Big data is exactly what it sounds like - an abundance of data that modern marketers now have at their fingertips. But that doesn't make it any less challenging. In fact, when data is available in massive amounts, finding the most relevant and actionable insights that offer results through marketing strategies can be a tough task.
Even then, organizations are making huge investments in developing and acquiring talent, business, and technology processes aimed at collecting massive amounts of data to analyze them and create actionable business insights to offer nothing but value and quality to the customers. The only thing businesses need to pay attention to is the ability to improve digital transformation to convert data into useful understanding and knowledge that leads to timely and meaningful action.
How Big Data Analysis Can Help
Businesses can also consider data science training to enable an employee on-board to learn how testing and building models, crunching numbers, and understanding customer behavior on various social platforms can help predict what the customer what and how offering them exactly that can offer benefits to the business. Again, the idea to reach to that position is, to begin with data analysis. It's the procedure of processing through massive amounts of raw data to grab useful insights that can help an organization make a better decision. It is the big data, where the patterns about the social customer behavior, their growing demands, and user experience will be hidden.
With big data training, an employee is fully equipped with the training and knowledge required to extract that valuable information to make sales, marketing, and all the other departments of an organization to gain maximum benefits and do more business. There are three different classes of analytics that trained employees can use. The first one is the descriptive analytics. This one includes a detailed summary of historical data that has been collected over the period of time. According to experts, the major chunk of data will fall into this category.
That's why descriptive analysis is the first step. After extracting enough data out of it, you are in a better position to further process it and build it into the predictive analysis. Once you become more sophisticated with the information you have a collection, the last and essential step is to carry out prescriptive analytics. This is where you will build a prescriptive model on the available data, as well as on the feedback and action data to guide the authorities to make a better business decision. Since the last class models should be actionable, there should be a constant data stream of feedback and social analytics.
Other Methods to Gain Actionable Insights from Big Data
Here are a few more strategies that organizations can implement to turn raw data into worthy insights:
Optimize What You Can Measure
That's the rule! Every business model is different from the other one and should be treated accordingly. Thus, it's crucial to measure the right things that are relevant to your business. For instance, if you have established an e-commerce website, these are the factors you probably would like to measure:
- Most successful channels that offer maximum conversions
- What factors do you consider as your leaking buckets (where leads quit your website)
- The different devices used to access your app or website
- The look-to-buy ratio for every product category
- Which landing pages are weaker
To be able to focus on the right insights for decision making, it is essential that you are concise and clear about your business goals.
The Segmentation Method
One of the best ways to have a more precise approach to your data is to segment it. By categorizing big data, you can focus on one aspect at a time, which allows you to dig deeper. Data science training also helps the employee to prioritize the segments based on the problem you are trying to solve. The key is to correctly segment data to enhance your understanding of customer behavior pattern and reaction towards your products.
Use Visualization
The way you present data can make a significant difference in the results. Using visualization is a great way to present data. Once the data is extracted through the process, it is important to articulate it into a data story before presenting it. This will not limit your data to words and numbers only. In fact, it will convert it into useful insights that can be used for profitable decision-making.
Don't Ignore Qualitative Data
Since it is all about the consumer, don't forget to factorize qualitative data like reviews, feedback, comments, and more. If you conduct surveys or interviews, it is important to take your time to analyze that information. This data can add more sense to the overall insights you are collecting. It adds meaning to the quantitative data analysis.
Optimize
Conduct as many tests and analysis as you need to gain access to who is responding and what. Scattering big data to extract only relevant information can give you a clear idea about customer behavior and product trends. It's a great way to find out if a particular offer is more enticing for your customers than others and what channels are moving more consumers closer to your product(s). The information extracted through future-proof analytics can help an organization make major shifts to the marketing plan. However, it is important to keep repeating this process as the change in behavior is inevitable after a specific time period. Once you capture something measurable, optimize it and make decisions accordingly for maximum results.
Practical Data Science Training Benefit
Data science training does not only help data scientists with effective analysis procedures but also help marketers to make smarter marketing decisions. It is clear how technology is changing and its adoption accelerating, which naturally translates into changing consumer demands and behavior. With data analysis, and organization can collect information on a timely basis that's critical for business success.