9 Key Technologies That Enable Customer Data Analysis
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Customer data analysis is one of the core principles for success in the digital world. You see, since the beginning of time, businesses depend on their understanding of their customer base. What do they need? How big of a priority is it? How much are they willing to pay for it? What makes them like/dislike or interact with various businesses?
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In the age of the internet, we finally have the means to figure some of these things out. Still, in order to do so, it is important that entrepreneurs embrace and implement several tech concepts and principles. Here are some of them
Customer Data Analysis Technologies
The main thing about customer data analytics is a matter of motivation. Sure, you can gather customer data via on-page tools but what do you do with all this data. How do you turn this into actionable information? Devising a strategy is like playing chess. Predictive analytics starts with a business goal: to use data to reduce waste, save time, or cut costs.
The process harnesses heterogeneous, often massive, data sets into models that can generate clear, actionable outcomes to support achieving that goal. These can be less material waste, less stocked inventory, and manufactured products that meet specifications. You need to think three moves ahead but your thought process needs to be impacted by the prediction of actions by the other party. With customer data analytics, prediction of future customer behavior becomes a lot more reliable. This is pivotal for the future development of strategies and preparing for various eventualities.
An in-memory database (IMDB; also, main memory database system or MMDB or memory resident database) is a database management system that primarily relies on the main memory for computer data storage. It is contrasted with database management systems that employ a disk storage mechanism. Main memory databases are faster than disk-optimized databases since the internal optimization algorithms are simpler and execute fewer CPU instructions. Accessing data in memory eliminates seek time when querying the data, which provides faster and more predictable performance than disk.
The in-memory database is a technology that allows a certain dataset to be stored in RAM in its entirety. The advantage of this is the fact that it provides much faster access to the data. This means that processing data creates a scenario in which real-time information processing is a possibility. With the availability of these databases, the majority of parties interested in in-memory databases and their application will have a much easier job of pulling this off. Also, this is one of the ideas that are the easiest to implement into the already existent model.
Big Data Security Solutions
When talking about big data, the majority of people talk about its analytical potential. What a lot of people ignore is the nightmare scenario where this data falls into the wrong hands. This is why the issue of big data security is so important. It is also why big data security solutions take such a prominent spot on the list of technologies relevant to present-day customer data analytics. The challenges in securing big data are mostly vulnerability to fake data generation and the security inside the system. The latter is the case because the majority of databases focus on points of entry and exit.
Picture 1. Big data security
Artificial Intelligence (AI)
Artificial intelligence (AI) refers to complex software that performs tasks in a way similar to human brains, often by sensing and responding to a feature of their environment. This could mean learning to solve problems in unexpected ways, recognizing the nuances of speech, or exhibiting some form of human-like creativity.
The question of the usefulness of AI in the field of customer data analysis is incredibly relevant. First of all, the use of AI can provide the user with new insights. Second, it increases the average accuracy rate on the majority of AI predictions. Finally, it provides the unification of analytics and customer data. The challenge of using AI for these purposes lies in the integration process. To facilitate integration, it is necessary to educate the staff on the subject matter. This is achieved through reliable materials and conferences.
Interaction design offers yet another way of considering your audience. From a design standpoint, it is an important concept that falls under the user experience umbrella. However, interaction design also branches into other areas (some of which also overlap with UX) such as content strategy, visual design and information architecture.
Interaction design addresses the communication between a company’s products and users. If a website has well-thought and well-executed interactions, it means a user can efficiently achieve his or her goals. According to web design Gold Coast specialists, the way in which the majority of this data gets processed is through interactive elements on the website. How these elements are introduced may affect how the audience interacts with your brand. This means that the interactive elements may increase the efficiency of the data gathering process. This only goes to show that customer data analytics need to be taken into consideration during the website development process.
Picture 2. Interaction design
Customer segmentation involves grouping customers into specific marketing groups, perhaps narrowing them down by gender, interests, buying habits or demographic. The process requires a thought-out strategy, understanding how to manage and group your customers and which data you will use to do this. By differentiating their customer base, businesses can better target individuals and maximize sales, link-sell appropriately and provide more tailored shopping experiences.
Observing your customers as a single entity is never a smart move. Instead, you need a reliable way of segmenting your audience. Doing so is not that simple but it does provide you with a more accurate customer data analytics result. The only thing worth noting here is that these customer groups need to be recognized before the analytical process starts. Therefore, data recognition plays a major role in figuring this out.
Machine learning is the process that powers many of the services we use today—recommendation systems like those on Netflix, YouTube, and Spotify; search engines like Google and Baidu; social-media feeds like Facebook and Twitter; voice assistants like Siri and Alexa. The list goes on.The concept of machine learning is huge in the customer data analysis industry. Previously, we’ve talked about AI and the ability to independently process this data after integrating the concept. In order for these AI-based tools to remain relevant for a prolonged period of time, the concept of machine learning must live up to its full potential. Combined with the cutting-edge technological concept of deep learning, these tools might soon become more independent than you would expect.
Minimization of Wasteful Spending in Marketing
Speaking of motivation, reducing wasteful spending in marketing was always one of the top priorities for all entrepreneurs in the market. Marketing is a highly competitive and expensive business to be in these days. A critical component of success in the current environment is effective targeting and the elimination of wasteful spending.
A long time ago, John Wanamaker said that while he’s aware that half of the money he spends on marketing goes to waste, he can never know which half. With customer data analytics, this is no longer the case. By getting better insight, the majority of entrepreneurs can now learn the specifics of how their visitors got there and which means creates the most paying customers.
How often do you complete a large project, from start to finish, on your own? I’m going to guess the answer is never, especially if you work at an organization with lots of employees. No matter what department you’re in, it’s highly likely you need to work across departments to get work done. We can’t do it all on our own, especially when a project includes specialties outside our wheelhouse. Yet cross-departmental collaboration goes beyond simply needing to tap into the skillsets of others.
The internal structure of every enterprise is quite delicate. Previously, we’ve already explained the importance of customer data analytics for sales and marketing. How about public relations? Isn’t there a way for the customer support to benefit from this insight, as well? Of course, there is. There probably isn’t a single department in your organization that wouldn’t benefit from greater accuracy of your customer data analysis process. By centralizing your enterprise, you would be able to send these results wherever they are needed, thus increasing the overall success rate.
Data will be the new oil in the years to come. You will see a lot of companies making use of big data and analytical techniques to make deep understanding and provide their customers with even better products and services. In the end, you need to understand the fact that doing customer data analysis and doing customer data analysis accurately aren’t one and the same thing. For this process to give the desired effect, you first need to know what you’re looking for. Second, you need to implement all the right tech principles and concepts, which will ensure that the information you get is dependable. It might not be simple, but it’s worth the effort.
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