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Data Science

Data Science, Startups

Engagement: The Key to a Data Driven Culture ft. @UseNotion

Image created by Yasmine Sedky (@yazsedky).

A “Data-driven culture” is what so many businesses aspire to be. They think that if they have the data, organize the data, and use the data to make decisions, that they will automatically infuse it into the very fibers of their corporate culture.

If only it were that easy.

Yes, obtaining data and organizing it may be the first necessary steps towards creating a “data driven culture,” but unless you can get your team excited about using that data – it’s close to worthless.

Having a data-driven culture means that everyone – not just analysts, product managers and marketers – understands the data you’re collecting, can find the data you’ve collected, and knows how to use that data to improve and optimize their work. And, of course, it’s not about your team just knowing how to use the data, but feeling empowered, encouraged, and even excited to make data an integral part of their work.

For that to happen, employees need reasons why adding data to their lives will make their work easier and get them better results.

But, engendering enthusiasm can be an uphill battle, especially if your mountains of numbers tend to scare people away.

Read More on Notion


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Data Science, Visual Communication

Spreadsheets are not enough. You need Data Visualization. ft. @UseNotion

spreadsheets-are-not-enough-You-need-Data-Visualization.

Image created by Yasmine Sedky (@yazsedky).

Whether you’re using Google sheets or Excel, spreadsheets are an incredible tool – versatile, robust and necessary for every business and every analyst. But today’s business landscape has shifted dramatically from when Excel was first designed. Even though there are added features, computations and capabilities that help close the gap, the tool still falls short of the needs of most business users.

Plus, it’s boring.

It’s Not Easy to Love a Spreadsheet

You know why spreadsheets don’t exactly excel in the user-friendly department as well as we do. But this has to be the worst: The Terror of the Blank Page (not just for writers anymore!).

With no template, no starting model, not even a hint of what you should (or could) do, the basic blank grid of a spreadsheet is intimidating. And even when you make it over that hurdle, you’re faced with an insurmountable obstacle: it’s nearly impossible to make an Excel spreadsheet look good. Sure, there are table, chart and dashboard functions, if you’ve been trained how to use them, but even then every spreadsheet looks the same.

It doesn’t reflect the nuances of your business.

But it’s not just the looks of the thing that make the program dated.

Functionalities we’ve come to expect in other programs, like collaboration and commenting features, just aren’t there.

You have to explain every little thing. Intuitive it is not.

And, we’ve all been at that meeting where the presenter is referencing numbers from one Excel doc (or a previous version of an Excel doc), while everyone else is looking at numbers from a completely different document. Confusing much?

Hey, Excel isn’t the enemy. And neither is Google sheets. But they’re not exactly our friends either. Collecting, storing and displaying data are vital functions…but there has to be a better way.

Read More on Notion


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Content Marketing, Data Science

Here’s How to Combine Storytelling and Data to Produce Persuasive Content ft. @VisageCo

1

Can you recall Don Draper using statistics in a quote? Neither can I.

Draper’s pitches were successful because they focused on stories. (Remember the famous Kodak Carousel pitch?) He was onto something: Research highlights stories as key to capturing an audience’s attention.

Jennifer Aaker, a social psychologist and professor at Stanford’s Graduate School of Business, cites a study in which students were asked to present a one-minute persuasive pitch to their class members. Each pitch included an average of 2.5 statistics. Only one of those pitches included a story. Ten minutes later, the researcher asked the students to pull out a sheet of paper and write down every idea they remembered. Only 5% of the students remembered a statistic; 63% of the students remembered the story.

For most people, numbers aren’t memorable. Stories are.

stories

Numerous studies have shown that stories aren’t only more effective in making a message memorable, they’re also more emotionally persuasive. Pair this with research that shows we make decisions primarily with emotion (using logic to justify them later), and you have the power of story in a nutshell.

Read More on Moz


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Content Marketing, Data Science, Metrics, Tools

Data Storytelling 101: Helpful Tools for Gathering Ideas, Designing Content & More by @NikkiElizDeMere

data-storytelling

It’s an exciting time to be a content marketer. But it’s also a challenging time.

As more companies continue to jump on the inbound marketing bandwagon, the influx of content seems to be turning into a bit of a traffic jam. And few things have the power to cut through this noise like data storytelling.

Combining the visual appeal of images with the trust engendered by raw data, data storytelling is a force to be reckoned with. Marketers are using data storytelling to support every part of the buyer’s journey, from attraction and consideration to conversion and delight. What better content to offer a consideration-stage buyer than a comparison chart between your services and your competition’s?

Not a data analyst? No worries. Check out the list of tools below. From data collection to design, this roundup of resources is designed to make it easy for anyone to get started with data storytelling.

Read More on HubSpot


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Customer Success, Data Science

Customer Success by the Numbers: Using Data Science to Predict the Future by @NikkiElizDeMere

Customer-Success-by-the-Numbers--Using-Data-Science-to-Predict-the-Future

Image created by Yasmine Sedky (@yazsedky).

A little precognition is a handy skill to have – too bad it’s not readily available. What is available? Data. Lots and lots of data.

Your company sits on a goldmine of data – which you can use to predict user behavior. How? By finding meaningful patterns that allow you to see which customers are most likely to churn. 

Let us gaze into our crystal data-sets…

Maybe you only log 5 types of user events: views, clicks, purchases, tutorial views and logins. Based on a user’s activity in the first week (or month) after downloading the app, can we predict whether that user will still be active in 6 weeks? Considering some statistics show that 80% of users only use a new app once – once! – we can easily predict a high attrition rate, but that’s just a guess. However, by looking at the views, clicks, purchases, tutorial views and login data, we can develop an early warning system for attrition.

