With over 300 billion emails sent every day and over 2 billion people shopping online daily, you can only imagine how much data is there for analysts. And businesses, both big and small, generate massive amounts of data. So, why not use it to address a number of business problems?
While data analytics calls for additional resources, small business owners should understand the results it delivers—and that these results are worth the investment. Let’s explore what benefits you stand to receive from using big data solutions. Specifically, we’ll be talking about their value for small businesses with tight budgets and a small workforce.
Most Important Types of Data Analytics for SMBs
Wasteful spending and negligible results are some of the worst outcomes for growing businesses. However, this can easily happen if you don’t know what type of data you need and why. If you start without a plan and strategic guidance, you’ll unlikely get the valuable insights you’re looking for. A solution to this: narrowing your focus.
Here are several areas that small businesses should focus their data analytics efforts on.
Workforce
Most small business owners can agree that labor accounts for a significant portion of operational costs. For some, it’s the biggest expense. If you’re trying to squeeze in more productivity without paying more, a data-based approach can help.
The answers about your workforce, specifically, the opportunities for increased efficiency, can be found in data analysis. Measure metrics like time-to-hire, retention rate, revenue per employee, total costs of the workforce, etc.
Use business data creatively. Depending on your goals, you’ll be asking different questions and, as a result, generate different insights from data analytics. For example, you can establish an analytics-oriented workforce training program to reduce your employee turnover rate.
Sales
Another area where you can optimize processes through data analytics is sales. While employee cost management is important, any small business should be looking for ways to bring in more revenue.
As part of your sales intelligence strategy, measure the following:
- Customer acquisition cost
- Average sale price
- Margins per product line
- Sales by product/service, by lead source
- Sales growth rate
- Customer lifetime value
- Lead conversion rate, and more
Raw purchasing numbers can only go so far. But for deeper insights, you’ll need advanced analytics—to help you understand and predict sales trends, identify your most important customers and products/services, identify stumbling blocks to conversions, and more.
The next key area to focus on is promotion – if you have marketing initiatives to begin with. If that’s the case, you already have a competitive advantage over 1 in 5 small businesses that don’t utilize digital marketing. As for the rest 4 out of 5, you’ll apply big data technology and analytical processes to marketing-related business data to get ahead.
The way you should approach marketing analytics is the same as other areas – understand, interpret, optimize. It’s worth going out of your way to integrate an analytics software suite into your marketing processes to have the best possible view of what’s working in your marketing campaigns and what’s not. For this, you’ll be looking at everything from clicks to brand awareness.
Conduct marketing analysis after defining key business indicators and the relationships between them. Here are a few examples of quantitative indicators that you can use to measure marketing activities:
- Acquisition – visits, impressions, installs
- Activation – conversion rate, subscribers, signups
- Retention – customer growth rate, clickthroughs, churn
- Referral – users sharing invites, invitation conversion rate
- Revenue – customer lifetime value, average receipt, revenue per account
Product
This area of business analytics helps gauge the success of a product and should tie back to the product strategy. In addition to facilitating intelligent decision-making throughout the product development process, product metrics also have value for sales and marketing teams. This proves once again that data is a unifying force for different parts of the organization.
The metrics the team chooses to track should collectively create a product’s North Star—the gold standard for product success. These can be the following.
- User adoption
- Feature use
- Actions per session
- Sentiment
- Customer satisfaction
- Support tickets
How Data Analysis Helps Small Businesses: 7 Main Benefits
So far, we’ve hinted that by leveraging data analytics, companies can get actionable insights and gain a significant competitive advantage. But we’ve yet to explain how it can happen.
Here is what big data can offer to your business processes if you use it properly.
Helps You Become a Data-Driven Organization
According to McKinsey Global Institute, data-driven organizations are more successful than their peers. If you become one, you can outperform other businesses across the full customer lifecycle. A data-driven organization is 23 times more likely to turn prospects into customers, 6 times more likely to retain them, and 19 times more likely to achieve above-average profitability.
