Learn how to approach some of the most frequently recurring questions industry data analysts are tasked with addressing
In the fast-paced world of data analysis, it’s not uncommon to find yourself experiencing déjà vu as you dive into new roles. You may have noticed a recurring pattern, where the same questions about data and business keep resurfacing.
But let me assure you, this is no coincidence.
Across different organizations, industries, and sectors, a remarkable similarity emerges. Despite the unique products, services, and business models they offer, organizations share a common hunger for insights derived from their data.
As a data analyst, understanding and addressing fundamental questions about your business is vital to your effectiveness. By framing your reports and analyses within the context of core business questions, you have the power to ignite more profound conversations with management and decision-makers.
With this article, I aim to equip you with the knowledge and insights necessary to address these recurring questions head-on. By preparing yourself to tackle these 10 fundamental questions, you will fortify your analytical prowess and establish yourself as an indispensable asset within your organization.
Here is what you can expect to be asked.
What is Product Revenue Against Benchmarks?
Probably one of the most obvious- organizations want to know how their finances are matching up against annual goals. While most common for a Finance Data Analyst to be reporting on this, it is still something most analysts should be prepared to answer.
When I have worked on FP&A reporting, organizations will typically have a set of targets / quota benchmarks for the fiscal year. This was provided on a monthly basis, as well as a cumulative total. It would say something along the lines of:
We want X product line to bring in $100,000 in revenue every month. Which means it should bring in 100,000 in the first month, 200,000 in the second…
To tackle this question, as a data analyst, your role would involve connecting various data sources, such as CRM systems or external lists set by C-Level leadership, with billing system results. By merging these sources, you can identify any surplus or deficit in revenue and provide meaningful explanations for any deviations from the expected targets. Your analysis would shed light on the underlying factors contributing to deficits, allowing management to make informed decisions.
This type of reporting not only demonstrates your ability to handle financial data, but also showcases your analytical skills in connecting and interpreting information from different sources. By effectively bridging the gap between financial targets and actual results, you enable decision-makers to gain a comprehensive understanding of their organization’s financial performance.
How do We Expect Revenue to Grow or Shrink?
Anticipating the trajectory of revenue growth or contraction is a crucial question that leaders seek to address. As a data analyst, you can play a pivotal role in providing informed predictions about future revenue trends, typically on a monthly and quarterly basis. Let’s explore some techniques that you, as a data analyst or part of the finance team, can implement to tackle this question effectively:
- A Timeseries Forecasting Model — This uses statistical techniques to predict a value over a date axis based on historical data. There are a number of techniques that can be used to execute a time series model which would be too long to include in this article — see the link I provided for more details.
- Leveraging a Sales Pipeline —A common Financial report is the “Actuals x Forecast” report. For example, the “3 X 9” would mean that we are presenting 3 months of Actuals vs 9 Months of Forecast. The Forecast elements would leverage data that is open in the CRM with a percent likelihood of closing by the end of the fiscal period.
- Manual Inputs — On some teams, Sales Leadership will want to have the ability to review what is in the pipeline and cherry pick which sales opportunities will close, when they will close, and their projected value as a final review. While this can still be effective, it relies on an individual’s personal judgement and cannot be programmatically streamlined. This will usually involve receiving a spreadsheet of values that need to be incorporated into a time series summary.
By accurately predicting revenue growth or contraction, organizations can proactively allocate resources, set realistic targets, and identify potential gaps in performance. Armed with this information, leaders can make critical decisions related to budgeting, investments, hiring, and operational planning. Revenue forecasts enable leaders to assess the financial health of the organization, evaluate the effectiveness of sales and marketing strategies, and make adjustments to ensure sustainable growth. Ultimately, revenue forecasting empowers leaders to navigate uncertainties, mitigate risks, and steer their organizations towards long-term success.
How Effective are Specific Sales Channels?
Sales and Financial leadership want to be able to see where different sources of revenue come from. Some of these questions might look like:
- What Sales Channels are growing and shrinking over time?
- What products perform best on which channel and what story does this tell about our customers?
A Sales Channel itself refers to the different ways that organizations can source revenue. The number of sales channels will vary across different organizations as it also depends on their business model. A typical set of Sales channels might look like the following:
- Direct Sales — Utilizing a Sales team that closes deals with clients and logs them in a CRM system such as Salesforce.
- E-Commerce — Customers purchase products directly on the organization’s website.
- Corporate Partnerships — Organizations can collaborate with other companies to sell their products or services. This can involve forming strategic alliances, joint ventures, or affiliate partnerships to expand their reach and tap into the partner’s customer base.
With Direct Sales being a large part of most organizations, I’ve found this type of channel reporting to be the most prominent. Sales teams will often implement certain sales initiatives to promote a specific product line for a certain period of time. This usually sparks the creation of a dashboard to show progress against these initiatives, how we are meeting the overall goal, and how each sales representative is performing. Expect this type of reporting to be most heavily scrutinized, as it likely serves as a reference for sales commission…
It’s important for organizations to understand where they line up against their competitors. For example, leadership needs to have a grasp on the following:
- What is the total market size by customer spending and unit quantity?
- Of the market size, what percentage does our organization have by product category? by region?
This can be tracked in a variety of ways across industries. We can use the News Media industry as an example. Organizations in this field want to understand how many total visitors are being engaged on all news sites daily, and how many of those visitors are on each site. Vendors like Comscore offer a series of tools that allow analytics teams to evaluate the competitive landscape and see unique page views across major sites.
