Tech Insight : What Is Customer Journey Mapping Software?

In this tech insight, we look at what customer journey mapping is, its benefits, the challenges of using it, and also we look at some of the popular customer journey mapping software solutions available.

What Is Customer Journey Mapping?

Customer journey mapping refers to a strategic process (that’s used by marketers) to visualise and understand the complete experience of a customer interacting with a product, service, or brand from the customer’s perspective. It’s a visual representation of the different interactions customers have with your brand, product, or service until they decide to purchase it from your business (and what happens beyond).

Identifying Key Interactions 

The customer journey mapping process involves identifying key interactions that the customer has with the organisation, understanding the customer’s feelings, motivations, and questions at each step, and recognising opportunities for improvement.

The map typically starts from the initial awareness or need recognition stage, through various touchpoints such as research, purchase, and post-purchase experiences, ending with loyalty or advocacy.

Who Does It? 

Broadly speaking, it’s used by marketing teams because they are directly involved in understanding and communicating with the target audience. Also, customer journey mapping can give marketers the kind of deeper understanding of customers’ experiences that enables them to improve how they target and serve prospects, which can ultimately deliver increased sales and loyalty.

More specifically, customer journey mapping is also carried out by:

– Product managers and development teams who use it to ensure that the product or service meets the customers’ needs and expectations at every point.

– Customer experience (CX) professionals, i.e. those specialising in understanding and improving the interaction between an organisation and its customers, and who want to identify gaps in the customer experience.

– User experience (UX) designers. These focus on the usability and overall experience of digital interfaces and tend to use journey maps to visualise how users interact with a website or app and identify areas for improvement.

– Service designers / those involved in designing and improving entire service processes, who use customer journey mapping to ensure that every touchpoint is optimised for the best customer experience.

– Senior managers who may also be involved in producing and reviewing the customer journey maps to make strategic decisions that align with customer needs and organisational goals.

Why Use It? 

Some of the key reasons for conducting customer journey mapping include:

– Enhancing customer understanding, i.e. helping organisations to see things from a customer’s viewpoint, leading to deeper insights into their needs and preferences.

– Identifying ‘pain points’ and bottlenecks – where customers face difficulties or get stuck, thereby allowing for prioritisation of improvements.

– Improving customer satisfaction, loyalty, and advocacy by addressing issues and optimising the journey.

– Aligning teams around the customer experience by providing a shared understanding across departments.

– Driving and informing strategic business strategies and decisions, such as for product development, and service improvements that align with delivering customer value.

– Optimising ‘touchpoints’ and channels – helping understand and optimise interactions and leveraging high-performing channels for a cohesive experience.

– Identifying gaps in the marketing strategy, e.g. by revealing channels and touchpoints that aren’t being used effectively, thus offering opportunities for optimisation.

Common Challenges

Although customer journey mapping can be very useful and deliver some important insights, it’s not without its challenges. For example, some popular challenges faced by marketers trying to use this type of mapping effectively include:

– Distinguishing fact from assumption. It’s crucial to rely on data to ensure the journey map accurately reflects actual customer behaviours rather than assumptions about how customers might interact with the company.

– Ensuring map accuracy. Inaccurate mapping can lead to misguided strategic decisions, potentially wasting resources and missing key opportunities to enhance the customer experience.

– Incorporating all touchpoints. For example, customers may interact with a brand through quite a variety of channels and actions, making it challenging to capture and understand every possible step in their journey.

– Dealing with the complexity of such a map. Comprehensive journey maps can become very detailed and complex, making them difficult to create, understand, and use effectively.

– Keeping maps updated. This is because customer behaviours and preferences evolve over time, necessitating regular updates to the journey maps to keep them relevant and useful.

– Tracking improvements. Once changes are made based on journey map insights, it’s important to measure their impact to ensure they’re driving the desired outcomes.

– Resource intensiveness. The process of creating, maintaining, and updating customer journey maps can be time-consuming and require significant effort from various team members, straining marketing resources.

– Adapting to digital tools. While traditional paper-based mapping can be cumbersome, transitioning to digital tools for journey mapping (i.e. customer journey mapping software), may involve a learning-curve and require additional resources.

Tackling The Challenges By Using Customer Journey Mapping Software 

As mentioned above, customer journey mapping software offers several key benefits to tackle the challenges of journey mapping efficiently, such as:

– Real-time data Integration, i.e. it enables accurate, live reflections of customers’ behaviour, facilitating quick strategic adjustments.

– Dynamic viewing and filtering. It simplifies the updating process, allows for easy navigation of insights, and supports decision-making with filterable maps.

