How do data and sales analytics help salespeople close more sales?

Your data is worth its weight in gold.

I am not teaching you anything here! But I like to remind you because it is important

Thanks to them, your company is able to learn about itself and its market with a level of relevance unthinkable ten years ago.

Your strategy and marketing teams have already understood this. As in many companies, they readily rely on this wealth of information to make the right decisions.

On the other hand, I bet your sales department is lagging behind. This is the case in most companies. From my observations, sales people are neither aware of this issue nor equipped to produce their data and analyse their sales.

In this article, I will show you how data can significantly boost the results of sales teams.

Above all, I'm going to give you a simple approach to implement to inject a good data culture into your team, and make sales analysis a pillar of your sales performance.

The goal: to make your salespeople more efficient and to increase your sales performance indicator. 

What else ?

Sales analysis, a goldmine for sales people

The €1,000 question: What is the use of data in a sales team?

My answer: almost everything.

To monitor the individual and collective performance of salespeople. To know the behaviour of prospects. Identify the right actions to take from the top to the bottom of the funnel to get the deal done the way you want.

Let's take a simple example: monitoring the performance of a salesperson.

In a "pre-data" world, the manager knows the overall track record of each of his team members. He has an idea of the number of calls made each week and the volume of sales made. If he records the calls, he can listen to them again and deduce some of the salesperson's good (or bad) habits. But he cannot objectively formalise the most effective practices.

In a data-driven world, the organised review of call content allows you to analyse sales precisely and identify exactly which strategies are effective and which are less effective for each salesperson. And then share them with all salespeople to improve overall performance. 

Again: what else?

The data reflex has yet to be built within the sales teams

And yet, according to a study published by Gartner, sales departments are currently among the least data-centric teams in companies.

Why the delay?

Let me offer some reasons from my own experience. 

Firstly: the use of data has not yet entered the radar of salespeople. For them, performance comes primarily from a good feeling and qualitative exchanges with prospects. In other words, factors that they don't think they can measure objectively. 

👉 the data is seen as off-topic.

Secondly , salespeople hate to feel that they are being monitored. Asking them to produce data goes against the deep-rooted culture of "what matters is the result". The proof: many salespeople are still reluctant to make full use of their CRM.

👉 the data is seen as binding.

Thirdly, managers are generally sensitive to the use of data, but they lack the tools to follow through on this. For example, they may know the average call duration of their best salesperson, but have no way of knowing whether this duration is actually one of the reasons for that salesperson's success.

👉 the data is unusable in sales analysis.

It is against these preconceived ideas that I invite you to fight... read more :).

Sales analysis supported by conversational analysis: a tool for sharing best practices

At Modjo, we believe strongly in the importance of a data culture in sales teams. 

Our equation is simple (and quite unstoppable, I think).

"Collecting sales data = enabling sales analysis = identifying channels for improvement".

Our tool of choice is conversational analysis: an artificial intelligence that highlights the different parts of a conversation, isolating the prospect's reactions, to draw conclusions about which interactions work and which do not. 

By aggregating results from a critical mass of conversations, this conversational intelligence delivers objective conclusions about good and bad sales practices. These can then be disseminated to the entire team. 

The advantages of this solution are that it is easy to use, the analyses are presented in a clear and visual way, and the conclusions are easy to draw. 

Here are some concrete examples of how to get the most out of these conversational analyses: 

• Replay conversations as a team: compare the best sale of the week to a call that didn't work out, for example. 
• Use them during one-to-one coaching: analyse the qualities and defects of a salesperson over one or more exchanges 
• Work on a particular phase of the sales exchange: the right response to a prospect's objection, for example. 
  

In concrete terms, how do you communicate the importance of data to your teams?

That's all well and good, but you sense that your salespeople are not going to be keen to change their habits. 

The good news is that you don't need to confront their culture head-on. The only method that works is to demonstrate, with evidence, that data-driven sales analysis can help improve their performance. Without disrupting the way they work. 

All you have to do is give them a simple science experiment.

Let's imagine that Baptiste, a good salesman, is convinced that the best time to make a "cold" call is between 2 and 3 pm. Baptiste has a certain aura within the team and has managed to convert his colleagues to this credo. Well done Baptiste. So your salespeople all make their cold calls religiously after lunch.

The question is: is this really good practice?

To find out, simply divide your salespeople into several small teams. Three, for example.

1. The former can continue to make cold calls at the usual time. 
2. The second is invited to pass them in the morning, for example between 10 and 11 am. 
3. The third is instructed to pass them later, for example between 4 and 5 pm.
   

Warning: for the experiment to be successful, the content of these calls must be fixed in advance and everyone must stick to the script. Just to avoid additional variables.

Run the experiment for at least two weeks. At the end of this period, collect the data from these calls and format it in the most visual way possible.

The answer to the initial question should be obvious. Do the results prove that the period 14:00-15:00 is really the most appropriate?

This small technique allows you to confirm or deny good practice across the team.

And finally, here is an example of conversational analysis from Real Life at Modjo.

The manager of the sales team of the neobank Qonto wanted to analyse sales by comparing two types of calls:

• A call in which all available offers are mentioned 
• A call that presents only the most complete offer.
  

Following the implementation of an experimental protocol, the result was that salespeople who spoke about a single offer were 20% more successful than those who did not.

This concrete result has been implemented at team level, with positive results for all salespeople.

And that, my friends, is the power of data. ⚡️

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Best,