With many companies ramping up their digital transformation initiatives, there’s been a massive surge in the implementation of data analytics.
- Deep learning is helping small businesses enhance decision-making capabilities.
- Machine learning is revolutionizing the way brands market to consumers.
- Dark data refers to information assets that companies collect, process or store, but fail to put to use.
- Data analytics provides SMBs with incredibly detailed insights into all aspects of operations.
With many companies ramping up their digital transformation initiatives, there’s been a massive surge in the implementation of data analytics. Big data adoption increased from a mere 17% in 2015 to 59% in 2018 – a 42% increase in just three short years.
Data analytics has a nearly infinite number of uses and can help small businesses become smarter, more productive and more efficient. And when used correctly, it can create a noticeable competitive advantage, while boosting both conversions and revenue.
Three data analytics trends that are impacting small businesses in 2019
1. Deep learning
We generate a staggering 2.5 quintillion (that’s 18 zeroes) bytes of data every single day. Machines are becoming more and more adept at putting that action to use, with deep learning capabilities growing immensely in 2019.
A subset of machine learning, deep learning utilizes artificial neural networks that learn from vast quantities of data in a similar manner to the functioning of the human brain. This helps machines solve highly-complex problems with incredible precision. As enterprise and cloud expert Bernard Marr says, “The more deep learning algorithms learn, the better they perform.”
2019 marks a critical juncture where deep learning is helping small businesses to greatly enhance their decision-making capabilities and take operations to new levels. For example, chatbots are developing at an increasingly rapid rate. Through deep learning, they’re able to respond more intelligently to a growing list of questions and create helpful interactions with consumers.
Deep learning provides a framework for chatbots to continually build upon knowledge so that the knowledge base grows exponentially.
2. Machine learning is becoming mainstream
Machine learning, the process by which machines learn information through training algorithms, is a form of sophisticated data analysis that’s become widespread in recent years. Some of the more notable examples include Netflix and Amazon generating suggestions based on previous queries and activities.
But in 2019, machine learning is being taken to new heights and opening new doors for companies across many industries, as the neural networks that make up the architectural design of machine learning are becoming more advanced.
And while machine learning and data analytics used to be considered fairly disparate, they’re becoming much more integrated – a trend that’s beneficial for modern small businesses. While there are multiple applications, machine learning is really revolutionizing the way brands market to consumers.
For example, companies can analyze a large volume of marketing data to create a fully optimized and personalized message. Often referred to as “hyper-personalization,” brands can deliver customized promotions based on factors like a prospect’s location, demographics and whether they’re a new or returning visitor.
Meanwhile, recommendation algorithms allow brands to suggest relevant products based on prior customer purchases and interactions, similar to Netflix and Amazon.
3. Dark data
There’s nothing threatening or sinister about dark data – it’s quite the opposite, actually. Dark data simply refers to information assets that companies collect, process or store but fail to put to use.
It’s that data that has value but slips between the cracks. Common examples include unused customer data, email attachments that are opened and left undeleted and old customer support tickets. With dark data predicted to account for 93% of all data by 2020, a growing number of organizations are taking steps to utilize it.
One way they’re doing this is by using the data from customer support logs to see which medium a customer used to initiate contact and how long the interaction lasted. This dark data allows a business to determine a person’s preferred method of contact so they can deliver a better customer service experience moving forward.
In terms of methods for generating unused data, web data integration is extremely effective. It involves converting websites into structured, usable data for in-depth insights that can then be integrated into analytics and business applications.
In one case, a financial services firm was missing sales opportunities due to the inability to immediately detect when a prospect’s legal filing indicated a change in lead status. But with Import.io’s web data integration, the firm was continually fed updated data whenever leads performed targeted actions, ensuring their leads were always up-to-date.
How can SMBs use data analytics?
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).
Data analytics can also be used to improve customer service. For example, machine learning tools can analyze conversations between sales representatives and customers on channels like email, chat, and social media. Thus, providing 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 are contacting a business and asking the same questions, it might make sense to create a dedicated page that addresses these questions in depth. In effect, this new website page 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 provide SMBs with incredibly detailed insights into all aspects of operations. For example, data analytics provides a detailed analysis of customer behavior. In turn, allows business owners to learn what motivates consumers to buy their products or services. This is incredibly valuable because small business owners can use this 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).
How can SMBs use data analytics on a budget?
The insights from data analytics help to reduce how much a business spends on marketing and product development. Rather than funneling big money into multiple marketing strategies that are only getting minimal results, by using data analytics, small businesses can concentrate on just a few proven ones that are generating high-quality leads.
And 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:
The bottom line is that there are countless analytics tools available that can generate data for nearly every aspect of business imaginable, that are either free or inexpensive.
With nearly two out of three companies now adopting data analytics, the process is strongly shaping the modern business world and is something most brands want to utilize. 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 some intensely practical applications that businesses should notice.
Are you taking advantage of these trends to grow your business? Share your experience in the comments below.