Posted on: November 23, 2020 | 3 min read

The Risk of Not Investing in Analytics

Although organizations across industries are implementing advanced analytics and Artificial Intelligence (AI) at a brisk pace, there are plenty of organizational leaders that are not ready to make the investment. There are valid reasons behind the reluctance. Modernization of legacy systems and applications may stand in the way. The technology behind advanced analytics and AI is complicated and daunting. There are competing priorities and many organizations struggle to create a value proposition that justifies the endeavor.

Unfortunately, organizations that do not develop advanced analytics and AI capabilities will lose market share to innovative competitors. Every year, new tech start-ups disrupt established industries and change the way business is conducted. Success in an ever-changing marketplace requires speed, efficiency and continual adaptation to consumer attitudes and behaviors, which vary by demographic.

For example, understanding the attitudes and behaviors of millennials will be critical for organizations that have previously underserved this group. According to a report by WealthEngine and the Coldwell Banker Global Luxury® program, millennials will hold five times as much wealth as they have today and are expected to inherit over $68 trillion from baby boomers. Millennials prefer peer-generated endorsements, third-party reviews and self-education over interactions with sales personnel. Millennials seek self-service channels to make purchases and enroll in services. In addition, they expect personalized experiences through these channels.

Addressing these needs requires organizations to process online and social media content to derive insights. It also requires organizations to invest in automation and predictive analytics to provide self-service capabilities and personalized experiences.

The organizations that will win are the ones that can:

1) Tap into various data domains to derive insights

2) Leverage insights to create efficiencies, new offerings and enhanced customer experiences.

There are many use cases that your organization can pursue to create new and better revenue streams or cost-saving efficiencies. You can start by leveraging automation and AI to perform tasks that are prone to human error or simply cannot be performed by employees. The following are a few common examples:

  • Use Optical Character Recognition (OCR) and Natural Language Processors (NLP) with AI to process large volumes of content such as emails, chats, and conversations to create repositories for various purposes such as tracking customer sentiment by interaction type.
  • Detect fraudulent claims and determine credit worthiness or default risk through machine learning.
  • Predict maintenance schedules, optimize resources and improve reliability of field assets by leveraging sensor data coupled with predictive algorithms.
  • Create targeted promotions and sales campaigns by evaluating purchases by region, demographic, channel and season to predict future sales.

Realizing the benefits of advanced analytics and technologies like machine learning may require a few years; therefore, it is critical that key executives understand the effort required and commit to the journey. There are a variety of initiatives that may be required including technology investments and projects to integrate and prepare data. The development of AI solutions requires time to configure and train models according to the business and organizational context. Once built, the resulting insights, predictions and automation need to be presented appropriately. This will require projects to integrate the solutions, align business processes and train employees.

Like any transformative change, it takes time for an organization to excel at using its new capabilities. Although the journey is difficult, your organization needs to begin right away to stay apace of evolving customer needs and competitive factors.

To get started, you should make sure to understand the current trends in your industry including consumer behaviors, regulatory changes, emerging technologies, new competitors and methods of delivery. Develop a common understanding of your proposed offerings. How do they meet current market needs? What are the benefits and features that your products and services will provide? What are the production and delivery costs? What are the outcomes you want to achieve?

In addition to determining your value propositions, you will need to convince key stakeholders in your organization to invest in the solutions by addressing critical questions. How do the enhanced revenue streams and cost savings created align to their priorities? What are their objections? How do you demonstrate that your solutions enhance their ability to meet their objectives or are simply critical to the organization’s success?

Explore how CCG can assist you on your analytics journey. We have helped dozens of clients develop compelling value propositions for their organizations and have enabled them through sustainable data and analytics strategies.

Written by CCG, an organization in Tampa, Florida, that helps companies become more insights-driven, solve complex challenges and accelerate growth through industry-specific data and analytics solutions.

 

Topic(s): Data Science
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