Using Data to Understand Metrics

People turn to data when they have questions and want answers. An integral part of a data analyst’s role is to use data to inform and influence the direction of your company by making sense of data and answering questions. Data is a vital instrument that has a major impact, especially if you get the results in front of high-level decision-makers.

By partnering across your company with teams ranging from Product to Sales to Marketing, you’ll be able to design A/B tests, run experiments, iterate on results and do the lion’s share of understanding patterns and trends in user behavior to find new opportunities for growth. However, to fully realize data’s power, you need to understand each department’s high-level metrics and run queries to answer those metrics.

There are a lot of business metrics. So, how do you understand what actually matters to your company? In short, you’ll want to measure and communicate metrics that every stakeholder will need to see to make decisions.

Company Metrics

A data analyst is expected to interpret data and effectively communicate insights at a company-wide level. So, when it comes to data and surfacing the right metrics to track, it’s important for us data people to ask the right questions.

The first question to ask is around understanding what your company wants to accomplish in concrete terms. Typically, at a high-level view, company Executives want to see metrics that move the needle forward. These metrics include:

  • Acquisition: net new business, or dollars into the company
  • Activation: number of customers that are actively using the product
  • Retention: number of retained customers over a defined period of time (monthly, annual)

Acquisition, activation and retention are all leading indicators of moving the needle forward, and company Executives want to see this because it predicts growth and overall company success. These metrics all closely align with the company’s goal and show a critical analysis on the health of the business.

In today’s cloud-based database world, siloed data is in the past. Luckily now, you’re able to blend your company’s CRM data and operational database (capturing application/product data) into an analytics platform to visualize the aforementioned metrics. For metrics at the company level, it’s important to analyze data with a view for success and choose metrics that track this.

Product Metrics

Much like how business metrics are crucial for keeping Executives, investors and the company informed with the health of the business, product metrics are equally telling. For product metrics, there’s no shortage in data. And just like business metrics, the first step in figuring out product metrics starts with asking questions. Here are a few questions to ask:

  • How often is each feature being used?
  • Which features are getting the highest return usage?
  • Which features are always used by the same users?
  • How much time are users spending using the product?
  • How much time does the average user spend using one part of the product?
  • What are some feature trends?
  • Where are areas for product improvement? 

Naturally, the above list of questions will vary based on your company’s product. But, understanding that the Product team intrinsically cares about usage is important. So, to surface the most telling product metrics, having as many relevant data sources integrated into your data warehouse is crucial in providing relevant rich information. With an analytics tools, you can test multiple hypotheses, draw conclusions, suggest trends and areas of improvement based on how users are interacting with the product.

A few high-level product metrics to track include:

  • User activity: how many users are engaged with the product, often formatted as Daily Active Users (DAU) or Monthly Active Users (MAU)
  • Average Session Time: this tracks the length of time a user is engaged with the product and can be used to gauge product sentiment. If a specific feature isn’t used that much, you can dig in and see if it needs to be improved

At the end of the day, product metrics should track the success of the product, and ultimately the overall success of the company as well. Metric-driven goals are crucial to building products because it ensures that you’re building a product that is actively used and users enjoying interfacing with on a frequent basis.

Sales and Marketing Metrics

Data is the lifeblood to both Sales and Marketing. Sales needs data to understand pipeline health and Marketing needs data to understand conversion rates. To understand what matters to your Sales and Marketing teams, it’s important to understand how their efforts are measured at a high-level.

Sales

In our blog post, How to Align Data with Business Strategies, we wrote that “data analysis is crucial to providing visibility into the sales pipeline by connecting CRM data with other sales-centric data sources.” For Sales, having a healthy pipeline of accounts to close is crucial for their productivity and revenue number. In addition to pipeline, here are a few key Sales metrics and CRM data can inform them:

  • Revenue: dollar amount that the Sales team is able to bring in by closing accounts
  • Win Rate: calculates the percentage of accounts closed within a specific time period and is directly related to sales performance

Marketing

For the Marketing team, their metrics directly relate to their overall performance across different mediums including campaigns, social channels, content marketing, etc. As a team that has multiple data sources, bringing it all together and tracking the following metrics gives them the ability to report on performance:

  • Customer Acquisition Cost (CAC): the estimated cost of gaining a new customer. Marketers want to spend less and get more customers. The data can show where and how marketers can achieve this balance
  • Website traffic: visibility into where website traffic is coming from by using a tool like Google Analytics. This is useful in showing marketers where to place ad campaigns and iterate on messaging
  • Conversion rate: identify where customers leave the conversion funnel. Use data to improve certain stages in the funnel to increase the overall conversion rate

Conclusion 

To be successful in data analysis, it’s important to have an innate ability to communicate cross functionally and across a diverse set of backgrounds and data competencies. In facilitating company success, you have to track the right metrics and understand what they mean in order to visually and verbally tell a story through data that fulfills the needs of your company.

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