A recent study published in Harvard Business Review found that companies relying on data see a better financial performance. The study showed that over 70% of the organizations that deployed analytics across departments reported improved financial performance, increased productivity, reduced risks, and faster decision making. This only proves that businesses need to focus on data-driven decision-making.
The need to stay ahead of the curve is a catalyst for both growth and innovation. So, it’s no surprise that data has surpassed oil in value and now holds the power to transform businesses and micro-economies. Data-driven agile business progression is no longer a choice but a fundamental business strategy. The ever-evolving analytics technologies provide decision-makers with the capability to make strategic business decisions and accurately predict changes in market scenarios with adequate risk assessment.
Benefits of data-driven decision-making
Facts and numbers back the shift towards digital data. For example, research suggests that data-centered businesses are 23x more likely to gain new customers, 6x as likely to retain them, and 19x as likely to make profits. In addition, a study by MIT Sloan School of Management professors and the MIT Center for Digital Business found that the data-oriented organizations achieved 6% more productivity and 4% more profits among the surveyed companies. Consequently, it shows that incorporating a data-driven decision-making culture reaps benefits in terms of profits and organizational efficiency.
Long-term benefits include enhanced organizational transparency and accountability. As a result, companies witness improvements in employee engagement and retention. It’s no secret that an efficient organization saves precious time while making better business decisions.
How does data analytics revamp decision-making?
Three essential segments reflect the expanse of opportunities. Let’s discuss these in detail to have a proper understanding of the scope of improvement in an existing decision-making process.
- Ability to identify consumer patterns: Data is capable of identifying patterns through predictive modeling and analytical CRMs. Recognizing patterns in your customers’ interaction with your business offers insights into buying habits and preferences. Unfortunately, the majority of companies today collect much of this information but in secluded bundles. Appropriate use of the right tools will help achieve the desired confluence. The supposed benefits include “customer segmentation and sales forecasting,” which help personalize marketing efforts, improve sales funnel, and proactively adjust sales strategies. Another benefit is the radically “improved customer experience” that enhances satisfaction and drives sales.
- Leveraging data to drive performance: Analytics and data optimization can reduce inefficiency and drive performance by “streamlining workflows to enhance productivity.” A better data-integration process across business domains, both internal and external, allows employees to spend less time searching for information and more time applying the results. Applying the same logic also helps achieve “pricing and cost-efficiency.” Data points can be used to evaluate historical, present, and predicted performance. It allows to ascertain cost-efficacy and estimate an optimal return on investment successfully. Competitor analysis is also a common avenue to establish ideal pricing points and drive performance.
- Alleviating risks: Statistical models of analyzing data help identify the best-suited strategy for business operations. Businesses can leverage risk analytics to identify threats and manage risks effectively while turning this imperative segment of data and analytics into an enterprise-wide approach. Setting up these primary bottom lines to mitigate and resolve troubles will allow businesses to incorporate risk considerations into their strategic decision-making processes, leading to better predictions and preparations for uncertainties.
Types of data analytics techniques and how they improve decision-making
The data analytics industry is growing at an exponential rate. This section discusses the commonly practiced data analytics techniques and their role in improving business decisions.
- Descriptive analytics: Descriptive analytics is where existing data is analyzed to derive meaningful and actionable insights. Studying the past to make future predictions offers unforeseen benefits. It is the reason that descriptive analytics is the most commonly practiced technique of data analysis. Businesses continually analyze past performances to recognize and decode the factors for success and failure.
A long-term strategy is to create and implement a business intelligence system to analyze real-time and historical data. The end game would be to extract insights for the future approach. A great example of leveraging descriptive analytics is IKEA, a reputed home retailer across the globe. IKEA started to analyze the correlation between people browsing their website and then visiting their store. They began with pulling data for metrics such as Products Added to “Shipping List,” Stock Availability Checks, Visits to local store pages, website searches, and products viewed. Using these metrics, they created an online conversion model and executed a targeted promotional campaign, resulting in an impressive 464% ROI.
- Diagnostic analytics: This technique helps in assessing a specific problem by identifying its root cause. It is a solution-focused technique providing insights to tackle and resolve diagnosed issues. Companies use this technique to figure the “why” in different business scenarios. In practical terms, diagnostic analysis’ most common application is AB testing, where two similar approaches are tested against each other to identify the one which outperforms the other. In the digital marketing landscape, diagnostic analytics is an essential part of the delivery methodology and real-time analysis of performance.
At Unyscape, we use diagnostic analytics every step of the way to achieve the results that we are after. For instance, say a brand comes to us with a specific problem statement, we devise an initial strategy keeping a one-year outer limit. So, if we are after 3x traffic website growth in a year, we implement the initial plan with a 6-month target of 1.5x improvement. Then, at the end of the sixth month, we analyze our progress, and if we achieve the target, we go ahead with the current strategy; if not, we diagnose the problem to identify the specific areas we need to tweak.
