Top 12 Process to Turn Data Into Actionable Insights

Priya Bawa

She has started her career as a Content Writer and writes on blogs related to career.

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Actionable insights, in their most basic form, are relevant conclusions derived from raw data in your analytics platform.  There's nothing wrong with telling a client what they can easily get from Analytics, but there is value in supporting them in understanding that the in-store action they ran in France last month was associated with a rise in normal basket values from male customers over the age of 45, says Nick Craig, Managing Director at Mackerel Media Ltd.

There is one distinguishing feature of actionable insights. They motivate others to take action, which leads to outcomes. Some actionable insights provide answers to queries concerning the effectiveness of your marketing. Other actionable insights urge taking action or solving an issue.  As an example, suppose you own a SaaS website. And you want to increase demos and revenue.

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You're checking through Google Analytics when you discover an unusually high bounce rate on your book demo page. Change the appearance of your sample page and increase the intent of your content as an actionable insight. Boost your Skills by learning: Digital Marketing
Table of Content:
Top 12 Process to Turn Data Into Actionable Insights:

Top 12 Process to Turn Data Into Actionable Insights:
1) Pay attention to the source of the data:
No such thing as "perfect data" exists. Business data originates from a variety of sources and is prone to common difficulties such as outlier numbers, missing information, and/or erroneous data entry. Data cleansing and preparation for subsequent processing are critical tasks. A bad dataset may result in incorrect insights, causing additional complexity for organizational decision-makers rather than assisting them. As a result, data integration becomes a critical responsibility. It not only makes business decisions smarter, but also faster. Collecting and linking data from many sources improves the company's understanding of its consumers' behavior and delivers superior insights. Data scientists often choose to examine a subset of data.
Data scientists often choose to evaluate a subset of data from a data mart initially since it provides faster insights and greater flexibility. They then broaden their reach to include studying larger and more complicated data sets.
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2) Place your data in context:
The more you comprehend the context of your data, the better you will interpret it, and the subsequent judgments will be more strategic. Context may be established by questioning what data means, determining its relevance, and comprehending its influence on the business.
3) Use segmentation to motivate people to take action:
If you would like to take action on your data, segment it! You might begin exploring deeper by categorizing visitors who have some characteristics. The segments you research are determined by the business question you are attempting to solve. Identifying segments will help you better understand how your clients act. You may use this information to create an optimization strategy. Digital analytics systems, such as Google Analytics, come with a plethora of pre-built categories and allow you to tailor them to your own requirements.

