Data-Driven Decision-Making Begins With Data Analytics Training

Data insights help organizations understand customer behavior, reduce costs, grab opportunities before competitors do, improve employee retention, and much more. But often times, there’s so much data pouring in from so many different sources, we go with our gut instead and the hard data is wasted. 

But the need to leverage data is growing. Gartner predicts that 65% of B2B sales organizations will transition from intuition-based to data-driven decision making by 2026. Time is ticking away.

For data insights to drive decisions, business professionals need a solid grasp of analytics. Organizations that provide training on using data benefit from organization-wide knowledge and a shared understanding of the value the data holds.

As an example of data-driven decisions, consider how one store manager might use data: An electronics retailer with a loyal customer base is experiencing a decline in sales and customer satisfaction scores over the past year. The manager decides to use data analytics to understand what’s going on. 

There are various sources of data they can analyze. They have a Point-of-sale (POS) system with its log of transactions, customer feedback surveys, website analytics, an inventory management system, and employee schedules and performance metrics. 

Using data analysis tools, the manager uncovers several things: 

1. There’s a strong correlation between long checkout times and negative customer reviews.
2. The store’s website traffic peaks between 7-9 p.m., but the physical store closes at 7 p.m.
3. Certain high-margin products are frequently out of stock during weekends.
4. Customer service scores are lowest during the lunch hour (12-2 p.m.)

Based on these findings, the manager makes some changes:

1. Checkout efficiency: Introduces mobile checkout devices for staff to use during peak hours to reduce wait times.
2. Extended hours: Proposes and implements extended store hours until 9 p.m. to align with online browsing patterns.
3. Inventory management: Adjusts the restocking schedule to make sure popular items are well-stocked for weekends.
4. Staffing schedules: Staggers lunch breaks and increases staffing during the 12-2 p.m. window to maintain consistent customer service levels.

Results after 3 months of implementing these data-driven strategies:

1. Average checkout time decreased by 40%, leading to a 15% increase in positive customer reviews.
2. The extended hours resulted in a 12% increase in evening sales.
3. Weekend sales of high-margin items increased by 25% due to improved product availability.
4. Customer service scores during lunch hours improved by 30%
5. Overall, the store saw an 18% increase in sales and a 22% improvement in customer satisfaction scores.

The use of data analytics in this example was a success. 

Talk to a SkillPath learning consultant to set up a private workshop for your team: Data-Driven Analytics for Beginners.

Data could obviously be used in many other business situations: 

HR - If there’s a growing increase in employee turnover, data would help HR. Of course, the data available would be different – performance reviews, time-off requests, exit interviews, project management systems. If the HR manager knows how to combine the information from these various sources, they would likely find insights to help them implement changes to help the situation. 

Sales - A data-literate sales associate could analyze point-of-sale data to identify purchasing trends and suggest effective product bundles. If a sales associate noticed that certain accessories were frequently bought with specific electronics, they could dig a little deeper into the data and propose an accessory bundle which could increase sales per customer.

Medical - A nurse or medical assistant can use data to track patient outcomes, identify trends in treatment effectiveness and contribute to improving care protocols. 

The volume of data available to organizations is growing. And using it to make business decisions is the goal, but that’s a lot of pressure for employees if they have little experience or are even data-phobic. 

Instead, data access and data-literacy can empower employees in every role. From training on foundational data concepts such as data cleaning, manipulation, and visualization, to data analytics and how to best communicate insights to stakeholders, these skills will build objectivity into decisions.