Your data is worth its weight in gold.

I'm not telling you anything new here! But I like to remind you, because data is gold.

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 what I've observed, sales people are neither sensitized to this issue, nor equipped to produce data and sales analysis. Indirectly damaging your customer relations.

In this article, I'll show you how data integrated into your sales policy can considerably boost the results of your sales teams, sales forces...

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

The aim: to make your salespeople more efficient, sign up a plethora of new customers (because yes, we like to grow our customer portfolio!) and boost your sales performance indicator.

What else ?

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Sales analysis, a goldmine for sales people

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

My answer: in business development, almost everything.

Sales analysis enables you to :

Monitor individual and collective sales performance. Know how prospects behave when prospecting. Identify the right sales actions to take from the top to the bottom of the funnel to get the negotiation going the way you want.

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

In a "pre-data" world, the sales manager knows the global 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 achieved. If he records the calls, he can listen to them again and deduce some of the salesperson's good (or bad) habits. But he can't objectively formalize the most effective practices.

In a data-driven world, the organized examination of call content enables precise sales analysis and pinpoints exactly which strategies are effective or less effective for each salesperson. This facilitates sales management by enabling sales managers to make informed decisions and adopt personalized approaches for each member of their team.

Again: what else?

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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?

Allow me to offer a few reasons drawn from my personal experience in the sales function.

Firstly: the use of data has not yet entered salespeople's radar. For them, performance comes first and foremost from a good feeling and qualitative exchanges with prospects. In other words, factors that they don't feel they can measure objectively, which underlines the need to support sales staff through sales coaching sessions, for example.

👉 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 counts is the result". The proof: many salespeople are still reluctant to make full use of their CRM. ‍

👉 data is seen as restrictive.

Thirdly: sales managers are generally sensitive to the use of data, but they lack the tools to take this approach to its logical conclusion. 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, for lack of dashboards enabling more in-depth analysis.

👉 the data is unusable insales analysis.‍

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

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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).

"Collect sales data = enablesales analysis = identify levers / 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: it's easy to use, thesales analysis is presented in a clear, visual way, and the conclusions are easy to draw for better steering and boosting your sales activity.

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 with a call that didn't go through, for example.
• Use them for one-to-one coaching: analyze a salesperson's qualities and shortcomings over one or more exchanges.
• Work on a particular phase of the sales exchange: the right response to a prospect's objection, for example.

From there, all you need to do is fine-tune your sales techniques to boost your sales performance.

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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 show them, with evidence to back it up, that data-drivensales analysis can help motivate them, improve their sales performance and, above all, help them achieve their sales targets. All without disrupting their working methods.

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 to reach and retain his targets is between 2pm and 3pm. As Baptiste enjoys a certain aura within the team, he has succeeded in converting (long live loyalty!) his colleagues to this credo. Bravo Baptiste, loyalty champion. So, your sales reps all make their cold calls religiously after lunch.

The question is: is this really good practice?

To find out, let's use a little experiment to analyze sales: divide your salespeople into several small teams of three, for example. And follow this experimental action plan.

1. The former can continue to make cold prospecting 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's an example of a sales analysis, or conversational analysis as we call it at Modjo, taken from our daily life!

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 ofsales analysis data! ⚡️

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Best,

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