python customer journey visualization

How to create a Python tool for customer journey mapping in retail

Customer journey mapping helps retailers understand their customers' experiences and identify areas for improvement. A Python tool can analyze customer data, visualize journeys, and provide insights to enhance marketing strategies and operational efficiency.

Key Benefits:

• Improved Customer Experience: Identify pain points and opportunities to streamline the customer journey, reducing churn and increasing loyalty.
• Data-Driven Marketing : Develop targeted campaigns based on customer behavior, preferences, and motivations.
• Increased Operational Efficiency: Optimize processes and supply chains by pinpointing inefficiencies and areas for cost reduction.

Core Components:

• Data Collection: Gather customer data from various sources like surveys, transactions, website analytics, and feedback.
• Data Preparation: Clean, transform, and merge data using Python libraries like Pandas and NumPy .
• Data Modeling: Structure customer interactions, touchpoints, and behavior patterns into a data model.
• Visualization: Create Sankey diagrams and network graphs to visualize customer journeys and identify patterns.
• Analysis: Use algorithms like PageRank and community detection to gain deeper insights into customer pathways and clusters.

Deployment Strategies:

By leveraging Python's data analysis and visualization capabilities, retailers can create a powerful customer journey mapping tool to drive business growth and customer satisfaction.

Requirements for Building a Python Mapping Tool

To create a Python tool for customer journey mapping in retail, you'll need to possess certain technical skills, knowledge, and tools. Here's a breakdown of the requirements:

Technical Skills

You should have:

• Python programming skills : A solid understanding of Python programming concepts, including data structures, object-oriented programming, and file input/output operations.
• Data analysis and visualization skills : Familiarity with popular Python libraries for data analysis and visualization, such as Pandas, NumPy, Matplotlib , and Seaborn .
• Data manipulation and cleaning skills : Knowledge of how to work with datasets, perform data cleaning, and handle missing values.
• Customer journey mapping concepts : Understanding of the principles of customer journey mapping, including customer personas, touchpoints, and pain points.
• Retail industry knowledge : Familiarity with the retail industry, including customer behavior, sales funnel, and marketing strategies.

You'll need:

By possessing these technical skills, knowledge, and tools, you'll be well-equipped to create a Python tool for customer journey mapping in retail that meets your business needs.

Collecting and Preparing Customer Data

To create a customer journey map, you need to gather and prepare customer data. This section will guide you through the process of collecting relevant data from various sources and preparing it for analysis using Python libraries like Pandas and NumPy.

Data Sources

You can collect customer data from the following sources:

Data Preparation

Once you've collected the data, you need to prepare it for analysis. This involves:

• Data cleaning : Handling missing values, outliers, and duplicates.
• Data transformation : Converting data types , aggregating data, and creating new variables.
• Data merging : Combining data from different sources into a single dataset.

Python Libraries for Data Preparation

You can use the following Python libraries for data preparation:

By using these libraries, you can efficiently prepare your customer data for analysis and create a comprehensive customer journey map.

Next, we'll explore how to create a data model for customer journeys using Python.

Creating a Data Model for Customer Journeys

Creating a data model for customer journeys is a crucial step in building a Python tool for customer journey mapping in retail. This model provides a structured framework for organizing and analyzing customer data, allowing you to identify patterns, trends, and insights that inform your customer journey map.

Selecting the Right Data Model

When selecting a data model, consider the following factors:

• Customer interactions : Identify the various touchpoints where customers interact with your brand.
• Customer behavior : Analyze customer behavior, including purchase history and browsing patterns.
• Customer feedback : Collect customer feedback from various sources.

Structuring Your Data Model

A well-structured data model should include the following components:

Visualizing Customer Journeys

Once you have a structured data model, you can use visualization tools to create a customer journey map. Sankey diagrams and network graphs are effective visualization techniques for illustrating customer journeys.

By creating a data model and visualizing customer journeys, you can gain a deeper understanding of your customers' needs, preferences, and pain points, ultimately informing data-driven decisions to improve the customer experience.

In the next section, we'll explore how to visualize customer journeys using Python libraries like Matplotlib and Seaborn.

Visualizing Customer Journeys with Python

Visualizing customer journeys is a crucial step in understanding customer behavior and identifying areas for improvement. In this section, we'll explore how to create informative visualizations using Python libraries like Plotly, Matplotlib, and NetworkX.

Creating a Sankey Diagram with Plotly

Sankey diagrams are an effective way to visualize customer journeys, highlighting the flow of customers through different touchpoints and identifying areas of high engagement and drop-off points. To create a Sankey diagram using Plotly, follow these steps:

1. Import necessary libraries : import plotly.graph_objects as go 2. Define nodes and links : nodes = [...]; links = [...] 3. Create the Sankey diagram : fig = go.Figure(data=[go.Sankey(node = nodes, link = links)]) 4. Customize the diagram : fig.update_layout(title_text="Customer Journey Sankey Diagram")

Creating a Network Graph with NetworkX

Network graphs are another useful visualization technique for illustrating customer journeys. To create a network graph using NetworkX, follow these steps:

1. Import necessary libraries : import networkx as nx; import matplotlib.pyplot as plt 2. Define nodes and edges : G = nx.DiGraph(); G.add_nodes_from([...]); G.add_edges_from([...]) 3. Visualize the graph : pos = nx.spring_layout(G); nx.draw(G, pos, with_labels=True); plt.show()

By using these visualization techniques, you can gain a deeper understanding of your customers' needs, preferences, and pain points, ultimately informing data-driven decisions to improve the customer experience.

In the next section, we'll explore how to analyze customer interactions and pathways using Python.

Analyzing Customer Interactions and Pathways

Analyzing customer interactions and pathways helps you understand customer behavior and identify areas for improvement. By using NetworkX algorithms, you can gain deeper insights into customer journeys and optimize the customer experience.