– It helps deliver personalised experiences / tailored customer journeys by segment, improving conversion and satisfaction rates through continuous optimisation.

– Efficiency and simplicity, thereby transforming previously time-consuming tasks into quick and straightforward processes, eliminating the need for physical mapping materials.

– Enhanced collaboration by facilitating sharing across the organisation, allowing teams to focus on relevant touchpoints and insights, promoting a customer-centric culture.

Popular Customer Journey Mapping Software

Here are some examples of popular customer journey mapping software options for businesses and organisations of different sizes, some of whose names you may already be familiar with (particularly Microsoft):

Microsoft Dynamics 365 Customer Insights 

This provides an AI-driven customer data platform that combines customer journey mapping with actionable insights, predictive analytics, and personalisation options. Since it’s from Microsoft, a significant advantage is that it integrates seamlessly with other Microsoft products that a businesses probably already uses. It’s perhaps most suitable for medium to large enterprises looking for a comprehensive solution that not only maps customer journeys but also leverages AI to drive decision-making and personalised customer engagements.


Features/benefits include integrating customer journey mapping with CRM, marketing, sales, and service hubs, offering a comprehensive view of the customer journey. Features include email marketing, lead management, analytics, and automation tools.

This platform is suitable for businesses of all sizes due to its scalable platform, while HubSpot is particularly beneficial for those looking to integrate their customer journey mapping with a broader inbound marketing and sales strategy.

Salesforce Journey Builder 

Part of the Salesforce Marketing Cloud, this tool allows for the creation of personalised customer journeys across various channels and devices. It offers powerful segmentation capabilities, real-time interaction management, and analytics.

Salesforce Journey Builder may be ideal for medium to large businesses already invested in the Salesforce ecosystem, seeking to leverage advanced customer journey mapping with extensive customisation and integration options.


This is a collaborative online whiteboard platform that’s quite versatile, enabling the visual mapping of customer journeys, brainstorming sessions, and project planning. It supports real-time collaboration, making it easy to work with teams remotely. Miro is well-suited for businesses of all sizes, especially those that value collaboration and flexibility in their planning and customer experience design processes.


This software offers detailed journey maps, persona creation, and stakeholder maps. It’s designed to provide a clear visualisation of customer experiences, including pain points and emotions, with exportable and shareable maps. Smaply may be best suited to SMEs or teams within larger organisations focused on service design and customer experience improvement projects.

Adobe Experience Platform 

This is a robust platform capable of delivering personalised customer experiences at scale, with real-time customer profiles, predictive analytics, and cross-channel journey orchestration. It’s probably ideal for large enterprises that require a powerful platform to manage complex customer journeys and deliver personalised experiences across various touchpoints.

What Does This Mean For Your Business? 

For UK businesses, the use of customer journey mapping (and popular customer journey mapping software) is helping to meet the need for a more nuanced understanding of the customer experience. By being able to clearly visualise the entire journey from initial awareness through to loyalty and advocacy, businesses can gain invaluable insights into customer interactions, pain points, and so-called ‘moments of delight.’ This strategic process not only helps businesses gain a deeper comprehension of customer needs and preferences but also highlights areas for improvement. In this way, it can help drive strategic decisions that enhance customer satisfaction and loyalty which, of course, can be of huge benefit to businesses.

Customer journey mapping software offers a way to address the common challenges associated with traditional mapping methods, i.e. it gives real-time data integration, dynamic viewing, and filtering capabilities, plus the ability to deliver personalised experiences. These software tools streamline the mapping process, making it faster, more accurate, and less resource-intensive, and facilitate collaboration across departments, ensuring a unified approach to improving the customer experience.

Businesses not currently leveraging customer journey mapping (software) therefore stand to gain a competitive edge by adopting it. The insights derived from journey maps can guide more effective marketing strategies, product development, and customer service improvements. Also, the use of customer journey mapping software can significantly reduce the time and effort required to create and maintain accurate maps, thereby saving costs, reducing risk, and allowing businesses to quickly adapt to changes in customer behaviour and market conditions.

In summary then, customer journey-mapping and the adoption of specialised software to facilitate this process offer businesses the opportunity to transform their understanding of the customer experience which can translate into greater business success. In a landscape where customer expectations are ever-evolving, the ability to swiftly and effectively respond to these changes is not just advantageous but essential.

Tech News : Your AI Twin Might Save Your Life

A new study published in The Lancet shows how an AI tool called Foresight (which fully analyses patient health records and makes digital twins of patients) could be used to predict the future of your health.

What Is Foresight?

The Foresight tool is described by the researchers as a “generative transformer in temporal modelling of patient data, integrating both free text and structured formats.” In other words, it’s a sophisticated AI system that’s designed to analyse patient health records over time.