4) Dismantle organizational silos:
Organizational silos are simply corporate units or divisions that operate independently and do not exchange information. It is in the organization's best interests to reduce and break down these silos. You should imagine an organization with no blind spots, where information flows easily between departments and sectors for decision-making and strategic planning. A strong organization serves as the foundation for all that is good. Instead of confronting, consider talking. In addition, be inspired, motivated, and curious about each dataset and its potential. This strategy prioritizes removing roadblocks and boosting communication between the company and analytics team leaders. 
5) Learn about the context of your data set:
Everyone possesses data as well as their own particular data-driven insight (opinion). In most circumstances, higher context comprehension leads to the best options. Make careful to put the facts you're looking at into perspective. What do these figures mean? Are they significant? Is it actually having an impact on the business? And how is the information gathered? Data without context is meaningless and can even lead to poor business choices due to incorrect interpretation.
6) Use segmentation to motivate good activity:
If you want to act on your data, you must segment it. You might begin delving deeper by classifying data that has a common feature, such as clients with similar consumption patterns or timetables. You will then pick whatever category to study based on the problem or questions you wish to answer. Through customer segmentation, you will gain a better understanding of consumer behavior and trends by identifying and providing a distinct identity to each category.
7) Create a robust optimization strategy:
To enhance your firm, use the "Define Measure Analyze Improve Control" (DMAIC) approach. It is one of the Six Sigma principles that you may immediately utilize in your scenario.In a nutshell, it boils down to:
  • To detect abnormalities, collect relevant data, and do basic analysis.
  • Control the modification by running (A/B) tests and tracking KPIs.
  • Analyze relationships and trends, and use your statistics and visualization abilities.
  • Define the problem or hypothesis, as well as the stakeholders and scope of the analysis.
  • Improvement based on insights, with numerous alternatives to consider.
8) Examine without prejudice:
If you have a certain sort of data in mind, you may overlook other actionable options. This is not to argue that you should disregard evidence that supports your objectives. Just don't become too focused on one data collection that you miss out on other fantastic prospects. Assess your business to determine the KPIs that are important. Keep track of these data elements to avoid missing out on critical information that can help you build your business. Work with a data analytics firm to determine the important data points that must be tracked and analyzed in order to generate relevant business insights.
9) Before you begin analyzing, have an objective:
Before you begin monitoring and evaluating data, take the time to establish clear goals. Your company objectives will keep you on track and put you in the right direction for the finest customer analytics solutions for your marketing aims. You'll know what data points to track to guarantee you're on pace to meet your objectives. Your business will lack direction if you don't have well-defined objectives, and you'll find yourself fluctuating between priorities that might take you away from your ultimate purpose. When creating goals for your business, avoid attempting to boil the ocean. Before taking on new objectives, focus on a few reasonable goals for your firm.
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10) Understand and categorize your target market:
Segmentation is an effective method for extracting the most value from your user data. Knowing your audience will allow you to categorize visitors that share similar characteristics, allowing you to provide more focused insights:
You will have a better understanding of how your target consumers act if you identify and analyze your client segments. This will assist you in answering specific questions about your company and developing a complete optimization strategy. Think beyond the traditional age and gender groups when developing client segments. Consider alternative techniques to segmentation, such as transactional value, economic level, lifestyle, and attitude toward communication technology. These segmentations can help you better understand your target consumers and direct what you need to do to better serve them.
11) Have the proper people on your team:
Data is collected via online tools and customer relationship software, but insights are created by individuals who understand the business. You must collaborate with knowledgeable people to obtain the correct consumer data and transform it into data-driven tales that provide useful business insights. This is a collaborative endeavor, therefore combining internal marketing expertise with external web analytics providers is your best chance.
12) Concentrate on data hygiene:
Having all of the data in one location does not imply that all of the data is usable. Because client data from many sources — CRM and LOS systems, contact centers, and social networks, for example — comes in various formats. In reality, up to 90% of the data we create today is unstructured, which means it does not fit into a single, clean format, such as an account number. This unstructured data must be maintained to guarantee acceptable data quality or data hygiene — in other words, to make it useable for analytics engines. Poor data quality is projected to cost the average corporation 30% of its sales — and costs US businesses more than $3 trillion every year. Again, a high-quality customer data platform or customer experience is required. Again, a high-quality customer data platform or customer experience platform will provide pre-built connections and APIs that ease the data hygiene difficulty — and best-in-class solutions will include professional assistance to assist mortgage lenders in addressing the data quality issue.
In their simplest form, actionable insights are pertinent conclusions drawn from raw data in your analytics platform. According to Nick Craig, Managing Director at Mackerel Media Ltd., there is nothing wrong with telling a client what they can easily obtain from Analytics, but there is value in assisting them in understanding that the in-store action they ran in France last month was connected to an increase in normal basket values from male customers over the age of 45.

Read More: How to Get Six-Figure Income as an AI Prompt Engineer?

What is the procedure for converting data into insight?

Definition of business analytics: The scientific process of converting data into insight in order to make better judgments. Used for data-driven or fact-based decision making, which is generally perceived as more objective than other decision-making methods.

What is the method for transforming raw data into actionable insights that lead to sound business decisions?

Data analytics is the process of transforming raw data into actionable insights using various analytical approaches. Data analytics is often used to impact corporate choices, identify trends in data, and make conclusions.

What are the three different types of data transformation?

The following are the most typical forms of data transformation:
  • Destructive means that the system removes fields or records.
  • Data transformation is a process that adds, copies, or duplicates data.
  • Aesthetic: The transformation standardizes the data such that it meets the standards or specifications.

What is an example of a data-driven actionable insight?

Actionable insights may help a company find areas where it can enhance its performance. Data analysis, for example, may demonstrate that a company's customer service is subpar, its goods do not fulfill client demands, or its marketing efforts are useless.

By anticipating and modeling future consequences, what transformed data into actionable insights?

Predictive analytics is a type of data analytics that uses historical data and analytics techniques such as statistical modeling and machine learning to forecast future results. Predictive analytics is a science that can generate future insights with a high degree of accuracy.

What are the five major categories of insight that comprise a brand's basic insights?

Real insight—the kind that inspires ideas and people to create new attractive products and services—can be boiled down to five fundamental principles: context, dilemma, why, motivation, and ideal.

What is the difference between raw data and actionable insights?

These are the most useful insights that deliver the best returns on data analytics efforts. The following method is used to get actionable insights from raw data. Data: Data is gathered from a variety of sources, including information databases, spreadsheets, plans, social media comments, phone records, and so on.

How do you develop research insights?

The process of transforming research into insights is divided into four stages: prepare, capture, comprehend, and interpret. The objectives should be proportionate to the scope of the project; a goal that is too wide will not be met.

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