Gone are the days when businesses would make decisions based solely on ones’ intuition or experience in the market. The vast amounts of data available today have made the traditional manual dataset exploration by statisticians and modelers extremely complicated and easily prone to wrong decision-making.
Computers have become more advanced and capable of running complex algorithms in the shortest time possible enabling a border and deeper analyses of datasets than previously possible.
Advancement in the processing power of computers is a double-edged sword that can fuel the rise or fall of a business. Competitors in various financial and business markets are constantly striving to lay hold of their customer base as technology yields its power to the most efficient and inventive business.
Today almost every business is instrumented for data collection considering it is widely available. Every event in the market domain such as market trends, incentives on customers by competitors, or external stimulants that affect customer’s decisions produces data. Such data provides valuable information on the market trend to anyone with the most accurate interpretation of the data, this process explains data science.
Here are some of the problems facing modern businesses and 6 ways how data science is used to solve them.
1. Customer Churn
The primary source of a business’s revenue is the customers, they are the target market. Every business is striving to establish and manage a large customer base as their capacity can withstand. It is the sole responsibility of the business to keep their customers interested by proving upgrades to their products or better incentives to the service provided.
However, a competitive business not only keeps the customers interested but also puts in place measures to predict the rate of customer satisfaction. Satisfied customers are one of the major causes of customer attrition, where customers are no longer interested in the products of a company and are considering terminating their subscription.
Customer attrition means less income to the business, less income means the business is dying. Data science not only enables the business to identify which customer is at the highest risk of customer churn but also helps in predicting customer churn in advance to enable the business to come up with the right incentives to prevent customers from abandoning the business through methods such as churn analysis process.
Data science helps to quantify which marketing strategies are likely to have the best customer retention.
The accuracy of churn prediction models and techniques deployed by data scientists is crucial to the proactive customer retention procedures established by a business.
Another business challenge that emerges as a result of customer churn is increased business expenditure. That is, a business that is seeking to prevent churn may incur expense in the name of incentives offered to the customer. Data science helps marketers to utilize resources efficiently when it comes to preventing customer churn by offering incentives only to the customers with the highest churn risk.
Prediction techniques developed by data scientists to combat customer churn are largely dependent on academic research and refined by years of testing to be reliable. This is to say, although it is possible to predict customer churn with decent accuracy, it is not a walk in the park.
Customer churn is very rampant in telecommunication firms. For example, in the mid-Atlantic, 20% of cell phone subscribers in that region do not renew their subscription once it expires. At the same time, completion in the wireless business is growing continually making the cost of acquiring new subscribers very difficult.
2. Business Intelligence and Data-Driven Decision making
Businesses around the world have noticed the broad availability of data and are taking advantage of it, hence the rise of the modern term, Business Intelligence (BI). Technologies put in place by a business to provide information about historical, current, and future business operations create new business opportunities for that enterprise thereby increasing the survival rate of the business in a fiercely competitive environment.
Functions such as benchmarking, prescriptive analytics, online analytical processing, process mining, and data mining comprise the intelligence technologies of the business.
These technologies are crucial when it comes to making business decisions. Decisions made regarding data acquired through business intelligence are termed Data-driven Decisions.
Data-driven decision-making improves the accuracy of the decisions made by a business by eliminating decisions based on intuition which can be highly inaccurate. However, most companies do not base their decisions solely on data but a combination of data-driven decisions and intuition.
For example, a marketing director could decide to set advertisements based on their life experience on market trends choosing by eye what proposed decisions might work or in contrast, choose to rely on the analysis of the business intelligence regarding how customers react to different ads.
Even better, use a combination of experience, personal expertise, and data-driven decision-making procedure.
Data-driven decision-making is heavily dependent on data science to drive meaningful insights to the data acquired through a company’s Business Intelligence. The wrong analysis of accurate data results in wrong decision-making, therefore a need for a decision-making process. Data science ensures that the decision-making process takes into consideration the context and nature of the business problem, ensures the data acquired is rightly quantified and the tools including algorithms that were used to come up with the solution were appropriate and unique to the problem unless it is a recurrent problem.
3. Increasing Business Productivity
Data scientists help develop individual metrics of every employee in the business and compare them to the business performance.
This helps identify employees that are feeding the strategies and goals determined by the business. This helps employers to better identify those that need to be promoted and those that need to be improved. Data science develops a data-driven culture by informing employees on the importance of data in their job roles.
Through workforce analytics developed by data scientists, employers can better track the success rate of individual employees and what strategies boost employee morale.
From the customers’ point of view, data science helps improve the overall customer experience. For example, calls made via a customer service line have traditionally been long calls where customers usually would be put through a process of answering lots of questions and probably be put on hold as their problem gets sorted.
Data science through means such as real-time reporting eliminates this problem by providing service providers with quick answers to the most asked questions by customers, thereby empowering the service providers to efficiently answer customer questions without going through a long process. This earns the business better interaction between service providers and customers, resulting in a credible customer experience.
Data science helps a business record customer behaviors over a considerable time. This means that a business can perform analysis on customer behavior and compare how their strategies are impacting customer behavior in the past and present. This helps the business make a better prediction of customer behavior in the future. It also gives the company insight on how to recommend certain products to different customers as opposed to others; this means data science gives better insight into customer types.
The ability to process historical data on the business interaction with customers can help improve the efficiency of the business and also produce new data once analyzed with a new lens.
4. Better Recruitment
Unemployment rates are continuing to increase by the day and this has become a problem for the recruiters. Recruiters are now faced with the problem of identifying the best employee for the job. They have to go through thousands of resumes from applicants sometimes for a single position.