Calculating PageRank

PageRank measures the importance of each node (touchpoint) in the customer journey. This helps you identify areas of high engagement and drop-off points. To calculate PageRank using NetworkX, follow these steps:

1. Import necessary libraries : import networkx as nx 2. Create a directed graph : G = nx.DiGraph() 3. Add nodes and edges : G.add_nodes_from([...]); G.add_edges_from([...]) 4. Calculate PageRank : pr = nx.pagerank(G)

Here's a sample output:

Community Detection

Community detection helps identify clusters of customers with similar behavior. This enables you to tailor your marketing strategies and improve customer engagement. To perform community detection using NetworkX, follow these steps:

1. Import necessary libraries : import networkx as nx; import community 2. Create a directed graph : G = nx.DiGraph() 3. Add nodes and edges : G.add_nodes_from([...]); G.add_edges_from([...]) 4. Perform community detection : partition = community.best_partition(G)

By applying these NetworkX algorithms, you can gain a deeper understanding of customer interactions and pathways, ultimately informing data-driven decisions to improve the customer experience.

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Improving and testing your mapping tool.

To ensure your Python tool for customer journey mapping in retail is accurate, efficient, and effective, it's essential to focus on improving and testing it. This section will cover best practices for refining your tool, optimizing code efficiency, enhancing visualization impact, and ensuring data accuracy.

Optimizing Code Efficiency

Optimize your code to improve the performance of your mapping tool. Here are some tips:

Enhancing Visualization Impact

Effective visualization is critical to communicate insights from customer journey data. To enhance visualization impact, follow these guidelines:

Ensuring Data Accuracy

Data accuracy is paramount to ensure reliable insights from customer journey data. To ensure data accuracy, follow these best practices:

By following these guidelines, you can refine your Python tool for customer journey mapping in retail, ensuring it provides accurate, actionable insights to inform data-driven decisions.

Deploying the Customer Journey Mapping Tool

When deploying the customer journey mapping tool in a retail environment, consider the following strategies to ensure a successful deployment:

Scalability

To handle increasing amounts of data and user traffic, consider deploying your tool on a cloud-based infrastructure. This will ensure that your tool remains responsive and efficient.

User Adoption

To ensure user adoption, provide adequate training and support to your retail team. Develop a comprehensive training program that covers the tool's features and best practices.

Maintenance

Regular maintenance is critical to ensure the tool remains stable, secure, and up-to-date. Schedule regular updates, backups, and security checks to prevent data breaches and system downtime.

On-Premise vs. Cloud Deployment

When deciding between on-premise and cloud deployment, consider the following factors:

By considering these strategies, you can ensure a successful deployment of your customer journey mapping tool and provide your retail team with a powerful tool to drive business growth and customer satisfaction.

Using Mapping Insights for Retail Strategy

By analyzing customer journey maps, retailers can make informed decisions, enhance customer experience, and guide marketing strategies. Here are some practical ways to use mapping insights for retail strategy:

Identifying Pain Points and Opportunities

Customer journey mapping helps identify areas of friction in the customer experience. Retailers can prioritize improvements to streamline the customer journey, reducing churn and increasing loyalty.

Informing Marketing Strategies

Customer journey mapping provides insights into customer behavior, preferences, and motivations. Retailers can use these insights to develop targeted marketing campaigns.

Enhancing Customer Experience

By understanding the customer's perspective, retailers can design experiences that meet their needs and expectations.

Improving Operational Efficiency

Customer journey mapping can help retailers identify operational inefficiencies and areas for cost reduction.

By applying these insights, retailers can create a customer-centric approach that drives business growth, increases customer satisfaction, and sets them apart from competitors.

Creating a Python tool for customer journey mapping in retail can help businesses gain valuable insights into their customers' behaviors and preferences. By using Python, retailers can analyze customer data, identify pain points, and develop targeted marketing strategies to improve customer experience and drive business growth.

Key Takeaways

By prioritizing customer-centricity and continuous improvement, retailers can create a loyal customer base that drives long-term success. Python is a powerful tool for customer journey mapping, offering a range of benefits and opportunities for retailers to stay competitive in a rapidly changing market.

Remember, the key to successful customer journey mapping is to understand your customers' needs and preferences. By putting the customer at the heart of your retail strategy, you can create a loyal customer base that drives business growth.

In conclusion, Python is a valuable tool for customer journey mapping in retail, offering a range of benefits and opportunities for retailers to improve customer experience, drive business growth, and stay competitive.

Additional Learning Resources

To further improve your skills in creating a Python tool for customer journey mapping in retail, we recommend exploring the following resources:

Online Resources

These resources will help you expand your knowledge and skills in Python and customer journey mapping, enabling you to create a more effective and insightful tool for your retail business.

Related posts

• How to create a customer experience analysis tool in Python for hospitality
• How to use Python for customer segmentation in retail
• How to create a market research analysis tool in Python
• How to use Python for consumer behavior analysis in e-commerce

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Best tutorials on how to visualize user journeys with Sankey Diagrams with Python

In this article, you will find the best python tutorials you can use to visualize user journeys on your app or website!

User journey analysis is tricky

Coding in python and sql as a data analyst.

Here is my story.

I have been data analyst for several years for several app and website companies. Some were social networks, some were group chat apps, some were even dating apps. I also did some analytics on e-commerce, as well as one-pager websites - believe it or not, there is a lot you can analyse in one single landing page!

While some metrics are very easy to analyse ( DAU , Conversion rates , Event counts ), some are a bit more challenging ( Retention rates , Stickiness metrics - the famous DAU/MAU and some other ratios). Overall, with the most popular tools and softwares on the market such as Excel , Looker Studi o (previously called Data Studio ), Power BI , or even Tableau , almost everything can be looked at without coding too much .

Of course, sometimes a little bit of code is needed . Coding adds great flexibility when you need to come up with a more custom solution. In my honest opinion, when it comes to behavioral data analysis in an app or a website, the coding part is not too hard. I'm far from being the best at coding, and I quite easily managed to get the work done after a couple of weeks of learning. I would not say the learning curve is too steep, and they are many great free tutorials online. 

For SQL queries , BiqQuery is intuitive, flexible, and the documentation around it is quite rich. Since it can be plugged to Looker Studio, it makes almost everything possible to analyse. For investigation that requires some Python code , pandas and matplotlib libraries are great and simplify the work a lot.

