Have Retail Companies Missed The Data And AI Revolution? – A Holistic Approach to Becoming a Data-Driven Company

A new era began when Google announced it would cease using third-party cookies in Chrome by the end of 2024. This announcement triggered significant shifts, with end-user companies redirecting their focus from third-party to first-party customer data. This paradigm shift extends beyond end-users to encompass customer success teams, data platform developers, and the revenue models of digital tools.

In industries like finance, gaming, marketplaces, and video streaming, rapid evolution is inherent. Meanwhile, businesses in the retail sector faced a game-changing turning point—the AI revolution, accelerated by the ChatGPT revolution. While some countries continued traditional trade methods, the increasing e-commerce share, especially through digital apps post-Covid-19, could not be ignored. This unexpected catalyst, akin to a Black Swan event, not only altered existing business models but also spawned new types of businesses.

As the retail sector grapples with new data privacy regulations and the integration of offline and online transactions, the AI revolution takes another step forward. For many firms in retail business, creating a customer data platform with their online and offline transactions with complete uniqueness and getting rid of duplications was seen as a success. However, they started to take their actions according to very basic and limited data compared to fastly digitalized sectors and their segmentation of customers was very far away from becoming super granular. Digitized companies have already chosen their super-connected sales/marketing/service cloud ecosystems, analytical tools, or new-age chatbots while retail were still trying to integrate their checkout systems, payment options, and customer data.

• Senior management prioritizes IT projects in Sales, Payment systems, Finance regulations, etc., over Customer Data and Experience.
• The entrenched belief that Customer Experience is a cost center and not part of the short/mid-term company plans.
• Lack of dedicated CRM teams within the company.
• Use of various tools within the company for different tasks, time-consuming integrations.
• CRM/Marketing teams’ inability to adequately demonstrate incremental numerical results to top management.
• Lack of communication between top management and CRM teams for brainstorming and finding creative ways to monetize CX actions.

1. 1SIMPLICITY Adopt a holistic approach to projects: ‘Done is better than perfect!’
2. 2INTEGRITY Choose and use ‘easy-to-integrate,’ ‘plug-in,’ and ‘inter-communicated tools’: ‘If you don’t have a task force, outsource!’
3. 3SYNCHRONIZATION Instead of interpreting a lot of unstructured cold data, focus on gathering instant useful customer data.
4. 4EXPERTISE Form dedicated and specialized teams for customer experience and data touchpoints.
5. 5CULTURE Prioritize and invest in data, making it a part of the company culture by considering it a long journey.
6. 6MONITORING Continuously measure and listen to your company activities from the voice of customers, not just your data, and detect discrepancies.
7. 7ARTIFICIALIZATION Embrace more AI-driven business for both employees and customers.

Imagine, are these a dream?

• A well-trained custom GPT from the previous social media posts widget is put on the company intranet for the social media specialists and content writers to analyze and fine-tune the social media content according to the best impression.
• A custom GPT embedded the fashion retail company e-commerce application which enables customers to attach their clothes photo and instantly browse the web or create visuals for a matching outfit combo.
• An AI algorithm of the sports shoe retail company listens to call center calls, reads social media comments scores customer-wise emotions, and creates a daily overall score for company managers. Embed the scores to the analytical churn prediction models and satisfaction measurement scores. Track the daily scores with the marketing activity days to measure success.
• A food company puts an AI add-in to its website which allows its customers to create an order basket without exceeding a total calorie and gives recipes by using the foods in the basket.
• A cosmetics company AI model allows customers to import the photo of their skin tone (with a small piece, not the whole face) and pick the best color suggestions for the proper lipsticks. According to real personal purchase results, it improves itself!

This article first appeared in Cloud4Feed’s CX Kaleidoscope Yearbook 2024 and is published here as written by the author. Part 7 of 8