Data-Driven Solutions in the Age of Informed Intuition

Turning information into insight

According to Fortune Business Insights, the global market for data analytics is currently valued at more than $348.21 billion and will grow to $961.89 by 2032. North America takes nearly one-third of that market share.

By 2030, global data creation is projected to surpass 612 zettabytes, according to Statista. A zettabyte is 1 billion terabytes (TB), which is approximately all the grains of sands on all the world’s beaches. Just as it would be impossible for a team of humans to sift through each grain of sand on even a single beach, this amount of data necessitates automation.

Though the term “mining” has been used to describe the process of data analysis, this analogy is too simplistic, suggesting a false dichotomy of useless rock versus precious stones. Going through large data sets is more like searching through an antique store or flea market. Among piles of useless, outdated hand-me-downs and cheap trinkets, there are priceless artifacts and useful pieces, hiding behind mounds of old rags without any real sense of organization.

Admittedly, “data mining” has a better sound than “data antiquing.” Regardless of the metaphor, when seeking data-driven solutions to problems in business, there are some proven tools and strategies for getting the best use from large data.

Data mining makes use of machine learning, artificial intelligence, and statistics to glean patterns, oddities, and trends in data. Though intuition, human connection, and a keen eye for best practices will remain a significant part of operations for any business, analytics can improve overall company health.

The most successful hospitals use data analytics for risk stratification, quality of care, and patient flow/demand forecasting. Restaurants can use data to follow trends in social media to create popular seasonal items. A coffee shop or café can gather and analyze data for optimization of its menus and customer satisfaction, answering questions, such as, “How long will pumpkin spice lattes be in demand this year?” 

Businesses that utilize data analytics to improve customer relations can boost marketing effectiveness, optimize operations, and increase profitability. Though email surveys and suggestion boxes may be the most affordable option, even small businesses can benefit from using professional analytics on the pricing of goods and services.

Before venturing into the data field, it is important to set goals and ask the correct questions.

Are you trying to increase your customer base or expand into a new market? Are you suddenly losing profits and need to ascertain why? Do you simply want a redesign of the brand and want to know if the timing is right? (Here’s looking at you Cracker Barrel. Too soon?)

These goals will determine the categories required for collection. For rapid results, there are data platforms and marketplaces such as Datarade, Snowflake, and AWS Data Exchange. There are also specialized data providers, such as Experian, Equifax, or ZoomInfo. Google Cloud Analytics Hub, IBM, and CoreLogic are other well-known data resellers. Many of these companies also offer visualization packages and other services at a premium.

For small businesses, more affordable options exist (see sidebar). However the data is collected, the next step is classification into common categories (clustering). For readers with a penchant for programming, R and Python have nice packages for data analytics and visualization
of results.

Once the results are in, it is crucial to understand the meaning behind associations discovered between variables.

Predictability does not equate to inevitability. Finding a relationship between variables is the true value of data analytics; however, correlation does not imply causation. If there is an association between winter and an increased demand for peppermint-flavored products, then it would be wise to carry a few items for that target demographic. But market fatigue could result in a reduction in profits when trends suddenly shift. How long should these products be offered to optimize profitability? This is another important question that might require additional analytics, which brings us to the heart of the power of data analytics: Refine your goals, and iterate through the data when possible. ▪

Effective methods for collecting data

Small businesses collect customer data through direct methods like surveys, interviews, and online forms, and indirect methods like website analytics, social media monitoring, and transaction tracking.
By using a combination of these techniques, businesses can gain insights into customer demographics, behaviors, and preferences
to improve products and services.

Direct methods

→ Surveys and questionnaires: Ask customers directly for feedback on their experience, preferences, and demographics through online or physical forms.

→ Interviews and focus groups: Conduct one-on-one or group conversations for more in-depth, qualitative feedback.

→ Online forms: Collect information
when customers sign up for newsletters, create accounts, or use contact forms on
your website.

→ Contests and promotions: Offer incentives for customers to provide their contact information and other relevant details.

Indirect methods

→ Website analytics: Use tools to track how customers interact with your site, including pages visited and time spent on each page.

→ Social media monitoring: Observe who interacts with your brand’s social media pages and what they are saying about your products and services.

→ Transaction tracking: Analyze purchase history to understand buying habits and preferences from point-of-sale or e-commerce platforms.

→ Observation: Watch how customers interact with your product or service in a physical or digital environment to see their behaviors in real-time.

→ CRM systems: Use customer relationship management software to centralize and organize customer data, interactions, and purchase history.