Why data analytics is critical for small businesses

Businesses, both big and small, operate and generate large volumes of information. Data that is analyzed wisely can be the key to business success.

By leveraging data analytics, businesses can increase efficiency and productivity. Data analytics can also pave the path to a competitive advantage that increases revenue.

While it is true that data analysis calls for additional resources, the results it delivers for businesses are worth the investment.

Let’s find out more about why data analytics is so critical for small businesses and what benefits you stand to receive by implementing it.

5 key types of data analysis techniques used by small businesses

Small businesses benefit from using different types of data analysis techniques based on business and technology. There are five key data analysis methods essential to building business resilience in 2023:

• Data mining (Text analysis)

Data mining, also sometimes referred to as text analysis, helps discover a pattern in large data sets using databases or data mining tools. With its help, a business data analyst can transform raw data into valuable business information.

What does data mining help discover? Data mining allows for extracting and examining data to identify patterns and relationships, and interpret the data to make informed business decisions. Data mining comprises the following steps: data sourcing, data exploration, modelling, and deploying models.

• Statistical analysis

Statistical analysis is the process of collecting, exploring and presenting large amounts of data to discover patterns and trends. Five steps are taken to complete statistical analysis, including:

1. Data description
2. Building connections between the data and the underlying population
3. Creating a model to summarize these connections
4. Proof of the model validity
5. Predicting future trends.

• Diagnostic analysis

Diagnostic analytics is a form of advanced analytics that examines data or content to answer the question, “Why did it happen?” It is characterized by techniques such as drill-down, data discovery, data mining and correlations (Gartner). Diagnostic analytics implies using data to determine the causes of trends and correlations between variables. This method can be employed across a number of use cases, such as examining market demand, analyzing customer behavior, improving company culture, etc.

• Predictive analysis

Predictive analytics is used to make predictions about future outcomes based on historical data and statistical modeling, data mining techniques and machine learning. Businesses use predictive analytics to assess information that helps evaluate the company’s past behavior and predict its future, identify future risks and opportunities based on data analysis.

• Prescriptive analytics

Prescriptive analytics examines data or content to provide a recommendation on the steps a business should take to resolve an issue. This form of analytics employs a wide range of instruments and techniques, including graph analysis, simulation, complex event processing, neural networks, recommendation engines, etc.

Why is data analytics important for small businesses?

With the help of the right technology, data analytics can do its magic. Small businesses enjoy such benefits of data analytics as reduction of operational costs, business model improvement, and revenue increase. Companies that use data analytics have an advantage over their competitors by using data to provide a more personalized customer experience. Better yet, manual data analysis processes can be easily replaced with automated business solutions.

Using data wisely can help a company save money by optimizing operational processes, identifying cost inefficiencies, and making data-driven decisions. By analyzing various aspects of their business through data, businesses can pinpoint areas where resources are being underutilized or wasted, enabling them to streamline operations and reduce unnecessary expenses.

Data-driven insights can guide small businesses in making smarter investment choices, allocating resources more effectively, and negotiating better deals with suppliers. Ultimately, harnessing the power of data allows for making informed choices that lead to significant cost savings while improving overall efficiency and profitability.

Let’s explore 6 ways you can boost your business performance with data analytics.

1. Establish a data-centric approach to business

Source: Amadeus

When companies have the ability to analyse business data and massive amounts of information associated with their business and the market in general, they can create a complete picture of the customer journey.

For instance, you’ll know how people get to know the brand, what they buy, why they do it, how they shop, at what time, from which device, why they abandon the carts, and what makes them choose a brand again and again.

With this kind of information, small businesses can change the way they interact with potential consumers, stick with the preferred communication channels, and resolve issues faster.

Data analytics provide insights that illustrate a complete business overview, eliminating the need to make decisions based only on guesswork and what others do, allowing companies to act according to their business’s and clients’ needs.

The critical point here is that such a data-centric approach relates not only to customers but to the overall performance of a business. Having data gathered and analyzed is the way to act in a data-driven way in order to streamline operations, improve HR activities, and more.

2. Add clarity to financial processes

Even small businesses can spend a fortune on advertising, but all of it may be in vain. This can happen due to limited knowledge about your target market and methods for using digital marketing tools.

