Small businesses that use data analytics software are able to transform data into actionable information, allowing them to work smarter rather than harder. But before they can select the best software, companies need to understand the distinction between data mining and predictive analytics and the ways in which these technologies can be leveraged to gain advantage over the competition.

In the past, small businesses typically lacked the financial or human resources to effectively implement and use data analytics software. Fortunately, advanced data analytics software has been democratized in recent years. Powerful technology that was formerly reserved for only the largest enterprises is now available to small business at affordable prices.

Furthermore, cloud-based versions of these systems are commonly offered, allowing small businesses to invest in them without needing to add costly IT infrastructure. So the question isn’t whether small businesses should employ data analytics—it’s which type of analytics suits them best.

In this article, we’ll discuss two primary categories of analytics software: data mining and predictive analytics. We’ll also explore examples of these technologies in action to help you decide which is best for your business.

Data mining: Analyzing what happened

Data mining software allows organizations to glean useful information from large sets of raw data (i.e., data that has not been processed). The data generated by a business, its products, and customers is free and can be extremely valuable if used effectively. Businesses use data mining to aggregate, clean, evaluate, and visualize data to identify helpful trends that might otherwise go unnoticed.

However, to mine data, businesses must first collect data that exists in two basic forms: quantitative and qualitative.

Quantitative vs. qualitative data

Quantitative data Qualitative data
Generally structured Generally unstructured
Numerical values Primarily text
Fits comfortably in a relational database Difficult to analyze
Often used to answer how questions (e.g., How many products did we sell last quarter?) Often used to answer why questions (Why did sales increase last quarter?)

Due to the inherent difficulty of analyzing qualitative data relative to quantitative, methods such as the Likert scale were developed to make it easier to gain nuanced insight from quantitative data. Likert scales survey respondents using a range of predetermined opinions (e.g., very important, moderately important, unimportant).

Example of a Likert Scale

Example of a Likert Scale

New forms of this type of research have appeared in recent years with the use of emoji feedback systems to gauge everything from the frustrations of airport travelers to the statuses of health care patients.

In early 2016, Facebook moved beyond the standard like reaction and added the ability to react to posts with a love, haha, wow, sad or angry emoji. As a result, businesses now discern more meaning from reactions to social media posts.

Facebook's emoji rating system

Facebook’s emoji rating system (Source)

Data mining to identify opportunity

Online retailers apply data mining in countless ways to increase web traffic and improve sales. For example, a major problem for online retailers is cart abandonment. A recent study by Statista estimates that more than 69 percent of all online shopping carts were abandoned in 2017.

Data mining can help online retailers determine whether customers abandoned their carts because of exorbitant shipping costs, glitchy web browsers, confusion about the checkout process, or other factors. In doing so, they could make changes to increase the percentage of customers who complete their purchases.

Data mining can also help companies determine which types of customers are more likely to need specific products or services. It does this by building detailed customer profiles using information such as website interactions, purchase histories, and demographic information.

Demographic research in Quantcast

Demographic research in Quantcast (Source)

Identifying distinct customer profiles that align with product offerings can make marketing outreach more efficient. For example, if you’re a home improvement retailer, marketing efforts to sell lawnmowers and gardening tools are probably more effective with customers who live in the suburbs than in the city center. Similarly, offers related to more apartment-relevant tools, such as hammers and screws, are likely better aimed at city dwellers.

Likewise, determining the right customer for the right product allows businesses to make targeted offers that increase customer satisfaction. A 2017 study by digital advertising company Adlucent revealed that 75 percent of consumers favored personalized ads and fewer ads overall.

The same study also showed that respondents were twice as likely to click on ads from unknown brands if the ads were tailored to their interests.

Mining data to connect the dots

Another benefit of data mining software is that it can help mitigate fraud and theft. Data mining is just as effective at finding patterns as it is at finding information that doesn’t fit patterns.

financial institutions iconFinancial institutions

Financial institutions commonly use behavioral analytics to build a profile of a customer’s account activity over time. If the same customer’s current activity doesn’t align with established patterns, the bank will suspend the account while it investigates a possible breach.

online retailer iconRetail organizations

For retail organizations, shrink (i.e., inventory that is lost through theft or error) can be reduced by analyzing which products are stolen during what time and at which point in the supply chain. This information can be combined with other data, such as employee keycard records and inventory audits, to inform investigations and narrow fields of suspects.

Predictive analytics: Determining what action to take

Predictive analytics software harnesses data generated in the past and present to identify trends and patterns that might predict what will happen in the future. Knowing the likelihood of impending situations or events that might impact business can make a big difference in gaining a competitive advantage.

Predictive analytics uses machine learning algorithms based on models built from data mining to continuously fine-tune recommendations. Accordingly, businesses use predictive analytics for everything from the development of customer loyalty programs to forecasting the effects holidays will have on sales.

Predictive analytics to gain advantage

One particularly powerful use of predictive analytics is enhancing lead generation for marketing and sales by combining factors such as geography, demographics, and income levels. Companies can identify leads that have a higher likelihood of being converted into customers.

