When it comes to data analysis, people often think of some dense digital tables, or advanced data modeling technology, or gorgeous data reports. In fact, "analysis" itself is everyone's ability; Data analysis is to analyze business problems and draw conclusions in a quantitative way.
There are two key words: quantification and business. Let's talk about quantification first. Quantization is to unify cognition and ensure that the path can be traced and copied. Only by unifying cognition can we ensure that people from different levels and departments can discuss and cooperate in the same direction, and can we avoid people in the company guessing the current business situation with "I feel" and "I guess".
Traceability and replicability of path means that many optimization methods can be found and replicated through quantitative results. The same is the optimization of conversion rate. It can be predicted which effect is better and how much better between Scheme A and Scheme B..
To achieve quantification, we need to do three things: establish a quantification system, clarify the key points of quantification, and ensure the accuracy of data.
Establish a quantitative system
Mainly in accordance with the "indicator design method", the core indicators of business+disassembly indicators+business indicators are designed, and finally the indicator dictionary and dimension dictionary are all common in the whole company. This kind of work is generally done by data analysts or data PM. In this way, a comprehensive and systematic quantitative analysis framework of the whole company can be initially established to ensure that the daily analysis can be decomposed layer by layer, with no emphasis or omission.
Clarify the key points of quantification
At each stage, the current business priorities should be clearly defined. The quantification system needs to change the emphasis and method of quantification according to the business stage. This also means more detailed indicators and greater monitoring and promotion efforts.
For example, in the early days of the take-away industry, it experienced the process of paying attention to the number of orders, reaching the order amount, reaching the number of new customers+subsidy rate, and reaching the number of new customers+capital use efficiency (transaction completion progress/expense completion progress).
We can see that with the continuous escalation and changes in the development stage of the war, from establishing market share regardless of cost, to taking a fancy to the quality of orders, and then to almost competing for the stock market, we began to consider the number of new customers and control subsidies at the same time, until the war became normal, we began to control the overall subsidy amount and beat our opponents by fighting for efficiency.
At each stage, it is necessary to judge the current focus according to different battlefield conditions, so as to establish a 360-degree analysis and monitoring system without dead ends around this focus.
Ensure data accuracy
On the topic of data accuracy, data products have mature data quality management methods, involving data source monitoring, index calculation, data presentation and so on.
From a commercial point of view
In addition to quantification, another key word is business. Only by solving business problems can analysis create value. Value includes personal value and company value.
How to think from the perspective of business? To sum up, it is eight words "worry about what they are worried about and give them what they want". This is not only applicable to the post of analyst, but also the most important thing for suppliers to accurately understand each other's needs in all the interaction processes with supply and demand as the main relationship.
These small decisions are actually based on the data points in our minds, which is the process of simple analysis. For business decision makers, it is necessary to master a set of systematic, scientific and commercial data analysis knowledge.
Strategic thinking of data analysis
Whether it is products, markets, operations or managers, we must reflect: where is the essential value of data? What can the team learn from these data?
The goal of data analysis
For enterprises, data analysis can help enterprises to optimize processes, reduce costs and increase turnover. We usually define this data analysis as business data analysis. The goal of business data analysis is to use big data to make fast, high-quality and efficient decisions for all professionals and provide scalable solutions. The essence of business data analysis is to create business value and promote business growth.
The role of data analysis
In the enterprise growth model we often say, a business platform is often the core. Among them, data and data analysis are essential links.
By providing products or services to target users through enterprises or platforms, the interactions and transactions generated by users in the process of using products or services can be collected as data.
According to these data insights, the customer's needs are pushed back by means of analysis, and more value-added products and services that meet the needs are created, and then users put them into use, thus forming a complete business closed loop. Such a complete business logic can really drive the growth of business.
Evolutionary theory of data analysis
We often position the different stages of data analysis by the rate of return on business, so we divide it into four stages.
Phase 1: What about the observed data at present?
First of all, the basic data display can tell us what happened. For example, the company launched an advertisement for a new search engine A last week, trying to compare how the new channel A compares with the existing channel B a week later, how much traffic A and B bring respectively, and what is the conversion effect? For example, how many users like the newly launched products and how many people register for the newly registered traffic. These all need to show the results through data, all based on the "what happened" provided by the data itself.
Stage 2: Understand why it happened.
If we see why channel A brings more traffic than channel B, then we should further judge the reason of this phenomenon in combination with business. At this point, we can further split the data information. Perhaps the traffic brought by a keyword may be that the channel has gained more users on the mobile side. This kind of data in-depth analysis and judgment has become the second advanced stage of business analysis, and it can also provide more business value.
Stage 3: predict what will happen in the future.
When we know the traffic level brought by the two channels, A and B, we can predict what will happen in the future according to the previous knowledge. When channels C and D are pushed out, it is guessed that channel C is better than channel D. When new registration process and new optimization are pushed out, we can know which node is more prone to problems. We can also automatically predict and judge the difference between C and D channels by means of data mining, which is the third advanced stage of data analysis and predicts the future results.
Stage 4: Business Decision
The most meaningful thing in all work is business decision-making, judging what to do through data. The purpose of business data analysis is business results. When the output of data analysis can be directly transformed into decision-making, or the data can be directly used for decision-making, then the value of data analysis can be directly reflected.
EOI data analysis framework
EOI architecture is the basic way for many companies, including LinkedIn and Google, to define and analyze project objectives, and it is also the basic and necessary means for managers to think about business data analysis projects.
Among them, we first divide the company's business projects into three categories: core tasks, strategic tasks and risk tasks.