Take your event log data for the first month, or first week, whatever the most useful timeframe might be, then look at which users were still active during week 6.

Let’s say the crew of Serenity is interested in your new navigation app. Their user events might look like this during the first week.

User Date Week Event
Captain Mal Oct 1 Week 1 Viewed a deal
H. Washburne Oct 2 Week 1 Viewed a deal
Simon Tam Oct 3 Week 1 Viewed a deal
Kaylee Frye Oct 1 Week 1 Viewed a deal
Kaylee Frye Oct 2 Week 1 Clicked a deal
H. Washburn Oct 2 Week 1 Viewed a deal
H. Washburn Oct 3 Week 1 Clicked a deal
H. Washburn Oct 3 Week 1 Bought a deal
Kaylee Frye Oct 3 Week 1 Bought a deal
H. Washburn Oct 4 Week 1 Viewed tutorial
Kaylee Frye Oct 4 Week 1 Viewed tutorial

When you review the data from 6 weeks later, you see this:

User Date Week Event
H. Washburn Nov 6 Week 6 Logged in
H. Washburn Nov 7 Week 6 Logged in
Kaylee Frye Nov 8 Week 6 Logged in

Now you can go back to that first week and look for patterns between who did, and did not, log in during week 6. (Note: logins alone don’t matter.) Mal and Tam were clearly just looking, but Kaylee and Wash both viewed deals, clicked, bought and viewed the tutorial. Of those four, viewing the tutorial might be the best indicator of engagement. And, if I were to take this chart further, I’d look for other engagement indicators like re-viewing the tutorial or clickthroughs of all the app pages and options.

You don’t need a lot of data to find informative patterns. Even if you only track views, clicks and purchases, you can see whether users who click more than X times in Week 1 are more likely to be active in Week 6.

No, you don’t need a Data-wizard, there’s an app for that

You don’t have to hire a data-wiz or find these patterns on your own (which is great, because you probably have a lot more data than just four characters from Firefly). Tools like BigML can look at your data and find useful patterns to predict churn.

With the help of a churn-prediction tool, you can see which segments of users are more likely to churn in a given period of time, and which segments are more likely to keep coming back. Most importantly, you can find the specific behaviors that indicate someone may leave soon – and that’s your chance to swoop in with your Customer Success team to find out why your product isn’t helping that user meet his or her desired outcome. If your product is a good fit for that user, you stand a solid chance of not only re-engaging the user, but really impressing them!

But, you might find that the user segment most likely to leave is leaving for good reason – they thought your product solved a problem it doesn’t. This is a cue that your marketing messages may be off, and by changing them, you can bring in more of your ideal customers who are more likely to remain active and engaged for weeks, months, and years.


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Data Science

Digging into Data With #rStats [Includes Video]

digging-into-r

Image created by Yasmine Sedky (‏@yazsedky).

R is an open-source language and environment for statistical computing and graphics which has been described as: “The single most important tool for computational statistics.”

In other words, when you want to mine data, R helps you do it and allows you to present your findings in an attractive visual.

What’s so great about R?

  • It’s very well supported with a huge community, which means it’s constantly growing and evolving.
  • 6,000 libraries of pre-developed packages for a wide variety of applications.
  • The data frame is analogous to an Excel Spreadsheet or MySQL table.
  • No software licensing costs.
  • Reproducible analysis!

However, until recently, R didn’t have an interface to the Google Analytics API. Google being Google, they promptly fixed that problem by introducing the RGoogleAnalytics library. Now you can use R to analyze your Google Analytics data to do things like:

  • Custom reports with up to 7 dimensions and 10 metrics.
  • Batch data extraction.
  • Create larger queries with tens of thousands of records.
  • Predict product revenue.
  • Calculate the long-term value of marketing campaigns.

Let’s look at that last item: Calculating the long-term value of your marketing campaigns.

As you know, with Google Analytics it’s relatively easy to look at a given advertising campaign and see how many sessions and transactions it generated. GA shows you your sessions, revenue and transactions for each campaign, but doesn’t account for all of the people who may have discovered your business for the very first time as a result of one of those campaigns.

Those first-timers can result in a lot of revenue over the next several months. Wouldn’t it be nice to track the lifetime value of that marketing campaign? Yes!

The “R”ough Guide for Tracking Long-Term Value of Marketing Campaigns

Complete the one-time setup by creating a project on the Google Dev console, activating the Google Analytics API for your project, and copying your project’s client ID and client secret to your R script.

With R in your toolkit, you can now create a graph to visualize the cumulative transactions over time, adding incremental or daily transactions from users of a particular campaign to see the full value of that campaign.

Your query for new customers acquired from a specific campaign might look like this:

Query.list <- Init (start.date = “2015-01-01”,
End.date = “2015-02-01”,
Dimensions = “ga:date”,
Metrics = “ga:transactions,ga:transactionRevenue”,
Segment = “users: :sequence: :
^ga:userType==New Visitor;
dateOfSession<>2015-01-01_2015-01-07;
ga:campaign==Campaign A;
->>perSession::ga:transactions>0″,
sort = “ga:date”,
table.id = tableId)

Here, the segment we’re targeting is new visitors from a specific campaign. This should allow us to include all sessions for users who match these conditions. The “sequence” narrows the list down even further to those who visited during a specified time frame and made a purchase at some point.

From there, you can compare multiple campaigns to find out which result in more transactions over time. You may find that one campaign worked better for driving short-term revenue, and another campaign – which may have had a slower start – actually earns you more revenue over the course of a few months. Very valuable information!

Perhaps most importantly, you can then use R to make an attractive graph of this information to present to your non-techy clients. They’ll be very impressed.

Google Analytics Data Mining with R (includes 3 Real Applications):

Additional Resources


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