What can Data Scientists learn from Sales Experts? - Part 2 - My Datafication

08 July, 2017

What can Data Scientists learn from Sales Experts? - Part 2


The previous post showed how data scientists can use sales best practices in order to improve how they communicate their analysis and work in general. If you haven't still read this post, please check it here and then come back for the next lesson. But anyway, let's recap the first 5 lessons quickly:

  1. Find what intrigues them
  2. Offer many "hot to buy" options
  3. Use stories they can relate to
  4. Tailor your "sales" presentation
  5. Master the art of storytelling

Lesson #6 - Prove your customers that your analysis actually works

It might seem obvious to us, who have spent hours and hours on an analysis, but your audience might hear things for the first time. They are not sure that you solve their problem, and even if you do, they are not sure if you solve it the right way. In order to prove them the actual value, try to use previous cases where your analysis could be applicable and then relate it to the future. The best way to prove value is to quantify it, because numbers are a worldwide language. For example, instead of saying that your algorithm can predict customer churn, you can claim that using this algorithm, the company can reduce customer churn by X% and save Y euros in the next fiscal year.

Lesson #7 - Use a simple "buying" procedure

This lesson indicates that you should provide your solution in an easy-to-access way. For example, create a dashboard that is easy-to-access and easy-to-understand, so that they can view it frequently, e.g. every morning on a daily basis. Making it easier for them, increases the chances of acceptance. On the other hand, if they cannot access the analysis or if they need to ask you for the update of the numbers, it might seem not so interesting or even time consuming. Specifically, when it comes to usability providing mobile responsive dashboards is always more fancy because many people, and especially managers or directors might prefer to access the data (& analysis results) on their smartphone or tablet.

Lesson #8 -  Practice active listening 

While talking and presenting is the best way to sell your product, sometimes it is just better to step back and listen to what the customer has to say. Feedback (positive and especially negative) is always constructive, and should be taken strongly under consideration. When you listen to your customer you can get a good understanding of their needs and expectations. Even before the analysis, a data analyst should take some time and listen carefully to the requirements and the business need. Just hearing is not enough, you should focus on listening!

Lesson #9 - Develop compelling goals and an actionable plan

So, you have the idea and you know how to achieve it. But success comes when you also create an action plan to show the path towards your target. Goal-setting and planning are critical to any project, e.g. data analysis project. Settings goals assists the development of expectations from the customer-perspective, since they know what they will receive, as well as the accompanying timeline.

Lesson #10 - Maintain a positive mindset

Of course this is not something new, everyone is being told to stay positive and think positive. However, this is a life lesson to remain positive in good and tough times. Developing a positive attitude has many benefits. A positive outlook helps problem solving and enables creativity. When you remain positive, you are more likely to handle stressful situation successfully and be perceived as the reliable person.

So, that's all folks. I hope these 10 lessons seem useful to you, as they have to me! Please let me know in the comments below if you can think of any other best practice that helps our professional lives. And stay tuned for more interesting posts! :)

If you’ve found this post informative or interesting, let me know in the comments or like "My Datafication" on Facebook to stay up-to-date with the latest posts!









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