Marketing and sales teams are the left and right hands of bringing new customers to your organization. We know they work hand-in-hand, but do you treat your organization’s data that way? Combining data is a challenge – we see it all the time.
The truth is, many of our partners have faced this problem (maybe you’re in a similar boat). You know that multiple channels, such as Facebook and Google Ads, influence your marketing qualified leads (MQL) and sales qualified leads (SQL). You also know that sales activities like phone calls, emails and demos influence your SQL success rate and your close rate.
But can anyone at your organization say confidently, with data, how all these factors work together? Does Facebook have a better SQL rate but lower MQL rate than Google? What about Facebook versus email? Helping you answer questions such as these is why we built our Multi-Touch Attribution Model for marketing and sales touches.
The Multi-Touch Attribution Model takes into account both touches from Google Analytics (GA) and your customer relationship management system (CRM)*. Our model combines data from GA and your CRM to break out conversions equally between multiple touchpoints.
Now, not only can you see individual customer paths, but you can also see how each of your channels influence and interact with one another.
The best part is that we leverage technology that is easy to use. Hedy & Hopp takes care of the heavy lifting in terms of data extraction, storage and analysis. You will always have access to your data through Google BigQuery, and your data will automatically populate in a Google Data Studio report that you can customize and share.
*Note: So far, we have integrated with Hubspot, ActiveCampaign, and SugarCRM. More CRMs to come!
If you use Google Analytics for reporting, you likely use the default attribution model called “last click.” More importantly, GA can’t account for any offline data in your CRM. Using the basic attribution models in most CRMs, you end up with an incomplete view of your customer’s journey.
We work around this by building our model from scratch using the Google Analytics API data. We look at all of the digital marketing touches for a user, even ones before they convert. So, if a user comes to your site from Facebook, and then converts a week later on Google Ads, we will include both sources in our model.
But the biggest advantage of our Multi-Touch model is that we integrate your Google Analytics property and your CRM to track the same user from Google into your CRM. We then look at all of the different touches in your CRM system and add that to the attribution model.
The process can be set up in two weeks, and as your prospects convert to customers, the data begins to populate! Depending on your prospect-to-customer lifecycle, you can start getting actionable data in just a few weeks.
Imagine knowing that Facebook converts better with an introduction email, while Google Ads converts better with an early demo!
By now, most people have heard of GDPR, but many are still confused about who it applies to and its implications for businesses. For those of you who haven’t heard of GDPR, it stands for General Data Protection Regulation. GDPR is a set of rules passed by the EU to give users more control over personal data online and protect against the misuse and mishandling of data. However, even if you aren’t from the EU, keep reading — GDPR may still apply to you. AND it is now looked up to as the gold standard for data privacy laws, so some of the regulations in GDPR will likely be popping up elsewhere. In fact, the California Consumer Privacy Act (CCPA) is a fantastic example of how this has already happened.
In addition to all EU companies, GDPR applies to any company worldwide that interacts with companies or individuals in the EU. This means if you market to EU businesses or individuals, GDPR applies to you. If you sell products in the EU, GDPR applies to you. If you have a form on your website that collects data about individuals or companies in the EU, GDPR applies to you. GDPR is more encompassing than many people realize. If, based on the above criteria, you believe you’re subject to GDPR, then start making compliance a priority. Large companies such as Equifax, Facebook, and many others have already been fined hefty amounts for their misuse or mishandling of personal data.
GDPR applies to all personal data. This includes information that can be tied back to you directly, such as a social security number, full name, home address, etc. It also includes information that can only be tied back to you when it is paired with other information about you, such as an IP address, an employer, a job title, race, gender, etc. Note: the examples above are only meant to be a guide for what could be considered personal data; they do not serve as a complete list.
There is much more to GDPR compliance than what we can cover in this short post, but here are some of the most common concerns we deal with in our office day to day:
Organizations have data flowing in from all angles. Mapping out exactly how data was collected, what data was collected, why the data is needed, how it’s stored, and how it will be disposed of — this is all crucial in determining if you are GDPR compliant. Through this process, you will find data that you are not using that can be properly disposed of. You will find data you didn’t even know you were collecting. Plus, doing these things will make it easier to identify potential processes that put you at risk of violating GDPR. Implementing an action plan to help resolve these vulnerabilities and documenting them will help you maintain GDPR compliance.
One of the most important things you can do is to update the privacy policy and terms and conditions that are listed on your site. These should describe exactly how data is being collected, used, and stored. They should also mention the steps that you are taking to be compliant with GDPR.
The #1 concern clients raise about GDPR is in regard to adding a cookie consent banner. If your site uses cookies, you are required to let users know what information is being collected and how it will be used.
What About Google Analytics?