However, there is one field in user behavior analytics that I find particularly tricky : User journey analysis (sometimes called customer journey analysis ). 

• Looking at what ONE user did before or after a specific event is easy . 
• Looking at what ONE user has done two, three or more steps before or after a specific event is already more challenging , but can be achieved with some fancy approaches (I often used the window function LEAD or LAG on Google BigQuery to get the insights I needed)
• Looking at what ALL users have done before or after a specific events, sometimes 10 steps before or after a specific event, is really hard . 

The number of possible paths grows exponentially the more steps you are looking at , and figuring out what is the most common path requires to plot the aggregates of the most used paths in a clever way . This is where Sankey diagrams come in!

Sankey diagrams can help identify areas of the application that are causing users to drop off or get stuck, which can be useful for improving the user experience. Additionally, Sankey diagrams can provide insights into the most common paths that users take through the application, which can help inform decisions about which features or content to prioritize. In my opinion, this is one of the best and most underrated data visualization technique. 

Using Sankey diagrams to plot user journeys with Python

As data analyst, when I figured out that Sankey Diagrams were the way to go to analyse user journeys, I knew my coding skills in Python would not be enough. 

Lucky me, the python delvelopers community is awesome, and they are great 100% free tutorials online that explain how to visualize customer journey using python. 

In this article, I will list the 3 best Python tutorials or documentation I found online to plot my Sankey charts and better understand my users paths!

SankeyJourney - the no-code solution to visualize customer journey with Sankey charts

Before starting, for those searching to skip the code part, here is an easy and very flexible solution for you.

Funny enough, I got so convinced by the great potential of flow charts in the specific context of user journey analysis that me and my dad developed SankeyJourney , a no-code tool to generate Sankey Diagrams in seconds.  All you need to do is to import your csv file containing your events data, and... that's it!

Your Sankey graph is generated automatically, and you can interact with it.  Real time interaction is the biggest advantage I see with SankeyJourney compared to alternatives such as python to build sankey graphs . You can decide the number of the steps to display, the events to filter on, you can filter out some details, and there are many other features. You may want to give it a try!

Tuto 1 : Visualizing In-App User Journey Using Sankey Diagrams In Python

This tutorial has been created by Nicolas Esnis . You can find his complete tutorial here. 

Let's start with what is the best tutorial ever created for this purpose in my opinion. 

The article presents a Python script that reads data from a CSV file containing information about user behavior in a web application. The input data consists of a sequence of events for each user, where each event is represented by the fields user_id, time_install, event_name, and time_event . The script uses Pandas to process the data and create a dictionary of nodes and links. Each node represents a page or screen in the application, while each link represents a transition from one page to another.

The script then uses Plotly to create a Sankey diagram from the nodes and links dictionary. The resulting diagram shows the flow of users between different pages or screens in the application, as well as the number of users who followed each path and the average time it took them to make each transition.

Tuto 2 : A Python Plotly example of the customer journey

This tutorial has been written by Summer He . You can find the complete tutorial here .  The tutorial provides a step-by-step guide for creating a Sankey diagram using Python Plotly. The guide starts with a made-up dataset on customer behavior, which includes columns for user ID, event name, platform, and timestamp. The dataset is transformed and aggregated to generate the source and target data for the Sankey diagram, which is then plotted using Python Plotly.

The code first creates a simulated dataset of user events, which includes a user ID, the time of the event, the name of the event (e.g. "Home", "Cart", "Purchase"), and the platform on which the event occurred (e.g. "iOS", "PC").

The main function in the code is user_journey , which takes the dataset as input along with a starting step (i.e. the event that the user started their journey with) and an optional number of steps to include in the visualization (default is 5). The function sorts the dataset by the time of the events and selects the users that have performed the starting step. It then aggregates the first n steps of the user journey for each of these users and counts the number of identical journeys. The resulting dataframe is transformed into a source-target pair format suitable for the Sankey diagram. The function also defines the colors of the nodes and links based on the events in the dataset.

The code then creates a list of labels for the nodes and a list of colors for the nodes and links based on the user_journey function output. Finally, it creates the Sankey diagram using the plotly library and displays it in the output. The Sankey diagram shows the flow of users between the different events on the website/app, with the thickness of the links representing the number of users that followed that particular path.

Along the way, the tutorial provides code snippets and explanations of the various parameters that can be customized to create a more effective Sankey diagram. These parameters include labels for annotation, node colors and link colors, and unique integer IDs for both source and target.

Tuto 3 : Google Charts and its Sankey Diagrams

Google Chart has an excellent documentation for generating Sankey diagrams , making it a great choice for visualizing complex user journeys on an app or website. Sankey diagrams are particularly useful for showing how users navigate through a series of pages or steps in a process, and Google Chart makes it easy to create these diagrams with its intuitive interface and powerful tools.

For our project SankeyJourney , we have used Google Chart extensively to develop our no-code tool, which helps businesses visualize user journeys on their apps or websites. With its extensive range of customization options and straightforward documentation, Google Chart has allowed us to create visually stunning and informative diagrams that are both easy to use and understand. We had to mention them in our top 3 !

And that wraps it up! Hopefully, these 3 tutorials will make it easier for you to generate your own Sankey Diagram. And remember ! If you are searching for a great easy-to-use solution for building your own sankey diagrams to better understand your users or customers' paths, SankeyJourney is here for you! If you like tutorial listings, you could like this article about the best tutorials to generate Sankey diagrams on Excel.  ‍

Visualizing the Complete Customer Journey: Mapping and Tools for Success

The customer journey is a pivotal aspect of any business’s marketing and sales strategy. But how often do we truly visualize the customer journey in its entirety? And why is it so crucial to do so? If you’re a director-level or higher marketing expert, have you ever pondered how a comprehensive customer journey map can revolutionize your strategies? Or how the right tools can make this visualization more effective?

1. Understanding the Essence and Benefits of Customer Journey Visualization

The customer journey is the complete sequence of events and interactions a customer goes through from the moment they become aware of your brand to the point of purchase and beyond. It’s not just about the buying journey; it’s about every touchpoint, every emotion, and every decision that influences the customer. But why is it so essential?