Digital marketing can be a tricky thing — you have the potential to attract a lot of new customers, yet you need to know how much you are spending on advertising and what your return on investment is in the end.

With the help of data analytics and management, you can track how much money you give away to onboard new consumers and how much profit you receive from them. It is important to bear in mind that the revenue clients deliver to your small business should cover all the advertising expenses.

From a financial standpoint, data management can keep your company’s financing and reporting clean. For example, you can set up Excel integrations and get all the financial data from different tools in one place. This helps with reporting, understanding your cross-department expenses, salaries, fixing up financial processes, and more.

3. Improve inventory management

It may seem that inventory issues aren’t critical for small businesses. In fact, not having an inventory management strategy, keeping unclear inventory records, over/under ordering, and other mistakes can damage your business. You can improve all these with data analytics.

With data analytics, you can upgrade operational efficiency to avoid stockout, set proper stock levels, empower order fulfillment, set accurate product descriptions, and eliminate excessive warehouse deliveries and errors.

In this way, you collect and evaluate data to optimize your existing process and build an effective inventory management system.

You’re also encouraged to use data as a basis for predictions. For example, you can conduct stock optimization based on weather forecasts, current trends, and recent consumer queries. Instead of stocking up on too many items, you can only get the most popular things and avoid stock overload. Whenever you can track what’s happening on the market and foresee how it will change, you can massively improve inventory management and save costs.

4. How to better understand the market and competitors

If you can dive into your business data, you may be able to identify valuable insights about your competitors and their niche. There are many ways a business process analyst can understand the type of clients your competitors are targeting, what products they buy, and how much profit they earn.

Such information can be of great use for small businesses. They can adjust faster to the market’s needs, decrease costs, and gain more consumers by offering the appropriate products and services. Understanding the market’s tendencies and the moves of competitors can help small businesses beat the competition and emerge at the very top of their industry.

5. Skyrocket marketing and promotional efforts

With a better understanding of how things work on the market and how competitors win over their clients, small businesses can easily predict where the trends are heading, how much in demand certain products are, and more.

Leveraging a data-centric approach eliminates the guesswork and provides facts that you’ll be able to implement in marketing your efforts. Data analytics will give you insights into what your consumers want and allow you to track them down and promote things from their wishlist.

You’ll be able to:

• analyze website performance
• adjust your marketing strategy
• choose digital marketing tactics
• use suitable channels
• set up promo campaigns

You can measure all the data down to the minor details — how people move around the online store, how they interact with your brand, how much time they spend on certain pages, etc.

There is no need to spend money on promoting items you think people might like. Instead, you can invest in marketing products people want at this very moment and lay down a budget for the next season, already knowing what they will search for in a few months.

6. Refine customer service and the customer experience

Using business data analysis, small companies can enhance their customer experience and provide greater customer service. By knowing how people think, behave, and drive their decisions, you can change the way your company is interacting with consumers.

There are many factors that could cause people to leave — a slow website, a higher price, a lack of responsiveness from a customer support representative. Whatever it is, you can use analytics for small business to track where people fall out of the loop and make sure you are ready to catch them next time.

People tend to stick with those brands that make them feel valued. Even a small business can have many loyal consumers when the service it provides is outstanding. Based on data analysis, personalized customer service helps to engage with every client at the right time and through a proper channel, providing clients with a great experience and driving sales.

What are two ways that data analytics benefits retailers?

Data analytics offers a world of benefits to retailers. So, let’s explore two of the most significant advantages:

1. Better customer insights and personalized experiences:

Imagine having a magic crystal ball that reveals your customers’ deepest desires and preferences. By using data analytics and insights, retailers can gather and analyze heaps of customer data from various sources like online transactions, loyalty programs, social media, and website interactions.

By diving into this data treasure trove, retailers gain valuable insights into what makes their customers tick. They can discover popular products, understand peak shopping times, and identify different customer segments with unique preferences. Armed with such knowledge, retailers can create personalized marketing campaigns and offers that truly resonate with individual shoppers.

The result? Happy customers who feel like their favorite store knows them inside out. This personalized experience not only fosters loyalty but also boosts customer satisfaction and sales.

