As a business leader in the digital world, you prioritize customer experience (CX). You know you need data to understand and improve how customers interact with your brand. However, you may not be fully aware of how big data fits into the conversation — specifically, how it differs from traditional data and why it’s so closely tied to CX.
The amount of data that businesses have access to is growing every year, but few companies are using these large datasets as effectively as they should. Read on for a better understanding of what big data is and how you can use it to elevate customer experience.
This post will cover:
What is big data, and how is it different from traditional data?
Big data refers to datasets so large and complex that they are difficult to manage all at once. That might sound subjective, but it’s more technical than it seems. To be considered “big data,” it typically must exhibit all three Vs:
- Volume: There must be a lot of data. Think of every user’s clicks tracked across multiple platforms and channels. Even a single source might contain an enormous amount of information, though it’s often low in density. The meaning of this vast activity isn’t easy to interpret quickly, and most companies track far more than just one source.
- Velocity: Data must arrive promptly and be handled efficiently. It becomes more useful when you can receive, record, and respond to it in real time.
- Variety: Data must come from multiple sources and in multiple formats. It can be structured — like numbers in a spreadsheet — or unstructured, such as text, video, or audio. It may also not flow at a steady rate, coming in large waves at different times, with sources that shift and evolve.
Think of the difference between traditional data and big data as the difference between water flowing from a sink faucet versus water gushing from a fire hose — or even several at once. Normal data is easier to control, monitor, and process, either manually or using simple tools. However, traditional data techniques and software are often unable to process large amounts of data. Standard data analytics simply can’t keep up.
How can big data improve customer experience?
With this flood of digital information, you might think that big data is solely about crunching numbers and is disconnected from real people. However, when you know how to handle it, big data opens the door to connecting with many customers at an individual level. And those interactions with real people is what customer experience is all about.
You can use big data to improve customer experience in these six ways:
1. Elevate customer personalization.
- Treat every customer interaction with your business as a valuable data point. From website clicks and purchase history to customer service calls and social media mentions, each touchpoint contributes to a comprehensive, real-time customer profile.
- Use this aggregated data to create hyper-personalized ads or recommend specific products with uncanny accuracy. Imagine a customer browsing hiking gear: big data allows you to show them ads for waterproof boots they almost clicked on or suggest a compatible backpack based on their past purchases, rather than generic promotions.
- Help customers feel genuinely important to the company with personalized content and a custom user experience. This goes beyond just adding their name to an email — it means tailoring their entire journey, from the layout of your app to the product suggestions they see, making every interaction feel uniquely designed for them.
2. Minimize friction across the customer journey.
- Learn which parts of your website are moving too slowly, causing confusion, or leading to abandonment, so that you can fix them and make the site more intuitive and useful. Big data reveals patterns in user navigation, highlighting conversion bottlenecks or areas where customers consistently drop off.
- Discover precisely which aspects of your customer service are leading to dissatisfaction, where the system itself needs work, or where your support team requires additional training. By analyzing call logs, chat transcripts, and sentiment from feedback, you can pinpoint systemic issues or common customer pain points.
- Make the work of customer service agents easier and faster by equipping them with robust, real-time data. Agents can immediately access a customer's full history, including previous interactions and current issues, enabling them to offer quick, informed, and relevant solutions without needing to ask repeated questions.
3. Understand customer behavior and motivations.
- Learn why customers are acting a certain way without having to ask them directly through complex behavior pattern analysis or advanced sentiment analysis of their unstructured data (like reviews or social media posts). This allows you to infer intent and preferences from their digital footprint.
- Enhance personalization significantly by targeting customers based on their observed behavior. For example, suppose big data shows a customer has repeatedly viewed a particular product but hasn't made a purchase. In that case, you can follow up with a targeted discount or relevant user reviews. If you understand why a customer bounced from a page, you can proactively address that specific issue and notify them of the resolution.
- Understand and appeal more effectively to the emotions and underlying needs of new and existing customers. Big data helps segment customers not just by demographics, but also by psychological profiles, allowing for more empathetic and resonant messaging.
4. Target the right customers.
- Track comprehensive customer interactions and attributes to create a highly accurate customer profile, providing a clear picture of what's common among your best, most loyal customers. This allows you to build a reliable ideal customer archetype.
- Then, you can target new prospects who possess similar traits and behaviors, significantly increasing the efficiency of your acquisition efforts. The more data you gather, the more confident you can be in defining and reaching your ideal customer segments.
- Determine whether your marketing campaign is effectively reaching the target audience by examining granular engagement data. See if specific segments are clicking through, engaging with content, and converting, then make necessary, real-time adjustments to campaign parameters or messaging to improve ROI.
- Attract individuals who are already likely to be interested in your brand or product, rather than wasting effort on a general audience and hoping they don't bounce. This intelligent targeting minimizes wasted ad spend and improves conversion rates by focusing on high-potential leads.
5. Predict future trends.
- Track nuanced customer trends across diverse datasets to forecast what might become popular in the future, from product demand to preferred communication channels. The biggest benefit of big data is that, due to its immense volume, it provides a statistically substantial sample size for analysis, leading to highly reliable predictions.
- Gain actionable insight into what's happening now to accurately predict what might happen next, enabling your business to stay prepared for future shifts. This means anticipating market demand for seasonal products, predicting churn risks, or identifying emerging customer service needs.
- Reduce the risk of disruptive trend swings or critical stock shortages by preparing in advance. By predicting demand, you can optimize inventory, staffing, and even pricing, thereby preventing customers from encountering potential problems and ensuring a seamless experience.
6. Build customer loyalty.
- Stay ahead of evolving customer expectations and proactively build customer loyalty through personalized engagement and predictive service.
- Consistently track long-term trends and intricate customer behavior patterns to identify and continuously improve the parts of your customer experience that fall short, thereby keeping customers happy and engaged over time. This iterative improvement based on real-time feedback and long-term data fosters trust and reduces churn.
- By consistently demonstrating that you understand customer needs and are responsive to their feedback, big data helps forge stronger emotional connections, turning customers into loyal advocates. This extends beyond transactional relationships to build genuine brand affinity.
The relationship between AI and big data.
Big data delivers vast information and deep insights, but AI is the force that truly amplifies its impact on how customers experience your business. AI can process massive amounts of data quickly and accurately, far beyond human capacity. This empowers businesses not just to understand what customers are doing, but to predict what they’ll do next — and act on it before it happens.
AI uses big data to deliver:
- Hyper-personalization at scale: AI can analyze billions of data points to create individual recommendations, content, and journeys for millions of customers simultaneously.
- Intelligent automation: From AI-powered chatbots resolving routine queries to predictive routing of complex issues, AI streamlines service and sales processes.
- Proactive engagement: By identifying subtle patterns in big data, AI can predict customer needs or potential churn risks, allowing businesses to intervene with relevant solutions before a problem even arises.
- Continuous optimization: AI constantly learns from new data, refining personalization models, improving service responses, and optimizing customer touchpoints in real time, ensuring CX is continually improving.
In essence, AI transforms big data from a powerful analytical tool into a dynamic, adaptive system that drives brilliant and seamless customer experiences.
Get started with big data and customer experience.
With big data, you can gather information and recognize patterns that would otherwise be impossible to understand — then respond in real time to create consistent, memorable experiences that will keep customers coming back for more. People appreciate it when you cater to their needs and make them feel valued. Big data helps you save them time and deliver a service that benefits both sides.
The first step in using big data to enhance your customer experience is having a platform that enables real-time customer interactions. Adobe Experience Platform does that and more.
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