Customer Retention: Customer Churn and Renewal Rates?
Leaders want to know how they are growing their customer base. This means providing aggregated totals at given points in time, but also understanding what the churn rate of a typical customer is. If there is a pattern of customers dropping your service after x number of months, why? And what story does this tell about the customer lifecycle?
Understanding customer retention, and the likelihood of churn gives leaders the ability to shift paths or provide sales teams with the ability to save a customer if they are likely to churn. When teams can master churn reporting, they will have a robust understanding of why customers may be dropping off, and will have developed a standardized process to keep them billable.
Customer Segmentation: Who Are Our Typical Types of Customers?
Mapping a series of customer profiles is a powerful analysis to provide to product and sales leaders. This allows teams to have the ability to tailor services to the most relevant demographics (in B2C), or most relevant types of organizations (in B2B).
This is a quintessential machine learning problem that uses a K-Means Clustering Model, which is an unsupervised model that groups records into a set number of distinct groups, based on a series of inputs. Executing customer segmentation models is a topic of its own — Ceren Iyim wrote a great article on this topic called Customer Segmentation with Machine Learning.
What Features of Our Product / Service are Most and Least Used?
Organizations with digital products are constantly monitoring what features that users are taking advantage of most. Understanding patterns behind the “click path” that users are taking allows products like Instagram and TikTok to be so immersive and engaging (one could argue these services are too effective in this area, but that’s a separate article…). When product teams can understand what is and isn’t working within their product, they can build features that are more impactful to users, which ultimately relates to a higher customer retention.
This also applies to organizations that aren’t built around digital products— including service, retail, and hospitality. Data may be collected in a different format through the means of customer surveys. Leadership can still gather valuable findings from direct customer feedback, or even from deriving insights through customer transaction history. Amazon customer reviews are a great example of this- analytics teams can develop a general sentiment about the product and report on key words most commonly found in reviews.
What is our Online Brand Engagement (Website / Social Media)?
Companies want to have a strong grasp on their Brand awareness. As a result, digital marketing campaigns are created to attract attention and engagement about their organization, which ultimately becomes a sales tool. It’s no surprise that customers are more likely to buy from a brand they are familiar with and trust.
For this reason, Data Analysts can be tasked with reporting on Social Media engagement or Website analytics data. In this scenario, you might be answering questions like:
- How many impressions, interactions, and shares are we receiving on our posts? And how is this changing over time?
- How are certain pages or posts performing better than others?
When leaders can answer these questions, they can tailor their brand engagement strategies to content that is most effective, and ultimately increase views & awareness.
What Are Our Customer Satisfaction Rates?
Similar to Brand Engagement, leaders also need to understand the general customer sentiment about your Product and Brand. There can be a number of data points that teams reference to deduce what overall satisfaction rates are, including the following:
- Comments, tweets, and posts on the organization’s social media account(s) using open source or paid API feeds.
- Customer satisfaction surveys
Insights from customer satisfaction will be able to describe a greater trend about what customers perceive is going well, vs what are the biggest pain points so their products and services can be positively influenced. Here are some examples where this has been relevant:
- Apple and iPhone: Customer feedback played a significant role in the introduction of new iPhone models. Apple actively seeks customer input through surveys, user testing, and customer service interactions. Feedback related to battery life, camera quality, software features, and design preferences has influenced Apple’s decisions to enhance these aspects in subsequent iPhone releases.
- Netflix and Offline Viewing: In response to customer feedback, Netflix introduced the option to download content for offline viewing. Many users requested this feature, particularly for situations with limited internet connectivity. Netflix listened to their customers’ preferences and introduced the download feature, increasing customer satisfaction and expanding the service’s convenience.
What Inefficiencies Exist in Our Operational Processes?
Mostly for organizations who offer physical products, understanding what pain points exist from order to fulfillment are crucial in improving overall quality of service and scaling order volume. Operational leaders are always looking to find ways to reduce lead time. A common approach to measure this is a Cycle Time Analysis, which is a timeseries report to understand how long each segment of the process takes. For example, a fulfillment process might look like this:
- Customer Places an order.
- Order is Received in the Order Processing System.
- Operations team reviews order and requests the required materials from the manufacturer(s).
- Goods are produced from the manufacturing order.
- Materials are Packed and loaded to a delivery vehicle.
- Product is in transit to Customer.
- Order is delivered to Customer.
In this process, we would assign a time value to each step, and produce a dashboard that rolls up the average / median length of each step for all orders with filter criteria (by product line, within a certain date range, customer location etc.)
Another approach to improve operational processes would be through Error Rate Monitoring, which is reporting designed to capture the number of anomalies, operational mistakes, or exceptions to a standardized process. For example, why are orders failing on x step? Or what customer order scenarios are not accounted for in the process that are resulting in manual work? Once these questions can be answered, leaders can take action to resolve them or limit their frequency.
This article provides a wide array of organizational questions. And mastering them all at once will be overwhelming. So here’s my suggestion; focus on one area of the business that I have mentioned above. Know that area inside & out, and understand what questions management is asking about it. In almost every analytics role I’ve had — data analysts who were subject matter experts in their domain were also the ones who were recognized the most.
While this list isn’t fully encompassing of every organization’s reporting needs, it should provide you with a general framework of what questions need to be asked at a high level. In any role, the closer your work relates to the bottom line (revenue), the more irreplaceable you are as an employee. In the case of being a Data Analyst, when you have a strong grasp on some of these questions that I have discussed, you are likely to be more effective in your role, and source actionable outcomes.