Mapping the Customer Journey: Mapping is the process of creating a visual representation of this journey. A customer journey map is a visual tool that allows businesses to understand the customer’s experience at every stage of the journey. It highlights customer touchpoints, pain points a customer might face, and opportunities for improvement.

Benefits of Customer Journey Visualization: Visualizing the customer journey offers numerous advantages. It provides insights into customer needs, pain points, and preferences. It also helps businesses improve customer experience, leading to better customer retention rates.

2. Types of Customer Journey Map Templates

Not all customer journeys are the same. Depending on the business, product, or service, the type of customer journey map you create can vary.

Basic Customer Journey Map: This is a straightforward visualization of an end-to-end customer experience. It’s ideal for businesses just starting with journey mapping.

Comprehensive Customer Journey Map: A more detailed map that covers the entire customer journey, including pre-purchase and post-purchase stages. It provides a more in-depth look into the customer’s experience.

Future State Customer Journey Map: This map visualizes the desired customer journey in the future, based on insights and data available.

3.  How to Create Customer Journey Map with Tools and Templates

Creating a customer journey map from scratch can be daunting. Thankfully, there are tools and templates available to simplify the process.

Customer Journey Map Tools: These tools offer features to create, visualize, and analyze the customer journey. Wizaly, for instance, is a platform that provides a comprehensive suite for customer journey visualization.

Free Customer Journey Map Templates: For those on a budget or looking for a quick start, free customer journey templates are available. These templates provide a foundation upon which businesses can build and customize their maps.

4. The Role of Personas in the Customer Journey Mapping Process

Understanding your customer is at the heart of the customer journey. Customer personas play a pivotal role in this understanding.

Creating the Customer Persona: This involves gathering data and insights about your target audience. It helps in visualizing the steps a customer might take and the potential challenges they might face along the journey.

How Personas Influence the Journey: By understanding the customer persona, businesses can tailor the journey to better meet their needs, leading to an exceptional customer experience.

5. Enhancing Customer Retention through Journey Mapping

Journey mapping isn’t just about understanding the customer’s path. It’s also about enhancing their experience to increase loyalty.

Identifying Pain Points: By mapping the customer journey, businesses can pinpoint areas where customers face challenges. Addressing these pain points can lead to increased customer satisfaction.

Proactive Customer Service: Journey mapping can point out areas where proactive customer service can make a difference. A proactive customer service strategy can lead to a surge in customer loyalty.

Investing in the Customer: The insights gained from the customer journey can help businesses invest in areas that matter most to their customers, further enhancing the customer experience .

Visualizing the complete customer journey is more than just a marketing exercise. It’s a strategic move that can lead to better customer experiences, increased loyalty, and business growth. By understanding every stage of the customer journey, from awareness to purchase and beyond, businesses can tailor their strategies to meet and exceed customer expectations.

Ready to take your customer journey mapping to the next level? Reach out to Wizaly today and discover how our platform can revolutionize your marketing attribution strategies.

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Customer Profiling and Segmentation in Python | A Conceptual Overview and Demonstration

Lillian Pierson, P.E.

Playback speed:

If you’re a data professional interested in marketing, mastering customer segmentation and profiling should be at the top of your priority list. Not every product or service that your company makes will be right for every customer, nor will every customer be equally responsive to each of your company’s marketing campaigns.  In the age of personalization, those who fall back on mass marketing techniques will fail. However, those who work to understand their customers’ unique tastes and preferences will thrive. 

If you want to be doing work that impacts your company’s profitability and bottom line (and gets you recognized as top talent!) , customer segmentation is an absolute must. It helps generate MORE sales from your existing leads and customers. 

Customer segmentation has been on my mind these days as I work on my business’s own marketing automation and product development. I realize I’ve learned a whole lot these past couple of months as I doubled down on marketing new offers. And I wanted to update this blog post to share this new information with you!

What you’ll get if you read this post:

• A conceptual training on a need-to-know data science skill
• A badass practice problem so you can apply your knowledge
• A step-by-step coding tutorial to help you master segmentation
• A FREE Downloadable source code + toy dataset for you to use on your own machine and apply at your own company

If you’re new around here, I’m Lillian Pierson and I regularly share resources and training for data professionals. This is to helps uplevel their skills and start creating more profit for their organizations through data strategy so they can land their next promotion.  To date, I’ve trained over 1 million workers on the topics of AI and data science and consulted for 10% of the world’s Fortune 500 companies!

This coding demonstration on customer segmentation and profiling is just one way to improve your organization’s bottom line.

So, what’s customer profiling and segmentation .

• Demographic characteristics,
• Psychographics,
• Desired benefits from products, and/ or;
• Past purchases

One benefit of such customer profiling and segmentation is that you can start targeting your customers more strategically based on the different segments you create. 

Before heading over to the case study, let’s have a look at how clustering is done.

Clustering analysis 101

By now you see how segmentation can help you better target specific audiences within your customer base. So, let’s get into a little bit of data speak .

Want to access the full training on Python for segmentation? Access the entire training in my LinkedIn Learning course, Python for Data Science Essential Training – Part 2.

When you use segmentation analysis to break customers into similar groups (or “ market segments “), the customer groups that result are called “ clusters “.   For a formal definition of clustering analysis ,  “c luster analysis is a class of statistical techniques that can be applied to data that exhibit natural groupings”.

With cluster analysis, your algorithm breaks customers into similar groups based on similarities in the attributes that describe the customer. So, for example, you could use one model to break your customers into separate groups. This will be based on how similar the customers are in terms of the following four attributes:

• Email responsiveness,
• Product channel affiliation, and;
• Income bracket.

Now, the similarities between your customers here will be calculated simultaneously. So, the model will quantify how similar customers are based on all four attributes at the same time .

Since you’re not providing the model labels to instruct on how you want it to break the customers into groups, it has to look at each of the customers and figure out similarities for itself. Then it’ll assign the customers into groups, as it defines them. That’s what we call unsupervised machine learning. We haven’t given the model any labels to describe the data it must learn from, so it has to discover groupings on its own.

