Growing need for machine learning

The Growing Need for AI and Machine Learning in Customer Experience

The Growing Need for AI and Machine Learning in Customer Experience   The global eCommerce

The Growing Need for AI and Machine Learning in
Customer Experience


The global eCommerce market is expected to reach $5,879 billion by 2022; more than doubling the $2,682 billion generated in 2017. Postal customers in today’s environment have a choice when it comes to choosing who they entrust their parcels with. It’s no surprise then that posts are looking for ways to increase their parcel share by looking first to the customer.

The customer experience, including convenience, is emerging as a key differentiating factor in how parcel customers are making their choices. What is impacting their behavior? How can we learn from it? And perhaps most of all, how do we find all this out?

Leveraging Data

Posts today have the ability to gather greater amounts of data about the customer than ever before. What’s more, this data can be collected at the customer’s point of interaction to deliver a clear, in-the-moment response from customers about their experience.

How this data gets used is the key factor at play. With Escher, posts have more control over what data is being collected and how to extract actionable insights from it. This trend towards leveraging artificial intelligence and machine learning tools has not missed this industry. Today, we’re doing more than ever before with AI and machine learning to create value and assist posts in delivering compelling experiences for their customers.

We see the growing role of technology and data as beneficial in four main areas. Speed, transparency, convenience, and value. Through these four cornerstones, a compelling customer experience can be built.

An integrated method of data collection at the point of interaction is a crucial starting point. By gathering customer feedback at the point of sale (such as a retail counter or kiosk), posts can derive insight into the customer’s experience as well as their perception of that experience.

From there, AI and machine learning algorithms can unpack the massive amounts of data being collected and make real-time alerts and recommendations at the point of interaction such as through a point of sale system. Additionally, with this technology, posts can provide real-time streaming analytics to a cloud environment for business insight and longer-term, larger-scale planning and improvement.

AI & Machine Learning in Action

As part of an integrated strategy of customer success, we’ve provided a few key areas of opportunity where AI, machine learning, and supporting technology can assist postal operators.


  • Staffing and Resource Allocation: Machine learning algorithms can identify peak traffic periods and recommend or even automatically allocate resources such as additional staffing or trucks.
  • Staff Training: AI and machine learning tools can determine high performing employees based on transaction volume, upsells, or other attributes. Conversely, the same tools can identify lower-performing employees and make recommendations for additional training or performance improvement plans.
  • Anti-Money Laundering: In many locations, post offices serve multiple roles in their community. Financial services such as sending money orders, bill payments, and other electronic payment services are common. However, these services are ripe for fraudulent activity. Machine learning tools can detect suspicious activity, including transactions, and flag them for later review. These tools can also recommend changes in operation to prevent or further investigate other transactions with similar attributes. Occurring on the back-end, this removes the need for counter personnel to function as “police” and investigate transactions at the point of interaction.

The Value of Technology Moving Forward

The true application of technology lies in the ability for postal operators to derive actionable insight from their customer’s experience, enhance that experience through real-time recommendations, and develop better processes for long-term growth and operations.

The chief benefit, and a primary motivating factor for adopting this technology and these approaches, is speed. Posts can cut down the time it takes to collect and analyze data while in many instances, receive proactive recommendations for a course of action at the point of sale.

Speed and efficiency are enhanced and as a result, the customer receives a more positive experience which leads to repeat business and increased parcel share for the operator.





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