If you recognize that data analytics is the gateway to new opportunities, you’ll need more than just data. You’ll also need to establish a data-driven culture, which aims to leave behind the traditional decision-making approach based on assumptions. This kind of workplace approach has a number of benefits on its own.
Gives You Financial Clarity
Are you spending your money in vain? Are your marketing efforts paying off? Are you overspending? Once again, the answers are hidden in big data.
Find out how much money you are spending on onboarding new consumers, cross-department expenses, customer returns, and the scrapping of products. These processes, along with many other ones, affect your company’s financial situation. The least you can do is to learn about them through data analytics and reporting. The next step would, of course, be to convert the results into actionable insights.
Remember that owning and operating a business without data-based financial awareness is like driving blindfolded.
Allows You to Understand the Market and Compete With Bigger Brands
In most industries, established competitors will leverage data-driven strategies for their own benefit. You may think that with the amount of data they can aggregate and analyze, your dataset will not stand. But as a new entry, you can capture value even with a smaller amount of data to work with.
If you dive into publicly available data on top of your business data, you can learn a lot about your competitors and your product/service niche. For example, you can turn to government and academic data, social media, traditional media, and journalism.
You may not be able to climb to the very top of your industry. But keep at it nevertheless. Use the findings to offer the appropriate products and services, react to trends, adjust your pricing, etc.
Optimizes Inventory Management
Businesses that are just starting out may not have the warehouse and supply chain capabilities that bigger brands have. So, that forces you to be smarter about your decisions on how to store supplies and products.
You can leverage historical data and forecast future events—seasonal changes, a rush of orders, etc. For instance, data allows you to identify patterns in demand and, with the help of predictive analytics, suggest better allocation and replenishment strategies. Just make sure to have the right people interpreting the data; suggestions don’t come from numbers and visualizations alone.
Inventory data can also provide you with insights into customer behavior. So, you can streamline operations across departments.
Ensures Effective Hiring
Data-driven recruiting can increase the quality of hires. It can show you and the human resources team variables that you may not have seen. Analyze your best hires—who they are, where they come from, and some common qualities they all share—and look for candidates that are consistent with these variables.
Many business owners will also appreciate the cost savings that come from data-based hiring. The better you understand your most effective recruitment platforms, the less money you waste on your least effective platforms. The more successful you are in finding the best hires, the higher the productivity.
Helps You Meet Your Ideal Customer
Let the data analytics tool do the grunt work to find your happiest existing customers. Have it look for your best-performing customers and highlight what characteristics they share. And then, find your worst-performing customers and decide whether they’re worth changing for.
Identifying your perfect customer to focus on makes everything else easier. You will be able to stop wasting your marketing efforts on those who don’t fit the profile and create personalized offers for those who do. On top of doubling down on whatever makes your current customers happy, find new leads who genuinely want to hear about what you have to offer.
Improves Your Capacity to Scale
One of the most compelling benefits of making data analytics a key element of your business model is that it helps you grow.
Big data can paint the big picture that no one person is capable of. It includes information about your company’s footing in the marketplace, opportunities to optimize expenses and increase revenue, and predictions about future outcomes, among other revelations.
Structure & Stages of Modern Data Analytics
A modern data stack is now replacing the traditional model to accommodate the growing needs of data-driven organizations. The stack includes a variety of tools in five main categories.
Data Ingestion
The first step is obtaining and importing data for immediate use or storage. It can be a continuous flow of internal data generated by various sources and/or data collected by third parties.
Tools you can use for this stage: Segment, Snowplow, Fivetran, Airbyte.
Data Orchestration
At this stage, it makes sense to automate data management processes and prepare data for further analysis. Data orchestration tools may also include source preparation and monitoring to approach data collection effectively and systematically.
Tools you can use for this stage: Dagster, Prefect, Airflow.
