In the digital age, businesses are facing unprecedented challenges and opportunities. With the rise of the internet, the Internet of Things (IoT), social media, and e-commerce, the volume of data has exploded, providing businesses with unprecedented insight and decision-making potential. Data analysis of business metrics is becoming increasingly important as a quantitative tool for interpreting this data. They not only help companies understand the complex and dynamic market dynamics, but also guide them on how to optimize operations, enhance customer experience, and ultimately achieve business success.
Business metrics are key to measuring your performance, monitoring market trends, and predicting future performance. In the massive amount of data, how to accurately capture those indicators that can truly reflect the business situation has become the common pursuit of enterprise decision-makers and data analysts. Effective data analysis business indicators can reveal key information such as customer behavior patterns, market trends, and product performance, so as to provide data support for the strategic planning and daily management of enterprises.
In this article, we will explore the concepts and categories of data analytics business metrics. Whether you're a marketer looking to optimize your marketing strategy, an operations manager looking to improve operational efficiency, or a business leader working on data-driven decision-making, we'll provide you with valuable insights and a practical framework. Let's embark on a journey to explore data analytics business metrics and discover how they can accelerate your digital transformation journey.
1. How should I understand a piece of data after I get it?
1. Master the meaning of data columns
When we get an Excel data sheet or any other form of data set, the first task is to thoroughly understand the meaning of each column in the data. Each column may represent data points that correspond to different metrics, categories, or attributes, and this information is critical for subsequent data analysis.
2. Classify the data
By systematically categorizing data, we are not only able to better organize and store data, but also provide clear direction for later data analysis, mining, and decision support. In practice, data is often divided into three broad categories, each with its own unique attributes and analytical value.
(1) User data: depicting individual portraits
User data forms the basis of our understanding of who I am. It involves a series of basic information about an individual user that provides us with a comprehensive user profile. User data typically includes, but is not limited to:
- Personally identifiable information: name, gender, email address, etc., to help identify and distinguish between different users.
- Demographic information: age, home address, education level, etc., which can help with market segmentation and targeting specific user groups.
- Occupation information: The user's professional background, which can reveal their potential needs and spending power.
- Contact information: telephone, social media accounts, etc., for user communication and service feedback.
- User preferences: personal preferences obtained through questionnaires, user registration information, etc.
(2) Behavioral data: track user behavior tracks
Behavioral data records a user's activity on a particular platform, reflecting what the user "did". This type of data is critical to understanding user needs and improving the user experience. Behavioral data mainly includes:
- Interaction behavior: how long a user stays on a product page, browsing history, click-through rate, etc.
- Purchase behavior: the type of goods that users buy, how often, when they buy, etc., which can help with sales forecasting and inventory management.
- User feedback: User evaluations, reviews, and ratings of the product reflect user satisfaction and product pros.
- User journey: The user's path through the platform, including the order of pages visited, the conversion funnel, and more.
(3) Product data: the full picture of the product
Product data provides details of "what is sold" on the platform. Whether it's an e-commerce platform's product, video content, or other service-based products, product data is key to understanding product performance and market performance. Product data typically includes:
- Basic product information: product name, category, specifications, price, etc.
- Inventory information: the amount of inventory of the product, the replenishment cycle, the sales speed, etc.
- User interaction: User reviews, ratings, favorites, and retweets on a product.
- Sales data: product sales volume, sales, market share, etc.
- Product life cycle: time to market, maturity period, decline period, etc.
2. What are the criteria for measuring data: common business metrics
In business analytics, to ensure consistency and impartiality in assessments, we need to rely on a standardized set of business measurement tools, which are what we call business metrics. Business metrics are a set of quantitative measures that assess and reflect the performance and health of a business against specific criteria. To gain a deeper understanding of the business implications behind the data, we'll explore the key metrics involved in user data, behavioral data, and product data, respectively.
1. Commonly used business indicators to measure user data
(1) The number of new users per day
"Daily new users" is a crucial metric for data analytics that measures the number of new users added in a day. The new users are not only the fresh blood of the enterprise user pool, but also an important basis for evaluating the effectiveness of marketing activities. Through in-depth analysis of the source of new users,
Enterprises can gain insight into the contribution of different promotion channels to optimize their marketing strategies:
- Channel performance analysis: Segmenting new users by their source channel can reveal the specific contribution of each channel to user growth. This analysis helps businesses identify which promotion channels are most effective and which need to be improved or abandoned. For example, if one social media advertising channel brings in significantly more new users than others, a business may decide to increase its investment in that channel.
