This is too much! Sales analysis can be done so well?

In the world of data analysis, sales analysis is often mentioned, but analysis that can truly transform data into sales momentum is rare. From operational guidance for grassroots sales to strategic decision-making for senior management, the article carefully analyzes the needs for data analysis at different levels and provides practical solutions.

The slogan “Data helps business” has been shouted for many years, but when it comes to data analysis, people still habitually talk about: Excel, Python, SQL, data cleaning, data calculation, and visualization.

Few people have a serious discussion about what kind of data analysis the business department needs. Today, we will take sales as an example to see what kind of data analysis is useful.

1. The secret to making data useful

Let me ask you a simple question: when you use your phone, do you know how the memory works and how the CPU is processed? No! You only care about how to turn on the phone and how to open the game as quickly as possible so that you can play it happily. This is true for all technology products: users care about the value to themselves, not the scientific principles of the product itself.

The same is true for data analysis in enterprises. Although there are complex principles behind data analysis, such as mathematics, statistics, operations research, computational science, and machine learning, the business department neither understands nor cares about it. You just need to tell me: "What can I do to achieve results?"

Especially the sales department. The sales department faces customers every day and bears huge pressure. They are not willing to listen to reason. Therefore, if you want to make data useful to the sales department, the first thing to do is to deeply understand the sales department's processes and carefully observe their actual difficulties (as shown in the figure below).

It should be noted that the sales department has its own organization. As long as the company has a certain scale, the sales team is large, with different business lines (telemarketing, sales teams in different cities, online sales, etc.), and a city/team management mechanism. People in different positions focus on different things. Therefore, it is necessary to carefully understand the company's sales organization in order to distinguish the needs of senior, middle and grassroots levels.

2. How to make data useful to the grassroots

Frontline salespeople are the hardest-working, most exhausted, and most stressed people. Imagine the sales calls you call and the salesgirls you don't even look at in the mall. Yes, frontline salespeople work hard to sell products despite customers' cold stares.

What they don’t need the most at this time are indicators like sales volume, number of buyers, and average order value that they can’t even understand. What they need is clear action guidance: what should they do?

Therefore, if they want to make data useful to them, they have to carefully disassemble the operation process to see which links can help. For example, taking telephone sales as an example, the operation process and potential problems may look like this (as shown below):

As the saying goes: business only needs to talk, data only needs to run. After breaking down the process and understanding the pain points, you will find that there is no perfect sales analysis model that can solve all problems at once.

For example, the simplest one: "Which phone should I make first and which one should I make next?" may involve:

● Which customers are high-value?

● Which customers are likely to respond?

● Which time period has a higher response rate?

● Which ones are suitable for secondary follow-up?

A single problem may require analysis of several points to be fully supported, and data calculations are required to provide a solution with a higher response rate than random calls. This requires data analysts to be particularly patient when working and tackle problems one by one.

What’s interesting is that although data analysis has done a lot of work, when it comes to outputting to the frontline, you have to be very restrained: don’t talk about things that are irrelevant to the frontline. For example, “Which phone should I call first, and which one should I call later?” Finally, just output it directly on the operator’s dial list, and put the priority number at the front. Only by hiding the complex process and improving the convenience of frontline operations can the frontline really use it (as shown in the figure below).

Similarly, by carefully sorting out the problems in each link, many opportunities can be found, such as:

1. Improve the success rate of dialing

2. Choose more effective words

3. Reduce the difficulty of searching the database

4. Related promotion information

There may be 2-3 data analyses to be done for each point. Although it is hard work, it is more effective to improve the success rate of the front line than to talk about any scientific principles (as shown below)

3. How to make data useful to middle-level managers

When dealing with middle-level managers such as outbound call team leaders, city managers, and sales team managers, you have to change your mindset. As middle-level managers, front-line operations at the bayonet level are only one aspect they care about. More work will be focused on how to make plans, how to organize work, and how to motivate/constrain subordinates (as shown in the figure below).

Note: Unlike the busy frontline workers, middle managers have time to sit down and think carefully about the plan and look at the data. However, at different time points, they spend different amounts of time on the data.

for example:

● On a specific day, you may only have 20 minutes to look at the data before the morning meeting.

● When it comes to weekly summaries, I may spend 1 or 2 hours looking at the data.

● When it comes to the monthly summary report, I may spend half a day carefully preparing the PPT

Therefore, the output data results must conform to the other party's working habits. Even if there are a lot of results to be thrown out, they must be output in a restrained manner and in different occasions to avoid information explosion. If time is short, read less; if time is long, read more (as shown below):

With a clear output scenario, the output content should also focus on the scenario. For example:

● The daily morning meeting is just to check progress, motivate subordinates, and output a few key information

● Weekly meetings require inventory of resources, checking execution status, and solving problems, which requires analysis from multiple dimensions

● At the monthly meeting, we need to review the work of the month. The more in-depth the review, the more data support is needed.

A hierarchical progression can greatly assist management work (as shown below):

4. How to make data useful to senior management

For regional managers and department director-level leaders who manage an entire business line, or manage multiple teams and multiple maps, their tasks are not only to give speeches, motivate people, and shout slogans, but also to be responsible for taking on tasks given by higher decision-makers and coordinating with supporting departments such as brand/promotion, and supporting departments such as supply chain, customer service, and after-sales.

Otherwise, if there are only sales but no products, no promotions, no publicity, no supply, and no guarantee of supply quality, the goal cannot be achieved (as shown in the figure below).

These managers also spend a lot of time in the office every day, so they have time to read more analysis reports and think about more in-depth issues. Therefore, simply outputting result reports cannot meet their needs.

From the perspective of thinking about the problem, the core entanglement is: Can I complete the task independently? As a senior manager, coordinating resources, ensuring support, and eliminating potential problems (passing the buck to others) are more important than getting down to work directly. Therefore, the logic of thematic analysis can be based on how to distinguish sales/other department collaboration issues, and multiple topics can be separated for in-depth interpretation (as shown below):

5. Why are the things we usually do useless?

After reading the above, you may have discovered why the sales analysis you usually do is useless: most companies’ data and sales are very disconnected. As a data analyst, you don’t understand the sales process, organizational structure, or sales skills. You just write the formula of sales = number of customers * conversion rate * average order value over and over again, breaking it down into different cities. This kind of thing is definitely useless.

Many salespeople in many companies do not pay attention to analysis. They only look at the total number of sales results every day, and then start to shout slogans: "As long as you are not dead, work hard." Even if they have a CRM platform, they do not use it properly, and secretly develop various black technology operations every day. As a result, there are more and more unconventional methods, and not many serious sales results are achieved.

In short, if you want data to play a role, the investment of both teams is necessary. As data, you should go deep into the business, think about problems from the business perspective, and solve real pain points. As business, you should respect data, strictly implement processes, and accept new methods to achieve good results.