Actionable Insights - How to Gather and Transform Business Data

Actionable Insights March 19, 2021

Priyanka Bhadani

Introduction to Actionable Insights

Collecting data from inventory, sales, and customer management systems, although essential, is not enough to make impactful innovations for business expansion. Management must be able to digest and create a plan of action from this information, or else the impact of vital metrics can be overlooked.

Actionable insights ensure that companies can develop a strategy to improve various business operations and functions from gathered data. In fact, a study conducted by Forrester found that insight-driven companies grow up to 8 times faster than the global gross domestic product (GDP).

Therefore, collecting data and generating effective actionable insights not only supports process optimization but business growth as well.

What are Actionable Insights?

Actionable insights are sets of big data from which management can develop effective data-driven actions. These insights can be drawn from raw data, typically software, such as customer, sales, and inventory management systems, to generate comprehensive reports and analyses. From these reports, businesses can determine what operations need further improvement.

For example, customer surveys provide insight into where consumers buy their items, their satisfactory levels, and the overall performance of the item. With this information, management can innovate products, marketing strategies, and services to improve customer satisfaction and experience.

Actionable insights allow management to view their business performance from different perspectives to make informed decisions on enhancing their operations and customer service. In fact, a Lumao's State of CX report shows that over 38% of organization leaders use actionable insights from feedback to improve and personalize customer experience (CX). Insights can lead to impactful actions that promote average sales, operational efficiency, and customer satisfaction.

Additionally, Forrester's case study shows that while 74% of organizations have the need for insight, only 29% are successfully collecting and acting on their analytics. Therefore, companies that seek to make data-driven decisions have a competitive edge when using actionable insights to progress their business.

6 Key Characteristics of Actionable Insights

Businesses that struggle to convert their collected data into actionable information should acknowledge that not all data is actionable insight material. In fact, when using a pyramid model, actionable insights only make up the tip of the pyramid, followed by general information and data.

Raw data is plentiful and is converted into information such as graphs and reports, but only a small amount of this information is actionable. Management can determine what sets of data are considered actionable insights by identifying six key characteristics.

1. Alignment

Information that is directly linked with a business's needs and goals is likely to contain actionable insights. For example, if a company is looking to improve customer experience, feedback with reviews and suggestions from buyers would align with their goals and provide actionable insight. However, if a particular reported metric or qualitative information fluctuates or does not affect the focal company goals, the data may be unnecessary.

On the other hand, insights based on key performance indicators (KPIs), such as net sales, revenue, and profit margins, provide crucial metrics that can be acted on immediately. Therefore, KPIs and metrics that align with company goals can be interpreted and easily converted into actionable insights.

2. Context

Generally, data that lacks background and context is considered to be unhelpful. Oftentimes, a reference, benchmark, or previous report is needed for comparison.

For example, if a weekly report shows a retail store sold 400 units, the owner may be initially pleased. However, if previous reports show the average weekly sales are 600 units, this would indicate a 33% drop in sales.

With this data, management can also collaborate information from demand trends to determine why sales have declined, whether it was due to a slow season or ineffective marketing tactic. From these detailed actionable insights, plans can be developed to promote sales and revenue.

Therefore, providing ample background and details to support data ensures that management can understand the full picture regarding operational performance.

3. Relevance

Although this is a critical characteristic, relevance is subjective as insight can hold value but not necessarily contribute to a business's goals. Therefore, in order for data to have relevance, it must be delivered to the appropriate project manager at the right time. Otherwise, the information may be discarded.

Insights can also lose relevance if they remain within analytics tools without being accessed for extended time periods. Therefore, data should be analyzed and passed on to project managers promptly.

4. Specificity

Broad information can help businesses gain holistic perspectives but is not necessarily useful when handling actionable insights. Specific and detail-oriented insights provide enough information for management to act on immediately.

For example, a report that shows revenue has increased by 25% may seem promising but may be the result of an external factor rather than an intentional promotion. Therefore, insights should be supported by an explanation that defines precisely how an event occurred.

5. Unique Perspectives

With the plethora of incoming data, unique insights can sometimes prove to be more helpful than routine checkups and provide unorthodox ways to improve operations. While scheduled reports and analyses should remain, finding new ways to innovate functions may be worth the extra effort. When trends and patterns emerge, such as demand and sales, companies tend to monitor the information over time.

However, there may be indirect causes of these trends that go unnoticed. Therefore, running unique analytics to monitor functions from new perspectives and identify unusual data can allow managers to approach projects in an innovative way.

6. Clarity

Valuable insight that is muddled with excess information can lose its clarity, resulting in stakeholders not understanding its significance. Therefore, effectively portraying actionable insights is essential for managers to be able to acknowledge and work with data. Aids such as graphs, images, and reports can adequately visualize the impact of the data insight.

How to Turn Data Analysis into Actionable Analytics

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Once businesses narrow down what information can be used to create a plan of action, they must understand how to do so. Some best practices for forming data into actionable insights include-

Measure the Appropriate Functions

First and foremost, businesses must collect metrics from operations they wish to improve on. For example, an e-commerce company should run analytics on what channels drive the most traffic, look-to-buy ratios, what pages are losing customers, and what devices buyers are using to access the website. These insights allow marketers and developers to implement effective changes to ensure that online shoppers are attracted to the website and finalizing their transactions.

