Companies using data analytics are 23 times more likely to outperform competitors
Data analytics is a valuable tool for businesses aiming to increase revenue, improve products, and retain customers. According to research by global management consulting firm McKinsey & Company, companies that use data analytics are 23 times more likely to outperform competitors in terms of new customer acquisition than non-data-driven companies. They were also nine times more likely to surpass them in measures of customer loyalty and 19 times more likely to achieve above-average profitability.
Data analytics can be broken into four key types:
· Descriptive, which answers the question, “What happened?”
· Diagnostic, which answers the question, “Why did this happen?”
· Predictive, which answers the question, “What might happen in the future?”
· Prescriptive, which answers the question, “What should we do next?”
Each type of data analysis can help you reach specific goals and be used in tandem to create a full picture of data that informs your organization’s strategy formulation and decision-making.
Descriptive analytics can be leveraged on its own or act as a foundation for the other three analytics types. If you’re new to the field of business analytics, descriptive analytics is an accessible and rewarding place to start.
What Is Descriptive Analytics?
Descriptive analytics is the process of using current and historical data to identify trends and relationships. It’s sometimes called the simplest form of data analysis because it describes trends and relationships but doesn’t dig deeper.
Descriptive analytics is relatively accessible and likely something your organization uses daily. Basic statistical software, such as Microsoft Excel or data visualization tools, such as Google Charts and Tableau, can help parse data, identify trends and relationships between variables, and visually display information.
Descriptive analytics is especially useful for communicating change over time and uses trends as a springboard for further analysis to drive decision-making.
Here are five examples of descriptive analytics in action to apply to your organization.
5 Examples of Descriptive Analytics
1. Traffic and Engagement Reports
One example of descriptive analytics is reporting. If your organization tracks engagement in the form of social media analytics or web traffic, you’re already using descriptive analytics.
These reports are created by taking raw data—generated when users interact with your website, advertisements, or social media content—and using it to compare current metrics to historical metrics and visualize trends.
For example, you may be responsible for reporting on which media channels drive the most traffic to the product page of your company’s website. Using descriptive analytics, you can analyze the page’s traffic data to determine the number of users from each source. You may decide to take it one step further and compare traffic source data to historical data from the same sources. This can enable you to update your team on movement; for instance, highlighting that traffic from paid advertisements increased 20 percent year over year.
The three other analytics types can then be used to determine why traffic from each source increased or decreased over time if trends are predicted to continue, and what your team’s best course of action is moving forward.
2. Financial Statement Analysis
Another example of descriptive analytics that may be familiar to you is financial statement analysis. Financial statements are periodic reports that detail financial information about a business and, together, give a holistic view of a company’s financial health.
There are several types of financial statements, including the balance sheet, income statement, cash flow statement, and statement of shareholders’ equity. Each caters to a specific audience and conveys different information about a company’s finances.
Financial statement analysis can be done in three primary ways: vertical, horizontal, and ratio.
Vertical analysis involves reading a statement from top to bottom and comparing each item to those above and below it. This helps determine relationships between variables. For instance, if each line item is a percentage of the total, comparing them can provide insight into which are taking up larger and smaller percentages of the whole.
Horizontal analysis involves reading a statement from left to right and comparing each item to itself from a previous period. This type of analysis determines change over time.
Finally, ratio analysis involves comparing one section of a report to another based on their relationships to the whole. This directly compares items across periods, as well as your company’s ratios to the industry’s to gauge whether yours is over- or underperforming.
Each of these financial statement analysis methods are example of descriptive analytics, as they provide information about trends and relationships between variables based on current and historical data.
Credential of Readiness | Master the fundamentals of business | Learn More
3. Demand Trends
Descriptive analytics can also be used to identify trends in customer preference and behavior and make assumptions about the demand for specific products or services.
Streaming provider Netflix’s trend identification provides an excellent use case for descriptive analytics. Netflix’s team—which has a track record of being heavily data-driven—gathers data on users’ in-platform behavior. They analyze this data to determine which TV series and movies are trending at any given time and list trending titles in a section of the platform’s home screen.
Not only does this data allow Netflix users to see what’s popular—and thus, what they might enjoy watching—but it allows the Netflix team to know which types of media, themes, and actors are especially favored at a certain time. This can drive decision-making about future original content creation, contracts with existing production companies, marketing, and retargeting campaigns.
4. Aggregated Survey Results
Descriptive analytics is also useful in market research. When it comes time to glean insights from survey and focus group data, descriptive analytics can help identify relationships between variables and trends.
For instance, you may conduct a survey and identify that as respondents’ age increases, so does their likelihood to purchase your product. If you’ve conducted this survey multiple times over several years, descriptive analytics can tell you if this age-purchase correlation has always existed or if it was something that only occurred this year.
Insights like this can pave the way for diagnostic analytics to explain why certain factors are correlated. You can then leverage predictive and prescriptive analytics to plan future product improvements or marketing campaigns based on those trends.
5. Progress to Goals
Finally, descriptive analytics can be applied to track progress to goals. Reporting on progress toward key performance indicators (KPIs) can help your team understand if efforts are on track or if adjustments need to be made.
