How sales analysis helps sales reps close sales

Your data is worth its weight in gold.

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

Thanks to them, your company can learn more 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 can easily draw 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 sales data and analysis. Indirectly damaging your customer relationships.

In this article, I'm going to show you how data integrated into your sales policy can considerably improve the results of your sales teams, your sales forces...

First and foremost, I'm going to give you a simple approach you can implement to instill a good data culture in your team and make your sales analytics process a pillar of your sales performance.

The objective: to make your sales force more effective, recruit a plethora of new customers (because yes, we like to expand our customer portfolio!) and boost your sales performance indicator.

What else?

‍

Sales analysis, a goldmine for sales reps

The €1,000 question: what use is data to a sales team?

My answer: when it comes to business development, almost everything.

Sales analysis lets you :

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 so that the negotiation goes the way you want it to.

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

In a world "before data", the sales manager knows the global track record of every member of his team. 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 certain good (or bad) sales habits. But he can't objectively formalize the most effective practices.

In a data-driven world, the organized review of call content enables precise sales analysis and identifies exactly which strategies are most or least 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.

Once again: what else?

‍

The data reflex has not yet been built up within sales teams

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

Why the delay?

Let me give you a few reasons drawn from my personal experience in sales.

First of all, the use of data is not yet on salespeople's radar. For them, performance depends above all on a good feeling and qualitative exchanges with prospects. In other words, factors they don't feel they can measure objectively, which underlines the need to support sales staff through sales coaching sessions, for example.

👉 the data is considered irrelevant.

Secondly : salespeople hate to feel they're being watched. Asking them to produce data goes against the deep-rooted culture that "results are what counts". The proof: many salespeople are still reluctant to make full use of their CRM. ‍

👉 the data is considered restrictive.

Third: sales managers are generally sensitive to the use of data, but lack the tools to take this approach to its logical conclusion. For example, they may know the average call duration of their top salesperson, but have no way of knowing whether that duration is actually one of the reasons for that salesperson's success, for want of dashboards that allow deeper analysis.

👉 data is unusable inanalysis ofsales. ‍

It's 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're convinced of the importance of a data culture within sales teams.

Our equation is simple (and almost impossible to stop, I think).

"Collect sales data = activatesales analysis = identify levers/channels for improvement".

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

By aggregating the results of a critical mass of conversations, this conversational intelligence enables objective conclusions to be drawn 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, sales analysis is presented in a clear and visual way, and conclusions are easy to draw to better manage and boost your sales activity.

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

• Relive conversations as a team: compare the best sale of the week with a call that didn't go through, for example.
• Use them for personalized coaching: analyze a salesperson's qualities and shortcomings over the course of 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 improve your sales performance.

‍

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

That's all well and good, but you have a feeling that your sales staff won't want 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 is based ondata-sales analysis can 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 2 and 3 pm. As Baptiste enjoys a certain aura within the team, he has managed to convert (long live loyalty!) his colleagues to this credo. Bravo Baptiste, champion of loyalty. 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 do a little experiment in sales analysis: divide your sales force into several small teams of three, for example. And follow this experimental action plan.

1. The former can continue to make cold calls at the usual time.
2. The second is invited to pass them in the morning, between 10 and 11 a.m. for example.
3. The third is responsible for passing them on later, between 4 and 5 p.m. for example.
   

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

Run the experiment for at least two weeks. At the end of this period, collect the data from these calls and format them as visually as possible.

The answer to the initial question should be obvious. Do the results prove that the 2:00 pm to 3:00 pm period is really the most appropriate?

This little technique helps to confirm or invalidate good practice within the team.

And finally, here's an example of sales analysis, or conversational analysis as we call it at Modjo, taken from our daily lives!

The head of the sales team at neobank Qonto wanted to analyze sales by comparing two types of call:

• 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 talked 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 sales staff.

And that, my friends, is the analytical powerof datasales! ⚡️

‍

Better,

‍