Changing regulations, new technologies, increased risk exposure, and a hybrid workforce are just a few challenges that small businesses like yours face today. Data analytics can help address these challenges by enabling small businesses across every industry—including retail stores, auto shops, HVAC businesses, and med spas—to make informed choices related to cost management and performance optimisation. This can help convert more leads, drive sales, optimise efficiency, improve customer retention, and more.
Below we’ll explain the concept of big data and the benefits it offers small businesses. We’ll also go over common data analysis techniques and applications for big data in small business operations.
What is Big Data?
Big data is data that must be processed using advanced data processing software due to its volume, velocity, and variety. It includes both structured and unstructured data. Structured data refers to highly organised and easily searchable data sets, while unstructured data refers to raw, unorganised data sets.
The concept of “big data” originated around the time of the internet, when individuals began generating massive amounts of data at such a fast rate that traditional computing systems couldn’t keep up. For example, in 2010, it’s estimated that two zettabytes of data were created, captured, copied, and consumed globally. This amount increased rapidly over the next decade. In 2020, it’s estimated that 64.2 zettabytes of data were created and replicated. This is projected to grow to more than 180 zettabytes by 2025.
Big data is significant in the modern business landscape because it provides more complete answers to questions about a business’s operations, customers, and products. These insights can help you improve your decision-making, efficiency, and overall performance. As a result, you’ll have the information you need to convert more leads into money at scale.
We’ll cover the benefits of big data in more depth below.
Benefits of Big Data for Small Businesses
There are several benefits of big data for small businesses. Below we’ll cover some of the most important ones.
1. Informed Decision-Making
One of the greatest benefits of big data is that you can gain valuable insights into various aspects of the business. This means making more informed choices about everything from marketing strategies to staffing to resource allocation. You can also more accurately forecast future sales, trends, and customer behaviour by analysing historical data.
2. Improved Inventory Management
Big data can be particularly beneficial for demand forecasting and inventory management. By analysing historical sales data and external factors such as seasonality, your small business can make more accurate predictions about future demand. This will help you maintain appropriate inventory levels without overstocking.
3. Enhanced Customer Experience
Another major benefit of big data is the insight it provides into your small business’s customers. It allows you to better understand customer preferences, behaviours and needs so you can then tailor your products or services accordingly.
Analysing data like customer feedback and reviews, in particular, can identify areas for improvement and enable your small business to continuously respond and adjust to customer needs.
4. Improved Operational Efficiency
Finally, big data can help you identify inefficiencies in your processes or supply chain. Use this information to streamline your business operations, allocate resources more efficiently, reduce costs, and improve productivity overall.
4 Types of Data Analysis Techniques for Small Businesses
You can use several techniques to gain valuable insights from your small business’s data. Below are four of the most common techniques.
1. Descriptive Analysis
The goal of descriptive analysis is to answer questions about what happened. It involves using data collection, processing, analysis, and visualisation to summarise large datasets to describe outcomes to stakeholders. This technique is commonly used to provide essential insight into past performance.
2. Diagnostic Analysis
The goal of diagnostic analysis is to answer questions about why something happened. It involves trying to find the cause of what happened by finding dependencies and patterns in the descriptive data sets. This technique is commonly used to diagnose either a drop or an improvement in performance.
3. Predictive Analysis
The goal of predictive analysis is to answer about what will happen. It involves using statistical modelling, machine learning, and data mining techniques to identify trends in historical data and determine if they are likely to occur in the future. This technique is commonly used to reduce risk and identify new opportunities.
4. Prescriptive Analysis
The goal of prescriptive analysis is to answer questions about what should be done. It involves using AI, big data, and machine learning techniques to find patterns in large datasets and analyse past decisions and events. This technique is commonly used to help predict outcomes and prescribe a course of action.
5 Ways in Which Small Businesses Can Use Big Data
Let’s look at five ways in which small businesses effectively use big data to optimise operations.
1. Customer Analytics
Small businesses can use big data to gain deeper insights into their customers, including their purchase history and browsing patterns. This helps in identifying and segmenting customer groups based on preferences, demographics, and behaviour. Small businesses can then provide personalised product recommendations or marketing campaigns that resonate with specific groups to achieve higher engagement rates, increased conversions, and improved customer satisfaction and retention rates.
Amazon is well-known for using customer data to provide personalised recommendations. Its software, called the Collaborative Filtering Engine (CFE), pulls information from customers’ purchase histories, wishlist items, shopping cart saves, and product reviews as well as their most searched products to recommend products.
Learn more about customer analytics and how to use them in your small business in this ultimate guide.
2. Marketing Optimisation
Big data can also help small businesses optimise their marketing strategies in other ways. For example, with attribution modelling, small businesses can better understand how customers are interacting with their website and when they’re converting. They can then use this information to allocate their budget across different channels and campaigns.
They can also use big data to conduct A/B tests on landing pages, ads, emails, social media posts, and other marketing collateral and to monitor and analyse their competitors’ marketing efforts. Both provide valuable insights that help small businesses improve their performance.
