Data analysis empowers sales, these strategies are amazing

In today's highly competitive business environment, data analysis has become a key tool for improving sales performance. This article explores in depth how to empower sales through data analysis, and proposes a systematic approach from understanding the sales process, formulating precise strategies, setting replicable benchmarks to designing effective incentive mechanisms. I hope it will be helpful to everyone.

Sales analysis is the most important analysis as it relates to company revenue. However, many companies do not do it well. The conventional practice is to list the target achievement rate by region, team, and product, and then nothing else. Although there are many such reports and charts, it is difficult to draw more conclusions except that "Team A did not meet the target."

So how can we find problems and opportunities? Today I will explain it systematically. The article is very long. If you haven't followed Teacher Chen yet, please remember to follow and like it first, and then read it slowly.

1. Understand the Sales Process

Essentially, the sales process is:

1. Design sales strategies to meet customers’ product/price demands

2. Optimize sales process and improve sales success rate

3. Motivate sales staff and improve sales motivation

If you want to draw useful conclusions, you need to conduct in-depth analysis from multiple angles such as products, prices, incentives, sales processes, etc., and you cannot just list the target achievement rate.

2. Find a strategy

The so-called strategy is to "launch appropriate products + price plans based on customer needs", rather than blindly offering discounts, selling all products and offering discounts regardless of user needs.

Based on the distance of toB business, in toB business

1. Customer business volume

2. Customer purchasing scale

3. Customer's technical requirements

4. Customer's price requirements

5. Customer lead sources

It will affect sales strategy

When developing your strategy, consider:

1. Differences between new and old customers

2. Difference in order size

3. Does our company have competitive product advantages?

4. How our company acquires customers

To formulate supporting strategies, so that each segment has a certain competitive advantage, thereby increasing the success rate (as shown below)

Note! Strategies are developed through practice and cannot be analyzed 100% accurately at the beginning.

Especially for new products, new customers, and newly developed markets, more trials are needed. Therefore, data analysis should be monitored well.

Modify the execution of the strategy (as shown below)

3. Setting a Benchmark

The best way to optimize the sales process is to set a benchmark. By comparing with the benchmark, you can find the gaps and stores that can be optimized. However, when setting a benchmark, you cannot just focus on performance.

Simply ranking sales performance and setting the first one as the benchmark will result in:

1. Winning by doing nothing with fixed big customers

2. Those who only know how to offer discounts will win

3. Get lucky and win a big order

In the eyes of others, these are either luck or innate and difficult to replicate, so the benchmark loses its meaning.

Reproducibility is the first requirement of benchmarking, which is where data analysis comes in handy: we need to eliminate the influence of luck, discounts, old customers, etc., and find replicable points in the sales process. Take toB sales as an example:

1. Time of first contact after receiving the clue (indicator)

2. Number of follow-up visits with customers and the interval between two follow-ups (indicators)

3. Which product combination (label) should be promoted based on customer needs?

4. When customers question the price, you should use the dialogue (label)

The above are replicable factors. First list the analysis assumptions, and then check them one by one (as shown below):

Generally, companies are reluctant to interfere with sales that have good performance, fearing that they might cause problems by making unnecessary interference.

Therefore, in the analysis, we often start with new sales or sales with poor performance. If we find that copying some of the benchmark's methods can improve performance, we will continue to promote and expand the effect (as shown below):

4. Determine Incentives

There are some common misunderstandings about incentives:

1. Only take extra bonus incentives and ignore strategy optimization

2. Only take extra bonus incentives and ignore benchmark copying

3. Only take extra bonus incentives and ignore spiritual incentives

4. Only reward sales results, ignoring process incentives

At their core, salespeople care about making money. Therefore: maximizing total revenue is the motivational goal. If adopting a new strategy/learning a new practice can increase revenue, it has the same effect as giving an extra bonus, and it can also save sales expenses.

Therefore, setting incentives is the last thing to consider after “finding strategies” and “setting benchmarks”. If a good strategy or benchmark is found to be replicable, data analysis should first calculate: how much more money salespeople can earn if this strategy or benchmark is adopted. This helps the sales operations department convince salespeople.

The analysis of specific incentives can be done using the same template as for activity analysis, but it should be noted that salespeople often distort their behavior in the short term because of incentives, such as:

1. Complete the incentive tasks this month, hide some orders and release them next month

2. If there is an incentive for new products this month, customers will be urged to stock up. Once the incentive is withdrawn, the sales will drop.

3. Force (negative incentive) to complete 100 calls a day, but the calls are made randomly and the conversion rate is low

Therefore, incentive analysis generally takes a longer time frame and focuses on overall performance (rather than a single incentive activity) so that problems can be discovered in depth.

summary

Of course, many students will complain:

1. Our sales have no strategy and only offer discounts

2. Our company's sales rely on connections, not skills.

3. Our product line is messy and there is no combination

4) The company’s CRM system is not working and behavioral data cannot be uploaded

These problems do exist, but they do not prevent us from organizing our thoughts and outputting analytical conclusions in a reasonable way. In this way, we can accumulate more experience and leave such low-end companies as soon as possible.