Data Warehouse
This stage concerns a central repository of information. Moving on from standard relational row-based data warehouses, organizations can turn to columnar tools. They offer faster performance and let users skip over all the non-relevant data very quickly.
Tools you can use for this stage: BigQuery, Snowflake, Clickhouse.
Data Transformation
The modern approach to extracting information from raw data revolves around the ELT flow—Extract, Load, Transform. This data flow system focuses on in-database transformations, from which you can create dashboards, build machine learning models, etc.
Tools you can use for this stage: dbt, Matillion.
Data Intelligence & Visualization
Finally, data needs to be presented to business experts. The key is to present it in a way that minimizes their reliance on developers and analysts—which is exactly what intelligence & visualization tools aim to do.
Tools you can use for this stage: Metabase, Superset, Google Data Studio, PowerBI.
Why Small Businesses Should Use Different Big Data Analytics Tools
With the benefits that data analysis can possibly provide, it’s essential to find business intelligence tools that are right for your needs.
A few examples of data analytics tools you can use are:
Combining Data From Multiple Sources
It’s likely that you have data coming from different sources and stored in multiple places. Take advantage of data integration tools to get it all into one software. You’ll need to transform all datasets into an accessible format, and this way, different people will be able to access them (on a need-to-know basis, if needed).
Small and medium businesses can start integrating data from customer relationship management platforms, sales systems, and marketing software.
Getting Data Ready for Processing
First of all, you shouldn’t analyze data without cleaning it. You simply don’t need all the data. Second of all, you will likely need to pre-process it. If you don’t have a dedicated person in your team for it, a data analytics service provider can structure and organize your business data for you.
Making Data More Trustworthy and Manageable
You can eliminate unnecessary work by having data quality policies in place. Use the tools to identify and correct errors, typos, and redundancies, handle deduplication and standardization. Overall, make sure your business data is accurate, complete, and consistent before it arrives at the data analysis stage.
Ensuring Data Is Well-Governed
Effective data governance leads to better data analytics. It ensures the accessibility, integrity, and security of information and instills confidence in the data you use. While doing so, it will also help you avoid lawsuits you wouldn’t be able to afford financially and reputationally.
Best Data Analytics Practices for Small Businesses
Make the most out of your business data by following these practices:
- Automate as much as you can – Use technology to avoid wasting your workers’ time on data analysis. For example, have one of them set up automatic scanning and analyzing reports and identify those that share a specific trait.
- Simplify administration – Consider all the requirements and dependencies, including those for installation, configuration, and management. Shoot for the tools that you know you can handle.
- Prioritize visualizations – Don’t allow good insights to get lost in poor visualizations. Find data visualization tools to create impacting, powerful visuals that everyone on the team can make sense of.
- Tap into social media data – Don’t assume that you can find all answers just with your business’s internal data. Take the time to explore different types of external data, starting from social media.
- Use solutions that scale – Your business goals will likely change over time. Should demands arise, it’s helpful to have tools with flexibility and scalability needed for business growth.
- Take training seriously – Think about what skills your employees need for the kinds of models you want to build and teach them these skills. As you grow your team, make sure to bring new less-technical employees up to speed.
- Offer ongoing skills development – No matter how good people already are at their job, ongoing training will help take their skills to the next level. Plus, data science is constantly evolving, so there is always plenty of new information to learn.
Small Business Data Analytics: Takeaway
There are many big data solutions available for small businesses, from ready-to-use tools to custom services. And it’s important for small business owners to understand that while you need resources to extract value from raw data, it’s absolutely possible to make it fit even a tight budget. Besides, analytics solutions help the company’s bottom line, so the investment can be recouped and then bring even more.
It’s never too early to start tracking and analyzing data. So, it’s time to get some help from a data mining specialist to guide your way through your digital ecosystem!
You need this guide, because it includes:
- evolution of data stack
- reasons why many well-known tools do not meet the challenges of the new world
- vital structure of modern data stack
- the newest tools for every part of modern data stack
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