Industry giants are not the only ones who profit from data power. Emerging businesses and startups can also benefit and use insights to guide their decision-making. Adopting a data-driven approach in business can assist in speeding growth and altering a small or mid-sized organization internally and externally, even though the development can be delayed due to scale. In this blog, we will see how exactly data science benefits small and medium-sized businesses.
How to Use Data to Grow a Small to Mid-Sized Business?
There are various obstacles to overcome while expanding from a small to a mid-sized company:
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The organizational structure has more levels.
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The majority of employees lack a direct connection to leadership and are in need of increased revenue.
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Direct opposition from established industry players
So how can new enterprises deal with these? Data science.
Yes, data-driven decision-making is one of the finest approaches to handling these problems. For instance, having well-defined KPIs and business objectives centered on reaching them gives everyone in the firm a feeling of direction and a shared objective. Furthermore, data analysis might provide suggestions for how to handle competition.
Generally, it is not a good idea for a mid-size company to compete against much larger competitors. It would be wiser to use customer data and work to identify market segments that can be defended.
How Should a Mid-Sized Company Use Data-Driven Business Strategy?
Why is data crucial for a profitable business? Let’s look at real-world examples of how data-driven decision-making may be extremely important for a mid-sized organization.
Business Model
Initially, management’s approach was to release audiobooks in several genres to appeal to a larger audience. The idea was to release specialized titles that specific clients would want to listen to entice them to subscribe to the service. Thanks to this business model, the platform had a competitive advantage over its competitors.
Audimax’s business model had evolved several months following the Series B financing, but not in the way the management had hoped. Expanding the content collection was one of the company’s major commercial objectives in order to compete more effectively with bigger audiobook players. However, the newly hired voiceover and production personnel found it difficult to uphold earlier standards. Visit Learnbay’s Data Science Certification Course in Hyderabad to discover more about how small businesses use their data by building business models.
Business Challenges
At the same time, rising customer attrition caused recurring revenue to stall. It may also have been influenced by price rivalry from one of the biggest digital retailers in the world, which recently launched an audiobook platform, though it’s not clear to what extent. The mid-sized business had no hope of competing with the retail behemoth’s size, customer base, and capacity for niche marketing.
However, they made the proper choice in their decision-making. The evidence was that Audimax’s commercial expansion had caught the notice of its rival. In retaliation, the retailer started a price war with some of the most well-liked Audimax titles as ammunition.
Data Science for Business
As a result, the business used some of the funds from the Series B funding to establish a data team. The company hired a number of data scientists and analysts on a full-time basis. They set out to better comprehend the clients and deal with Audimax’s churn issue.
The data team eventually reached the following conclusions:
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Compared to past publications, ratings for recently released titles were much lower.
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Customers listened to several of Audimax’s new titles for noticeably fewer listening sessions.
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There were titles in the company’s portfolio that hardly any customers were listening to.
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These revelations brought to light a practical problem. As we’ve already seen, the production quality had declined, and the intention to diversify the content library’s genres had not yielded the desired effects.
Data-Driven Business Solutions
Management decided on the following remedial actions to stop a downward spiral:
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Carrying out quality control
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Utilizing machine learning methods
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Identifying the Targeted audience
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What’s Next
The real-world example demonstrates data’s value to businesses by highlighting areas for improvement and offering workable solutions. A high-quality data talent investment or internal team upskilling can significantly alter decision-making.
The road to becoming a data-driven company is long and twisty, but the rewards can be great. Learn more about the data science techniques and approaches used in businesses with an industry-relevant Data Science course in Hyderabad, designed in collaboration with IBM.
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