If you are tracking user behavior on your site with the most basic Google Analytics setup, then you do not need a cookie consent banner. However, if you enable features like demographic reports, use the remarketing features, add custom dimensions for user IDs, or utilize other more advanced functionalities within GA (which we definitely recommend you do), then you will need to add a cookie consent banner to your site if you operate in the EU or directly market to EU residents. If your company is not located within the EU and you don’t directly target EU residents, we recommend that you consult your legal team for guidance around implementing a cookie consent banner, as the regulations are still unclear for most American companies. The cookie consent banner that is added to the site needs to provide users with an easy way to opt out of any tracking.
No personally identifiable information should ever be stored in Google Analytics. Not only will it put you at risk from a GDPR perspective, but it is also against Google Analytics’ privacy policy. Personal data often gets pulled into Google Analytics through URLs or page titles. On some sites, data that is captured in a form will be appended as URL parameters on the thank you page after the form is submitted. This results in URLs that look like the following:
www.example.com/thanks/?first=Bob&last=Smith&email=bsmith@gmail.com
These parameters need to be removed from the URL completely, not just filtered out in GA.
It is not uncommon for websites to have a pre-checked box on their forms next to a statement that reads something along the lines of “I wish to receive future promotions from [company name].” This puts companies at risk for GDPR violations. These boxes need to remain unchecked. Additionally, once a user has signed up to receive emails, they need to be presented with an option to unsubscribe from the email list at any time. The process to unsubscribe needs to be quick and easy for the user to initiate.
GDPR has now been in effect for almost two years, and it is not going to fade away anytime soon. In fact, just the opposite is true. It is becoming the standard for other legislation around the world. Big-name companies have already been fined high dollar amounts due to non-compliance. GDPR is an issue companies need to address head-on to avoid the intense repercussions that come along with it. If you are concerned about how you are collecting, processing, storing, or using data, we highly recommend seeking professional guidance.
This blog is meant to provide a starting point for your journey to become GDPR compliant. It is not a complete guide to ensure GDPR compliance.
If you’re unsure about your level of GDPR compliance, reach out to us to discuss an audit.
As marketers in the healthcare industry, it’s a fine line to walk to try to offer personalized and helpful digital experiences to patients while also ensuring tactics are HIPAA compliant. As an agency that specializes in helping our clients in the healthcare space improve their digital marketing and analytics practices, we have deep experience with sourcing and testing tools. We thought it would be helpful to aggregate a list of our favorite tools that we find ourselves leveraging time and time again.
Note: Currently there is no official training or certification for HIPAA compliance. At Hedy & Hopp, all of our employees go through HIPAA BAA training during their first week of employment (along with Google Analytics certification) to make sure every team member understands the fundamentals when dealing with PII and PHI. Our training platform of choice is www.hipaatraining.com.
Some of our favorite tools:
Website Analytics:
User Experience Optimization:
Business Intelligence and Data Storage/Management tools:
Call Tracking/Attribution:
Patient texting/messaging platforms:
Polling and Research tools:
Manage location listings, online reviews, social media:
Appointment Setting/Management Platform:
Form Submissions:
A final note: Even if you are using the best tools, it’s important to audit access to these tools on a regular basis. Who has login capability and why? Remove access for any non-essential employees and have plans in place to audit access immediately if required. Also, making sure all members of your marketing team have received training to identify PHI and feel comfortable raising potential issues is a really important step. Your legal department is likely focusing most of their attention on the clinical side of the business, so take steps to clean up the marketing and analytics tools before they become an issue.
A well-established professional association had a membership database containing a wealth of information on their 33,000+ members. The association wanted to use this data to help understand why some members were more engaged than others and boost membership attendance at events held by the association. However, with such a large database, the association didn’t know where to start when it came to turning all of that data into insights they could actually use to help accomplish their goals.
Our Analytics + Decision Science team recognized right away that this was the perfect type of problem to solve with machine learning. Broadly speaking, machine learning is the use of statistical algorithms to find patterns within large amounts of data. Machine learning has been the key to recent breakthroughs in artificial intelligence, and there are also lots of common applications that we encounter in our everyday lives, like when Netflix recommends something to watch or Google autocompletes a search query.
In order to solve our client’s problem, we used machine learning to analyze their membership database and organize the members into different groups, or clusters, based on a variety of attributes, including professional experience, membership tenure, past engagement, and others. After the machine learning algorithm broke the database into different clusters, our analysts drilled down on the results. Using tools like principal component analysis, we could see what attributes the computer weighted most heavily when dividing up the clusters.
The next step was to work with our Digital Marketing + Strategy team to identify which clusters had the most potential for growing engagement. In the end, we found five clusters that had clear potential based on their attributes. Then, we took those five clusters and created five distinct member personas, just like the customer personas we’re familiar with as marketers. Now, using these personas, the professional association can create personalized marketing campaigns with specific content tailored to each persona.
