Next-Gen Insights: Key Elements Driving Customer Analytics

If organizations can fill the following fundamental gaps, this could be the year when AI solutions gain enough momentum to transform the way companies perform customer analytics.

5 Min Read
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It may feel like the newest AI solutions are already applied everywhere, but that’s not the case -- yet. Despite the media and industry discussion in 2023 about the potential applications of AI and advancements in analytics technology, we’re still on the cusp of widespread adoption in business. For example, at the end of 2023, a survey showed that more than 80% of companies agreed that the ability to stay competitive will depend on using AI for customer analytics to improve customer experience (CX), but less than a quarter were doing so. Our experience with consumer-packaged goods (CPG) manufacturers has shown us there’s wide variability in customer analytics maturity across and within industries.  

A few organizations are already creating advanced insights with the latest AI solutions, leveraging natural language processing for predictive analytics, generative AI, or recommendation engines. However, many are not doing so because they struggle with other foundational components of a successful customer analytics program. For example, they may lack a well-defined data strategy, have siloed data that prevents a customer-centric view, or need skilled talent to use digital analytics tools. If organizations can fill these fundamental gaps, this could be the year when AI solutions gain enough momentum to transform the way companies perform customer analytics.  

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Delivering Better CX

Clearer insights allow companies to provide better experiences at many points in the customer journey. Leveraging customer analytics effectively can strengthen customers’ feelings of recognition and ensure offers and communications are better targeted and timely. By analyzing customers’ responses to improved AI-generated product recommendations, companies can forecast demand more accurately, reduce the likelihood of stockouts, and get valuable insight on services and product experiences. Leveraging that data for continuous improvement allows businesses to refine their offerings and enhance customer satisfaction.  

Some CPG brands are already using analytics and AI for better CX. One global CPG company collects and analyzes customer data to understand customer needs and how customers think their products compare to competitors’ offerings, sentiment around specific ingredients, how customers talk about the company’s products online, and which influencers the company should work with.  

Making Better Decisions Faster

Real-time customer analytics give companies immediate insights that they can use to improve their interactions with customers or share to help their customers improve their own customer experience. 

Related:How CEOs and IT Leaders Can Take the Wheel on Responsible AI Adoption

Some B2C companies are already using AI technology in their customer support channels to analyze contacts for customer stress levels and the quality of support provided. For example, AI-powered chatbots can generate contextually relevant and human-like text to respond to consumers in real time -- expanding and improving customer service and advice. Later, insights from these interactions can help in adjusting the support experience to reduce stress and minimize customers’ need to seek help.  

Improving Sustainability

Better customer analytics capabilities can also help businesses address increasingly urgent sustainability priorities. According to recent data, 61% of organizations see a lack of sustainable practices as “an existential threat,” and close to half think climate change will be the leading disruptor of business operations over the next 10 years. As a result, 52% of business leaders are investing more in sustainability initiatives this year.  

Analyzing customer feedback and preferences can provide valuable insights into desired product features, functionalities, and sustainability attributes. Businesses can use this information to design and develop products that meet customer needs while also minimizing environmental impacts throughout the product lifecycle, from sourcing of materials to end-of-life disposal. For example, one global luxury brand is using predictive analytics for demand forecasting to avoid overproduction that would generate waste and extra expenses related to the disposal of unsold products.  

Related:Is Innovation Outpacing Responsible AI?

Customer analytics can also help business track key sustainability metrics and KPIs such as carbon footprint, energy usage, waste generation, and recycling rates. By monitoring performance overtime and benchmarking against industry standards, businesses can identify areas for improvement and implement strategies to enhance sustainability across their operations. 

More than AI Tools: Success with Customer Analytics

AI is the engine that powers these customer analytics use cases, but other elements provide the fuel. Without these critical components of a strong customer analytics program, AI investments in this area can’t maximize ROI.  

Customer centricity: The organization needs a truly customer-centric mindset and culture that prioritizes thinking about customer data in ways that allow for innovation. 

Structure: Organizations that still have data in silos need to finally break those down, democratizing it so teams can derive insights across the full customer value chain. This kind of restructuring both reflects and strengthens customer-centric strategy and culture.  

Talent and expertise: Your analytics teams need strong data skills and a customer-centric mindset. Whether they are in-house specialists working in a center of excellence or external support from an experienced partner, the blend of analytical acuity and customer focus is critical. 

Data collection and analytics solutions: Gathering, integrating and analyzing data from existing and emerging touchpoints is one of the biggest challenges for many organizations.  

Technology: The right AI tools can help organizations reach many of the outcomes discussed above. However, the return on investment for analytics solutions will depend largely on the quality of the other elements in the analytics program.  

 The examples we’ve shared above, of companies using customer data and AI for CX improvements and innovations, are possible because those companies also have customer analytics program elements in place. As more companies move to adopt AI and related technologies this year, the foundational elements above will be the keys to making the most of those next-generation investments. Getting the basics right will help companies do more, faster, with their new technology investments for customer experience and business decision making. 

About the Author(s)

Katja Tiefenbacher

Senior Director, Capgemini Invent North America

Katja Tiefenbacher is a Senior Director at Capgemini Invent North America, where she leads the Data Strategy practice. With a Ph.D. in Information Systems and 16+ years in consulting Katja is a key expert for turning data into value through initializing organizational setups and integrating corresponding solutions. A broad range of clients - particularly in automotive and CPR - have come to value her advice and passionate delivery when it comes to the development and realization of AI strategies, target operating models and data management for scaling AI. 

Nadia Kiani

Managing Consultant, Capgemini Invent North America

Nadia Kiani is a Managing Consultant at Capgemini Invent North America within the Data Driven Transformation team with core expertise in the Consumer Products and Retail industry. She has a strong background driving impactful AI solutions for global brands helping them make insights driven executive decisions. Nadia's expertise lies in delivering compelling analytics strategies, showcasing strong leadership in the field of customer data analytics. 

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