Sales pipeline in Insightly

Sales pipeline in Insightly (Source)

Recommendation systems are usually based on predictive analytics. Anyone who has been suggested a product by Amazon, or a TV show by Netflix, has been the target of these systems. Customers’ previous selections and activities are compared with similar profiles to anticipate future interests.

One interesting example of predictive analytics involves Target Corporation’s strategy to gain a valuable demographic in its infancy. As pregnant women are a particularly profitable market segment, Target uses predictive analytics to gain prescient knowledge about which of their customers are expecting. This allows them to a maximize sales of diapers, formula, and a host of other baby-related items.

Data scientists at Target discovered that women in their second trimesters tend to buy a lot of unscented lotion, as well as other specific products such as vitamin supplements and hand sanitizers. By gathering this information from purchase histories, coupon redemptions, and other sources, the company is able to crunch data to market toward customers whom they surmise are pregnant.

Predictive analytics also helps businesses maintain awareness of the customer life cycle, alerting them to intervene before potentially losing customers to the competition. Moreover, predictive analytics can help companies decide when to begin persuading customers to buy a new product or upgrade an existing one.

It’s especially applicable to businesses that sell products likely to wear out over time. For example, a backpack manufacturer might evaluate sales histories and warranty claims to get an idea of how long their products tend to last. This information can help them predict when existing customers might be in the market for a new backpack.

Selecting data mining and predictive analytics software

Data mining tends to be most beneficial for organizations that want to discern detailed information about their customers. Predictive analytics tends to be more advantageous for companies that are concerned with customer or employee churn. It can also benefit those wanting to identify services and offers that will increase customer satisfaction.

In essence, data mining allows you to explore the data you have to understand what happened and why. Predictive analytics builds on the foundation formed by data mining to help you figure out what to do next.

Though fictional, the following case studies are realistic examples of how data analytics software can be put to use by small businesses.

Cookie iconHypothetical case study: Determining why the cookie sales crumbled

Following several years of success, a cookie delivery company expanded its operations from two stores near the city’s university to eight spread across the city. To the owners’ surprise, the two most popular flavors at the original locations, chocolate chip and double fudge, began to plummet at two of the new stores.

Meanwhile, sales of snickerdoodle and oatmeal raisin, which historically came in fourth and fifth overall, rose unexpectedly. This led to both surpluses and lack of stock, costing the company money. To address the problem, the company’s distribution manager suggested they invest in data mining software to better understand their demographics.

After looking at the data for a few months, the company realized that people aged 18-25 preferred the chocolate chip and double fudge flavors. This explained why they were so popular at the university locations. The data also revealed that snickerdoodle and oatmeal raisin were most popular with more mature demographics.

This explained their popularity at the two struggling stores, which happened to be located near two retirement homes and a hospital. After adjusting inventory levels and marketing efforts to account for these factors, losses were stemmed and sales began to rise.

sailboat iconHypothetical case study: Forecasting whether reps will sail away

A young startup is focused on renting out spare office space using an Airbnb-style business model. Sales reps earn above average salaries trying to convince local businesses to join the company’s marketplace. Suddenly, the company’s top sales rep reveals that she is leaving the business.

Two weeks later, another productive rep puts in his two weeks’ notice. The company is shocked at the abrupt loss of two of their best reps. As a result, they decide to implement predictive analytics to evaluate employee behavior and try to anticipate who might quit next.

Soon, the software identifies several reps who have been working outside of normal business hours, a flag that could indicate flight risk. When a couple of the reps are asked about the extra hours, they express feelings of burnout and explain that the time is needed to enter sales data into the customer relationship management (CRM) system.

In response, the company hires data entry clerks to handle the more mundane tasks so that reps can focus on more satisfying aspects of their jobs. Two years later, only one rep has voluntarily left the company, and the former top sales rep has returned to the fold after hearing about the changes.

Data analytics software offerings

Many analytics software options are suitable for small business owners who have little or no IT staff and are looking to graduate from basic data sorting in Excel to something more powerful.

Other software is more appropriate for midsize businesses with at least some dedicated IT staff who can take advantage of more advanced features that include significant learning curves.

The misuse of data analytics, or misunderstanding of results derived from these systems, can sometimes do more harm than good. For this reason, we recommend that you conduct your own research to decide which software best aligns with your needs and resources.

Next steps

In a matter of a few short years, using data analytics will be standard practice. Organizations will no longer need to think in terms of data mining vs predictive analytics because both will become part of the framework of ordinary business.

Budgets for these technologies will be part of small business plans right from the start, rather than a luxury afforded only to large, established enterprises. This means that by embracing data analytics today, your business will be in a better position to outpace the competition tomorrow.

You can take the next steps in getting acquainted with data analytics software by consulting GetApp’s data mining and predictive analytics catalogs. There, you can find relevant articles, apps, and reviews that can help you to further define your needs and find the best software for your business.

Note: The information contained in this article has been obtained from sources believed to be reliable.The applications selected are examples to show a feature in context, and are not intended as endorsements or recommendations.

Share This

Share this post with your friends!