Data scientists through strategies such as clustering and similarity matching can help recruiters choose the best applicant for the job. Similar matching identifies individuals with similar traits based on data. Clustering groups individuals with similar behavior.
A company can deploy such data-science procedures including technologies such as image recognition to automate the best applicant for the job or to face out those that do not precisely match the company’s requirements as the others are booked for human interviews.
Furthermore, a company, through data science, can have the capability to analyze potential applicants in the job market. Having the ability to recognize job-seekers helps a business to reach out to the ones with the most refined skills before their competitors get the chance to hire them.
Processing employees with data-analytic thinking is very valuable to any business, especially considering how rare employees with decent data-analytic skills are. Data-analytic mentality with a company’s employees helps to provide clarity to the employees conserving the company’s goals. A data-analytic culture with a company helps employees identify weak points in the business efficiently and implementing the right structures to resolve that particular aspect of the business instead of analyzing the whole business structure.
5. Timely Execution
The ability to buy and sell is the backbone of the very business. The skills that businesses deploy in their buying and selling are what sets the business apart from its rivals. Data science helps a business identify when to restock more or less and when to withhold their products.
Here is an excerpt from a New York Times Story from 2004,
“Hurricane Frances was on its way, barreling across the Caribbean, threatening a direct hit on Florida’s Atlantic coast. Residents made for higher ground, but far away, in Bentonville, Ark., executives at Wal-Mart Stores decided that the situation offered a great opportunity for one of their newest data-driven weapons … predictive technology. A week ahead of the storm’s landfall, Linda M. Dillman, Wal-Mart’s chief information officer, pressed her staff to come up with forecasts based on what had happened when Hurricane Charley struck several weeks earlier. Backed by the trillions of bytes’ worth of shopper history that is stored in Wal-Mart’s data warehouse, she felt that the company could ‘start predicting what’s going to happen, instead of waiting for it to happen,’ as she put it. (Hays, 2004)”
Although predicting people in the path of the hurricane would need to buy more bottled water and therefore increasing the stock is useful, that is pretty obvious and does not need data mining. However, the ability to discover that a particular DVD in Wal-Mart stores sold out in the region in the hurricane’s path could reveal unusual patterns associated with hurricanes. Such information can provide insight on what to restock in the future occurrence of hurricanes.
The New York Times (Hays, 2004) indeed reported that mined data revealed that certain products aside from safety kits such as flashlights sold more during the hurricane. For example, Strawberry Pop-Tarts were found to sell more than their normal sales rate before the hurricane. Therefore, before a hurricane is advisable that stores increase their stock intake on Strawberry Pop-Tarts and anticipate more profit.
Wal-Mart’s competitor, Target made headlines in 2012 for a stunning data-driven decision-making case of its own. Target deployed data science with the aim of understanding customer inertia, what influences customer shopping habits. Target’s data analyst’s understood well that customer shopping habits are very hard to change but special events can significantly change a customer’s shopping habit. One such event is the arrival of a new baby within the customer’s family.
Most retailers already had the information that, once a customer buys baby products from their store, they are likely to buy everything else from the same store, so, retailers competed to sell baby products to new parents. The data providing information to retailers as to who is pregnant was already widely available, different people post about their pregnancies online.
Target wanted to beat the competition so they employed data-science procedures to try and predict when target customers are likely to be expecting a baby. They did this by looking into their historical data on the customers that were later discovered to be expectant. Customer behavior such as women changing their wardrobes, diets, and vitamin regimens was extracted from historical data and plugged into predictive models, and fitted in marketing campaigns.
6. Testing Business Decisions
Data science helps businesses to test decisions meant to be a forecast of the occurrences in the future in a safe environment. This is don’t thorough a variety of hypothesis test models and testing tools.
For more accurate results that would mimic the actual performance and results of the business under the indented data-driven decisions, companies have to take different considerations. These considerations include their customer requirements, the business financial ability, and company goals. For accurate results, the companies have to a proper understanding of their database and ensure the intended data-driven decisions are in the right context with actual information revealed by the data.
Compared to the traditional way of analyzing business decisions, which relayed on ‘gut feelings’, data-driven decisions are more accurate. Data-driven decisions are usually dependent on a vast amount of data and a variety of processing and testing technologies are used to influence decisions. This is in contrast to the traditional way, which can save a business a lot of money and increase the confidence required by businesses for investing. When companies need to make calculated data-driven decisions, data science is the go-to.
7. Statistical Evidence and Better Manufacturing
A critical aspect of any business is the design process of the business, it is the plan that results in the products or services offered and meets the set goals.
Designers have everything to gain when they take advantage of the plethora of available information, it is the key to desiring the challenges that they must overcome to meet the desired results.
The ability to predict customer behavior gives clarity to the design team on how to design their products and what strategies to follow when it comes to marketing ad different time seasons.
Feedback by customers such as customer reviews and online recommendations of business products can greatly impact a company’s priorities when it comes to future decisions on what features to incorporate into their services and what to eliminate.
Every customer interaction by a business is made efficient by data science. A business can make better interaction, recognize customer purchasing patterns, and more importantly, a business can know why certain products sold more than others and what impacts do new strategies and designs have on customers in the present and the long run.
Conclusion
When it comes to acquiring investors for the business, data science empowers the business to explain how their business is growing and provide the reasons behind the results. Providing statistical data and backing it up with the data-analytic knowledge and arguments that explain the company’s results, takes the convincing power to a higher level. This helps the business marketers to market new business strategies to investors with a higher success rate leading to more income to the business.