- Market penetration strategy: Data on new users can also guide companies to develop or adjust their market penetration strategies. By understanding where new users are coming from, businesses can better target their target market and design products and services that meet their needs, thereby increasing market penetration.
- Product and service optimization: In addition, the characteristics and behavior data of new users can provide valuable information for product and service optimization. Based on the feedback and preferences of new users, businesses can adjust product features and improve user experience to increase user satisfaction and retention.
(2) User activity rate
Active users can define based on the characteristics and business goals of different products. For example:
- For some apps, user sign-in may be sufficient to be considered active.
- For other apps, a user may need to take a specific action, such as using a feature within the app or completing a purchase, to be considered an active user.
Therefore, when evaluating the number of active users, it is important to first understand the product-specific definition of active users.
- Daily Active Users (DAU)
Daily active users are the number of users who perform at least one active action in a day. For example, if opening an Official Account article is defined as an active activity, the number of daily active users refers to the total number of users who have opened an Official Account article in a day.
- Weekly Active Users (WAU)
Weekly active users are the total number of users who performed at least one active action in a week. Continuing with the above example, weekly active users are the number of users who have opened an article on the Official Account at least once in a week.
- Monthly Active Users (MAU)
Monthly active users are the total number of users who have had at least one active activity in a month. Similarly, taking an official account article as an example, the number of monthly active users is the number of users who have read at least one article in a month.
The activity rate is the ratio of the number of active users to the total number of users, and it is an important metric to measure user engagement and product appeal.
By analyzing the activity rate, enterprises can evaluate the stickiness of users to the product or service, understand the real engagement of users, and optimize product features and improve the user experience accordingly.
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Precautions
- Consistency of active definitions: Ensure that the definitions of active users are consistent across the statistics over time.
- Data accuracy: Use accurate data sources to count the number of active users and the total number of users to avoid calculation errors.
- User overlap problem: In multi-day or multi-week statistics, it is important to note that users may exhibit active behavior in multiple time periods, and double counting should be avoided.
(3) User retention
Retention rate is often used to measure how well a user continues to use a product or service after a certain period of time after using it for the first time. It can be used to evaluate product appeal, user loyalty, and the effectiveness of marketing campaigns.
Retention rates can be categorized by different time periods, common ones include:
- Next-day retention: The percentage of users who still use the product the day after they first use it.
- 7-day retention rate (weekly retention rate): The percentage of users who still use the product within 7 days of using it for the first time.
- 30-day retention rate (monthly retention rate): The percentage of users who still use the product within 30 days of using it for the first time.
The retention rate can be calculated using the following formula:
Thereinto:
- Retained users: The number of users who are still using the product at the end of the time period.
- Starting Users: The total number of users at the beginning of the time period.
By analyzing changes in retention rates, product teams can discover why users churn and optimize product features accordingly. Retention can also be used to evaluate the effectiveness of marketing campaigns, especially new user acquisition. A high retention rate is often an indication of high user loyalty to the product, which helps build a stable user base.
Improving retention often requires companies to take the following steps:
- Optimize the user experience: Ensure that the ease of use and performance of the product meets user expectations.
- User onboarding: Provide clear guidance and education to help users quickly understand the value of the product.
- Personalized services: Provide personalized content or recommendations based on user behavior.
- Community Building: Establish a user community to increase interaction and participation among users.
- Regular updates: Regularly update content or features to keep your product fresh and engaging.
2. Behavioral data indicators
In the field of data analytics, behavioral data is an important window into how users interact with products or services. Metrics related to behavioral data can reveal user behavior patterns, preferences, and engagement, which are critical to optimizing user experience and business performance. Metrics related to behavioral data include: PV and UV, retweet rate, conversion rate, and K-factor.
(1) PV and UV
In web analysis and user behavior research, PV (Page Views) and UV (Unique Visitors) are two fundamental and key indicators. They provide a quantitative perspective on how users interact with a website or app.
- Page Views (PV)
PV measures the number of times a user visits a page within a specific period of time. Each page load or refresh is counted as a separate PV. For example, if a user opens the same web page 10 times in a day, the page's PV count will increase by 10. PV is an important metric for evaluating the popularity of content and user engagement.
