Case Study: SLT

Using SLTrib.com as a Case Study for A/B Testing

Using the SLTrib.com as a Case Study for A/B Testing

Overview

SLTrib.com is serving as a case study for implementing A/B testing directly within a newsroom environment. Our goal is to assess how different design and content strategies impact user engagement. By segmenting users into control and experiment groups, we can measure the effectiveness of changes to our website.

User Segmentation

We are segmenting users using a client-side approach that ensures a consistent experience across visits. Our method assigns each user to either a control or experiment group based on their browser cookies and a randomization function. This segmentation allows us to test variations in design, layout, and content delivery.

Key Metrics for Analysis

After implementing A/B tests, we will track and analyze the following metrics:

• Clicks: How users interact with elements such as headlines, buttons, and links.

• Newsletter Subscriptions: Whether the experiment group results in more newsletter sign-ups.

• Donations: The impact of content changes on reader contributions.

Implementing Your Own A/B Testing with JavaScript

Below is a generic implementation of our segmentation logic that newsrooms can copy and modify for their own testing needs and code stacks. This script assigns users to either a control or experiment group and caches their assignment to ensure a consistent experience.

import React, { useState, useEffect } from 'react';
import PropTypes from 'prop-types';


function getBucket() {
  const SEGMENT_KEY = '__user_segment';
  const storedSegment = localStorage.getItem(SEGMENT_KEY);
  if (storedSegment !== null) {
    return storedSegment;
  }
  try {
    const bucket = Math.floor(
      (document.cookie
        .split('; ')
        .find(cookie => cookie.includes('user_id='))
        ?.split('=')[1]
        ?.split('')
        ?.map(char => char.charCodeAt(0))
        ?.reduce((acc, curr) => acc + curr, 0) * Math.random()) % 2
    );


    const group = bucket === 0 ? 'control' : 'experiment';
    localStorage.setItem(SEGMENT_KEY, group);
    return group;
  } catch (e) {
    console.warn('Segmentation error:', e);
  }


  localStorage.setItem(SEGMENT_KEY, 'control');
  return 'control';
}


const ABTestWrapper = ({ children }) => {
  const [bucket, setBucket] = useState('default');


  useEffect(() => {
    setBucket(getBucket());
  }, []);


  return bucket === 'control' ? children[0] : children[1];
};


ABTestWrapper.propTypes = {
  children: PropTypes.arrayOf(PropTypes.node).isRequired,
};


export default ABTestWrapper;

How to Use This Code

• Replace user_id in the getBucket function with an appropriate cookie or identifier for your site.

• Modify the children components passed to ABTestWrapper to include your control and experimental elements.

• Analyze user interactions based on your test variations.

This approach provides a lightweight, reusable A/B testing framework that can be easily integrated into any website or application. By combining segmentation with analytics tracking, organizations can make data-driven decisions to optimize user experience and engagement.