Ask Stakeholders Questions

Stakeholders' concerns should be addressed to resolve any challenges they may be facing. Therefore, analysts should ask questions to determine what data should be collected; otherwise, time could be wasted researching irrelevant operations.

Utilize Segmentation to Drive Action

Splitting up and categorizing data can simplify the analysis process. Researchers must choose what type of segments the business is focusing on, such as customer experience, inventory control, or average sales. By grouping similar reports, project management can quickly sift through and comprehend the actionable insights. In fact, advanced integration software provides tools that aggregate and categorize data from all systems to generate comprehensive analytics. These solutions are often customizable so organizations can program the functions to fit their insight needs.

Understand the Data Context

While the majority of data is valuable, it can be overlooked without the right context as figures without adequate background information can mislead management into making poor decisions. Therefore, supporting information should be collected to develop well-rounded insight, from which data-driven decisions can be made. Context should explain why the insight is valuable, how it impacts the business, and what the values mean.

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Create an Optimization Plan

By using the Define Measure Analyze Improve Control (DMAIC) process, businesses can build a personalized optimization plan. The DMAIC method consists of five steps-

Define the issue, stakeholders, and scope of analysis.
Measure any relevant data and generate reports to find unusual trends.
Analyze patterns, statistics, and cross-examine metrics.
Improve operations based on actionable insights.
Control the implementation by monitoring respective KPIs.

Develop a Hypothesis

Before beginning an analysis, a detailed hypothesis must be developed that addresses a prediction and plan of action using the following template-

If (an event) occurs, then (the plan of action).

For example, if sales increases due to the newest promotion, then the company will invest more in marketing schemes to maintain heightened revenue.

Integrate Data From Other Systems

Integrating systems leads to more detailed reports and data insights that streamline decision-making processes. Instead of mining through various systems looking for specific information, an integrator automatically combines necessary information so users can access a collective database on one interface. Aside from customer management solutions, companies can also connect inventory control, point-of-sale (POS), replenishment software, and any other existing systems. Linking automated software streamlines business processes and communication, allowing management to access data and retrieve actionable insights efficiently.

Dissect Organizational Silos

Many companies form information "silos" to organize their stakeholders into segments according to the type of data they work with. While segmentation is excellent for collecting information, businesses should seek to break down barriers that can hinder communication between departments. Analytic leaders should aim to improve data exchange outside of their assigned sectors, as there may be unique insights that indirectly affect their operations.

Gathering Customer Data Sets

A Harvard Business case study revealed that the fastest-growing businesses implement data gathering to discover how their technology affects CX. This feedback allows companies to make quick changes to improve customer service and satisfaction.

There are several options for collecting customer reviews and feedback, such as-

CSAT

While customer satisfaction (CSAT) is a term referring to the consumer's happiness, it is also a standard survey many companies ask buyers to fill out. The review typically focuses on one aspect of the experience at a time, using a 5 or 10 point scale to measure the customer's satisfaction. However, some reports can be more in-depth by asking a few simple questions that are later used to calculate a quantitative score.

NPS

Net Promoter Score (NPS) surveys are simplistic and usually only require customers to answer two questions. The first inquiry asks for a 1-10 recommendation score to see how likely customers are to refer associates to the business. The second question asks for an explanation for the first answer. Unlike CSAT, NPS surveys focus on the overall CX rather than a single interaction and are shorter, increasing the likelihood of customers completing them.

CES

Unlike the other two surveys, the Customer Effort Score (CES) focuses on how hard customers had to work to fulfill their orders. This inadvertently explains the company's customer service proficiency, whether in-store or over customer support calls.

Other Sources of Actionable Data Sets

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Customer reviews and feedback are great sources of actionable insight. However, for businesses that take advantage of several management solutions and system integrators, there are many other sources to extract data from.

With a POS system, management can extract quantitative data on inventory turnover rates, average sales, employee performance, and customer purchase history. This information can be easily converted into statistics and actionable insights to show how demand and sales fluctuate throughout the year. Departments, such as marketing and human resources, can then discuss ways to enhance item performance, sales, and revenue using this information.

When integrating POS services with inventory control and demand forecasting software, it is easier to see how external factors, such as weather and events, affect consumer demand and turnover rates. For example, retailers may experience spikes in demand for raincoats due to people attending outdoor events on a rainy day. Without this context, management would not be able to prepare sufficient stock to meet the surge in traffic.

Analysts can observe these types of insights to determine sales trends and implement procedures to optimize inventory levels. By increasing or lowering stock held in the warehouse based on forecasted demand, companies can save on handling costs, promote sales, and increase profit margins.

Actionable insights are vital for transforming lagging operations that are preventing business expansion and profitability. By investing in advanced analyses and reporting, organizations can efficiently monitor their performance and continuously develop innovative solutions.