What Does It All Mean? 

The “generative transformer” type of AI is a machine learning / large language model (an ‘LLM’) that can generate new data based on what it has learned from previous data. The term “transformer” is a specific kind of model that’s very good at handling sequences of data, like sentences in a paragraph or a series of patient health records over time (temporal), i.e. a patient’s electronic health records (EHR).

Unlike other health prediction models, Foresight can use a much wider range of data in different formats. For example, Foresight can use everything from medical history, diagnosis, treatment plans, and outcomes, in both free text formats like (unorganised) doctors’ notes or radiology reports and more structured formats. These can include database entries or spreadsheets (with specific fields for patient age, diagnosis codes, or treatment dates).


The researchers say that the study is aimed to evaluate how effective Foresight is in the modelling of patient data and using it to predict a diverse array of future medical outcomes, such as disorders, substances (such as to do with medicines, allergies, or poisonings), procedures, and findings (including relating to observations, judgements, or assessments).

The Foresight Difference 

The researchers say that the difference between Foresight and existing approaches to model a patient’s health trajectory focus mostly on structured data and a subset of single-domain outcomes is that Foresight can take a lot more diverse types and formats of data into account.

Also, being an AI model, Foresight can easily scale to more patients, hospitals, or disorders with minimal or no modifications, and like other AI models that ‘learn,’ the more data it receives, the better it gets at using that data.

How Does It Work? (The Method) 

The method tested in a recent study involved Foresight working in several steps. In the research, the Foresight AI tool was tested across three different hospitals, covering both physical and mental health, and five clinicians performed an independent test by simulating patients and outcomes.

In the multistage process, the researchers trained the AI models on medical records and then fed Foresight new healthcare data to create virtual duplicates of patients, i.e. ‘digital twins’. The digital twins of patients could then be used to forecast different outcomes relating to their possible/likely disease development and medication needs, i.e. educated guesses were produced about any future health issues, like illnesses or treatments that might occur for a patient.

The Findings 

The main findings of the research were that the Foresight AI tool and the use of digital twins can be used for real-world risk forecasting, virtual trials, and clinical research to study the progression of disorders, to simulate interventions and counterfactuals, and for educational purposes. The researchers said that using this method, they demonstrated that Foresight can forecast multiple concepts into the future and generate whole patient timelines given just a short prompt.

What Does This Mean For Your Business? 

Using an AI tool that can take account of a wider range of patient health data than other methods, make a digital twin, produce simulations, and forecast possible health issues and treatments in the future, i.e. whole patient timelines until death could have many advantages. For example, as noted by the researchers, it could help medical students to engage in interactive learning experiences by simulating medical case studies. This could help them to practice clinical reasoning and decision-making in a safe environment, as well as helping them with ethical training by facilitating discussions on fairness and bias in medicine.

This kind of AI medical prediction-making could also be useful in helping doctors to alert patients to tests they may need to take to enable better disease-prevention as well as helping with issues such as medical resource planning.  However, as many AI companies say, feeding personal and private details (medical records) into AI is not without risk in terms of privacy and data protection. Also, the researchers noted that more tests are needed to validate and test the performance of the model on long simulations. One other important point to remember is that regardless of current testing of the model, Foresight is currently predicting things long into the future for patients and, as such, it’s not yet known how accurate its predictions are.

Following more testing (as long as issues like security, consent, and privacy are adequately addressed) a fully developed method of AI-based health issue prediction could prove to be very valuable to medical professionals and patients and could create new opportunities in areas and sectors related to health, such as fitness, wellbeing,  pharmaceuticals, insurance, and many more.

Tech Insight : Python in Excel … So What?

Following the announcement that Microsoft is releasing a public preview of Python in Excel, we look at what this will mean for Excel users and how it could help businesses.

What Is Python? 

The initial version was created in the late 1980s by Guido van Rossum, with its first official release, Python 0.9.0, coming out in February 1991. It was named after the eponymous Monty Python Show, after having been developed as a successor to the ABC language and was intended to be easy to read and allow for concise code, among other goals.

It’s regarded as a good general-purpose programming language that’s relatively easy to learn due to its simple and straightforward syntax. Python is often used in creating web applications and artificial intelligence applications, and it is the language behind platforms like Pinterest and Instagram.

Added To Excel 

Last week, Microsoft announced that is releasing a Public Preview of Python in Excel, thereby enabling the combination of Python and Excel analytics within the same workbook, with no setup required. Microsoft says: “With Python in Excel, you can type Python directly into a cell, the Python calculations run in the Microsoft Cloud, and your results are returned to the worksheet, including plots and visualisations.”  In short, this means that Excel users will be able to carry out advanced data analysis in the familiar Excel environment, by accessing Python from the Excel ribbon.

