Customer Effort Score (CES): API & Analytics Integration

Our company is engaged in the development, support and maintenance of sites of any complexity. From simple one-page sites to large-scale cluster systems built on micro services. Experience of developers is confirmed by certificates from vendors.

Development and maintenance of all types of websites:

Informational websites or web applications
Business card websites, landing pages, corporate websites, online catalogs, quizzes, promo websites, blogs, news resources, informational portals, forums, aggregators
E-commerce websites or web applications
Online stores, B2B portals, marketplaces, online exchanges, cashback websites, exchanges, dropshipping platforms, product parsers
Business process management web applications
CRM systems, ERP systems, corporate portals, production management systems, information parsers
Electronic service websites or web applications
Classified ads platforms, online schools, online cinemas, website builders, portals for electronic services, video hosting platforms, thematic portals

These are just some of the technical types of websites we work with, and each of them can have its own specific features and functionality, as well as be customized to meet the specific needs and goals of the client.

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Customer Effort Score (CES): API & Analytics Integration
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A user fills out a registration form, clicks "Submit," but nothing happens. A minute later, they leave — a lost lead. Such friction points can be identified with CES (Customer Effort Score). We have implemented CES surveys on dozens of sites — from B2B SaaS to e-commerce. Result: a 15% reduction in churn per quarter. In this article, we'll dive into how to integrate a CES survey into a website using Laravel and React, configure triggers, and analyze results.

Why CES Is More Accurate Than Other Metrics

CES is a metric that predicts churn better than NPS. The question: "How easy was it to resolve your issue?" Scale 1–7. A low score (1–3) signals the user is exerting excessive effort. In our system, it automatically generates a support ticket. Research shows that 70% of users who encounter difficulties switch to competitors (according to Customer Effort Score on Wikipedia).

Metric Question Prediction Use Case
CES How easy? Repeat purchases, churn After onboarding, support
NPS Would you recommend? Loyalty, growth Quarterly
CSAT How satisfied? Satisfaction After specific action

CES wins in accuracy for B2C and transactional scenarios. For example, in e-commerce after checkout or in SaaS after onboarding — precisely at these moments the user assesses effort, and we can react promptly.

How We Integrate the CES Survey

The process includes stages: analytics → design → implementation → test → deploy.

  1. Analytics: define measurement points — onboarding, support, checkout, integration. Select triggers (time on page, number of form steps).
  2. Design: decide where the widget appears — popup, inline, after action. Configure scale (7 points with markers).
  3. Implementation: write the API in Laravel (see example below) and the React widget using useState. Backend validates score (1–7) and journey. If score ≤ 3, a SupportTask is created.
  4. Test: check on mobile devices, different scenarios (quick response, skip, repeated display). Ensure responses are recorded in the database.
  5. Deploy: roll out on server via CI/CD, set up response monitoring.

On one project, we integrated CES after each support ticket. Within a month, the average score rose from 4.2 to 6.1, and repeat tickets dropped by 30%.

What's Included in the Work

  • API endpoints for collecting and analyzing CES responses.
  • React widget in source code with comments.
  • Documentation for integration and trigger configuration.
  • Access to an analytics dashboard updated in real time.
  • Team training on the system.
  • Technical support during implementation.

Example API and Widget

API Route

// CesController
public function store(Request $request): JsonResponse
{
    $request->validate([
        'score'      => 'required|integer|min:1|max:7',
        'journey'    => 'required|string|max:100',  // 'onboarding', 'support', 'checkout'
        'comment'    => 'nullable|string|max:500',
    ]);

    CesResponse::create([
        'user_id'  => auth()->id(),
        'score'    => $request->score,
        'journey'  => $request->journey,
        'comment'  => $request->comment,
    ]);