In my experienc e, t wo places where I see a lot of clients struggle is that they either:

(1) have too much data and are overwhelmed with the idea of how to begin making sense of it or

(2) they don’t have enough data about their customers to begin using data science to generate business value.

The good news is, whether you fall into either of the above-two camps, you can begin using clustering analysis to improve your business’s bottom-line.

You can easily improve your organization’s bottom line with clustering analysis because it’s easy to deploy on survey data. If your company is data-rich, then you’re sure to have lots of customer survey response data sitting around. If your company is data-poor, it’s fairly easy to create a survey and begin getting your customers to provide feedback.

Once you have your data source(s) pinned down, it’s not hard to use clustering analysis on  your survey response data to group survey respondents into clusters. 

Now that you understand a bit of the background on what customer profiling and segmentation is and how you can use it, let’s dig a little deeper into how clustering algorithms work. 

Essentially, the primary method for classifying your customers into groups requires that the algorithm computes a quantitative distance value for similarity and dissimilarity between customers. Frankly, the algorithm has no way of knowing whether it’s grouping customers, or fruit, or any other type of item. It just looks at the data and uses math to find patterns. In the case of customer profiling and segmentation, each customer is described by a “row” in a data table (otherwise called an “ observation “, a “ case “, or a “record”).

So the algorithm goes in and calculates mathematical distances between rows, where each row represents a customer in this scenario. There are several mathematical methods from which to choose when instructing the algorithm on how to calculate similarity between customers, and this is an important choice to make. For this demo, however, we’ll be calculating similarity based on the Euclidean distance.

The Euclidean distance metric

The Euclidean distance metric is calculated according to the following equation:

To make things clear, let’s look at a quick example.

Imagine you have a small sample of data that describes  three customers. These three customers were each asked two questions:

• How much money do you spend on expensive hotels?
• How much money do you save?

The customers are asked to rate themselves between 1 to 7, where 1 indicates that the customer spends the least amount of money whereas 7 indicates the customer spends the most amount of money. So, 1 – 7 is the scale of measurement , and each of the customer’s responses are categorical (in other words, they can only rate themselves as belonging to one class, out of seven classes total)

When the Euclidean distance is calculated between customers A, B, and C, you can see that the distance between customer B and C is less than the distance between customer B and A.

This means that customers B and C are more similar than are customers B and A. You use these distances to segregate these customers into groupings based on similarity in their responses…m akes sense, right?

The k-means algorithm.

Now that you see how the distance between customers is calculated, let’s look at how to create clusters from these distances. For data scientists, we know that K-means clustering is an unsupervised clustering algorithm and that it belongs to the non-hierarchical class of clustering algorithms.  

But I imagine that some of the people reading this aren’t data scientists, so if that’s you, don’t worry. You don’t need to get into the nitty-gritty details right now – this is just an intro to customer profiling and segmentation, after all. Suffice it to say, the advantages of K-means over other clustering algorithms are:

• K-means method is appropriate for large data sets
• K-means is able to handle outliers extremely well

There are four main steps to that the K-means algorithm follows when you deploy it. Those are: 

• We start off by picking a random number of clusters K. These form the centers for the clusters (aka; the “ centroids “).
• In step two we assign the centroids a value taken from any observation. This is done by calculating the Euclidean distance between the centroid and the observation.
• Now, we compute the distance between the centroid and the nearest observations, and then average those out. This gives us the new values for the centroid. This ensures that the total intra-cluster variation (aka; total within-cluster variation) is minimized.
• Repeat Step 2 and 3 until none of the cluster assignments change. And the within-cluster sum of squares is at the minimum value. The within-cluster sum of squares is calculated by the following equation:

Determining the number of clusters

Now that we know how the clusters are computed we can start implementing the algorithm. However, we are missing one vital piece of information! We must determine the number of clusters to be used. There are various methods to figure this out. W=In this demo, we’ll be using the elbow method. This method works according to the steps described below:

• Start by computing the cluster algorithm for different values of K.
• For each value of K, we calculate the total within-cluster sum of squares.
• We plot the number of clusters against the within-cluster sum of squares
• We choose the number of clusters where the bend is prominent – this area is the point where we know that adding more clusters does not add more meaningful information.

A customer profiling and segmentation Python demo & practice problem

Now that we’ve covered the inner workings of k-means clustering, let’s implement it in a practice problem.

Consider that you’re a marketing manager at an insurance firm and that you want to customize your offerings to suit the needs of your customers. You’ve decided to try out customer profiling and segmentation. After much thought, you decide on the two factors that you think the customers would value the most. Those are:

• A savings on premium paid
• The local availability of nearby insurance agents

The importance of these factors will be measured using something called the “likert scale”, wherein a rating of 1 represents not important and a rating of 7 represent very important. Sound familiar?  ⍨

Now you ask your potential customers to take the survey. The survey data that I am using here is a randomized set of data . Assuming that you survey a lot of people, you are bound to see clear clusters. Let’s start off by importing the required libraries.

Next, we use pandas to read in the data set.

Since we are calculating Euclidean distance, we need to scale the data.

Now, let’s have a look at our data.

We have 150 observations without any null values, which is a good thing. Next, let’s scale the data.

Before performing K-means clustering, let’s figure out the optimal number of clusters required. As discussed above, we’ll use the elbow method.

As you can see, there’s a massive difference between the WSS (within-cluster sum of squares) value of cluster 1 and cluster 2. So let’s go ahead and choose two clusters. Let’s plot the figure to get a clearer picture of what’s going on.

A clear bend can be seen at the 2nd cluster. Cool!

Next, fitting the k-means algorithm on the data…

And, looking at the cluster determined for each observation…

Since there are only two clusters, we can have a look at the calculated centroid values:

Lastly, we’ll visualize the data with the clusters formed.

By looking at the above, you can easily see that there are two segments of potential customers. Those are:

• Segment 1 – Potential customers who want agents in their neighborhood and who are willing to spend more on premiums.
• Segment 2 – Potential customers who don’t really care whether there’s an agent in their neighborhood but do, however, demand to pay lower premiums on their insurance policies.