Data analysis projects have different goals for these three types of tasks. For the core task, data analysis is an aid (e) to help the company make better profits and improve its profitability; For strategic tasks, it is to optimize (o) how to assist strategic tasks to find directions and profit points; For the risk task, it is * * * and entrepreneurship (1), trying to verify the importance of innovative projects.
Managers need to have a clear understanding of the company's business and development trends, rationally allocate data analysis resources, and set data analysis goals.
The basic idea of data analysis
Faced with massive data, many people don't know how to prepare, how to proceed and how to draw conclusions. The following introduces the basic idea of data analysis, hoping to bring you help in the practical application of data analysis.
Basic steps of data analysis
Above, we mentioned the importance of correlation between data analysis and business results. All business data analysis should start with business scenarios and end with business decisions. What should be done before data analysis? On this basis, we put forward five basic steps of business data analysis process.
Step 1: Dig the commercial significance.
First of all, we should understand what the marketing department wants to optimize, and measure it with Polaris index. For the evaluation of channel effect, it is important to transform business, with emphasis on how to measure the transformation effect through data means; The operation strategies of different channels can also be further optimized according to the transformation effect.
Step 2: Make an analysis plan.
Take "investment and financial management" as the core transformation point, allocate a certain budget for traffic test, and observe and compare the number of registered people and the final transformation effect. Write down the number of times these people repeatedly buy wealth management products to further judge the quality of the channels.
Step 3: Split the query data.
Because we need to compare the channel traffic in the analysis scheme, we need each channel to track the traffic, landing page residence time, landing page pop-up rate, website visit depth, orders and other data for in-depth analysis and landing.
Step 4: Refine business insight.
According to the data results, compare the effect of advertising, and observe the results according to the two core KPIs of traffic and conversion, and infer the business significance. Assuming that the mobile search effect is not good, we can think about whether the product is suitable for the mobile customer base; Or carefully observe whether the performance of the landing page can be optimized and so on. And need to find business insight.
Step 5: Make business decisions.
According to data insight, guide channel decision. For example, optimize the mobile landing page, change the user operation strategy and so on.
Dos thinking
The idea of DOSS is to split a specific problem into the overall impact and find a large-scale solution from a single solution. DOSS is an effective way to rapidly, massively and effectively grow the solution.
Eight methods of data analysis
Figures and trends
Looking at numbers and trends is the most basic way to display data information. In data analysis, we can quickly understand the market trend, order quantity, performance completion and so on through intuitive figures or trend charts, so as to intuitively absorb data information and contribute to the accuracy and real-time decision-making.
Dimension decomposition
When a single number or trend is too macro, we need to decompose the data through different dimensions to get more detailed data insight. When choosing a dimension, we need to carefully consider its influence on the analysis results.
User grouping
Classifying users who meet certain behavior or background information is a user segmentation method we often say. We can also create a group of portraits of users by refining their specific information. For example, users who visit shopping websites and send addresses in Beijing can be classified as "Beijing" users. For the "Beijing" user group, we can further observe the frequency, category and time when they buy products, so as to create a portrait of this user group.
Conversion funnel
Most business realization processes can be summarized as funnels. Funnel analysis is one of the most commonly used data analysis methods, whether it is registration conversion funnel or e-commerce order funnel. Through funnel analysis, we can restore the path of user transformation from beginning to end and analyze the efficiency of each transformation node.
Among them, we often pay attention to three points:
0 1) What is the overall conversion efficiency from beginning to end?
02) What is the conversion rate of each step?
03) Which step loses the most, and why? What are the characteristics of lost users?
Behavior trajectory
Paying attention to behavior trajectory is to really understand user behavior. Data indicators themselves are often just abstractions of the real situation. For example, if web analytics only looks at such indicators as user visits (UV) and page views (PV), it is absolutely impossible to fully understand how users use your products.
Restoring the user's behavior trajectory through big data means helps the growth team to pay attention to the user's actual experience, find specific problems, design products and launch content according to the user's usage habits.
Residue analysis
In the era of fading demographic dividend, the cost of retaining an old user is far lower than the cost of acquiring a new user. Every product and service should focus on the retention of users to ensure that every customer is satisfied. We can understand the retention situation through data analysis, and we can also find ways to improve retention by analyzing the relationship between user behavior or behavior groups and return visits.
In addition to paying attention to the overall user retention, the marketing team can pay attention to the user retention obtained from various channels or the return visit rate of registered users attracted by various contents, and the product team pays attention to the impact of each new function on the return visit of users. These are common retention analysis scenarios.
A/B test
A/B testing is used to compare the effects of different product designs/algorithms on the results. A/B testing is usually used to test the effects of different products or functional designs during the product launch process. The market and operation can complete the effect evaluation of different channels, contents and advertising ideas through A/B testing.
A/B testing has two essential factors: first, there is enough time for testing; Second, the data volume and data density are high. Because when the product flow is not large enough, it is difficult to get statistical results when doing A/B test. A big company like LinkedIn can conduct thousands of A/B tests at the same time every day. Therefore, A/B testing is often used more accurately in the case of large company data, and statistical results can be obtained faster.
mathematical modeling
When a business goal is related to various behaviors, portraits and other information, we usually use mathematical modeling and data mining to model and predict business results.
When we need to predict and judge customer churn, we can build a churn model through user behavior data, company information, user portraits and other data. Use statistical methods to calculate some combinations and weights, so as to know which behaviors users conform to, and the possibility of churn will be higher.
We often say that we can't grow without measurement, and data analysis plays a vital role in improving the business value of enterprises. Of course, it is far from enough to master simple theories, and practice makes true knowledge.