Improving the customer experience (CX) is among the crucial steps in ensuring better business performance. How is that?
Suppose you want to boost sales. A person will hardly buy if the website is too complicated to navigate, the pricing is vague, and the customer care specialists are incapable of resolving clients’ issues.
Even if you launch the best marketing campaign, the customer experience is key. It can also strengthen loyalty, attract more clients through positive recommendations, and increase sales. Among the various ways to improve the customer experience, there is one that stands out. It’s big data.
Big data is not just big. It’s enormous, complex, and scattered. It contains so much information about your audience that no other tool or channel can provide. In this article, we’ll overview some tips on leveraging big data for better customer experiences. We’re also going to dive into how cutting-edge AI tools can break down big data barriers, making it super accessible and actionable for your team.
In short, everything around us is data. But not everything you monitor from your dashboard is “big data”. Compared to regular data, there are two criteria to consider it big:
Traditional data is structured and has a well-defined schema, which makes managing, tracking, and processing it more straightforward. Sometimes, you can even do this manually or without advanced and pricey technologies.
With big data, things get much more complicated. It can also be structured or semi-structured, but more often, it’s unstructured. It should also possess the following characteristics, known as the “Five V’s”:
Put simply, it’s the size of information stored in your systems. The more it gets, the more powerful computers and storage systems you’ll have to purchase. For example, 204 million emails sent every minute across the world are big data only based on volume criteria.
The new information is generated every second. Users click, make purchases, and comment on social media posts, so it all comes to your system. One of the essential things here is to capture, process, and respond to this data as fast as possible to understand better and serve prospects in real time.
This “V” refers to diversity. Big data includes numbers, pictures, videos, emails, and voice recordings. Your task is to be able to process the information in different formats, which may also appear at a different speed depending on the current trends.
Can you trust that data? You shouldn’t aggregate all of the insights available but focus on accuracy as well. The big data should be genuine, reliable, and devoid of inconsistencies. Why? The reason is that you need a solid ground to build your decisions on.
When gathering information, ask yourself, “How is that data applicable to your business?” If you collect it just for the sake of collecting, it won’t get you far. But it's valuable if you do it, for example, to learn more about consumers and personalize offers based on their purchase patterns, demographics, and feedback.
Regardless of your industry, you need to delve deeper into audience analytics. Why is it important? Because it lets you comprehend buyers better to sell what they need, not just what you want them to buy. To tailor your offers to their tastes, also consider practices like a comprehensive conversion optimization audit to check and enhance your website.
You possess a mammoth collection of insights about your clients, from shopping behavior to feedback. Use every byte of this data to find a key to their hearts. Here is how:
Scattered data can lead to disjointed strategies, missed opportunities, and costly mistakes. That’s where you need to pull all that information together into one centralized hub. This step can provide you with the following benefits:
Analyzing customer interactions starts with choosing metrics to benchmark your performance. Some can show the real state of affairs, while others require a more thorough investigation.
Take the average handling time (AHT) of a customer query as an example. This metric is low, so your team works efficiently, addressing questions promptly, right? Yet, it’s not always true. Quick issue resolution may also indicate insufficient attention to the problem, perhaps failure to resolve the customer’s concerns in-depth.
The first contact resolution rate (FCR) is another metric that calls for further analysis. High FCR could mean customer issues are handled after the first contact.
However, if coupled with low customer satisfaction ratings, it may indicate unsolved underlying problems that lead to customers giving up. But look at customer satisfaction scores. If they’re not so good, it may mean persistent underlying issues that lead to clients giving up eventually.
Examine operational challenges, such as:
As previously stated, every consumer activity adds layers to their digital persona. All of this lets you craft unique experiences. Take Amazon or Netflix, for instance. Their bespoke product or content recommendations aren’t just randomly curated. They arise from deep dives into user behavior, including the duration of their viewing sessions, items in their cart, products they paused to look at, and browsing patterns in general.
Sales are driven by emotions. So, if you manage to use data to foster meaningful connections, you’ll be able to boost conversions and revenue.
The user experience is among the crucial aspects of converting prospects into customers. Imagine browsing a store with a convoluted checkout process or a slow-loading page. It directly affects bounce rates, conversions, and even your search engine rankings. For example, a growth of loading time from one second to 10 seconds results in a 123% increase in mobile bounce rates.
While some issues may go unnoticed, big data provides a magnifying glass to dive into details, such as website navigation, service interactions, pages with a tendency to have higher bounce rates, and also where users spend an excessive amount of time.