If this was a real-world example, you could use what you learned in this analysis to help you craft targeted offers and optimized marketing messages.

There you have it! That was the basics of customer profiling and segmentation in Python.

So, what’s next?

First of all, pat yourself on the back from getting through a somewhat technical (yet necessary!) discussion on customer profiling and segmentation. As a next step, think about how you might go about applying what you’ve learned to your business. How can you go even further with your new knowledge?

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Python customer-journey-map

Python customer-journey-map Projects

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retentioneering-tools

Retentioneering: product analytics, data-driven CJM optimization, marketing analytics, web analytics, transaction analytics, graph visualization, process mining, and behavioral segmentation in Python. Predictive analytics over clickstream, AB tests, machine learning, and Markov Chain simulations.

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customer-segmentation

Here are 27 public repositories matching this topic..., retentioneering / retentioneering-tools.

Retentioneering: product analytics, data-driven CJM optimization, marketing analytics, web analytics, transaction analytics, graph visualization, process mining, and behavioral segmentation in Python. Predictive analytics over clickstream, AB tests, machine learning, and Markov Chain simulations.

• Updated Dec 1, 2023

mesudepolat / RFM_ANALYSIS

customer segmentation with rfm

• Updated Apr 29, 2021

sonwanesuresh95 / rfm

Python Package for RFM Analysis and Customer Segmentation

• Updated Mar 2, 2022

mtahiraslan / flo_cltv_prediction

FLO wants to determine roadmap for sales and marketing activities. In order for the company to make a medium long -term plan, it is necessary to estimate the potential value that existing customers will provide to the company in the future.

• Updated Mar 7, 2023

AyseGokceyrek / Rule-Based-Classisfication

A game company wants to create new level-based personas using some of their customers' features.

• Updated Feb 10, 2023

aadimangla / Mall-Customer-Segmentation

Customer segmentation is the practice of dividing a customer base into groups of individuals that are similar in specific ways. You can provide different value propositions to different customer groups. Customer segments are usually determined on similarities, such as personal characteristics, preferences or behaviours that should correlate with…

• Updated Sep 8, 2020

snaffisah / Customer-Segmentation-Using-Deep-Learning

Create a classifier model to identify group of customer using deep learning and return result in a new csv file

• Updated May 19, 2022

10mudassir007 / Customer-Segmentation-using-KMeans-Clustering

Mall customers segmentation with clustering

• Updated Mar 10, 2024

aurelienmorgan / online_retail_growth

Laying some of the "Know Your Customers" / "Know Your Markets" fundations to strategizing on how to grow a business (from insight dug from the business data).

• Updated Jan 5, 2021

dayanacavalcante / CustomerSegmentation

Customer Segmentation by RFM

• Updated May 25, 2022

HrayrMuradyan / CustomerProfile

An easy-to-use package for customer segmentation using Python.

• Updated May 16, 2023

nilhanatalay / CRM-Analytics

This is a case study in my Data Analyst Path by Miuul.

• Updated Jul 8, 2023

icodeitnl / Unsupervised-Machine-Learning-Algorithm-K-Means-for-RFM

Client segmentation. Recency, frequency, and monetary value.

• Updated Jan 10, 2020

nafisalawalidris / Building-a-Clustering-Model-for-Customer-Segmentation

Customer Segmentation Using Clustering: This repo applies clustering algorithms to a customer transaction dataset, grouping similar customers together based on their purchasing behavior. Targeted marketing strategies can be developed by analyzing distinct customer segments.

• Updated Jul 23, 2023

yakupkaplan / Customer-Segmentation

In this project, we will first firstly implement RFM Analysis to group customers according to RFM metrics and then the same customers will be segmented by using K-Means and Hierarchical Clustering algortihms.

• Updated Oct 28, 2020

aylinghsr / Customer_Segmentation

Customer Segmentation using Data Mining methods

• Updated Nov 4, 2022

KimaruThagna / Segmente

A journey through understanding customer segmentation using python with the general goal of encouraging data driven decision making

• Updated Jul 1, 2020

TalhaFarook / SegmentWise

SegmentWise: Unveiling Customer Insights for Exploratory Data Analysis (EDA) and Customer Segmentation

• Updated Aug 2, 2023

abhijitpai000 / customer_segmentation_rfm

Generated customer groups by giving each customer a quantitative score based on the Recency, Frequency & Monetary Value of their historical purchases using the K-Means Clustering algorithm.

• Updated Oct 3, 2020

rpradeepsurya / mlnd-customer-segments

Udacity Machine Learning Engineer Nanodegree, Unsupervised learning project (Nov 2018)

• Updated Dec 1, 2019

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20+ User Journey Map Examples and Templates

13 min read

Looking at user journey map examples can help you come up with a visual representation of your customer’s journey.

Customer journey mapping research also allows you to identify areas of opportunity in your processes and plan to reduce those friction points. For instance, you might discover that you need user onboarding software to retain users after the first three months.

So, we’ve compiled 20+ examples and templates of customer journey maps to help you get inspired.

Let’s get started!

• A user journey map is a document that shares the steps a user must follow to accomplish a goal. It can also include sentiments, thoughts, and friction points.
• The terms user journey maps and customer journey maps are usually interchangeable. These are similar because they both tell the story of a customer’s journey . However, they vary in scope, definitions, and goals.
• For instance, a user journey map is usually reduced to a specific product or service; while a customer journey map shows the end-to-end of a customer’s activities involving your product or service, e.g., advertising.
• The touchpoints in a user journey map refer to in-app experiences, and customer touchpoints in a journey map refer to every interaction the customer has with your app.
• Usually, the user journey map influences product design, while the customer journey map informs business strategies.
• There are four types of customer journey maps:
• Current-state maps. To illustrate the customer journey in the present.
• Future-state maps. To speculate on potential user journeys in the future.
• Day-in-the-life maps. To show what a user does in relation or without any link to your product/service.
• Service-blueprint maps. To define everything that needs to happen internally to properly serve users.
• Examples of user and customer journey maps include companies like Hubspot, Zoom, Mailchimp, Dropbox, and Userpilot.
• Templates of user journey maps include service blueprints, customer empathy maps, user touchpoints, and effective journey maps.
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What is a user journey map?