For example, Capital One analyses the demographics and spending habits of its customers and then, based on that data, determines the optimal times to present various offers to increase the conversion rates of its marketing communications.
3. Supply Chain Optimisation
Small businesses can also use big data to evaluate the performance of their suppliers based on factors like lead times, quality, and reliability to make informed decisions when selecting, negotiating, or renewing contracts with their suppliers.
They can also use this data to identify areas where they can reduce costs or optimise their supply chain in other ways.
For example, Amazon uses big data to locate the closest warehouse to a customer to reduce the overall shipping costs and fulfil their order as quickly as possible.
4. Customer Service Optimisation
Big data can significantly enhance a small business’s customer service. For example, a small business can implement chatbots or another automated customer service solution powered by big data to handle simple or common inquiries more efficiently. This frees up human reps for more complex issues.
They can also use data from their platform, social media, customer reviews, and support interactions to address common pain points and improve the overall customer experience.
For example, Netflix uses data to optimise the experience of every consumer. They not only provide personalised recommendations based on your viewing history, but they also customise the artwork and trailers shown to each viewer.
5. Financial Analytics
Finally, small businesses can leverage big data to gain deeper insights into their financial performance. For example, by analysing historical data as well as expected income and expenses, small businesses can better manage their cash flow to ensure they have enough liquidity to cover their obligations. They can also analyse data to identify potential financial risks, such as market fluctuations or acquisitions, assess their potential likelihood and impact, and mitigate those risks accordingly.
For example, Starbucks uses big data to determine the potential success of opening up a new location. Using information about location, traffic, area demographics, and customer behaviour helps mitigate the financial risk of opening a new store.
Starting With Big Data for Small Businesses: How?
Now that you understand the benefits and use cases of big data for small businesses, you can use the step-by-step guidance below to begin your big data journey.
1. Define Goals and Objectives
To start, define the specific goals and business objectives you want to achieve with big data. For example, your objectives may include improving the customer experience, optimising operations, or increasing revenue.
At this stage, you may also want to identify key performance indicators (KPIs). KPIs can help you assess how much progress you’re making towards these goals and objectives. For example, if one of your objectives is to improve customer retention, you may select churn rate or customer acquisition cost as a customer retention KPI.
2. Inventory Your Data Sources
Next, take inventory of the data sources within your organisation. Data sources may include a customer relationship management system, chatbot, website analytics platform, social media channels, customer review sites, and other must-have tools for small businesses.
When choosing data sources, consider factors like the quality, volume, and diversity of the data available through the source. (Based on your data needs, you may need to find additional data sources.)
3. Select Appropriate Data Analytics Tools and Technologies
Next, select the data analytics tools and technologies that align with your data needs and objectives. You might need a data analytics platform, database management system, visualisation tool, or a combination of these.
When selecting these tools, consider your budget, the availability of resources and skills at your organisation, as well as the data sources and formats you’ll be working with.
4. Implement Data Collection and Cleaning
Now you’re ready to set up data collection processes to gather data from the sources you identified. This might require you to implement tracking codes, integrate APIs, or use data extraction tools.
You’ll also need to set up processes to ensure that this collected data is clean, accurate, and error-free. This might involve removing duplicates, handling missing values, and standardising formats. When evaluating data analytics tools and technologies, consider whether they automate any of these tasks for you.
6. Monitor, Evaluate, and Iterate
Once you’ve implemented data collection and cleaning processes and begun using that data to optimise your operations, you can monitor and evaluate your performance against your goals and objectives. Based on these performance insights, you can continuously iterate and improve your data analytics strategy.
Take Your Small Business to the Next Level With Podium
As a small business, implementing a data analytics strategy can be intimidating and difficult. Podium can help.
By using Podium’s communication tools, CRM integrations, and features like Reviews and Payments, you can centralise vast amounts of customer data. So instead of spending time tracking down and integrating multiple data sources, you can focus on analysing that data to better understand your customers and make more informed decisions that help you optimise your operations and customer experience—and ultimately, make you more money.
FAQs
Q: What are the four types of business analytics?
A: The four types of business analytics are descriptive, diagnostic, predictive, and prescriptive analytics. Descriptive is about answering what happened. Diagnostic is answering why something happened. Predictive is answering what will happen. Finally, prescriptive is answering what should be done.
Q: Do small companies need data analytics?
A: Small companies can benefit significantly from using data analytics. For example, data analytics can help them allocate resources efficiently, understand customer behaviour, optimise operations, respond quickly to market trends, better predict future sales and trends, drive sales, and boost website engagement.
Q: Is Google Analytics good for small business?
A: Google Analytics is a good tool for helping small businesses understand and optimise their online presence. It provides detailed insights into website traffic, user behaviour, conversion rates, and more. E-commerce businesses can also use Google Analytics to monitor sales performance, track revenue, and analyse customer behaviour during the purchasing process.
