This episode is sponsored by Knit. Click to get your free consultation today.

Part 1

Part 3

Part 2

Part 4

Part 5

Click to Subscribe: 

About Our guest

Jenn Sturgill - Founder and Owner of Analytics Angels

Jenn is a marketing professional with 10 years of experience working primarily in the high-tech industry. Her unique background includes work in analytics, program management, and marketing operations.

Jenn works with data and marketing technologies to help organizations glean actionable insights and drive critical business decisions.

Jenn Sturgill.jpeg

Show Notes

Part 1

Jenn’s background and the Analytics Angels - (3:00) 

I have been in the marketing technology space since 2010, and thanks to my engineering and statistics background, I was always interested where all of this data was going. The Analytics Angels was founded with the idea to help organizations and businesses use data and marketing technology in a more useful way. 

The Right Way to Capture Data - (4:52) 

You want to think about what kind of business questions you are trying to answer and what kind of information do you need in order to be able to answer those questions. 

A clear objective is necessary, and that is the first step. The next step is knowing which different tools you already use in your business. 

Setting a Broad Enough Net With Data Capture - (6:45) 

You need to put yourself in a measurement framework. Meetings with marketing and IT departments are very important because they help you better understand what you already have and what you need in order to get to that solution.  Discover what KPIs and metrics you need along the way. 

Partnering with the Engineering Team and Understanding Their Decisions - (8:28) 

The most important thing is bridging the gap between different teams, as they rarely talk to each other. Their KPIs and reporting are all different. Building strong relationships with the leaders from each team is extremely important. Also, having a measurement strategy that everyone can refer to can make such a huge difference. 

Part 2

Good Ways to Capture and Store Data - (3:27) 

First, we usually do a tech audit of what business already have and based on that we give recommendations on how to optimize the tools that they already use for data collection. 

Replace PowerPoint and Excel with some kind of data warehouse. Create a database where you have all the different information from different teams in your company and merge these datasets together.

The Importance of Having a Centralized Hub for Data - (7:00) 

Not having a centralized hub for data makes it harder to do A/B testing, email marketing, and all other optimization tests. 

You can start with a lighter version of Red Shift or SQL server and see how this works for your business. 

Having different reports from different teams, makes it more harder to find the right solution and this doesn’t help your business excel. 

The Rules of Thumb for Collecting Your Data - (8:26) 

You can build your own database, but most of the major players already have APIs where you can feed data directly to your server and from there on you can transform it for various needs. 

Part 3

Data Cleanliness - (3:40) 

If you don’t have clean data, it is extremely difficult to set any actual targets and make predictions. Once you’ve merged all the data, you need to go through it with your IT and analytics department and see if it all makes sense. This can be done by charting out some visualizations for example. 

Sometimes, analysts can spend about 90 percent of their time just trying to clean the data. For example, if you are looking at financial data, you must pay special attention to the currency and make sure that everything is alright. 

Analyzing Data Efficiently - (7:05) 

Spend some time on the front-end and create some custom reports. They can be sent to your email inbox directly. Smaller organizations can set up different tasks to make sure that everything is being covered and to avoid having to do this manually. 

The Most Common Areas With The Lack of Data - (8:05) 

Most of the problems arise when something is not set up properly. We’ve come across teams who were trying to use this merged data but this had effects on the future data coming in. It's good to set up some checklists in place and make sure that everything is working properly, at least once a month. 

Part 4

The Best Tools for Data Processing and Visualization - (3:03) 

Some really great ones are Google Data Studio, Microsoft Power BI, and Tableau. Each one is specific in its own way. Tableau has a fantastic storytelling feature and it also takes some cleaning that otherwise you'd have to do manually. On the other hand, Microsoft Power BI offers fantastic visualizations. 

Differences Between These Tools - (6:15) 

Google Data Studio doesn’t have as many integrations as other two tools, but it’s easy to use. The other two tools allow you to transform the data more easily.  You can create custom dimensions, do data cleaning etc.

Data Studio is a free tool, and Microsoft Power BI comes with your Microsoft Office. Tableau has a premium monthly versions unless you want your data to be public. 

Best Tools for Heavy Data Processing - (8:27)

We’ve used Azure for machine learning in the past. 

Best Recommendations for the Actual Visualization Part - (9:34) 

Keep it simple and straightforward. Waterfall charts and cohort reporting are the best because they allow you to see the actual impact over time. Avoid pie charts as they are confusing. 

Part 5

Outsourcing Analytics and Data - (3:30) 

Some companies have fantastic marketing teams but unfortunately, they lack an analytics expert. For example, we work with a lot of startups who understand the importance of data but they don’t have the time or expertise to delve deeper into this.

We also coach our clients along the way so they’ll get a better understanding of what they need to do in order to move forward. 

The Cost of Building a Good Quality Analytics - (5:13) 

This depends on a lot of different things - what the business needs, their budget, their business model, the number of their clients and more. Projects can range from $5,000 for Google Analytics implementation to $500,000 for database creation. 

Good Resources for Learning About the Analytics - (7:47) 

I’m a big fan of knowledge sharing, but if you’d like to learn more about this topic you can go to an online school or check out webcasts. Also, take a look at the tools such as Tableau and Google Analytics.

Coursera and Lynda are other good resources.