Cloud-based machine learning in an omnichannel world: Decoding customer satisfaction

 

Retail and e-commerce are moving fast and merging along the line into a singular platform. Amazon has recently moved into stationary retail and Walmart has marked its territory in e-commerce. However, what is driving this behavior for these mega-entities? Maersk recently threw its hat into end-to-end logistics solutions to better hedge against their current sea-liner market volatility. What is the one factor which is influencing such shifts and pivots?

 

It’s all about consumption and enablement. The marketplaces as we knew them have been transformed into a live and reactive medium where consumers are more adherent to multi-factor influencers. Key market expectations have since normalized (or disrupted, based on how able a company is to fulfill them). People want better products and services, quicker and with absolute convenience.

 

Decoding customer satisfaction: It’s all about incremental benefits

 

Initially, across the development of multiple industries, the focus was on key product features which would create lasting benefits for the consumer. With the evolving consumption patterns, it becomes clearer that the focus has been stretched to include timely and valuable delivery of the product or service.

 

Consider the mobile phone. Earlier it was the epitome of communication and connectivity. It was designed to create lasting benefit and value for its consumers. Now, as technology races ahead, the actual benefit of any ‘new’ phone is only incremental. The drive to consume something isn’t backed with the expectation of perfect rationale but more with the experience of the purchase.

 

Enter, cloud-based omnichannel enablement for businesses. All the businesses I mentioned earlier, are creating vast consumer-centric value movement metrics. One of the key reasons Amazon invested in Whole foods was to leverage the latter’s influential local following and network. It’s about getting into fast and immediate disbursement of services with an added connect with the consumer regarding the product.

 

In simpler words, Amazon would be able to tap into the demography of the local Whole Foods to make deeper inroads into the market. They would also create a retail persona which would act as a validator and influencer at the same time. The last time you bought a bag, wasn’t it nice when your friend appreciated your choice and thought it was a good deal? That’s exactly the experience and validation a local touch can create.

 

Machine learning to enhance the overall delivery experience

 

Cloud-based machine learning is the keystone for enabling this omnichannel experience. When you buy groceries online and would like to pick it on your way home from a local retail outlet, the mechanics that make that happen in a seamless fashion is efficient last mile movement. Last mile delivery is nothing but the final leg of merchandise movement where the last packaged unit is either delivered to the retail outlet or handed over the consumer. Last mile delivery efficiency means that you would be getting your delivery on-time and in good quality, backed by clear invoicing and validation.

 

The technology behind this seamless last mile movement involves perfect schedule planning and routes optimized for traffic and other disruptions. The idea is to ensure on-time deliveries for all the orders in a milk-run, ensure perfect digital validation of the order being received, proper feedback capture, robust transaction processing, and eventual logistics cost optimization.

 

How does this work and why is cloud the best way to go about it?

 

The mechanics of optimization behind selecting the best route which would fulfill the highest amount of on-time deliveries while reducing overall overheads requires an agile, simple, and yet, scalable solution. Agility is important as last mile delivery movement are prone to disruptions and delays arising from random everyday events.

 

There might be a rally or a vehicle breakdown which would delay a single or even a batch of deliveries. It is important to note here that such delays are not tolerated in the same spirit as they were before. We have a generation built on the immediate gratification of next-day or same-day deliveries. Such last mile delays might cost the company more than an irate customer, it might mean the erosion of market stickiness. Reaction times are very small. Any and all notifications and alerts have to be synced in real-time with the system to make decision making easier.

 

The technology must be simple because it would eventually go through multiple users and multiple interpretations. The technology or interface, be it a web-dashboard or a delivery person’s mobile app, should offer a simplified and optimized process. Communication flow across the network should be seamless and instant. Imagine a live wire where the impulse at one end is immediately felt at the other. Within layered technology covering a dynamic last mile universe, this is done by creating a singular, yet comprehensive, interface which is decipherable by all and would eventually lead to a single interpretation of the process.

 

The technology must be scalable. We have recently seen large retail and e-commerce event dominate shopping patterns. This trend would only grow over the next five years. Consumption patterns may become even more precise.

 

Scalability, or even the ability to adapt quickly, would be the key to sustaining businesses. Imagine having a thousand order to deliver in a day using a hundred resources. The schedule and planning could be managed using planning algorithms. However, when the orders expand to maybe two thousand, there is a sudden resource crunch and your algorithm now must deal with exponentially more permutations. The planning would be stuck if it isn’t backed by machine learning.

 

Future for businesses in an omnichannel world

 

Machine learning, here, means the algorithms ability to learn from past experiences, decipher multiple restrictions such as customer delivery windows, resource capacity, location intelligence, local traffic patterns, feasibility and durability of the products, multiple SLAs associated with the delivery process, cost of logistics movement, cost of any market-sourced resources, etc. All these patterns are then merged and turned into optimized and precise routes and schedules which would help the company ride the surge without any major delays or hassles.

 

The fact that an advanced scheduling and dispatch management software is working in your favor puts you in great space to deliver better. Delivery dispatch software gets you up and going for your last mile deliveries, greatly reducing lead time.

 

This agility, simplicity, and scalability within the last mile delivery universe and related field inside analytics is what is creating value for the big players and helping them create a proper omnichannel experience for the average consumer. It is going to grow much further ahead and having proper SaaS optimized logistics would result in lower logistics costs and higher resource utilization.

 

The need is clear, and the logistics are in place. It’s time to put machine learning to its actual and intended use. Doing fast, precise, repeatable, and scalable planning.

 

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