---
title: Challenge
slug: hol-aep/challenge
description: Learn how to use flow visualization to gain valuable insights into customer journeys across various channels. This document provides step-by-step instructions on creating and configuring dimensions to analyze customer behavior. Discover how mixing and mat
docTags: 
createdAt: 2023-10-26T15:01:08.076Z
---

## Guidelines:

- Maximum Team Size is 5.
- Data available for the Lab is limited to Retail industry. Date range for the data is between 12 Nov 2023 - 20 Jan 2024. 
- Sample use-cases have been shared below. However, you are free to define your own use-cases. Feel free to explore the metrics/dimensions that you can leverage to refine your insights.
- Each team will get 2 mins to present their dashboard.

## Evaluation Criteria:

- **Data Usage**: Assessing how effectively you leverage diverse datasets, ensuring comprehensive utilization of available data sources to uncover nuanced customer behaviors.
- **Visualization**: Evaluating the clarity, creativity, and effectiveness of data visualization techniques employed by participants, emphasizing intuitive and engaging representations that enhance understanding of complex customer journey dynamics.
- **Insights**: Analyzing the depth and relevance of insights generated from data analysis, focusing on actionable findings that offer strategic guidance for optimizing customer experiences and driving business growth.

## Sample Use-Cases:

As an analyst, you have been assigned below tasks:

### Example 1: Online Process Optimization

**Description**: In the journey we covered previously, user called the call centre while making the purchase. This is leading to increased number of calls to the customer care and hence increase in operational costs. You need identify the pain areas of the order process and provide the insights to optimize it.

:::hint{type="info"}
**Hint**: Analyze the impact of online application form steps. You can find the website pages under **`pageName`**Dimension.
:::

### Example 2: Product Performance Evaluation: 

Evaluating the performance of product categories across different channels that can provide insights into customer satisfaction, profitability, and areas for product improvement or innovation.

:::hint{type="info"}
**Hint**: 

1. Analyze the product category **`Retail_Product_Category_ProductID`** to evaluate the cross-channel performance by **`total_orders`** / **`total_revenue`.**
2. Evaluate performance by **`Interaction channel`**. Apply attribution to see the channel impact of for different attribution models.
3. Evaluate brand loyalty by identifying overlap between multiple products.
:::

### Example 3: Cross-Sell/Up-Sell

**Description**: As an analyst, I need to identify the cross-channel experiences/interactions that can lead to successful customer cross-sell/up-sell.

:::hint{type="info"}
**Hint**: 

1. Analyze the pageName **`purchase:thank you`** pathing journey to identify next products **`Retail_Product_Category_ProductID`** for Cross-Sell/Up-Sell.
2. Analyze the **`Retail_Product_Category_ProductID`** to find which 2 products are brought together by creating . You can create filters by dragging dimension and holding the shift key while dropping on panel filter area.
3. Analyze the overlap between different product categories to find cross-sell opportunity. 
:::

### a: Cross-Selling and Up-Selling Opportunities

Analyze customer cross-channel data to uncover opportunities for cross-selling and upselling additional products, increasing revenue per customer.

### b: Customer Segmentation: 

Analyze usage data from various channels such as online, mobile apps, and in-person interactions can help segment customers based on their financial needs, behaviours, and preferences, allowing for personalized product offerings and targeted marketing campaigns.

### Example 4: Marketing Optimization 

**Description**: In the previous use-case, you have have identifed the cross-sell product/segment. As a next step:

:::hint{type="info"}
**Hint**: 

1. Analyze the **`Markerting Channel`**  for performance.
2. Analyze the performance of marketing delivery method **`campaign_delivery_method`**.
3. Identify top campaigns **`Retail_Fashion_Campaign`** leading to **`total_orders/total_revenue.`**
4. Leverage **`Attribution`** to find the impact.
5. Perform the cohort table analysis to identify retention and point of diminising return.
:::

### a: Campaign Analysis 

You need to provide the insights to marketing team for running the campaigns effeciently by analysing the historical data.

### b: Campaign Monitoring 

Based on your insights, marketing has ran several campaigns across various delivery channels.You need to provide a dashboard to monitor the performance of the campaigns.

### Example 5: Customer Retention: 

Analyze data from various channels to identify at-risk customers based on their interactions and behaviors, allowing for targeted retention efforts and proactive measures to prevent customer churn.

:::hint{type="info"}
**Hint**: Analyze the **`emails_clicked`** against users who made a purchase **`total_orders`**.
:::