A user journey map is a document that shows the steps a user follows to reach a goal with your product or service. It’s usually used for UX visualization as it tells the story of a person navigating your product and their interaction with different touchpoints.

This document also helps you gather information about the user and the functionality they find most relevant.

In your first version of a user journey map, you may only add the user’s actions. But as you expand it, you should also add their emotions and thoughts.

User journey maps vs customer journey maps

User journey maps and customer journey maps are almost interchangeable terms. They both show the road a person takes to achieve goals and help improve the user experience .

However, these vary in scope, definitions, and goals:

• Scope. User journey maps usually showcase the way a user experiences and interacts with a specific product or service. On the other hand, customer journey maps visualize the end-to-end experience of a customer across various brand touchpoints.
• Definitions. The word ‘touchpoints’ has different meanings in each case. A touchpoint in a user journey map refers to interface interactions and in-app experiences. However, a touchpoint in a customer journey map means every moment a customer interacts with or becomes aware of your brand — e.g., advertisements, customer support, or at checkout.
• Goals. User journey maps are useful to influence product design improvements. The insights of a customer journey map , usually inform broader business strategies and customer engagement efforts.

Types of user and customer journey maps

Using customer or user journey maps for different purposes allows you to influence different aspects of your business. For instance, a day-in-the-life journey map lets you spot areas of your customer’s routine where you can participate.

Here are different types of user and customer journey visualization to implement in your business:

• Current-state map. Illustrates the critical user journey as it is now. It helps you visualize the current state of the user experience based on facts. This type of journey map allows you to identify the strengths and opportunities of your current process.
• Future-state map. Design how you wish the customers’ journey could look in the future. This type is aspirational and it’s useful when speculating potential customer paths. It’s mostly based on data from the current state map and creativity.
• Day-in-the-life map. Lists everything a customer does throughout the day despite those actions being related to your brand. A day-in-the-life map gives you an overall understanding of who your customer is, how they spend their time, and where your company falls. This map is based on user research data.
• Service-blueprint map. This is an internal document that states all the actions, policies, and processes that go behind customer-facing services. The service blueprint is usually for employees to know what they need to do to meet the customer across the journey.

User and customer journey map examples

Take a look at examples of user and customer journey maps from successful businesses and get inspired to draft your own.

1. Userpilot’s current user state journey map

To examine the user’s current state, you can use Userpilot to perform a path analysis .

With path analysis, you can identify and understand how users navigate through your product, pinpointing the key interactions and touchpoints they encounter.

For example, to optimize conversion , you can look into how enterprise users navigate toward the conversion point. Then you can use the discovered insights to replicate the experience for new users.

2. Spotify’s music-sharing user journey map

Spotify is a music streaming platform with various features. This example shows a detailed view of a Spotify user’s journey when sharing music with friends and family.

The image shows the steps the user takes to find and share music as well as their thoughts, emotions, touchpoints, and actors. It starts with a user opening Spotify to listen to music at work. They look up the different playlists and feel excited to see the suggestions.

Once they find a song that makes them feel happy, they share it with a friend by sending the link through WhatsApp. They follow up and wait for an answer.

This example is particularly interesting since it includes the actions, thoughts, and sentiments of two different personas.

3. Uber’s first experience user journey map

Uber is a popular transportation company for booking rides. This current-stage customer journey map shows all the steps a user takes from the moment they choose to use Uber as a new user, up to when they arrive at their destination.

It includes screenshots that show exactly what the user sees when they go through each of the customer journey stages.

This example includes goals from the persona on the side. It also includes verbatim thoughts and emotion tags that give you deeper insights into the target persona.

The bottom part of the map shares critical insights that help marketing and sales teams understand the user on a deeper level and improve their experience.

4. Dropbox’s customer journey map

This journey map includes the user persona’s jobs-to-be-done (JBTD) and the path they follow from the problem-awareness stage.

Since Dropbox is a cloud storage platform, using it for business affects the day-to-day of all workers. Hence, this map includes a clever section named “cast” which includes the profiles of everyone who’ll be affected by the decision to use Dropbox.

As you can see, Sophia starts her journey when she discovers Dropbox. She researches alternatives, books a demo, and signs up for the application.

This looks like a future state journey map as it seems quite simplified for a current state map.

5. Mailchimp’s day-in-the-life customer journey map

Mailchimp is a popular email marketing platform. The customer journey map captures everything a marketing worker named Dani does every two weeks.

This customer journey map compiles all the little tasks she does before, during, and after she sends a marketing email.

It’s considered a day-in-the-life map rather than a current-state one because it includes more detail than simply outlining the steps Dani takes to send an email. Instead, it includes the digressions she takes before actually completing the task, as well as emotions and areas of opportunity.

6. Hubspot’s customer journey map

This platform offers multiple services for managing a business. This is the current state of Hubspot’s customer journey . It shows everything a user does from the moment they become dissatisfied with previous tools.

This map explains in detail how customers interact with Hubspot until they become paying users. It also includes all the other actors involved, the factors that lead to a positive or negative experience, and the decision points.

This map also includes thoughts and sentiments, friction points, customer touchpoints, and internal actors involved.

7. Netflix’s customer journey map

Similar to the Mailchimp example, this customer journey map explains the macro steps a user like Jen takes to watch a movie on Netflix.

As a media streaming platform, Netflix’s algorithm comes up with movies and TV show recommendations. This map shows how Jen disregards those recommendations and searches for a different movie instead, making it an area of opportunity for the Netflix team.

As part of the analysis, this map also includes Jen’s pain points, motivators, and emotions. This is an example of how breaking the journey down into smaller goals can simplify spotting friction points by showing an end-to-end process on a single screen.

8. Canva’s user journey map

Canva is an online graphic design platform, mostly suited for non-designers. This user map tells the story of Laura, a woman who isn’t a designer but wants to build beautiful flyers to promote her hobby.