- Unique Visitors (UV)
UVs refer to the total number of distinct users who visited a particular page within a certain period of time. Even if the same user visits a page multiple times in a day, the UV is only counted as 1. Continuing with the above example, even though a user opens the same web page 10 times, the UV count is still 1 because it is multiple visits by the same user. UVs help us understand how many different users are interested in the page and are a key metric for measuring the size of our user base.
Practical application of indicators
While different terms may be used for different products and platforms, the concepts represented by PV and UV are universally applicable. These metrics are crucial for webmasters, marketers, and product managers because they provide direct evidence of user engagement and the quality of website traffic.
- Content optimization: By analyzing PVs, you can identify which content is the most popular, which can guide content creation and optimization strategies.
- Go-to-market: UV trends can help evaluate the effectiveness of go-to-market campaigns, especially when it comes to attracting new users.
- User analysis: Combining PV and UV, you can get a more comprehensive understanding of your users' visit behavior, including user loyalty and content repeat visit rate.
(2) Forwarding rate
Retweet rate is usually defined as the ratio of the number of times it is shared to the total number of impressions within a certain time frame. This ratio can be used to quantify the dissemination of the content and the responsiveness of users to the content.
The forwarding rate can be calculated by the following formula:
Thereinto:
- Shares: The number of times a user actually shared the content with others.
- Total impressions: The total number of times the content was shown to users, usually the total number of views or views of the content.
A high retweet rate usually means that the content is of high quality and engaging, inspiring users to share. The retweet rate can be used as an indicator of user engagement, reflecting the user's loyalty to the brand and satisfaction with the content. By analyzing changes in forwarding rates, it is possible to gain insight into market trends and changes in user preferences.
(3) Conversion rate
Conversion rate is one of the key metrics to measure the effectiveness of marketing campaigns and user experience, and it is directly related to the revenue and growth of a business. The conversion rate reflects the ratio between the number of users who complete a specific target action (such as purchase, sign-up, download, etc.) and the total number of users in a certain period of time. Here's a closer look at the conversion rate:
Conversion rate is typically defined as the ratio between the number of users who successfully complete a conversion goal and the total number of users who reach a specific funnel stage within a specific time period. The calculation of the conversion rate depends on the specific business scenario, but the general formula for calculating the conversion rate is as follows
Thereinto:
- Conversions: The number of times a user completed the target action in a given time.
- Total reaches: The total number of unique users who reached a specific stage of the conversion funnel in a given time.
Conversion rates are crucial for businesses and marketers, and they help businesses evaluate the efficiency and return on investment (ROI) of their marketing campaigns. By analyzing the stages of the conversion funnel, businesses can identify and address pain points in the user experience. A high conversion rate usually means a good match between the product's features and the user's needs. Conversion rates can be used to predict revenue growth and business expansion potential.
(4) K-factor
K-Factor is a measure of a product's ability to acquire new users through promotion by existing users, especially when evaluating the effectiveness of referral marketing and the potential for virality. The K-factor is typically defined as the average number of new users that each user is able to invite over their lifetime, multiplied by the conversion rate of those invitations. It is a quantitative indicator that reflects the effectiveness of a user growth strategy.
The calculation of the K-factor can be done by the following formula:
K = average number of inviters × conversion rate
Thereinto:
- Average inviters: The average number of new users invited by each existing user.
- Conversion rate: The percentage of new users invited who actually become registered users or complete other conversion goals.
The K-factor is multifaceted for businesses and marketers, and a K-factor greater than 1 usually means that the user growth strategy has the potential to be self-sustaining, as each user can bring in more than one new user on average. And the K-factor can be used to evaluate the effectiveness of referral programs or word-of-mouth marketing campaigns.
3. Product data indicators
Product data is the key to market performance and product success. These data cover a wide range of indicators and can reflect the performance and market acceptance of products from different perspectives.
(1) Indicators of total business
In business analysis and business operations, metrics that measure the total volume of business are essential for assessing a company's market performance and overall health. These metrics provide intuitive data on the scale of a company's business and user engagement, helping management make data-driven decisions. Here are a few key business volume metrics:
- Transaction total
Gross transaction volume refers to the monetary sum of all transactions completed through the platform or channel within a certain period of time. This indicator is usually used on e-commerce platforms, and it can reflect the transaction size and market potential of the platform. GMV is an important indicator to evaluate sales performance and the effectiveness of market expansion strategies.