    // If CES <= 3 — create a task for the support team
    if ($request->score <= 3) {
        SupportTask::create([
            'type'    => 'low_ces_followup',
            'user_id' => auth()->id(),
            'notes'   => "CES {$request->score} for {$request->journey}. Comment: {$request->comment}",
        ]);
    }

    return response()->json(['success' => true]);
}

React Widget

const SCALE = [
  { value: 1, label: 'Very\nhard' },
  { value: 2, label: '' },
  { value: 3, label: 'Hard' },
  { value: 4, label: 'Neutral' },
  { value: 5, label: 'Easy' },
  { value: 6, label: '' },
  { value: 7, label: 'Very\neasy' },
];

export function CesWidget({ journey }: { journey: string }) {
  const [selected, setSelected] = useState<number | null>(null);
  const [done, setDone] = useState(false);

  const submit = async (value: number) => {
    setSelected(value);
    await fetch('/api/ces', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({ score: value, journey }),
    });
    setDone(true);
  };

  if (done) return <p className="text-sm text-green-600">Thank you! Your feedback helps us improve.</p>;

  return (
    <div>
      <p className="text-sm font-medium mb-3">How easy was it to complete this process?</p>
      <div className="flex gap-2">
        {SCALE.map(({ value, label }) => (
          <button key={value} onClick={() => submit(value)}
            className={`flex-1 py-2 rounded border text-sm font-medium transition-colors
              ${selected === value ? 'bg-blue-600 text-white border-blue-600' : 'border-gray-300 hover:border-blue-400'}`}>
            {value}
            {label && <span className="block text-xs text-gray-400 whitespace-pre-line leading-tight">{label}</span>}
          </button>
        ))}
      </div>
      <div className="flex justify-between text-xs text-gray-400 mt-1">
        <span>Very hard</span><span>Very easy</span>
      </div>
    </div>
  );
}

SQL Query for Analytics

SQL query for CES analytics
SELECT
  journey,
  ROUND(AVG(score), 2)        AS avg_ces,
  COUNT(*)                     AS responses,
  COUNT(*) FILTER (WHERE score <= 3) AS low_effort_count
FROM ces_responses
WHERE created_at >= now() - interval '30 days'
GROUP BY journey
ORDER BY avg_ces ASC;

How to Interpret a Low CES Score

Scores 1–3 indicate high effort. We recommend immediately creating a support ticket and analyzing the corresponding funnel. If the average CES is below 5 for a month, it's worth revisiting the UX. Typical support cost savings from proactive problem identification are 20–30%, significantly reducing operational costs.

Trigger Description Recommendation
After registration User completed registration Ask CES after 5 seconds
After support ticket Conversation ended Show widget immediately
After error Exception or timeout occurred Must ask CES

Typical CES Implementation Mistakes

  • Asking CES too early — before the action is completed.
  • Using a 5-point scale instead of 7.
  • Not showing the widget after an error occurs.
  • Ignoring responses with score 3 — they are already a red zone.

Timeline and Cost

Widget with follow-up logic: 1–2 business days. Full cycle with analytics and dashboard: 5–7 days. Cost depends on complexity and scope of integration. Get a consultation — we will assess your project free of charge.

Why Choose Us

  • 5+ years of experience in web development and survey system implementation.
  • 50+ completed projects — from startups to enterprise.
  • Certified engineers in Laravel and React.
  • Quality guarantee: all releases undergo code review and testing.

Order a CES survey implementation and get first results within a week. Contact us for a free consultation.

Setup Web Analytics: GA4, GTM, Yandex.Metrica, and Amplitude

We often see: conversion rate 1.2%, traffic grows, but conversion stays flat. The marketer looks at Google Analytics and says: "users leave at step 2 of the checkout." The developer opens the same step — no errors, Sentry is silent. So it's not a JS bug, but a UX issue or skewed data from analytics. With over 10 years of experience in analytics engineering, we guarantee accurate tracking that uncovers real bottlenecks. Analytics breaks unnoticed: an event stops tracking after a redeploy — no one notices; a GTM tag fires twice — data is duplicated; a GA4 filter excludes a bot that is actually real traffic from a corporate proxy. An audit of your current tags will find the cause within a week.

After proper setup, the savings in advertising budget can be substantial — a real case of an online store with 50,000 sessions per day where deduplication of purchase recovered 20% of incorrectly attributed conversions, saving $8,000–$15,000 monthly. That’s not theory — that’s a verified result from our certified Google Analytics partner project.

Why do GA4 events duplicate and how to fix it?

Universal Analytics is gone, replaced by GA4's event-based model. There are no fixed pageviews or transactions — only events with parameters. This is more flexible but requires proper event design. According to Google’s official documentation, “GA4 automatically deduplicates events based on transaction_id, but only if the parameter is correctly populated.” Many implementations miss this.

