Ereignisbezogene Muster: Aggregation von Customer Journeys

By Syed Zain Zaidi , Marium Gohr Fawad,

Januar 18, 2022

Januar 18, 2022

Ereignisbezogene Muster: Aggregation von Customer Journeys

With the onset of digitization, data available to modern retailers has increased exponentially in terms of volume as well as the number of data sources available. With ever-rising competition and an increase in omnichannel touchpoints, it is becoming vital for retailers to store, organize, integrate, analyze and activate this rich data to garner unique and actionable insights. Emphasis is moving towards customer analytics which allows organizations to analyze data in order to understand customer behavior. By doing so, organizations are able to offer personalized and seamless services to customers, deliver a better value proposition in terms of product offering and provide a holistic customer experience.

However, to perform various types of customer analytics, an organization must ensure that it collects data across all customer touchpoints, is able to identify customers across its data sources and can stitch data from these sources into one. Customer Data Platforms enables organization to bring data from multiple sources in one place and perform advanced analytics on unified customer data.

customer journey analytics

Once customer data is stitched across all available sources, value is realized only by activating this data for customer focused strategy & services.

AcuitySpark brings enterprise data from multiple sources into a centralized modern data platform. With its customizable out of the box rules and rule-based engine, it enables you to clean and standardize all data and create customer golden records with high accuracy. See how ML algorithms help you activate this data here.

Customer Journey Analytics

One of the most impactful analysis around customer data is done by mapping customer journeys. Customer Journey Analytics (CJA) involves the analysis of all customers’ touchpoints within an organization. It allows organizations to observe the most common journeys that customers take before they make a purchase and therefore helps to determine the key drivers and triggers for customers’ purchases. CJA allows Customer Insights & Digital teams to identify where most customers ‘drop out’ in their journey and do not proceed to make a purchase. This enables organizations to deep-dive into any bottlenecks or areas for improvement in their processes to make it easier for their customers to continue their journeys to point of purchase and beyond.

Modern CDPs enable digital analysts to visualize customer journeys across various touchpoints using one platform instead of multiple application silos. This presents a holistic view of customer behavior and results in more actionable insights for improving customer experience.

customer journey analytics

Customer Journey Analytics in Practice

Customer journeys are typically created starting from the first event or touchpoint a customer has with an organization. Any subsequent events of the customer are also mapped in chronological order to help visualize how customers are moving through their journeys and understand why they take certain actions.

The following graphic provides a visual flow of customer engagement through a normal use case. It demonstrates how many signals are left behind while customers go through a purchase experience and how they move from the initial awareness stage to the subsequent interest, purchase, service and loyalty stages.

customer journey analytics

Customer Journey Analytics in Action:

With millions of customers and interactions, it is efficient to analyze customer journeys by aggregating them to observe common paths and behavior shown by different customer segments. At any point of time, organizations can identify and deep-dive into individual customer journeys without breaching customers’ privacy or revealing PII data.

With a plethora of data sources available for customers, journeys can have a mix of touchpoints across multiple engagement or transactional channels. The chart below shows us how different customer segments start engaging with an organization and how they move to subsequent events.

customer journey analytics

These journeys enable analysts to zoom into event sequences before and after customers make a purchase and therefore generate insights around customer purchase behavior. This enables conversion of existing or potential customers that are in the middle of their journeys but have yet to make a sales transaction.

While CJA uncovers common patterns in customer journeys, it is also used to deep-dive into more narrow and specific touchpoints within customers’ journeys. Therefore, in addition to purchase transaction journeys, this blog will also showcase a specific CJA application around customers’ post-sale satisfaction. Analyses presented in this blog are based on the data of a leading slow-moving goods retailer in the US.

Customer Journey Applications

  1. Customers’ post-sale journey and behavior
  2. Analyzing drop-out rates at different stages of customers’ journeys
  3. Customer behavior and conversions post marketing interaction
  4. Customer behavior and conversions post engagement interaction
  5. Deep-dive into single touchpoint: post-sale satisfaction and product return analysis

Customers’ Post-Sale Journey & Behavior

The chart below allows analysts to visualize the most common first event for customers. In this case, 38% of the customers start their journey with a store-sale event, followed by 26% of those who visit the website and so on.

Of the customers who started with a store-sale event, 37% have no further interaction with the retailer in the second stage. However, 19% of these customers remain engaged over email followed by 15% of customers who interact over call and 10% who visit the retailer’s website.

The chart summarizes the most common activities and behaviors at each stage of different customers’ journeys. Using CJA, analysts are able to determine the next likely touchpoint for existing customers who are in the middle of their journeys.

customer journey analytics

Analyzing Drop-Out Rates

Customer retention rate is one of the most critical KPIs for organizations. One of the ways in which organizations retain customers is by engaging with them regularly through different mediums and touchpoints. The customer journey diagram below explores drop-out rates for customers who were reached out via emails after they made their first store sale. The percentage of customers who drop out at each stage is illustrated by the grey bar with the ‘No Further Event’ label.

customer journey analytics

As in the earlier example, 38% of customers had store-sales as their first transaction. Out of these, 37% of the customers dropped out while 19% responded to emails in the second stage. In the third stage, 27% of customers who responded to emails earlier dropped out while the remaining 73% were retained. Similarly, out of the 43% customers who responded to emails in the third stage, 41% dropped out and 59% were retained.

