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Small companies can gain valuable insights to make them more productive and efficient.
SMBs was just beginning. As a pioneer in the SaaS industry, Salesforce provided businesses with a platform that constantly monitored and recorded employee actions and tracked KPIs. The vast amount of data it captured and its robust reporting capabilities allowed business owners to analyze performance metrics for individuals, teams and companies.
Today, nearly all business applications, including CRMs, accounting software and human resource information systems (HRIS), record real-time actions and changes. These platforms generate massive volumes of data that seamlessly integrate with analytics tools. This integration provides SMBs with detailed insights to inform cost reductions, improve profitability and identify inefficiencies. We’ll look at current trends affecting SMB data analytics and share how businesses use data analytics today.
To remain competitive, businesses must use data to better understand their customers and find ways to work more efficiently. Here are five key analytic trends impacting small businesses:
A decade of investment in artificial intelligence (AI) and machine learning (ML) has boosted business growth on multiple fronts, such as the ability to turn text into images and tools that predict when deals will close and at what value. SMBs now have access to sophisticated operational, forecasting and planning tools previously exclusive to large enterprises with coding teams.
Kunal Madan, founder of online retailer Amarra, believes data analytics can significantly bolster SMB efficiency. “Analyzing customer behavior data can highlight peak purchasing times, allowing businesses to optimize their operational schedules, thus reducing costs,” Madan explained. “Detailed product performance data can inform targeted inventory management, preventing stockpiling and wastage.”
Here are a few examples of AI helping small businesses with data analysis:
The marketplace for AI plug-ins is growing quickly. AI-powered data analytics tools are available either as core or add-on services for numerous business apps. We expect SMBs to continue adopting this technology, as its insights will help business owners find rationalization and efficiency gains in a sluggish economy.
Sophisticated AI and ML tools rely on big data to parse and analyze information to better understand customer behaviors, preferences and trends.
Mary Zhang, head of marketing and finance at digital infrastructure provider Dgtl Infra, says data-driven decision-making is crucial to improving customer relationships because it reduces bias and guesswork. “When we shifted from gut-feel to data-backed strategies in our marketing, we saw a 40 percent increase in campaign effectiveness,” Zhang shared. “The key is combining data insights with industry expertise for truly impactful decisions.”
Consider the following AI-powered technologies that businesses use to deepen customer connections:
Deeper customer insights with natural language processing (NLP)
NLP technologies allow businesses to extract valuable insights from customer interactions and social media mentions so you can better understand the meaning and emotion hidden in transcribed phone calls, emails, live chats and social media messages. When combined with sentiment analysis, NLP can:
One practical example of NLP in action is Amazon’s Comprehend tool, which automatically summarizes consumer feedback on products and customer support interactions. This allows businesses to better understand customer sentiment and effectively address areas of concern.
Improved customer service with conversational AI
Conversational AI tools, powered by live data, help businesses understand and respond to real-time customer needs. This technology gives businesses a constantly updated stream of data on what customers want to know, what excites them and what concerns them. AI tools can also provide more effective and faster customer service at a fraction of the cost of traditional labor.
Here are two examples of conversational AI in action:
If a conversational AI tool encounters a question it can’t answer, it transfers the customer to a human agent for follow-up and prompts the agent with on-screen suggestions to help find a solution.
Enhancing data analytics with large language models (LLMs)
Large language model (LLM) tools like ChatGPT offer real-time, intelligent responses through a chatbot interface. This technology is fundamentally different from the rules-based conversation tools that preceded it.
Rules-based tools had the following drawbacks:
LLMs effectively address all of these points:
LLMs also operate on a broader scale. They can perform deep contextual analysis on large datasets, not just individual customers. This means SMBs can more easily identify marketing trends, personalize their marketing and run their business based on data-driven insights instead of gut instinct to stay competitive.
Data as a Service (DaaS) and Data Security as a Service (DSaaS) are rapidly growing IT trends that help businesses efficiently store, analyze and protect their data.
Gathering data is necessary for business success and growth. However, how well you process and analyze that data is critical, affecting the quality of the decisions you make about your company’s direction.
Data visualization tools transform complex data into visual formats like charts, graphs and dashboards, making it easier to understand and analyze. Many popular software packages, including the best CRM software and the best accounting software solutions, have powerful built-in visualization tools. However, specialized visualization packages are making data analysis even more effective, particularly when it involves large data volumes.
For example, Tableau is a popular data visualization tool that takes information from spreadsheets and databases and turns it into interactive charts and dashboards. These visuals can be shared across the organization to keep everyone informed and aligned.
Data monetization strategies help businesses make money or gain value from existing data.
This can involve your business selling data to other companies, using it to create better products, or finding new commercial opportunities.
Data monetization has internal and external approaches:
Ben Sporn, CEO of financial publisher Joywallet, emphasizes the importance of internal data monetization strategies for businesses. “Small businesses can use data to analyze customer behavior and identify patterns that inform targeted marketing strategies,” Sporn explained. “Instead of spreading their marketing budget thin, they can focus on the most effective channels and customer segments, which can lead to better ROI.”
Data is an asset, and it makes commercial sense to monetize it. However, with the right strategy, you can unlock new revenue opportunities, build valuable partnerships and better understand your customers.
Consider the following four impactful ways SMBs are using data analytics:
Leveraging data analytics is cost-effective for SMBs. Consider the following affordable resources that can provide small business owners with a wealth of information:
Zhang stressed that SMBs getting started with data analytics have numerous options. “We began with simple spreadsheet analysis and gradually moved to more sophisticated platforms like Google Analytics and Tableau,” Zhang shared. “These tools offer powerful insights without breaking the bank.”
Integrating data analytics into business processes can be complicated. But it’s worth it, according to Stuart Barber Yarborough, consulting business lead at global consulting firm Royal HaskoningDHV.
Before starting a data analytics project, be sure you have the following:
Consider the following best practices for starting a data analytics project:
Data analytics can bring transformative insights to businesses. Bruce Zheng, founder and CEO of strategic valve manufacturer NTVAL, shared how data analysis helped transform the company from a small local firm to an international provider.
The business analyzed its production cycle and discovered it overstocked 30 metric tons of raw materials annually. It adjusted its order cycles and saved more than $150,000 in inventory holding costs. “We used Excel to track daily production rates and find inefficiencies,” Zheng explained. “Tracking these metrics could reduce downtime by 60 hours per month and save about $20,000 in labor and missed deadlines.” The company now uses AI-driven tools to forecast valve demand. It also identified $300,000 in savings by optimizing its supply chain and restructured its capacity to produce an extra 10,000 units a week, generating additional revenues of $500,000 a year.
With results like these, it’s no surprise that 93 percent of businesses plan to increase investments in data and analytics, according to an EY study. The trends mentioned here indicate a swift evolution of data analytics and demonstrate their power to transform multiple aspects of operations.
Whether it’s mimicking the human brain’s knowledge acquisition through machine learning and deep learning or capitalizing on unused dark data to gain a competitive edge, the new era of data analytics has intensely practical applications that businesses should not overlook.