Two other key benefits of the integration highlighted by Microsoft are that it runs securely on the Microsoft Cloud, thereby keeping data private, and it is built to work with Teams. This enables colleagues to (seamlessly) interact with and refresh Python in Excel based analytics without needing to worry about installing additional tools, Python runtimes, or managing libraries and dependencies.

What Sort Of Things Can Be Done With The Excel/Python Combination?

Python’s ability to manipulate Excel tables will be of particular help to businesses that frequently work with data because it offers many practical benefits and uses. For example:

– Saving time by automating repetitive tasks in excel, e.g. formatting, or reorganising data.

– Potentially getting better data insights because Python enables the handling of large data sets and can be more efficient in processing and analysing that data.

– Saving time and doing a better job of data cleaning, e.g. Python is better at locating missing values, standardising formats, removing duplicates, and using techniques like regular expressions for pattern-based transformations.

– Improved data analysis and analytics due to the use of Python’s powerful data analysis libraries, e.g. Pandas, Matplotlib, and scikit-learn and the fact that Python in Excel leverages Anaconda (a popular enterprise repository) Distribution for Python running in Azure. This can help with complex calculations, statistical analysis, and data transformations that might be cumbersome or inefficient in Excel.

– Advanced visualisation. I.e., Python charting libraries like Matplotlib and seaborn enabling the creation of a wide variety of charts, spanning from conventional bar graphs and line plots to more specialized visualisations such as heatmaps, violin plots, and swarm plots.

– Helping to focus collaborative work efforts, e.g. where multiple people or systems are providing data in different formats or structures, Python acts as an aggregator, harmonising and consolidating diverse data sources into a single Excel sheet or structure.

– Python scripts can be scheduled to run at specified intervals, thereby making it easier to update or analyse Excel data even when you’re not around.

– Using Python as a bridge to enable Excel data to interact with other web applications, databases, or other external systems.

– Python scripts can be used to create custom functions not natively available in Excel, thereby expanding the scope of what can be done with Excel.

– Python can be used to periodically back up Excel files and even maintain versions (if needed).

– Python libraries like scikit-learn and statsmodels can be leveraged to apply popular machine learning, predictive analytics, and forecasting techniques, e.g. regression analysis, time series modelling, and more.


Some everyday examples of how using the power of Python in Excel could help businesses include:

– Making monthly sales reports better as well as faster and easier to produce. For example, if a sales manager needs to compile monthly sales reports and receives sales data from multiple regions in different Excel files, a Python script can be written to automatically consolidate all these files into a master report.

– Helping to track the expenses of a small business by using Python to automatically categorise and summarise expenses from an Excel sheet, thereby helping to track where money is being spent most frequently.

– In retail, a store manager could use a Python script to alert them when inventory for a particular item goes below a certain threshold (based on the data in the Excel inventory list).

– Financial analysts could predict future revenue or costs by using Python apply complex forecasting models on past financial data in Excel.

– In accounts, if a business needs to generate bulk invoices, Python can be used to save time by pulling data from an Excel sheet (like client details and amounts) and produce individual invoice files for each client.

– A business with critical data in Excel can have Python scripts scheduled to automatically back up these files at regular intervals, thereby ensuring data safety.

Other examples of what businesses can use Python scripts in combination with Excel include employee scheduling, e.g. generating shift schedules, quickly analysing any customer feedback collected in Excel, automatically highlighting best prices collected in Excel from different vendors, calculating commission for sales staff from figures collected in Excel, and analysing supplier delivery performance, e.g. delivery date and time records held in Excel.

What Does This Mean For Your Business? 

In short, releasing Python in Excel enables businesses (that leverage the integration) to effectively ‘supercharge’ their data processing and analysis capabilities, thereby giving them the ability to handle more complex tasks, larger data sets, and integrate with a broader range of technologies.

This could improve productivity, competitiveness, give new insights and reveal new business opportunities, save time, and produce better quality reports and visualisations which can improve transparency and business decision making. The fact(s) that Python in Excel doesn’t require any setup, integrates seamlessly with Teams, plus works securely in the cloud must surely also be attractive to businesses, many of whom now have remote and flexible working (all Teams users have access and security worries are minimised). Most businesses must, however, wait a little longer to start using the power of Python in Excel because it’s currently only available to users running Beta Channel on Windows and Microsoft 365 Insider Program members, although it will start to roll out with build 16.0.16818.20000, and then to the other platforms at a later date.