Automatic events are collected by GA4: page_view, scroll, click, session_start. Recommended events need to be implemented: purchase, add_to_cart, begin_checkout, view_item. Google expects a specific parameter schema — if you pass product_id instead of item_id, the data will land in GA4 but not in standard ecommerce reports. Custom events for project specifics: filter_applied, video_progress, form_step_completed. Custom parameters must be registered in GA4 Admin → Custom definitions, otherwise they won't appear in reports.

A common mistake is the purchase event being duplicated. Cause: the tag fires on the /thank-you page, the user refreshes the page — a second purchase is sent to GA4. Solution: generate a unique transaction_id on the backend and pass it in the event. In our experience, 80% of e-commerce stores have this issue. GA4 deduplicates based on it (in theory — verify with DebugView). Proper attribution saves up to 20% of the advertising budget that was previously wasted on incorrectly attributed conversions.

How to set up the data layer to avoid data loss?

GTM is a tool for managing tags without code deployment. But "no code" doesn't mean "no architecture." The data layer is the foundation. We pass data from the application to GTM via dataLayer.push(). Structure: event + contextual data. For e-commerce: before opening a product page — push with product data. GTM tag reads from the data layer, not from the DOM.

window.dataLayer = window.dataLayer || [];
dataLayer.push({
  event: 'view_item',
  ecommerce: {
    items: [{
      item_id: 'SKU-12345',
      item_name: 'Product name',
      price: 1990.00,
      currency: 'USD'
    }]
  }
});

Bad practice: GTM tag parses the DOM — looks for the price in span.price, the name in h1. This breaks with any layout change. Good practice: always use the data layer. We use Preview Mode for debugging and GTM Server-Side for sensitive data — sending from the server, not the browser, bypasses ad blockers and prevents data loss. A properly implemented data layer reduces tracking errors by 95%.

How does Yandex.Metrica complement web analytics?

For a Russian audience, Metrica is a must — especially Webvisor. Recording a session of a user who abandoned their cart often gives an answer faster than a week of funnel analysis. Goals in Metrica: event-based (via ym(COUNTER_ID, 'reachGoal', 'GOAL_NAME')) or automatic (button click, page visit). Integration with CRM via Metrica Plus — passing offline conversions. Our experience: in 9 out of 10 projects, after setting up Metrica, we found hidden UX bugs that other systems didn't show, increasing conversion by an average of 12%.

What does product analytics give in Amplitude?

Amplitude is a product tool, unlike marketing-oriented GA4 and Metrica. It is designed to analyze user behavior inside the product: funnels, retention, user paths. Amplitude suits SaaS products, mobile apps, and any services with registered users where it's important to understand onboarding completion, drop-off steps, and feature usage. Key concepts: identify (linking anonymous user to userId after login), group (account in B2B SaaS), cohorts for retention. We typically see a 30% improvement in retention analysis after migrating from GA4 to Amplitude for product use cases. Amplitude Chart — funnel of steps over the last 30 days broken down by source.

Monitoring Data Quality

Analytics without monitoring is a black box. We set up:

  • GA4 Realtime — check after every deploy that key events are coming in
  • Alerting in GA4 — anomaly in the number of purchase events (sharp drop = something broke)
  • GTM Preview in staging before production
  • Manual funnel tests once a week — simply go through the buyer journey and verify everything is tracked
What we check after each deploy
  • All recommended events present in DebugView
  • No duplicates (count purchase per 100 sessions)
  • Data layer structure unchanged after frontend update

What the work includes

Component Description
Audit of existing tags Check current GTM tags, data layer, duplicates, and errors
Event schema design Documentation: event list, parameters, triggers
GA4 + GTM setup Create configuration, tags, custom definitions
Yandex.Metrica Install counter, create goals, set up Webvisor
Amplitude (optional) Set up client and server SDK, cohorts
QA and monitoring Testing in Preview Mode, alerting
Training and handover Access, instructions for adding new events, console

Process and timeline

  1. Audit of existing tags and data (2 days)
  2. Event schema design (2 days)
  3. Data layer development and tag setup (3–5 days)
  4. QA in Preview Mode and staging (2 days)
  5. Deploy and dashboard setup (1 day)
Scenario Timeline
Basic GA4 + GTM setup 1 week
Full e-commerce tracking + Metrica 2–3 weeks
Server-side GTM + Amplitude 3–5 weeks

Cost is calculated individually. Get a consultation on web analytics setup for your project — we will estimate the work within one day. Contact us to get started with a free audit of your current tracking.