One of the illustrations of employing big data for speeding up business processes is in airlines. Companies like Delta Airlines do this to minimize delays and costs on compensation.
Big data is essential for avoiding costly mistakes like stock-outs during peak seasons or launching a product line at a less-than-ideal time of year. That’s where predictive analytics comes into play. You can see it at work when Netflix suggests a thriller because you watched a particular drama or when Amazon introduces you to a new book genre. Here are some steps to employ predictive analytics based on big data:
Pro tip: Combine predictive analytics with a robust CRM system. This step will allow for automated workflows, ensuring consumers get tailored recommendations with minimal interference on your side.
Another sphere to implement big data is improving long-term customer relationships. Why do you need it? First, it’s easier to ensure repeat purchases from customers who already did business with you. Second, they can become trustworthy brand ambassadors, spreading positive recommendations to their friends (known as word-of-mouth marketing).
Where should you begin when it comes to strengthening loyalty? Feedback. Those who’ve taken time to leave their thoughts deserve attention, appreciation, and issue resolution in case of a negative experience. When analyzing big data, you may find recurring patterns, highlighting areas for improvement.
Then, turn to purchase behavior. Do clients reorder particular items or services after a set duration? If you anticipate their needs and present the needed product, it can keep them returning.
Remember that you build connections with real people. Nobody likes their inbox flooded with irrelevant emails or being bombarded with ads that don’t align with their interests. Big data helps improve communication and determine the best time to step in. Let’s say data shows that a customer typically shops during lunch hours. One of the things you could do is to send them a notification or a deal during this period.
Open-source data is not the first thing that comes to your mind when you start searching for big data. Yet, it’s a vast resource and an alternative to proprietary databases and pricy analytics platforms. In essence, open-source data is data that’s freely available to the public. Think of it as a massive library of information without any entry fee. This doesn’t mean it’s of lesser value; quite the contrary. These datasets can give a fresh perspective, provide benchmarks, or fill gaps in your own data.
And here’s an addition: Don’t miss out on local open data repositories. Many local governments and institutions release data pertinent to specific regions, which can be a goldmine if you focus on local markets or want a more coarse view.
So, you’ve got access to these vast oceans of data. Now what? Here’s the game plan:
Merge with proprietary data. By integrating open-source data with your own, you can gain a holistic view of market trends and customer behaviors, shedding light on areas your internal data might not cover.
Look for gaps and opportunities. Open-source data can help you benchmark your performance against industry standards, letting you identify areas of improvement.
Tailor customer interactions. Understand broader market trends, then use this info to refine your interactions. Maybe there’s a health trend blowing up that you can cater to, or perhaps local holiday data can help you time your sales better.
Automate and integrate. Tools like Google’s Dataset Search can seamlessly slot into your workflow, making the process of finding and using open-source data much smoother.
First and foremost, you need to harmonize information. That’s where specialized tools like Triple Whale may help. Powered by OpenAI and machine learning, it lets you communicate with your marketing and sales data in the form of a dialog like you’d interact with ChatGPT. Triple Whale employs GPT 3.5, GPT 4, and Natural Language Querying (NLQ).
Wondering what this jargon means? NLQ makes it possible for you to ask questions, such as “Hey, what were the top-selling products last month?” or “Which marketing campaign had the highest engagement?” and get precise answers. It’s simple, learns with time, and connects to ChatGPT’s models to understand not just numbers but also images. And it doesn’t require special technical expertise to fetch insights.
Note: You shouldn’t overlook the importance of data safety and compliance when uniting everything under one roof. A centralized hub should operate under strict governing standards. Luckily, Triple Whale ensures data integrity, privacy, and compliance.
Tools like Triple Whale’s natural language chat feature are also there to help you recommend tailored products, services, or content. Thus, they streamline your campaigns, focusing on personalization.
If you need to optimize the use of AI in marketing efforts, consider Triple Whale’s free tool, the Ultimate ChatGPT Marketing Prompt Generator, designed for ECommerce businesses.
Big data lets you achieve various goals, from business growth to personalizing communication. By employing dedicated AI-powered tools, you can automate data analysis and explore what your customers expect, how they behave, and what they stay away from.
Big data is all about improving the customer experience. And the more information you collect, the more you move from a one-size-fits-all approach to a tailored, customer-centric strategy. The result? Satisfied clients, more sales, and stronger connections. Gather and analyze data, employ your intuition, and make your audience feel valued.
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