The margin and scope of the diagnosis depend upon how close we are to the set target. Then, we revisit to analyze goal fulfillment again at the end of the 9th month and then again at the start of the final month. This approach bears proven results and is the basis of all our long-term strategies. Not to mention that continuous data monitoring and analysis is an integral component of this strategy.
- Predictive analytics: It uses a confluence of “AI,” “machine learning,” “big data,” and other analytics technologies to offer broad-scale information to be utilized for numerous business objectives of any scale or size. It is easily the most complex data analytics technique and concurrently yields the best results, provided qualitative and detailed data is available for analysis.
In simpler terms, predictive analytics goes beyond the assessment of ‘what happened’ to focus on ‘what will/may happen.’ Some of the applications of this technique include predicting consumer behavior accurately, identifying and prioritizing qualified leads, launching the right products at the right time, and driving marketing strategies. For an in-depth understanding, please read our blog titled Predictive Analytics For Marketing.
- Prescriptive analytics: As the name suggests, this technique offers future-based solutions by analyzing historical data and empowers proactive decision-making. It is based on the fundamentals of predictive analytics but goes a step further to provide not only future-based information but recommendations for concrete decisions. Prescriptive analytics is an obvious progression from descriptive and predictive analytics to remove the guesswork out of data analytics. However, it is a common misunderstanding to assume prescriptive analytics is the same as predictive analytics. In Fact, the differences are pretty easy to neglect given that both the techniques use machine learning and Artificial intelligence.
Predictive analytics offers indications to the question “what will happen” but gives no guidance regarding decisions that need to be made. On the other hand, prescriptive analytics not only determines what will happen but also establishes the most appropriate decision for the business. A case scenario for leveraging prescriptive analytics is the implementation of suitable marketing and sales strategies.
At Unyscape, we use the technique to help the brand partners we work with the best options to revamp their marketing and sales efforts. We help businesses and brands to become more precise with their campaigns and customer outreach through an in-depth analysis of their historical data and future goals. Prescriptive analytics allows us to target and reach specific audiences and fulfill desired goals with conviction.
The prescribed course of action
Data and analytics have successfully penetrated the decision-making processes across industries. The influence will become even more conspicuous as acceptance reaches a pivotal mass. However, the catch is to treat data as an organizational asset and not just another departmental property. Most businesses do the opposite and treat data from sections as independent silos without having a structure for sharing and learning. This approach needs to be completely reversed, and data coming in from anywhere within the business should be accessible to all. A focus on capturing, cleaning, and curating meaningful data from across verticals will also promote frequent experimentation to learn and improve. Let us discuss in detail the essential steps to adapt and create a culture where data is treated as an asset.
- Tech Adaptation
Technology has a vital role in data and analytics, where machines are evolving and generating new capabilities. It helps solve problems and analyze risks, allowing businesses to develop significant value and be at the forefront of their industries. The critical fact is that organizations need to adapt to the digital world to utilize and harness its powers.
- Performance optimization
Digitizing existing frameworks and processes is essential to systematize decision-making. Gone are the days to blindly trust intuition and hope the past experience would help navigate the rough waters. Instead, the time is ripe to embrace business intelligence and analytics to make fact-based and informed decisions that will ultimately lead to overall growth. Furthermore, it is time to tie every decision to data and its various applications, such as success attribution, pattern-spotting, and visualization. Analyzing past performances and linking future decisions to interdependent and scalable data will help establish an effective decision-making process, one where optimizing routines would considerably become easier with time.
- Process standardization
Implementing the proper tools, hiring the right talent, and understanding and analyzing data will enable companies to incorporate a culture of data-driven decision-making, leading to a continuous supply of actionable information and accurate predictions backed by evidence and facts. In addition, businesses use third-party contingent for data analytics to achieve objectives, keeping their core structure stable enough to gradually build in-house capabilities while gaining valuable experience and analytics exposure.
The world became digital two decades ago, but data analytics gradually came to the forefront and is now leading the digital world to newer dimensions. Organizations may indeed be at varied data maturity levels, but each one has valuable customer data assets. If put through the churn of data analytics, these assets can prove to be vital game-changers for their respective organizations.
Possessing data capabilities alone does not imply impending success or fulfillment of objectives. On the contrary, it is the interdependence of data and the analytical prowess of an organization that defines the essentially required schematic for decision-making processes. Running behind the latest analytical technology might not bear the desired results, but evaluating present business scenarios through existing capabilities to establish and incorporate a culture of strategic data-driven decision-making would surely reap plentiful harvests.