As you see, the map walks us through the process of building a new design. It starts with Laura creating a board and ends when she exports the design. Similarly to the Netflix example, this journey map is also restricted to one scenario.

This user journey also includes actions, pain points, goals, expectations, and thoughts across the phases.

9. Zoom’s user journey map

This popular online meeting platform serves different purposes. This example is about Zoom for teachers and it’s broken down into three main categories: Action, emotions, and thinking.

This user journey map explains what a teacher does to give online lectures. It’s separated into five main action buckets with a breakdown of the tasks that go into each bucket. For example, for a teacher to “Start teaching” they need to open Zoom and roll the call.

You can also see how the teachers’ emotions and thoughts vary throughout the session. Plus, the design of this map lets us quickly identify opportunities just by looking at the emojis.

10. HeartiCraft’s user journey map

HeartiCraft is an online store for people who want to buy handcrafted products. The experience begins when the user researches and finds the website and ends when they decide to buy again.

It’s an interesting view of a user journey map as it exposes where HeartiCraft shines but also where it fails to delight users.

This map highlights four different stages and includes all the actions, thoughts and feelings, pain points, and delights under each of them.

11. Say Yeah!’s customer journey map

This company helps businesses deliver products and services that better serve neurodiverse users. To analyze this customer journey , you need to place your eyes on the left side of the screen and skim through the stages.

As you can see, this is the journey of an adult child looking for health support for their parents. It starts at the moment they discover a problem and ends after they’ve made a purchase.

This map includes the tasks, actors, emotions, media, tactics, and the thinking process of the user across the stages. It also shows how relevant each of those moments is for serving the customer properly.

12. Gartner’s B2B customer buying journey map

As a consulting firm, Gartner has a deep understanding of the B2B sales process. You can see that in this example because it paints the B2B buyer’s journey as a non-linear path.

This is likely informed by historic customer behavior, journey analytics , and user research. In the map, you’ll see four main actions across the user’s journey that allow them to buy a product.

However, there are internal discrepancies that Gartner manages to capture in this map. For example, showing that the person meeting with the company isn’t necessarily the decision maker and needs to go back and get the CEO’s approval before agreeing to make a purchase.

13. Service blueprint map for technical support

As mentioned above, a service journey map helps employees know what needs to happen internally to power customer-facing tasks. In this example, we can see how systems are interconnected and linked to company policies.

This map also shows the actions employees take to provide service, including the invisible back-end tasks and the evidence that supports each action.

Templates for user and customer journey mapping process

Explore the different templates included on this list, and edit them to fit your customer journeys:

1. User journey map template in Figma

You can leverage this template on Figma for your customer journey mapping exercise and uncover user activities and emotions across different stages – from realizing their needs to becoming a paid customer.

It allows you to add what you expect the user’s emotions, experience, and expectations to be at each of the stages.

You can include as many ideas as you wish on this canvas or even invite your teams to work on this together.

2. User empathy mapping template in Notion

An empathy map compiles your target user’s feelings, thoughts, and behaviors.

This Notion template follows the classical approach by including the four main categories:

• Says. Direct quotes or statements that provide insight into the user’s thoughts and opinions.
• Thinks. Reflects the user’s thoughts, beliefs, and feelings.
• Does . Includes what the user does in real life or during their interaction with a product or service.
• Feels . Fears, frustrations, joys, and other emotional responses.

3. Future state customer journey map template from Xtensio

Use your creativity and your current state journey map to fill out this template. Explore alternative customer paths to offer a better customer experience.

This template includes space to add:

• Stages of the journey.
• User’s thoughts and feelings.
• Actions and touchpoints.
• How this map is different from the current journey.

4. Service blueprint customer journey map template from Miro

This is a typical service blueprint template. Miro lets you edit it to your liking by following these steps:

• Define the customer service scenario to investigate.
• Plot customer actions in chronological order.
• Lay out processes, actors, and support systems.
• Add roles and responsibilities by specifying interactions, visibility, and internal actions.
• Illustrate cross-functional relationships.

5. Customer journey map template from Mural

Use Mural’s customer journey map template to have a better understanding of your target audience’s touchpoints, needs, motivations, and barriers.

Here you can:

• Establish a customer scenario, e.g., buying a shirt online.
• Define the customer steps, including big and small actions.
• List all customer interactions with your brand, either in physical or digital touchpoints.
• Determine your customer’s goals and motivations.
• Highlight the positive moments at each stage.
• Define the negative or frustrating moments across the journey.

6. Customer journey map template from Canva

Find many different customer journey map templates on Canva. These all let you edit the customer actions across stages, and depending on the option that you choose, you’ll also be able to add the user’s:

• Emotions and feelings.
• Thinking process.
• Physical or digital touchpoints.
• Barriers or pain points.
• Solutions to barriers.

7. Customer touchpoint map template from InVision

Map out the customer touchpoints on this InVision template. Here, you’ll be able to list all the different interactions between the user and your business, as well as mention all the involved actors. You can break down the actions by stages and teams.

8. Customer journey mapping template from Slidesgo

Slidesgo provides you with 29 customer journey mapping examples. You can choose the design that piques your interest the most and add the different stages, touchpoints, actions, and sentiments. These designs are mostly suited for journeys of up to five steps.

9. B2B customer journey map template from UXPressia

UXPressia developed a set of B2B/B2C customer journey map templates for you to use. This mix also includes persona templates to guide you when creating personas for your journey maps.

You can use these templates as-is to guide your thinking or adapt them to fit your specific project needs.

10. Customer journey map template from Conceptboard

This customer journey map template is a classical one. Open the file with a clear understanding of your user persona.

There, you’ll be able to add customer data concerning each stage, more specifically regarding their:

• Touchpoints and channels.
• Overall experience.
• Pain points.
• Areas of improvement.

Exploring user journey map examples can inspire you to enhance your customers’ experience by pinpointing critical areas, such as better onboarding processes.

To create an effective customer journey map, you need a deep understanding of your user and a clear mapping path, i.e., via conducting user interviews and contextual research.

Userpilot is an all-in-one product platform that can equip you with actionable customer journey insights. Get a demo to explore our powerful analytics capabilities!

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