2. Smoother operations and efficient inventory management:

Running a retail business is no easy task. But data analytics swoops in as a trusty sidekick, helping retailers streamline their operations and make savvy decisions.

By analyzing sales data and foot traffic patterns, retailers can optimize their store layouts and product placements. This clever move increases sales and ensures that customers enjoy a seamless shopping journey through the aisles.

Data analytics also becomes the secret sauce for efficient inventory management. Retailers can tap into historical sales data and predictive analytics to forecast demand accurately. Armed with this foresight, they can keep their shelves stocked with the right products at the right time, preventing frustrating stockouts and excessive inventory.

With these smart inventory moves, retailers save on costs and reduce wastage, making their business more profitable and sustainable.

All in all, data analytics offers retailers invaluable customer insights for personalized experiences while making operations more efficient and inventory management a breeze. It’s like having a retail Jedi on your side, ensuring your store thrives in the competitive galaxy of commerce!

How many businesses use data analytics?

The use of data analytics among small businesses has been steadily increasing over the past few years. According to SCORE Association, in 2020, only 45% of small businesses were actually using analytics, while 51% of respondents considered data analytics important. On top of that, 73% of small businesses prioritised finding new customers, while customer retention (67%) and customer experience improvement (65%) were named less important.

Allied Market Research predicts that the big data and business analytics market will grow by 13.5% from 2021 to 2030, reaching an estimated $684.12 billion. Over 90% of global businesses are planning to increase their business intelligence investments within the next three years, according to Zipdo.

At the same time, the more data is generated every day, the more use cases we see for data management and analytics. Unlike larger enterprises, very few small businesses can source, store, and analyse big amounts of data. Hence, the growing popularity of DaaS (Data as a Service) companies offering subscription-based services for data collection, storage and analysis.

What is Big Data?

Understanding Big Data is like delving into the massive amount of information businesses create daily. There are two main types:

1. Structured Data: Neatly organized and easily searchable.
2. Unstructured Data: Raw and challenging to collect, process, and interpret.

In the past, unstructured data was a hidden gem, but it lacked value due to the difficulty of analysis. Today, technology superheroes like machine learning (ML) and artificial intelligence (AI) make sense of this data.

Think of it as detectives finding hidden clues. Analyzing these patterns helps businesses predict trends, eliminate inefficiencies, and boost marketing efforts. It’s like turning data into a compass, guiding businesses toward informed decisions and greater success.

How can businesses benefit from using Big Data?

Big Data enhances decision-making, benefiting businesses in several ways:

1. Real-time insights:
   By setting up a Big Data analysis system, businesses can automatically incorporate new data. Algorithms instantly analyze this data, providing real-time insights. This agility allows for swift decision-making, turning your business into a dynamic and responsive entity.
2. Cost reduction and efficiency improvement:
   Businesses can use Big Data to identify inefficient processes and resource usage. Addressing suboptimal practices saves time and money, optimizing your company’s operations.
3. Enhanced quality control:
   While traditional quality control may miss specific issues, Big Data analyzes the entire production process, ensuring consistent high-quality products. This meticulous examination boosts customer satisfaction.
4. Improved marketing results:
   Big Data analyzes customer purchase and return behavior, revealing preferences and buying patterns. Businesses can utilize this understanding to personalize marketing campaigns effectively, offering tailored promotions.
5. Better customer experience:
   Seamless integration of better marketing, streamlined processes, and high-quality products results in an exceptional customer experience. Reduced friction fosters customer loyalty, creates brand advocates, encourages repeat purchases, and bolsters overall sales.
6. Competitive edge:
   Big Data delivers faster and more accurate information, giving businesses a competitive advantage. Swift market entry, increased profit margins, and efficient resource utilization outpace competitors. Even if competitors also adopt big data, staying informed helps you maintain market share and competitiveness.

Do small businesses need Big Data?

We described different ways your business can use data analytics above, lots of them intersect and can be empowered with Big Data.

While Big Data refers to large volumes of structured and unstructured information, small businesses sometimes mistakenly believe that they are too small for it, have no particular need to analyze it, or lack the budget for its implementation.

