Keeping Dealspotr productive

An established product still in use

Dealspotr was an established coupon community whose merchant pages helped shoppers find and use promotions. Those pages were also a key part of our monetization strategy: they handled code reveals, tracked interactions, outbound merchant visits, and commission-bearing offers.

By this point, much of the application was nearly eight years old. It was primarily server-rendered PHP, with jQuery powering client-side interactions. Years of product decisions were embedded in templates, shared state, promotion ordering, and card-specific behavior.

I led the work to keep this system useful. I did not try to disguise it as new software. I delivered the changes the business needed without adding patches that would make later work harder.

A simple control over years of assumptions

Adding promotion filters and sorting is a representative example. The request sounded contained: let shoppers narrow the feed to coupon codes, sales, or sitewide offers, then order it by Best, Newest, or Last Used.

The feed was not a conventional list. It mixed ordinary promotions with preferred placements, reseller and newsletter codes, unverified offers, competitor deals, financing, clearance, audience discounts, and house ads. Those cards did not share one complete data shape, and their position affected copy, reveal, voting, modal, analytics, and outbound behavior.

A client-side filter could therefore hide the wrong offers, override carefully curated ranking, duplicate click behavior, or break the action that made a promotion valuable.

Finding the implicit rules

Before changing the interface, I traced the full path from server-side promotion preparation through card rendering and jQuery enhancement. There was no authoritative promotion model. Health, type, placement, commercial priority, and fallback behavior emerged across several stages.

Before

database and cache
  -> prepare merchant promotion groups
  -> interleave cards and commercial modules
  -> render several card variants
  -> attach direct jQuery interactions

After

database and cache
  -> prepare a larger candidate pool
  -> preserve existing placement rules
  -> normalize card metadata
  -> filter and sort in the browser
  -> handle interactions through stable parent elements

That investigation established three rules for the change:

• The initial feed had to retain the server's curated placement and ranking behavior.
• Both card families needed to remain the source of their visible and interaction-specific values.
• Reordering a card could not change copy, reveal, vote, modal, analytics, or outbound behavior.

With limited automated coverage around these interactions, staged exposure, analytics, and production follow-up also had to be part of the safety model.

Isolating the promotion grid

A direct patch inside the existing page would have been fastest, but it would deepen the coupling. Replacing the feed with a modern frontend would create the opposite problem: too much risk before delivering any value.

I chose a smaller change. We extracted the dense promotion grid while preserving its ordering conditions and card renderers. The server continued to decide which offers existed and how to present them. jQuery added filtering and sorting to the rendered pool.

I added shared card metadata for promotion type, discount, creation date, last-used date, sitewide status, and health. The browser could now handle each card consistently without an immediate rewrite of every legacy promotion source.

Moving cards without breaking their actions

Filtering selected and reordered elements that were already rendered. Moving those cards exposed assumptions in direct click handlers, so key behaviors moved toward delegated event handling through stable parent elements.

The first version did not solve every interaction. Follow-up work corrected click handling, filtering, display limits, and default ranking. I coordinated that hardening with the team so the untouched All + Best state continued to respect the server's curated order.

The first merge was not the end of the work. We used production behavior to find and correct assumptions in the initial implementation.

From controlled exposure to everyday use

The controls began behind weighted cohorts. We attached experiment context to analytics, added overrides for reproducible testing, and gradually increased exposure before making the experience generally available.

This limited the initial blast radius and gave us a way to observe behavior while correcting assumptions. It does not, by itself, prove a conversion lift, so I treat general availability as a delivery outcome rather than an experiment win.

Reusing the extracted grid

The extraction proved useful beyond filtering. In 2025, I reused it to collect promotion data for Offer and WebPage structured data. Existing card values could feed machine-readable output without another implementation of promotion rules.

I applied the same incremental principle at the infrastructure layer. A selective Nginx allowlist moved a defined group of merchant routes to SimplyCodes while the rest continued through Dealspotr. The team then aligned internal links and expanded the migration over time. We could move traffic deliberately without requiring an all-at-once replacement.

Keeping Dealspotr changeable

Shoppers gained a generally available way to narrow and reorder a mixed promotion feed. The longer-term result was a set of clearer boundaries around one of the page's most complicated areas:

• Placement logic was isolated from the main merchant page.
• Different card types shared a contract for client-side behavior.
• Dynamic interactions no longer depended entirely on initial DOM placement.
• Structured data could reuse processed promotion information.
• Selected traffic could migrate while the legacy application stayed online.

I do not have retained reports that support a quantified revenue, traffic, or SEO claim. The defensible outcome is simpler: an established user and monetization channel continued receiving product work without waiting for a rewrite.

Debt left on purpose

The system remained procedural PHP and jQuery. Shared state, inconsistent legacy records, card-specific fallbacks, and limited automated coverage still made changes expensive. Some production behavior required follow-up corrections, and the structured-data and redirect paths would benefit from stronger retained validation.

I stopped short of broad cleanup because unrelated refactoring would increase risk without improving the immediate outcome. The next valuable investment would be characterization coverage around representative card combinations and critical actions, followed by moving promotion normalization into an explicit server-side model.

Why I did not rewrite it

Dealspotr was old, but it still served users and supported the business. Rewriting it would have put working behavior and revenue at risk. I needed to understand and preserve that behavior before changing it.

My role was to uncover implicit rules, isolate the smallest useful part, coordinate incremental delivery, and correct assumptions exposed in production. The system stayed useful and became easier to change in the areas the business still used.