- Number of deals
Deal volume refers to the total number of transactions for a product or service that were completed within a specific time period. This indicator can reveal the frequency of sales activity and market acceptance. Used in conjunction with GMV, businesses can analyze the value of individual transactions and adjust pricing strategies or promotions accordingly.
- Duration of the visit
Visit duration measures the average amount of time a user spends on a website. This metric is very useful for understanding user engagement and the attractiveness of your website's content. A high duration of visits usually means that users are interested in the content of the website and may be associated with higher user satisfaction and conversion rates.
(2) Per capita
In business analysis, the per capita indicator is an important tool to evaluate the contribution of individual users to the enterprise. These metrics help businesses understand the average value per user so they can optimize pricing strategies, improve user experience, and target marketing campaigns. Here are a few key per capita indicators:
- Pay per person
The per capita payment metric reflects the average revenue per user that comes to a business over a certain period of time. This indicator is calculated by dividing the total revenue by the total number of users, which can reveal the average level of consumption of the user group. Per capita payment is also called customer unit price in the e-commerce industry.
- Paying users pay per capita
The per paying user metric focuses on those user groups that have already generated consumption, and is measured by calculating the average spending of paying users. This metric helps businesses understand the paying capacity and spending potential of paying users.
- The average duration of visits per person
The average time spent on visit metric measures the average time a user spends on the platform. By analyzing this metric, businesses can assess the level of engagement and interest users have in a content or service.
(3) Payment
In business analytics, payment-related metrics are crucial to assess a business's profitability and user loyalty.
- Paid rates
The payment rate refers to the ratio between the number of users who complete the payment behavior and the total number of users (or the total number of active users) in a certain period of time. This metric reflects what percentage of the user base is willing to pay for a product or service. By tracking payment rates, businesses can evaluate the effectiveness of marketing campaigns, the rationality of pricing strategies, and the degree to which users recognize the value of a product.
- Repurchase rate
Repeat purchase rate measures the ratio between the number of repeat purchasers and the total number of users who purchase a product or service over a given period of time. This metric is an important indicator to measure user loyalty and satisfaction, and a high repurchase rate usually means that users have a high level of trust and dependence on the product or service.
The payment rate is directly related to the profitability of the enterprise and is a key driver of revenue growth; The repurchase rate reveals the user's loyalty to the brand and is the foundation for building long-term customer relationships. These two indicators provide feedback on the company's marketing strategy, help the company optimize products and services, and improve user satisfaction.
(4) Products
Product-related metrics are an important tool for companies to evaluate product performance and market acceptance. These metrics help businesses identify which products are favored by the market and which ones need to be improved or are likely to be withdrawn from the market. With a detailed analysis of product-related metrics, businesses can develop more targeted marketing strategies, optimize product mix, and improve overall performance. Here are a few key product-related metrics and how they apply to business decisions:
- Number of best-selling products
The number of best-selling products refers to the number of products that have the highest sales in a certain period of time. This metric can help businesses identify best-selling products, understand market demand and consumer preferences, and then conduct focused promotion and inventory management.
- Number of positive products
The number of positive reviews relates to the number of products that have received positive user reviews. Products with a high positive rating usually mean high product quality and a good user experience, and these products can serve as brand ambassadors for the business and attract more new users.
- Number of products with negative reviews
The number of negative reviews focuses on the number of products that have received negative feedback. By analyzing the reasons for bad reviews, companies can find the shortcomings of their products and make timely product improvements or adjust their marketing strategies.
4. Paid promotion indicators
When running a paid advertising campaign, accurately measuring and evaluating the effectiveness of your campaigns is critical to optimizing your advertising investment and increasing your marketing ROI (return on investment). Different paid advertising channels have their own characteristics and advantages, so it is necessary to examine the corresponding performance metrics in a targeted manner. Here are a few common paid advertising channels and performance metrics:
(1) Placement advertising
Placement ads typically appear in specific locations on websites, apps, or social media platforms. Key metrics include:
- Impressions: The number of times your ad was shown.
- 点击率（Click-Through Rate, CTR）：点击广告的用户数与看到广告的用户数之比。
- Conversion Rate: The proportion of users who complete a targeted action (e.g., purchase, sign-up) after clicking on your ad.
(2) Search ads
Search ads are displayed based on a user's search query and are commonly found in search engine marketing (SEM). The main indicators are:
- Impression Share: The percentage of your ad that appears in relevant search queries.
