Month: August 2021

Categories
Digital Customer Experience

Experience can make a huge difference – Get it right

Mastering the implementation of digital capabilities is the key to overcome experience disconnect

Would customers pay more for the delightful and seamless experience their favorite brand offers? In the coming years, most of them certainly will. Research shows that 86% of buyers are willing to pay more for a great experience. Experience has become a key brand differentiator, overtaking the price and product. Yet, many organizations may not grab this opportunity completely – as their customers may experience disconnect in the digital world.

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Getting the experience right requires implementing the right digital capabilities that consistently delight customers in every interaction.

The perception of a brand is built upon accumulated consistent interactions across multiple digital touchpoints. Getting the experience right requires an organization to implement the right digital capabilities that consistently delight their customers in every interaction. But as a matter of fact, most digital initiatives fail to reach their stated goals. One major reason is the gap between strategy formulation and strategy implementation. Incorrect implementation choices taken at the beginning of the project can make the organization rigid and unadaptable to deliver a seamless experience.

What can businesses do to make first-time right digital implementation choices? Critically evaluate the maturity of existing digital capabilities and plan the transition steps more methodically. Before starting the digital implementation journey, businesses must get the right answers to “Where to start?”, “How to start?”, and “How to get a head start?”.

Categories
Operational Excellence

Fiber is fast, but rollout needs to keep up

AI/ML can forecast delays before they occur, making the service delivery predictable and fast

The global pandemic has highlighted the fact that high-speed broadband is a necessity, not a luxury. And fiber is one of the ways to faster broadband. This appetite for fiber means that service providers need to roll out fiber-based connectivity services faster. However, with the rising complexities in the order management process, delivering the service within the specified timeline is becoming a nightmare. The main business issue is unpredictability, which may be as important as speed. Its absence means frustration for service providers and their customers.

The main cause of this lack of predictability stems from the structure of the process. In many cases, the enterprise service delivery process has evolved and grown organically. The most common causes of dysfunction are:

  • Multiple teams operating in silos prevent a clear view of the process and a single source of truth
  • Manual hand-offs leading to errors and delays
  • Dependency on external vendors, resulting in vendors operational issues being transferred to the service provider
  • Lack of strategies to forecast order delays
  • Lack of mechanisms for real-time tracking of service delivery flow

To overcome these challenges and tap into the next wave of opportunities, service delivery operations will require an advanced vision. AI/ML is at the heart of that vision. With AI/ML in service delivery, enterprises can predict and address delays before they impact the business. Enterprise AI can, over time, improve the prediction of potential delays and delivery dates at all points of the order journey. Over time, enterprises can achieve faster processing of orders with improved predictions.

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The appetite for high-speed broadband demands a faster rollout of fiber-based connectivity services.

Categories
Cloud

To treat, or not to treat: Increase marketing ROI with targeted campaigns, through uplift modelling

While running direct marketing campaigns, businesses must map the right customers to a given promotional offer to maximize the campaign effect. For example, which customers should receive a discount on subscription, to minimize the business overall churn rate.

Different methods can be used to identify the right set of target customers for campaigns, such as, manual spreadsheet-based statistical modelling and outcome modelling. These methods, however, have some limitations like:

  • Randomized and inaccurate list of target customers
  • Lack of granular details such as which customers are most likely to respond to marketing campaigns
  • Low marketing ROI due to poor response rate from customers

Machine Learning (ML)-based uplift modelling is a promising approach to overcome the above limitations. It allows businesses to categorize customers as the ones who are likely to respond positively to a campaign and those who would remain neutral or even react negatively.

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An uplift model increases marketing ROI by determining the right target customers.

A well-executed uplift model would improve a business marketing efficiency and help in driving higher incremental revenue. The successful implementation of the model requires the right set of enablers such as raw data acquisition, feature engineering, and AI/ML model development.