The chart above currently shows up to 4 levels of customers’ journeys but additional levels can also be added based on analysts’ requirements and objectives. The same analysis can also be performed for other engagement mediums such as calls or website activity.

Customer Behavior and Conversions after Marketing Interaction

This scenario explores customer journeys after customers’ interactions with marketing mediums. The diagram below measures the effectiveness of marketing emails.

customer journey analytics

There are 5% potential customers who start their journey after being contacted via marketing emails. Out of these, 21% make a sale transaction either in-store or online and 38% drop-out. The remaining 41% engaged via some other engagement source. 28% of customers are sent follow-up emails in the second stage out of which only 5% make a sale transaction in the third stage and 55% drop-out. Similar high drop-out rates are observed in the fourth stage as well.

Such insights allow analysts to measure the effectiveness of marketing emails in terms of customer conversion. In this example, we observe how most customers convert after the first email and follow-up emails don’t yield adequate conversions. It can be inferred that an alternative, more effective marketing medium should be opted in this case instead of emails, to try to engage remaining customers in this example.

Customer Behavior and Conversions After Engagement Interaction

Similar to the use case above, the following customer journey diagram explores the journeys and conversion rates of customers who start their journey with an engagement channel touchpoint. Here we observe that 12% of all customers start their journey through the calls channel. Out of these, 84% customers make a purchase following the call and 10% tend to interact via calls in the second stage. If a customer has contacted the company twice, there is a 67% chance that they will be making a purchase in the third stage. Eventually, most customers make a sale within the first 3-4 calls.

customer journey analytics

Deep-Dive into Single Touchpoint: Post-Sale Satisfaction and Product Return Analysis

This use case zooms into a narrower and more specific touchpoint to analyze customer behavior. In this scenario we deep-dive into the review sentiments of customers who returned products after purchase. Overall, we observe that 15% customers end up returning a product.

The review sentiment is based on ratings given by customers at the time of product delivery. In most cases, these ratings are based on the customer service and delivery experiences, while in some cases these ratings can also be about product-related issues which are identified at the time of delivery. Against each review sentiment, we also observe a return reason which specifies why customers ended up returning a product.

customer journey analytics

An analysis of the review data reveals that at the time of delivery customers typically review based on their shopping experience as well as the delivery service but afterwards, they can return a product if they are not satisfied with it. This means that customers whose initial rating sentiment was positive and later returned a product were satisfied along with other dimensions with the organization except for product related concerns. Therefore, if return reasons are analyzed and return rate is reduced, more customers would be left feeling satisfied with their holistic experience with the organization as well as the product. This leads to increased customer retention, higher customer loyalty and increased efficiency of operations.

Analyzing return reasons across different review sentiments show that customers end up returning a product because it is uncomfortable or does not meet their expectations. These reasons are subjective and vary from customer to customer. In this case, ensuring a convenient return process with excellent customer support ensures that customers remain satisfied even after their return and will be more likely to consider the organization for future purchases as well.

However, return reasons like receiving wrong or damaged products need to be looked into because they constitute a major proportion of returns. These types of returns can be reduced by implementing a quality control (QC) framework at the time of product shipment. The benefits of this approach are summarized in the diagram below.

customer journey analytics

Overall, implementing a QC framework in this case eliminate returns due to reasons such as wrong or damaged products being delivered. As such, it can bring down the return rate by 3%. Other intangible benefits of this include improved customer experience, stronger brand image, increased customer satisfaction, and higher likelihood of repurchase.

With this example we have seen how CJA is used to deep-dive into specific parts of customers’ journey, identify their pain points and create and implement a strategy to improve customer experience.

Wrapping up the Journey

CJA offers a customer-centric view by allowing retailers to understand the most common paths that customers take in their purchase journeys. It also allows analysts to identify any bottlenecks that hinder customers’ path to purchase. Furthermore, CJA enables organizations to streamline operations to improve customer experience and optimize internal processes. After aggregating and analyzing these insights for existing customers, organizations are able to leverage these findings to predict the journeys of new customers in order to provide them with a holistic and personalized experience. The value addition with using CJA allows retailers to observe noticeable improvements in customer retention and satisfaction, as well as more efficient and streamlined internal processes that translate into a better experience for customers.

If you want to reap the benefits and fully explore the analytical prowess of CJA, reach out to the Data & Analytics experts at Visionet today.

Writer:  Marium Gohr Fawad 

 

ContributorAsad MahmoodSaad Joiya

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