made under time pressure.

Dissonance theory has been heavily replicated. It also explains why the post-purchase touchpoints (welcome email, onboarding content, community access) are more than logistics — they actively reduce dissonance and lock in the choice.

Oliver's 1980 expectation-disconfirmation model is the standard account of consumer satisfaction. Satisfaction is not the absolute quality of the product — it's the relationship between perceived performance and prior expectation:

$\text{satisfaction} \;=\; f(\text{perception} - \text{expectation})$

Three regions, all empirically replicated:

• Positive disconfirmation (perception > expectation) → delight, recommendation, repeat purchase.
• Confirmation (perception ≈ expectation) → neutral satisfaction, baseline retention.
• Negative disconfirmation (perception < expectation) → dissatisfaction, complaint, churn.

The counter-intuitive consequence: a high-quality product with sky-high marketing claims can produce lower satisfaction than a lower-quality product with modest claims. Expectations are set by the seller's communication and the buyer's prior experience; perception is set at delivery. The gap determines the outcome.

For the buyer, this also explains the personal experience of disappointment in highly hyped purchases — and the surprise satisfaction of buying something unbranded and finding it adequate.

The often-cited figure — acquiring a new customer costs 5 to 7 times as much as retaining an existing one — comes from Bain & Company's 1990s work (Reichheld). The exact multiplier varies by category and the underlying paper is harder to source than its circulation suggests, but the directional finding is well-replicated:

• Acquired customer costs — paid acquisition, content, sales effort, trial discounts.
• Retained customer costs — light touch service, occasional product updates, sometimes nothing.

The compounding part matters more than the per-transaction asymmetry. A retained customer:

• Buys more often (frequency effect).
• Buys higher-priced items as trust accumulates (basket effect).
• Costs less to serve (familiarity effect — fewer support tickets).
• Refers others at near-zero marginal acquisition cost.

The modeling tool is customer lifetime value (LTV): the discounted sum of future contribution from a cohort. CAC : LTV ratios above 1:3 are the rough industry working threshold for sustainability — below, growth is unprofitable; above, retention investment usually beats acquisition investment at the margin.

Repeat purchase is not a single mechanism. Five distinct drivers, each well-documented:

Formal loyalty programs (cards, points) are a much weaker mechanism than this list suggests. Sharp and colleagues' "How Brands Grow" (2010) work argues that observed loyalty correlates more with availability and mental prominence than with any program design — and that brands grow primarily by acquiring new light buyers, not deepening loyalty in existing heavy ones.

The practical reading: design for the habit / switching-cost / network / identity mechanisms that fit the category. Treat loyalty cards as a measurement tool more than a retention lever.

The end of the loop: a satisfied customer recommends the product, which seeds a new buyer's consideration set (Step 2). When this happens at scale, the customer base does part of the acquisition work, and the cost-per-new-buyer falls.

The standard measurement tool is the Net Promoter Score (Reichheld, 2003): "how likely are you to recommend this to a friend?" on a 0–10 scale. Promoters (9–10) minus detractors (0–6) gives a single number. NPS is widely used and widely criticized — it correlates with retention but does not predict it more accurately than other simpler satisfaction measures (Keiningham et al., 2007). Treat it as a directional pulse, not a strategic dashboard.

The mechanism behind advocacy is more interesting than the measurement. Buyers refer when:

• Positive disconfirmation has occurred (satisfaction model, Step 5).
• The product is socially visible — easy to point at and recommend.
• Referring carries low identity risk — the buyer is not worried the friend's bad experience will reflect on them.

Advocacy is not engineered by a referral program — it is enabled by the conditions that make people willing to put their reputation behind a recommendation. Programs that pay for referrals can flip this: the friend now suspects the recommendation is paid, and the trust signal weakens.