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Enhancing the Stages of a Customer Journey Map

Customer journey mapping, CJM, Customer experience, CX, ML, AI,

Traditionally businesses were fixated on enhancing their products/services to get a better revenue outcome. But today, tables are turning towards customer-centric business models. The phrase “Customer is the King” aptly suits today’s situation. The journey of a customer with an organization tells a lot about how the future of the customer with that organization would look like.

Since –

A Customer Journey Map is a sequential pictorial depiction of the touchpoints and services that a customer has availed across various channels with a brand and their reactions towards them

– the customer data collected would be the foundation of it. Now, with every rising day, there are nuances in the customer behaviour based on the advertisements they interact with, the competitors that they study, and the kind of opinions they encounter in relation to the brand. Thus, it is imperative that customer experience managers have a constant eye out for the demographic and behavioural customer data. But is it feasible, to manually keep track of all the customers at every single point of the day?

No – that is when Artificial Intelligence-based Customer Journey Mapping (CJM) tools come into the picture. This AI-based Software as a Service (SaaS) tools continuously record and analyse customer behaviour in response to the decisions or actions taken by an organization. With the help of machine learning algorithms, future customer behaviour can be predicted, thus making the customer journey intelligent. With these tools in hand, organizations can get in the customer’s shoes to better engage with them and enhance their experiences.
Read more about customer journey mapping tools here.

Now that we know what a CJM is, let’s dive into its stages:

A CJM, as defined above, is the visualization of the customer experiences as a journey throughout the ‘purchase cycle’. It can thus be divided into 3 phases: Pre-purchase, Purchase and Post-purchase stage.

Pre-purchase phase

Awareness and comparison: This phase encompasses the time when the customers first realize that they have a need and try to find a solution.


Organizations take up a lot of effort in reaching out to their customers. For those efforts to reap results, the channels and customer engagement strategies that are bringing in maximum results should be precisely identified.
Decisions made for the customers must be made while trying to stand in their shoes. Voluntarily or involuntarily, customers leave a lot of data in the form of their digital footprint and online actions. This data should be leveraged to form a personalized customer behavioural profile.
Customer profiling, which is a method wherein segmentation of customers based on similar attributes via proper understanding of their pain-points/needs is done. With this in hand, the right customer can be provided with the right product at the right time, thus enhancing both awareness about the product/service amongst the right segment of customers and improve the customer journey with the brand.


There can be multiple vendors for the same product. Thus, promotional strategies should be framed by keeping in mind both the product as well as the competitors of the product.
Buyer personas are the customer segments created by grouping together customers based on the demographic data including customer age, browsing habits, operating system, location, advertisement interactions etc. Furthermore, Buyer personas can be fed to Machine Learning algorithms as parameters for the advertisement campaigns. These algorithms can help organizations in personalized marketing efforts. Targeted advertising increases the click-through rate of the customers in the pre-purchase phase.
Lead scoring: A lead is a contact of a prospect who hasn’t become a customer yet. With the help of the combination of predictive analytics tools and ML algorithms, previously available customer databases can be used for predicting the success rate of a prospect/lead. The data including successful as well as unsuccessful leads for the organization can be fed to the ML algorithms for them to find out patterns and thus transfer these characteristics into the prospect leads to identify most-likely-to-convert customers. These forecasts can significantly improve the conversion rates while reducing time and efforts for the marketing team as well.

Purchase Phase

Purchase and usage: This stage arrives when the customer has decided to pursue one brand out of all of the competitive vendors.

Product purchase:

For that brand to provide the customer with the right product, in the right quantity, at the required time, it is imperative that the product should be available and in stock. Demand forecasting helps you with just that. With forecasting algorithms organizations can predict the future demand based on historical orders, thus striking a balance between overstocking and understocking. Forecasts can also tell a customer’s buying potential based on their interactions with the services offered, thus helping organizations focus more of their energy on customers at the risk of churn, rather than the ones that are already loyal to the brand.

Product usage:

While using a product, the customer may have queries, reviews, or complaints. Addressing the customer at such times is one of the most important aspects of a successful customer journey. Customer service should be amenable and available across all touchpoints offered by the organization. Understanding the emotions and pain points of the customer, analysing their feedback, and using them for improvement can help in organizational growth as well as customer retention.

Post-purchase phase

Loyalty: This is the phase where organizations can reward their customers for buying from them and thus enhance customer retention.

CRM activities:

A customer relationship management system helps organizations retain their loyal customers, bring back former customers, as well all serve the ongoing customers better. An understanding of customers and their preferences can be obtained by the data collected in the CJM tool throughout the journey. This data can include their emails, texts, and all other interactions, in one place. Leveraging this data organizations can undertake loyalty programs, as a result of which the customers would feel cared for and prioritized.

Churn reduction:

Churn can be defined as the segment of customers that discontinue using a product/service offering of an organization. Once a customer has left, to bring them back takes a lot of additional offerings, time, and efforts – which might still not reap results. Instead, predicting which customers might churn beforehand, using the historical data analysis by AI-based software, and providing them with what they’ll be needing can significantly reduce the churn rate. Also, cross-selling suggestions that are relevant to the customers, made using predictive tools, could positively surprise them and thus improve CJ.


The journey of a customer starts even before they are in contact with an organization. Customer journey maps essentially help organizations look into a customer’s pain points and needs, with the proper understanding of which, they can provide user-specific solutions. CJM tools create insightful and actionable journey maps that are based on real-time data. Thus, the utilization of them can take organizations a step ahead in their CX initiatives.