Customer data analysis is a way of utilizing human behavioral and preferential data and patterns to improve key business indicators and boost the company’s success. Various websites, apps, and platforms specializing in consumer analytics use this type of data analysis to the fullest extent of its capability.
The primary purpose of all customer data analytics tools is to provide meaningful data and its proper analysis that has practical potential. Without this potential and the ability to act on it, such data remains meaningless. That’s why it’s necessary to build an analytics tool that can gather information based on its consistency and prospective convenience.
This article will guide you into the process of building an analytics platform and help outline the crucial steps. Keep reading to see what our team at DICEUS can offer you regarding consumer data analytics.
How to build customer data analytics platform for your organization
Customer data analytics is an enormous help while connecting with your target audience. By the nature of the collected data, customer analytics can be descriptive (covering business status quo), prescriptive (recommendations based on raw input), diagnostic (digging to the root of the problem), and predictive (possible scenarios).
The proper use of consumer data analytics platforms can give you the following advantages:
- effective customer database segmentation
- predicting consumer problems and their causes
- boosting customer journey comprehension
- understanding and anticipating client needs better
The most popular customer analytics use cases in modern companies are tracking important KPIs to enhance performance, prevent a possible churn, and adjust current and potential strategies to achieve more tremendous success. To reap the benefits mentioned above, take notes of the subsequent steps to build such a platform on your own.
Define your objectives
Select and define which short and long-term objectives you want to focus on. For example perhaps you wish your platform to predict the nature and the volume of future sales and customer feedback. You can choose from the following goals:
- focusing on behavioral patterns for a more successful client segmentation
- optimization of product performance and features
- invoking consumer loyalty, product satisfaction, and boosting customer retention
- personalizing advertisements and other campaigns better
- enhancing customer service experience, in which case you need to add customer service data analytics to the requirements
After defining your goals for the analytics tool, ask yourself and other stakeholders of your business as many questions as you can to set the details and possible limitations.
Gather requirements, including the metrics the software should track
It’s time to decide which data the tool will measure to meet the goals. You can start with all the information received from the actual sales and client transactions. Then, with each purchase, there’s a better grasp of potential patterns. You can add a series of direct customer service observations as well.
How clients are using your products or services is another metric applicable for valuable insights. Everything you can gather about consumers’ experience with the same or similar products should be included.
Next comes a person’s internet behavior. Knowing from which sources people found your company, how fast they respond and reply, how they engage with every element of your website — all this can help with conversions. Don’t forget to include scanning of reviews and feedback left on social media.
In terms of importance, the last but not least to include in consumer data analytics are clients’ personal details – socioeconomic and demographic factors, contact information, individual preferences, and reactions to promotional materials.
Analyze the data you want to use
Analyze and visualize the raw data mentioned in the previous step. Without visualization, it’s tough to make sense of any set of numbers or collected patterns. By visualizing the findings, you can also help the analysis process.
Whether it’s quantitative, qualitative, or behavioral data, you can use different analysis methods with accumulated client information. Numerical data representation fits best for pattern discovery and prediction. Any counting, measuring, or statistical analysis will then be presented in tables, graphs, or charts.
Every non-numerical piece of information may be a bit tricky to analyze due to its typical lack of structure, although it can still be appropriately visualized and described. It answers the question of “why” regarding your clients, not “how.” You can use natural language processing or data augmentation to facilitate the illustration and analysis.
N.B. Any data must be clean before analyzing it, which means extracting any inconsistencies, duplicates, errors, or unusable segments.
Evaluate how well the chosen model will perform
Any customer data analytics model should go through a thorough assessment of its future performance. The chosen model should fit the data and vice versa. Both categorizing and predictive analytics can be judged from two perspectives – business and science.
The business angle will help judge the model based on how well it adapts to the objectives named in the first step. Will the data and its visualization help make calculated and accurate decisions? Can it accommodate the ever-changing trends in marketing and technology?
Here are a few evaluation techniques from a scientific standpoint:
- Confidence intervals will help evaluate the reliability of any statistical estimates
- Check the data accuracy in a test environment that is close to a natural environment
- Adjust and assess the levels of statistical significance
- Use cross-validation to predict how well the analytics tool will perform on future data
- Check for possible performance degradations and visualization issues
Implement and automate your system
After testing is done and the model is validated, it’s time for implementation and automation. Model deployment can be done using two approaches. The persistence method allows any derived scores and values to be used by other applications and separates the model with its results. The semantic approach turns the model into a source code. That way, you can use the algorithms on any programming language by directly embedding them.
Finally, automate your analytics model using the needed data integrations. Over time, the system will deploy necessary algorithms on its own and flag unwanted data behaviors. Automation will also help to accelerate consumer analysis.
Based on our expertise in analytics tools, DICEUS can offer you both individual custom software development and solutions for enterprises. Either solution is bespoke and will be tailored based on which data analysis route you wish to take in your future analytics platform.
By applying our knowledge and experience in data science, we’re able to accurately implement any pattern detection and whatever algorithm you see fit to provide you with valuable insights about your customers.
You can also check out our most prominent customer data analytics solution named Customer 360 – an analytics tool for a Jordanian bank. It helped their conversion rates jump up to 25% and vastly improved client satisfaction.
It has always been the case of utmost importance to collect and correctly analyze the information that businesses receive from their customers. By implementing analytical tools, you can now effortlessly gather and utilize every piece of data, provided you have the correct software.
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