The truth is, regardless of the size of the business and the amount of information it generates, Big Data is the holy grail for those who know how to deal with it.

You can analyze your entire volume of information and leverage it for useful insights that result in better business process management and strategic moves.

Small businesses can leverage Big Data to:

1. Streamline expenses for improved financial efficiency
2. Identify and fix operational weaknesses and failures
3. Innovate by designing new products and services
4. Gain comprehensive insights through in-depth 360-degree customer reviews
5. Enhance marketing messages for better customer engagement
6. Dtect and prevent fraudulent activities
7. Boost overall efficiency in day-to-day operations
8. Develop pricing strategies for maximum profitability
9. Make informed and intelligent decisions based on data-driven insights
10. Expedite product or service launches for a competitive edge

On another note, there are Big Data solutions that will fit the tight budget of a small business. They can still provide actionable insights into user behavior, monitor current market trends, and reveal existing issues.

How can small businesses get started on implementing Big Data analytics?

Here’s how small businesses can get started on implementing Big Data analytics in their day-to-day operations:

1. Define your Big Data strategy: Determine the data to analyze and create a plan to acquire high-quality new data effortlessly
2. Identify credible sources of Big Data: Leverage existing information from sources like email marketing reports, sales receipts, social media analytics, the Internet of Things, website analytics, publicly available data, customer relationship management (CRM) software, etc.
3. Ask insightful questions to get the right answers: Beyond standard metrics, ask about vendor value, product line improvements, and delve into granular details for crucial insights
4. Create a system for Big Data: Identify how you’re going to store Big Data – onsite, in the cloud, data lakes, or use software solutions for efficient access, management, and storage
5. Develop patterns for data analysis: Consider hiring a data specialist or utilizing third-party data analysis solutions for efficient data processing. Here are some of the options for small businesses:
   – SAS Viya:
   Developed by the SAS Institute, this software suite extracts, manages, and analyzes data from various sources, including web, social media, and marketing analytics. It provides statistical analysis for customer intelligence in small and midsize businesses.
   – Kissmetrics:
   Tailored to enhance marketing ROI, Kissmetrics identifies customer behaviors, offering insights to understand, segment, and engage customers effectively.
   – InsightSquared:
   This platform seamlessly integrates with business software such as Salesforce, Google Analytics, QuickBooks, Zendesk, and more. It automates data collection and generates actionable insights, particularly in marketing, staffing, and sales analytics.
   – Watson Analytics:
   IBM’s Watson Analytics products make advanced, predictive business analytics accessible to small businesses without the need for data science expertise. The platform integrates extensive data sets from multiple departments, identifying patterns and potential issues.
6. Present the analyzed data: Utilize data visualization tools and interactive executive dashboards to present analyzed data clearly for informed decision-making.
7. Implement data-driven decision making: Based on the insights derived from big data, incorporate them into your decision-making process for enhanced business outcomes.

Data analytics vs. Data mining

Both data analytics and data mining play essential roles in getting data insights, but they have distinct objectives and methods. Data analytics provides a comprehensive understanding of the data and supports various types of decision-making, while data mining specifically focuses on uncovering hidden patterns and relationships within data to reveal valuable, previously unknown information.

Data analytics vs. Data mining

	Data analytics	Data mining
Purpose	To examine and interpret data to gain insights and make informed decisions.	To discover hidden patterns and insights in large datasets.
Main objective	To support decision-making and problem-solving.	To extract valuable and previously unknown information from data.
Scope	Broader scope, including descriptive, diagnostic, predictive, and prescriptive analytics.	Focused on discovering patterns and relationships in data.
Techniques	Uses statistical and quantitative methods for data analysis.	Utilizes advanced algorithms and machine learning techniques.
Timeframe	Focuses on understanding past and current data.	Often aims to predict future trends and behaviors.
Application	Widely used across industries for decision-making, performance evaluation, customer behavior analysis, etc.	Applied in various fields, including marketing, finance, healthcare, fraud detection, etc.
Use case	1. Customer segmentation based on historical sales data. 2. Forecasting sales for the next quarter. - Analyzing website traffic to improve user experience.	1. Identifying fraud patterns in financial transactions. 2. Recommending personalized products to customers. 3. Predicting equipment failure in manufacturing.

