You Ask, I Answer: Marketing Data Science for Traditional Sales?

Jackie asks, “How does all this fancy data science kung fu hubbub help us, we’re a traditional sales company that only makes phone sales cold calls?”

Data science is a methodology that can be applied to any data you happen to have. In the case of cold calling, as long as you have data about past successful and unsuccessful calls, you can start to build a hypothesis and test it. Watch the video for full details.

Can’t see anything? Watch it on YouTube here.

Listen to the audio here:

Download the MP3 audio here.

Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.

In today’s episode Jackie asks, how does all this fancy data science kung fu hubbub help us? We’re a traditional sales company that only makes phone sales cold calls.

Well, data science is a methodology.

It is a methodology that can be applied to any data you have remember that data science is the extraction of meaningful insights from data using the scientific method.

So, the question is, what is well, there’s a whole bunch of questions but first is what do you want to improve if you are selling via cold call? That is a very labor intensive practice.

You have to spend time, even if you’ve got the dialing part automated, as a lot of time and effort picking up the phone or talking to people, right? People take time.

And so you would like to be able to do more calls to be more productive, you would like to be able to have better outcomes right to be able to sell more stuff to get fewer people who say mean words to you and hang up hang really and more people who would be willing to give you you know, five or 10 or 30 minutes or however longer cold call process is to hear the pitch and possibly say yes.

So you have questions you need answered, right you How do I make myself more efficient? How do I make myself more effective? data science is one of those tools you can use a methodology you can use to answer those questions.

In the case of cold calling, I would hope that you have data from the past about what constitutes successful and unsuccessful cold calls.

So you should Do you have a list of numbers you’ve called who those people were, if you’d be to see the characteristics of those people where they came from, we’ve got that number from typical demographic information, such as like a household income, gender, etc.

for businesses, you’d be firma graphic information that you would have.

And you would want to use data science techniques, particularly statistical techniques to understand what do the people who are successful cold calls in the past? What do they have in common? Right? Remember that you’re going to have a lot of engineer data as well.

So you should have the date and the time that you made a call.

And so from that you would extract the year the month the day of the week, the day of the month, the day of the quarter, the hour of the day, the minute of the day.

How long each call was, how long did it take for someone to pick up? How many rings did it take? As you can see, just from a simple action, like picking up the phone and calling someone, there’s a lot of extra data you can engineer out of it.

And all of those statistics are things that you would then feed to data science software to figure out what features or, you know, data points, either alone or in combination with each other, lead to a successful outcome.

This is a technique, formal technique name with a specific technique name is called binary classification.

You have an outcome, successful call on successful call.

And then you have all the information that went into it.

And you would run a binary classification algorithm on the data and figure out are there things that indicate The likelihood of a successful call and maybe it is hour of the day maybe it is household income, or a number of family members.

on the business side, it might be annual revenue, yeah, could be domain name.

Whatever the thing is, you then find your classifiers.

Your have been called feature importance or how how important a particular data point is.

And then you begin testing.

You begin the scientific method if calling at lunchtime on Tuesdays is the thing that is most most likely recorded the album related to a successful outcome.

Then eat lunch early on Tuesdays doubled the number of calls that you do during Tuesday at lunchtime.

And you test you see how many successful calls or ideally you do it like a control tower.

where maybe you and a co worker, do calling on Tuesdays or around lunchtime.

And you make double the number of calls, your coworker makes the same number of calls.

And if that they normally do and an ideal you’re doing double the number of calls they are during that Tuesday lunch, and maybe they take half an hour off for lunch.

And then you compare your results after a few Tuesdays and say, Okay, now here’s our hypothesis, correct? Does calling on Tuesdays at lunchtime yield more successful results than not? And by having that control of doing things normally on a Tuesday, versus doing doubling the volume on a Tuesday at lunchtime? Do you see greater success rates? Do you see a statistically significant difference in success rate? That’s how you approach all these problems.

Just because you’re doing what we’ve considered traditional or old school sales doesn’t mean that you don’t have data, or that you can’t use data science techniques to improve those process.

is the same for, you know, postal direct mail postal direct mail can be very, very effective in specific certain circumstances in specific situations.

If you know what data you have, you can be very efficient in your mail, instead of spending a million bucks to drop, you know, postcards to every single person on the planet, you have maybe 500 households that you know, or that you’re going to test for likelihood, a much greater improved responses.

That’s how you use the stuff.

That’s how you apply the scientific method to your data, develop testing criteria, and then see if you get causation.

See if you get better results.

That’s data science.

That’s how you apply it to any of these situations.

So, step out on the mindset, you’re, you’re doing something traditional, and that was all this fancy new technology and step into the mindset of the scientific method.

What can can we prove or disprove about an important question to our business using our data? That’s where the change for you will come and make your make your business work no matter what it is make it better.

Thanks for the question.

As always, please leave a follow up comments in the comments box below.

Subscribe to the YouTube channel and the newsletter.

We’ll talk to you soon take care want help solving your company’s data analytics and digital marketing problems? This is Trust insights.ai today and let us know how we can help you


You might also enjoy:


Want to read more like this from ? Get updates here:

subscribe to my newsletter here


AI for Marketers Book
Get your copy of AI For Marketers (2019 Edition)

Analytics for Marketers Discussion Group
Join my Analytics for Marketers Slack Group!


Pin It on Pinterest

Shares
Share This