Whether you’re picking an outfit for the day or debating what to eat for lunch, you have to make choices—it’s a part of life. While going with your gut might help you make the right decisions in your day-to-day life, when it comes to running a thriving ecommerce business, strategic business decisions should be backed by data.
Every day, consumers share information with businesses. They reveal how, when, and where they like to shop, how much they’re willing to spend, and which product features or loyalty programs are important to them. As a business, there’s never been a better time to use this information to provide optimal experiences for your customers while maximizing your business operations. For example, should you invest in overnight shipping, or market buy online, pickup in-store (BOPIS)? Read on to learn more about data-driven decision-making and how to implement it for your ecommerce business.
What is data-driven decision-making?
Data-driven decision-making (DDDM) is the process of collecting and using quality data to guide strategic business decisions. This approach centers information like key performance indicators (KPIs), financial data, consumer insights, and market trends, avoiding assumptions that can misguide your decision-making process.
How to make data-driven decisions
Turning raw data into actionable insights is a crucial element of running a successful business—and being a great leader. Follow these key steps to successfully incorporate data into your decision-making process:
1. Define Objectives
When you’re faced with a choice, consider the outcome you want and make sure it aligns with your company’s goals. Those goals could be as specific as increasing sales by 3% over the next month or as broad as boosting brand awareness, but make sure your desired outcome helps you reach your business objectives.
2. Collect data
Now that you’ve clarified your objective, think about which associated factors you can measure. For example, imagine you have extra money in your marketing budget and are deciding whether to invest in your company’s TikTok or Instagram profiles. You might want to look at your two accounts’ conversion rates, post engagement, and follower growth over time. You can also conduct public surveys with your target audience to collect customer data.
3. Analyze data
Your research findings will often be in the form of raw data. In order to draw conclusions and develop insights from unstructured data, you’ll need to analyze each relevant data set. To make sense of it all, you can combine data into charts and graphs, using data visualization to easily identify patterns.
4. Make conclusions
Once you begin to see patterns within your data sets, you can start to extract insights that will direct your path forward. Remember to always keep your objective in mind to make sure you’re staying on track.
For example, let’s say you found that your brand’s Instagram account has more followers, but your company’s TikTok account has a better conversion rate, a higher post engagement, and a higher follower growth rate. You might conclude your TikTok account has more potential to convert future sales.
5. Act on findings
After analyzing data, you should have all of the information you need to extract actionable insights. Use graphs and spreadsheets to visualize why you’re making the decision you’re making. Make these resources available to your internal stakeholders and highlight the risks involved in your decision. This allows everyone to be on the same page as you push forward and implement your decision.
Benefits of data-driven decision-making
Let’s look at a few benefits of data-driven decision-making:
Confidence
Employees look to you to make tough calls and implement new strategies. Using a data-driven approach, you can make confident decisions with a clear understanding of how and why you made the choices you did. Repeating the data-driven decision-making process can also improve your data literacy, or your ability to understand data trends.
Employee satisfaction
If workers don’t understand the reasoning behind a high-level decision that affects them, their level of buy-in can suffer. Creating a data-driven culture is a great way to mitigate these feelings within your team. Your employees might better receive informed decisions backed by data-driven insights, leading to a culture in which workers not only understand the choices that affect them but value concrete reasoning for internal changes and changes to business strategy.
Cost management
Data-driven decisions can naturally improve operational efficiency: when you take careful steps to analyze concrete facts, you can avoid making the types of decisions that turn out to be detrimental to your business. Plus, you can also apply your data-driven decision-making framework to budgets, workflow, and company structures, ultimately allowing you to make choices that save time and money.
Examples of data-driven decision-making
Now that you know how data-driven decision-making works, let’s take a look at a few examples of how business leaders can use the DDDM framework:
Customer retention
You value your customers and you want to make sure they stick around as you grow your business. You can use data from reviews, customer complaint cases, surveys, and more to gain a clear picture of what’s benefiting and hurting your company’s customer experience, and ultimately, customer retention.
For example, let’s say you’re deciding whether or not to introduce conversational AI-powered chatbots into your company’s customer complaint process. You define your objective: You want to create a great customer experience and keep buyers coming back. You collect data from a customer survey and reviews, and after analyzing this data, you discover that your buyers frequently complain about slow response times from your customer service representatives. You also discover that 80% of customer questions center on the same few topics, which you know you can use AI to answer. In this case, you might decide to introduce conversational AI-powered chatbots.
Pricing optimization
Price optimization can make or break your profits, but it’s not always easy to determine the perfect price for your product. You can use data like customer price sensitivity and competitor pricing to establish an optimal price.
For example, let’s say you’re deciding the price for a new model of sneakers and your objective is to optimize profit margins. You analyze customer behavior and discover that your customers typically don’t buy shoes priced above $100. Then you collect data on the prices of similar sneaker models and find that your competitors price their goods between $80 and $110. With this information in hand, you can make an informed decision on the price that will allow you to maximize profitability while retaining a competitive edge.
Data-driven decision-making FAQ
What does it mean to be data-driven?
Being data-driven means that you rely on accurate data as the main factor in making business decisions. When you’re data-driven, you adhere to diligent data management and data analysis practices and employ critical thinking to make conclusions. You then use those conclusions to make choices backed by facts and figures.
What is an example of data-driven decision-making?
Using data such as return on investment (ROI) or click-through rate to decide advertising priorities is an example of data-driven decision-making. If an advertisement has a poor click-through rate, you might decide to invest in a different advertising strategy.
Why are making data-driven decisions important?
Making data-driven decisions is important because the process can improve outcomes of key business decisions. When you back your choices with data analytics, you can mitigate risk and ensure confidence in your company’s decisions.