For example, if your organization aims to reach 500,000 monthly unique page views, you can use traffic data to communicate how you’re tracking toward it. Perhaps halfway through the month, you’re at 200,000 unique page views. This would be underperforming because you’d like to be halfway to your goal at that point—at 250,000 unique page views. This descriptive analysis of your team’s progress can allow further analysis to examine what can be done differently to improve traffic numbers and get back on track to hit your KPI.
Using Data to Identify Relationships and Trends
“Never before has so much data about so many different things been collected and stored every second of every day,” says Harvard Business School Professor Jan Hammond in the online course Business Analytics. “In this world of big data, data literacy—the ability to analyze, interpret, and even question data—is an increasingly valuable skill.”
Leveraging descriptive analytics to communicate change based on current and historical data and as a foundation for diagnostic, predictive, and prescriptive analytics has the potential to take you and your organization far.
How Data Scientists Can Push Forward Warehouse Productivity
In the world of supply chain and logistics, companies are facing increasingly complex and resource-scarce environments. To stay competitive, you must take a more strategic view of your current workforce and focus on things like retention and productivity. Mining data can begin to give you the insights needed to build better processes and become much more proactive in planning and optimization strategies. However, if you don't know how to use your data effectively, it will be increasingly difficult to get meaningful insights. You need to align your data with scientists who have domain knowledge, a keen understanding of business challenges, and the objectives your organization needs to solve. They can define what’s important and then build systems that predict outcomes and optimize your resource allocation, increasing productivity while making warehouse jobs easier.
With most descriptive and diagnostic data, which includes things like raw data, “cleaned” data and standard or ad hoc reporting, you are in the mindset of sense and respond. It is very reactive, static, and also historical, capturing a particular moment in time. Presented generally in the form of dashboards, reports, and emails, you can possibly see trends, and utilize that in decision-making. However, by the time you use it, the data may have changed, making it inaccurate and less useful. Some insights descriptive data can offer:
1. How often did our order picking fall behind schedule causing shipping deadlines to be missed?
2. What are the velocities of our products and current order-picking productivity?
3. How productive is each of my pickers?
Jumping straight into using machine learning or advanced data analytics won't solve workforce difficulties alone and can be extremely risky. There are many layers to utilizing data, with each layer building upon the foundation laid by the others, like a pyramid. Prescriptive analytics powered by machine learning is the high-water mark, but without the rest of the pyramid beneath it, all you have is a shiny triangle in the dirt.
In order to make any decisions at all with data, you have to actually be collecting it. Data can come from a number of sources. This includes your ERP and/or WMS that capture and store general transaction data, or IoT (Internet of things) interconnected machines (conveyors or sorters with sensors, etc.), and devices (printers, mobile computers, etc.) that collect and share massive amounts of real-time data. With all of this data available within your business, you might be wondering where you start. Coming up with a plan, and collecting the right data, is a difficult thing to do.
Machine learning to drive warehouse productivity.
One of the best use cases of AI and machine learning is in measuring warehouse productivity. Traditionally this is done using engineered productivity standards.
Instead, if you use machine learning to measure productivity from the years of data and knowledge that you've built up on your operations, you can use these models to predict what your standards should be, much faster than traditional methods. The biggest benefit from this approach is that it's constantly adapting and learning from the data as the business continues every day - day in and day out. All of that data is going back into the model, and you're learning from it.
What are some specific examples in the warehouse? Seasonality, for instance, can be learned through the model over time. Knowing how productive your workers are allowing you to make your processes more efficient during peak periods. Maybe certain workers are better at certain types of picking, so you can allocate workers to zones or work areas that utilize that competitive advantage. You can apply the same principle to batching. When a picker is ready for their next assignment, you can assign them a batch of work that plays to their strengths. Maybe they walk very quickly so you give them a longer route. Maybe they pick items lower to the ground quickly, so we fill their batches with low picks. If you know how productive workers are, you can estimate where they are in their routes at any given moment and predict and build a batch that will avoid creating traffic jams.
Continued optimization through learning and adjustment
Having a machine learning model will provide you with the ability to consistently inject data and get to an elevated stage of prescriptive analytics. Starting from your optimizations from the past and understanding that things are changing, you can review slotting, current events, weather, and workforce adjustments, which is all information that can change decisions. You can play “what if” scenarios to have the model regenerate optimization scenarios that allow you to prioritize replenishment over picking or enhance safety and minimize congestion in the warehouse.
Think about some of the questions you could answer:
1. What should we do about order-picking assignments that are predicted to cause shipping deadlines to be missed?
2. What slotting changes should we make to improve order-picking productivity?
3. Which pickers are my most productive, independent of the type of work they do (case pick to pallet, each pick to cart, case pick to conveyer, etc.)?
Data analytics is a valuable tool for businesses aiming to increase warehouse productivity, increase revenues, improve customer services, and retain long-term loyal customers. Do you want to become a data-driven organization? Explore Warehouse Automation AI’s programs and solutions to deepen your company’s analytical skills and apply them to real-world business problems.