- 平均成本(Cost Per Click, CPC)或每次点击的成本。
- Quality Score: A measure of the relevance and quality of your ads, keywords, and landing pages.
(3) In-feed advertising
In-feed ads blend with what people are viewing, such as social media feeds or news feeds. Relevant indicators include:
- Engagement Rate: How often people interact with your ad (e.g., likes, comments, shares).
- 成本效益分析（Return on Ad Spend, ROAS）：广告投入与由广告带来的总收益之比。
- 观看完成率（Viewability Rate）：广告被完全展示给用户的比例。
At present, most major ad platforms offer the following three payment methods, allowing advertisers to flexibly choose according to product characteristics and marketing strategies:
- Pay-per-impression (CPM): Good for increasing brand awareness and product exposure.
- Pay-per-click (CPC): Good for getting potential customers to learn more about your product.
- Pay-per-performance (CPA): Good for driving sales or acquiring high-value user actions.
For new product promotion, it is usually recommended to use the Cost Per Download (CPD) model. This method is measured by the number of app downloads and is a cost-effective way to promote new products that have not yet established brand awareness. With the gradual establishment of brand influence, it is possible to gradually shift to a CPC or CPA model to achieve higher marketing efficiency and ROI.
3. How to choose such a wide variety of indicators
With so many data analysis metrics, choosing the right one is critical to gaining insight into the business situation and guiding decisions. When choosing an indicator, you should follow these two principles:
1. Prioritize proportional indicators
Good data metrics tend to be presented in proportional form. Proportionality indicators transform a single piece of numerical data into a meaningful benchmark that more accurately reflects performance or efficiency. For example, conversion rates, click-through rates, and so on are all calculated by dividing key numbers by total values such as total visits or total clicks. When evaluating an indicator, if you provide an absolute value rather than a proportion, try to convert it into a proportional form to better understand the meaning behind it.
2. Determine the North Star indicator
The North Star Indicator is a key metric that reflects the core values and goals of the business, and it guides all members of the company in a common direction like the North Star. The selection of the North Star indicator should be based on the current operational priorities and strategic objectives. It is important to note that the Polaris indicator is not fixed and will adjust as the company's business grows and the market environment changes. Different companies, or even the same company, at different stages of development, may have different North Star indicators. Therefore, enterprises need to identify and establish the North Star indicator at the current stage according to their own business characteristics and strategic needs.
How to choose the Polaris indicator
When choosing the North Star indicator, you can consider the following steps:
- Clear business goals: First, clarify the company's business goals and strategic direction.
- Key Result Areas: Identify the areas or processes that are most critical to achieving business goals.
- Data availability: Make sure that the data for the selected metric is easily accessible and accurate.
- Leadability of action: The North Star indicator should be able to guide concrete actions and decisions.
By carefully selecting and applying proportional metrics and identifying North Star metrics, businesses can more effectively use data to drive decisions, optimize business processes, and achieve strategic goals. The right choice of metrics not only helps businesses better understand the current state of the business, but also motivates teams to work towards a common goal.
IV. Conclusion
Faced with a massive number of data analysis metrics, organizations must take a systematic and strategic approach to selecting and applying these metrics. By prioritizing proportionality metrics, organizations are able to more accurately measure and compare different aspects of business performance. At the same time, identifying a clear North Star metric can help teams focus their efforts and move the business forward together.
In practice, companies should constantly evaluate and adjust their metrics to ensure they are aligned with current business goals and market trends. In addition, as technology evolves and the market changes, new metrics may become more important, while old ones may become less relevant.
Ultimately, the purpose of data analytics is to provide insights to businesses that help them make better decisions and drive continuous improvement and innovation. By carefully selecting metrics and in-depth analysis of the meaning behind them, businesses can gain an edge in a competitive market and achieve long-term success and sustainability.
In this context, FineBI, as an intelligent tool designed for enterprise-level data analysis, transforms complex data into easy-to-understand business insights through powerful data processing capabilities and intuitive business indicator calculation functions. Users can customize various business indicators to achieve multi-dimensional data analysis and use real-time data updates to ensure the timeliness of decision-making. FineBI's interactive data visualization brings data insights to life, while its ease of deployment and use makes it easy for non-technical users to quickly generate dynamic dashboards and reports. In addition, FineBI's high degree of customization and extensibility ensures that it can meet the specific needs of different enterprises, helping enterprises stay ahead of the curve in a data-driven business environment.