No matter if the data at the company’s disposal is raw or in the form of detailed reports, giving deep insights on the company’s operations, and uncovering hidden potential between collected facts, figures, and items of information, data is a helping hand in business.
Every business gathers data, but not each uses it to its own advantage
Data is a particular kind of asset, gathered somewhat by the way business processes take place. Equipment needs to be bought, services tailored, employees hired and managed, competencies built, products developed, sold, and dispatched. Any action performed by the company leads to information on its conduct and results – data to consider and draw conclusions from in future operations.
Pieces of information are generated in connection with all the processes, initially describing one example of performed action (purchases, recruitment, service, etc.), then another one, and another. In the long run, especially in the case of huge companies (but not only), gathered data can regard thousands and thousands of individual cases.
It would be a sin not to harness its potential to make it work for the company’s profit, wouldn’t it?
The start of working with data requires certain steps, covering the assessment of available datasets. The process distinguishes which data the company has, which will be useful, and what assets are available outside for free or commercially. This may include assets like demographics, data gathered by government agencies, weather information, and sources such as Google Trends. The combined power of all the above and properly executed analysis arms companies with a powerful tool to shape new paths in their fields.
How the shift to a data-driven company can improve operations and competitive advantages? The below examples show successful ways of moving towards a new business quality, possible through data processing.
1. Preventing churn
Customer retention – with the (anonymous) data on the behavior of customers & users, companies can analyze how the audience uses their services. Additionally, business owners can improve customer satisfaction on this basis, examining behavioral analysis to improve customer retention.
Companies notice who uses their products rarely and find ways to encourage users with additional options and services to satisfy their needs better. Offering new, alluring options often helps prevent users from changing the service provider, even before they start considering the shift visibly. Data-fed algorithms identify movements of clients, who after making certain decisions, will be most likely to e.g. cancel their subscription.
Understanding the patterns and rules governing the vehicle demand enabled us to provide a specialized data-based solution for a shared mobility platform. Knowing the customer behavior, the service provider can distribute the vehicles in recognized places and times of greater demand, creating a more user-friendly and tailored service.
2. New customer acquisition
With internal and external information, companies can identify areas where the product didn’t reach and develop a strategy for how to get there based on data analysis. Algorithm-powered insight enables more precise targeting, aimed at recognized needs.
One of the projects carried out by our data research team was aimed at identifying the cause our clients’ products weren’t chosen by public facilities in tenders. A brief overview showed that institutions invite providers to tenders executed for hundreds of products at once. In such cases, the analysis of data from previous processes helps in better adjusting the tender offer to make a product stand out and be easier choosable among others.
Being an expert in your own business, you know your target customers and which data to analyze to acquire new users and clients. Starting with pieces of information, companies can address a number of issues in attracting customers. This applies to both B2B acquisition, like the example above, and B2C, where data like demographics is utilized (e.g. services are used by the 25-30 year range, others don’t use it, and what can be done about it).
3. Recommender systems
Recommendations are one of the most recognized areas where data-powered solutions shine. Knowing what customers already like and choose, helps to forecast which offers may (and most commonly will) catch their attention. Among the most commonly known recommender systems, we can list those utilized by Netflix, Amazon, and Tinder.
Do you have a record of choosing products from particular vendors, or liking every medium-height blonde wearing glasses, interested in abstract painting and brewing their own beer? There’s a good chance that your personal choices and preferences behind them are already better known to the algorithms than they are to you. As a result, you’ll be most likely presented with either goods that may suit your needs best or people that will catch your eye.
4. Cost reduction
Cost reduction – having many subcontractors, companies can optimize processes based on data, execute better procurement processes, plan e.g. a year in advance in the case of large commercial chains (instead of vague feelings). Data-driven models help develop strategies predicting demand based on extensive external data, e.g. inflation, avoiding unnecessary operations, and targeting efforts at precisely identified areas.
Data analytics in recruitment enables using historical data for predicting future hiring processes and assessing candidates. With extensive data resources, Machine Learning can reject incoming resumes that do not meet the criteria. Some companies conduct first recruitment interviews with a “digital recruiter”, that performs tasks based on available data.
While machines still do have some deficiencies in comparison to the human touch and may overlook worthy candidates that do not match the pattern (individuals that a flesh and blood recruiter will recognize), data-driven recruitment solutions are useful especially in large-scale processes.
In the bigger picture, analysis of data in employment processes benefits the company not only in determining the most suitable candidates. The advantages include overall improvement of efficiency, accelerating time-to-hire, and more precise forecasting for future processes (how many CVs need to be considered to find the perfect match?). A meticulously designed process with properly trained algorithms can exceed the human-led recruitment, as data shows the crucial factors (education, experience, language skills) instead of gender or skin color.
6. Predictive maintenance
Algorithms help to identify how machines work, what the trends in their functioning are, can predict how they will behave, and estimate when they will malfunction, enabling prior reaction, instead of waiting for errors to happen. Internal systems gather information on the proper functioning of equipment, and observed abnormalities. Employing Machine Learning algorithms to learn from collected and classified data leads to uncovering patterns in real-life scenarios. ML can assess historical data to present predictive models, allowing staying a step ahead of machinery malfunctions.
However complicated and high-tech this may sound, predictive maintenance is widely applied, not only in industrial environments but also in everyday objects and devices, including refrigerators in restaurants. Sensor-based systems gather data on desired temperature and abnormalities, learning to recognize potentially undesirable scenarios, reacting in time to prevent downtimes.
7. Data-driven product development
Enterprises can utilize the extensive datasets gathered to streamline processes, e.g. building products based on AI suggestions. Taking advantage of data in product development breaks the stereotype that analysis of gathered information gives only an insight into the past.
It’s quite the contrary, as creative processes fuelled by data help identify areas in product creation, from minor improvements (like UX changes in cases where data analysis showed some app elements affect the way clients interact with the application) to building whole products (gathering customer data from all of the product development stages).
Perspectives for data exploitation in business
Companies utilize data analysis in building their market advantage, and the process once set in motion is not anticipated to slow down anytime soon. Building processes incorporating big data analysis leads to securing competitive advantages and creating innovative solutions, staying ahead of the competition.
The more data is gathered and examined, the bigger the picture that emerges for businesses to draw conclusions from. The findings are useful in product and service development, as well as in the way they are provided to customers. In the digital era, companies can answer needs before customers state them out explicitly. Sometimes, even before users formulate these thoughts. It is a game worth playing, isn’t it?
Does your company lack data-based solutions in key areas? If you wish to put your data to good use, let us know. Together we’ll see how your resources can be utilized in moving your company towards achieving objectives with the use of data.