When Salesforce launched its browser-based CRM software in 1999, the data analytics age for SMBs was just beginning. For many such businesses, Salesforce might have been the first piece of software that constantly monitored and recorded what employees were doing and whether or not they were hitting their KPIs. The amount of data it recorded and the ease with which reports could be created meant that, for the first time, owners could see exactly how well individuals, teams and the entire company were doing.
Today, most business apps, whether they’re accounting software or human resource information systems, record every action and change in real time. These apps create massive volumes of data, which they can share with data analytics platforms.
Data analytics platforms provide SMBs with detailed insights into all aspects of operations that they can use to cut costs, improve profitability, and identify inefficiencies. Below, we look at three major trends affecting SMB data analytics in 2023 and how SMBs use data analytics today.
A decade of investment in artificial intelligence (AI) and machine learning (ML) has led to innovations on multiple fronts, like the ability to turn text into images and tools that predict when a deal will close and for how much. SMBs now have the operational, forecasting and planning tools previously only available for larger companies with coding teams.
For managers and supervisors, AI-powered data analytics tools can analyze employee performance to understand why they’re missing their KPIs. These tools can even tell managers and supervisors which areas individual employees need specific training in. At the highest levels, CEOs are now using decision support systems to tell them the likely result of a course of action they’re considering, such as investments in machinery, which new markets to enter, and potential returns on product development.
AI data analytics for retailers can reduce the risk of ordering too many items or the wrong stock. It can even predict how much stock each shop and the online warehouse will need so that customers aren’t disappointed when they buy.
The marketplace for AI plug-ins is growing quickly. AI-powered data analytics tools are available either as core or add-on services for a wide range of business apps. We expect SMBs to continue adopting this technology in 2023, as its insights will help business owners find rationalization and efficiency gains in a sluggish economy.
For AI and ML to work, they need data — big data. As the accuracy of these tools increases, companies are digging deeper into the data they generate to see if they can identify new opportunities to improve.
One area of focus for companies in recent times is mining insights from interactions with their customers and mentions on social media. Natural language processing (NLP) tools can interpret meaning from transcribed phone calls and digital channels (like email, live chat software, social media messaging and more).
NLP, combined with sentiment data analysis, can recognize what consumers think about a business and its products, even detecting specific emotions through the words customers say or write. This technology can differentiate between statements and questions as well as determine whether what someone has said or written is an opinion, a question, a suggestion or a show of appreciation. Tools like Amazon Comprehend then use automatic text summarization to create concise summaries of consumer opinion from the unstructured data files created by term or topic (for example, feedback on “product A” or opinions on customer support).
Together with the instructions you provide to your software, these machine-generated insights powered by data analytics can improve customer outcomes.
Live conversational AI tools also rely on these insights. Two prime use cases that are growing in popularity are live chat software and call center software.
Conversational AI, which relies on live data analytics, can now handle customer queries, recommend products, handle technical issues, and take orders live by phone, email or messaging platform without involving a human agent. If the AI recognizes that it can’t resolve a situation, it transfers the client to an appropriate agent. Even at this point, AI helps agents achieve the right outcome by displaying on-screen prompts on what to say based on the actual live conversation.
Conversational AI’s ability to successfully resolve more calls will increase over time. This presents companies with opportunities to reduce personnel costs in customer service and technical support. Given the poor economic forecast for 2023, we expect to see significant uptake in and development of this field of data analytics.
Businesses are under a lot of regulatory pressure now to make sure that data is kept safe and used responsibly.
General Data Protection Regulation (GDPR) led the way when it was introduced in the EU in 2018. Gartner analysts predict that 65 percent of the global population will be covered by regulations similar to GDPR by 2023. In America, the federal government and state governments have also enacted new data security laws in recent years. The market also drives this issue, as 54 percent of consumers believe that companies should be legally compelled to implement better data security measures, according to the Thales Consumer Digital Trust Index.
Two factors make data security more difficult for companies, though.
First, companies store data on both the cloud and their own IT hardware. Second, employees who work from home can access that data using IT equipment that is often less secure than the equipment at the office. This gives cybercriminals many different attack points they can target to get at sensitive information especially when in transit. In response, a growing number of IT companies now offer data security as a service (DSaaS) to strengthen their clients’ data privacy and reduce risk.
As data and data processing have shifted to the cloud, there has also been a rise in the popularity of Data as a Service (DaaS) companies. These platforms offer companies a broad range of powerful pre-built tools that you can use to analyze your data. They’re also places for companies to pool data together and offer monetization opportunities if other clients use your data to plug gaps in their databases.
Data analytics are employed in many ways, but here are the four most important uses for SMBs:
One of the main applications of machine learning for small businesses is using it to track customers throughout the different stages of the sales cycle. Small businesses can use data analytics to determine a particular segment of customers that are ready to buy (and, more importantly, when they’ll be ready).
As we covered earlier in this article, data analytics can greatly improve customer service. We discussed one of these in our RingCentral review. This platform records all phone calls, email and live chat conversations over the past 90 days, analyzes them and then presents its results as a word bubble. This visualization is an effective way for supervisors to check for trending issues among customers.
Applications like the word bubble give you a greater level of insight into common issues customers are having that can be leveraged to ensure that customers have an amazing experience with a product, service or brand.
On a macro level, small businesses can use data analytics to identify overarching patterns and trends. For instance, if numerous customers contact a business asking the same questions, it might make sense to create an online knowledge base that addresses these questions in depth.
Some of the best call center software packages can now analyze incoming calls for content and provide you with suggestions on what to put in your knowledge base. In effect, this new part of your website could hypothetically increase sales as it addresses common questions that potential buyers face or help strengthen a brand’s unique selling point (USP) — all made possible through data analytics.
Data analytics provides detailed analyses of customer behavior, and these insights allow business owners to learn what motivates consumers to buy their products or services.
Small business owners can use this valuable information to identify which marketing channels to focus on in the future (i.e., save on marketing spend while increasing revenue at the same time).
The insights from data analytics help reduce how much a business spends on marketing and product development. Rather than funneling big money into multiple marketing strategies that only get minimal results, small businesses can harness data analytics to concentrate on a few that are proven to generate high-quality leads.
The great thing about this technology is that it doesn’t have to cost an arm and a leg. Here are some very affordable resources that can provide small business owners with a wealth of information:
There are many analytics tools available that can generate data for nearly every aspect of business imaginable, that are either free or inexpensive.
No-code and low-code plug-in platforms reduce companies’ need to hire specialist programmers and coders to grab the data from whatever internal or external sources are used.
About one in four companies are now driven by big data analytics, reported Zippia. 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 knowledge acquisition of the human brain 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 notice.
Additional reporting by Luke Fitzpatrick.