How does data mining help managers?

Data mining would be most helpful to a manager that wants to uncover hidden patterns, trends, and correlations within large datasets to make data-driven decisions and gain valuable insights into their business operations. By employing data mining techniques, the manager can sift through vast amounts of information, extracting meaningful and actionable knowledge that might not be apparent through traditional analysis methods.

For instance, a retail manager interested in understanding customer behavior might utilize data mining to explore purchasing patterns, identify customer preferences, and segment their audience based on buying habits. This information could lead to targeted marketing campaigns, personalized offers, and improved inventory management, ultimately boosting sales and customer satisfaction.

Similarly, in a financial institution, data mining can be used to detect fraudulent activities by analyzing transaction data for unusual patterns or anomalies. By detecting fraudulent behavior early on, the manager can take proactive measures to safeguard their customers and the business from potential losses.

Moreover, data mining can be a valuable tool in the healthcare industry, helping a medical manager identify trends in patient outcomes, predict disease progression, or analyze treatment effectiveness based on patient data.

In essence, data mining empowers managers across various industries to make well-informed decisions, optimize processes, and gain a competitive edge by harnessing the power of data hidden within their organization’s vast information repositories.

What companies use data mining?

Numerous companies across different industries strategically use data mining to obtain valuable insights, streamline operations, and bolster decision-making processes. Some renowned examples of these industry pioneers include:

1. Amazon: As a dominant force in e-commerce, Amazon relies extensively on data mining to curate personalized product recommendations, fine-tune pricing strategies, and forecast demand accurately, ensuring seamless availability of products when needed.
2. Google: At the forefront of technology, Google leverages data mining to optimize search algorithms, delve into user behavior patterns, and deliver more pertinent search results and precisely targeted advertisements.
3. Netflix: Known for its popular streaming platform, Netflix harnesses data mining to meticulously analyze user viewing habits, preferences, and interactions, ultimately delivering tailored content recommendations that keep users captivated and content.
4. Walmart: A globally recognized retail giant, Walmart expertly utilizes data mining to optimize inventory management, gain insights into customer purchasing trends, and pinpoint emerging market trends, thereby tailoring offerings and promotions for heightened effectiveness.
5. Facebook: As a leading social media platform, Facebook proficiently employs data mining to analyze user data and behaviors, crafting targeted advertising and content delivery to elevate user engagement and satisfaction.
6. Uber: A trailblazer in ride-hailing services, Uber relies on data mining to skillfully optimize ride pricing, accurately predict demand patterns in different areas, and enhance the efficiency of driver allocation algorithms.
7. Spotify: A renowned music streaming service, Spotify embraces data mining to grasp user preferences, seamlessly curate personalized playlists, and recommend songs that harmoniously align with users’ distinct tastes.
8. LinkedIn: The esteemed professional networking platform, LinkedIn, employs data mining to present users with relevant job opportunities, connections, and content that align precisely with their interests and activities.
9. Bank of America: In the financial sector, institutions like Bank of America adeptly employ data mining to swiftly detect fraudulent activities, meticulously assess credit risk, and intelligently identify promising investment prospects.
10. Airbnb: As a leading online lodging marketplace, Airbnb harnesses data mining to artfully furnish personalized recommendations for accommodations, deftly optimizing pricing based on demand patterns and user behavior.

These are just a few examples of companies that harness the power of data mining, firmly establishing its role as an integral component of modern business strategies. By making well-informed, data-driven decisions, these companies achieve a competitive edge, continually delivering enhanced experiences to their valued customers in today’s rapidly evolving data-driven world.

Final thoughts

Trying to figure out how the market will change next season or looking for ways to retain clients based on your inner beliefs tend not to net any tangible results unless your decisions are backed by data. You can keep guessing why your business spends a fortune on advertising but gets no consumers, or you can start leveraging data analytics to gain all the needed insights into your small business.

Data analysis can help small businesses change how they promote their products, engage with clients, and manage finances. It can not only provide actionable insights into user behavior but assist with reducing costs, driving revenue, and providing a one-of-a-kind customer experience that makes people stay.