Implement A/B Testing with BlipHQ: A Developer's Guide to Data-Driven Decisions

In this tutorial, we'll walk you through creating a server-side A/B testing system using BlipHQ and Python. We'll assume that necessary frontend functions for displaying variants already exist.

Step 1: Set Up BlipHQ

1. Sign up for a BlipHQ account at https://bliphq.com
2. Obtain your API key from the dashboard
3. Set up your preferred notification channels (e.g., Telegram, Discord) and nickname it "abtestbot"
4. Create a new project named "ABTestingProject"

Step 2: Implement A/B Test Variants

First, let's create a Python function to assign and retrieve variants:

import random
from flask import Flask, session, jsonify

app = Flask(__name__)
app.secret_key = 'your_secret_key'  # Set this to a secure value

def get_variant():
    variants = ['A', 'B']
    if 'ab_test_variant' not in session:
        session['ab_test_variant'] = random.choice(variants)
    return session['ab_test_variant']

@app.route('/get_variant')
def variant_endpoint():
    return jsonify({'variant': get_variant()})

Step 3: Apply Variant Changes

Now, let's create a function to determine what content to serve based on the variant:

def get_variant_content(variant):
    if variant == 'A':
        return {
            'button_color': 'blue',
            'headline': 'Welcome to our amazing service!'
        }
    else:
        return {
            'button_color': 'green',
            'headline': 'Discover the power of our platform!'
        }

@app.route('/get_content')
def content_endpoint():
    variant = get_variant()
    content = get_variant_content(variant)
    return jsonify(content)

Step 4: Track User Interactions

Let's use BlipHQ to track user interactions with our variants:

import requests

BLIPHQ_API_KEY = "YOUR_API_KEY_HERE"
PROJECT_NAME = "ABTestingProject"

def track_event(event_name, variant):
    url = "https://bliphq.com/api/push"
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {BLIPHQ_API_KEY}"
    }
    payload = {
        "message": f"A/B Test Event: {event_name}",
        "project": PROJECT_NAME,
        "nickname": "abtestbot",
        "tags": ["abtest", event_name, variant]
    }
    requests.post(url, json=payload, headers=headers)

@app.route('/track_view')
def track_view():
    variant = get_variant()
    track_event('page_view', variant)
    return '', 204

@app.route('/track_conversion', methods=['POST'])
def track_conversion():
    variant = get_variant()
    track_event('conversion', variant)
    return '', 204

Step 5: Frontend Integration

Assuming the frontend exists, here's a simple example of how it might interact with our backend:

<script>
async function loadVariant() {
    const response = await fetch('/get_content');
    const content = await response.json();
    document.getElementById('headline').textContent = content.headline;
    document.getElementById('cta-button').style.backgroundColor = content.button_color;
    
    // Track page view
    fetch('/track_view');
}

function trackConversion() {
    fetch('/track_conversion', { method: 'POST' });
}

// Load variant content when page loads
window.addEventListener('load', loadVariant);

// Attach conversion tracking to button click
document.getElementById('cta-button').addEventListener('click', trackConversion);
</script>

from scipy.stats import chi2_contingency

def calculate_significance(variant_a_conversions, variant_a_views,
                           variant_b_conversions, variant_b_views):
    contingency_table = [
        [variant_a_conversions, variant_a_views - variant_a_conversions],
        [variant_b_conversions, variant_b_views - variant_b_conversions]
    ]
    chi2, p_value, dof, expected = chi2_contingency(contingency_table)
    return p_value

# Example usage
p_value = calculate_significance(120, 1000, 150, 1000)
if p_value < 0.05:
    print("The difference is statistically significant")
else:
    print("The difference is not statistically significant")