On top of this, we even worked on a proof-of-concept machine learning algorithm that would identify which individual members would be most likely to sign up for a new event. This tool could help our client avoid fatiguing members who are not likely to attend an event, as well as determine which members to potentially target using more costly marketing like direct mail.
Our client was thrilled!
With machine learning, we were able to parse and analyze a massive amount of data in ways that would have taken human analysts months or even years to do.
Our client was able to transform a large, unwieldy database into five actionable member personas built from real data.
We helped our clients develop sophisticated email campaigns that included 5 workflows and 20 unique paths, targeting each persona with personalized content to guide them toward registering for an event. This new structure is allowing our client to leverage their large database in a strategic, measurable way with a focus on engagement rather than volume of messages sent—a huge improvement of user experience for the organization’s membership.
If you have a big, difficult dataset that you know holds potential but you’re not sure how to get started working with it, give us a call. We’d love to help your team leverage machine learning to tap into the potential of your data.
Few tools are as ubiquitous in digital marketing as Google Data Studio. It’s simple to use, getting better and better every day, and free to boot.
Yet, as much as we at Hedy & Hopp have grown to appreciate sharing data and insights using Data Studio, we have never felt like any Data Studio dashboards are completely up to our standards. As a tool, it has some serious weaknesses that have limited what we thought we could do. Over the last few months, we have worked hard to overcome these weaknesses, because ultimately Data Studio is our best option for creating digital marketing dashboards.
I can be a perfectionist in my work, and when it comes to visualizing data, I have very high standards. I love the ability to make highly customized reports and dashboards with tools like Tableau, Power BI, or Python. Not only can you make very complex concepts easy to interpret, but these tools also allow you to customize the visual experience.
There were a few issues that made us at Hedy & Hopp wary of using Data Studio for a long time. Not only were we stuck with fewer options for visualizations, but it has limited data processing and a page-by-page format that was not very interactive.
We wanted to move away from Data Studio. But it has one powerful strength. It has a very low barrier to entry. Like crazy low.
All of those other tools require some level of training. I can’t imagine anyone outside of our Decision Science team using Python, and we have worked with very few clients that have even used Power BI or Tableau.
Not only do these tools require you to learn their system, but they also have their own logins. Power BI’s complex user system is amazing for highly sensitive information but is generally overkill for Google Analytics and Google Ads data. Only our most data-savvy clients, with very complex data models, are using Power BI.
Data Studio is the opposite. All you need is a Google account to access and the interface requires very little training. If you can use a banking app on your phone, you would have zero problems using Data Studio in seconds.
Because it is so easy to use, it really expands our audience to practically anyone. This single feature makes Data Studio an awesome tool. If your audience can’t see your insights, it does not matter how powerful your tool is.
So, we decided we would tackle the biggest issues we have working with Google Data Studio.
When you use your bank app or Instagram, you don’t click from page-to-page. It is not a slideshow. You use the navigation to get to where you need.
In Data Studio, the default navigation is not intuitive and feels more like you are clicking through slides or pages in a book. Not only that, but it can be hard to find a specific page without clicking a dropdown.
If you are using Data Studio to build a report, and you are telling a story in a specific order, I think this feature is great.
The second option is to move the navigation to the left. This is actually not so bad, and it feels a lot more like an application. We thought this would be a good alternative, but it also has some weaknesses.
First, it has no hierarchy. So, all pages are treated equally. This is not bad if you have a few pages, but it can get out of hand in a hurry. If you have 10 or more pages, the left navigation gets quite large. Not only that, but it can be hard to identify subpages.
Our solution was to build our own left-side navigation that uses the common conventions of standard navigation setups. Plus it looks nice!
In addition to having top-level navigation, we used tabs to create secondary navigation when needed.
In order to make this navigation, we used a few different design elements including images that look like text, but the most critical part of the process is what we do before we build anything.
The first thing we do is design a sitemap. This allows us to identify all of the key pages we want to include and how they are connected.
From there, we review with all of the primary stakeholders to make sure we have covered the key information needed. This allows us to build our dashboards a lot faster.
We also love the new Community Visualization tool in Data Studio. It allows us to build any type of visualization we want, and it can bind to any user’s data.
We loved this feature so much, we worked with the Data Studio team at Google to have two of our custom visualizations be featured in the community visualizations gallery in Data Studio.
If you would like to use our statistical significance tool “Stats Analyzer” or the “Data Target Card,” you can access them in any report in a few simple steps.
Not only can you find the two tools we built, but there are more than 20 others in the gallery from companies like Supermetrics, Bounteous, or even the great developers at Google Data Studio like Yulan Lin.
One of the biggest differences between Data Studio and other business intelligence tools is that it does not have robust features for handling relational data. A few years ago they created the blended data feature, but it is not nearly at the level of what something like Power BI can do using Microsoft Power Query.
That being said, there are a few strengths of Data Studio when it comes to modeling data. The biggest is with Google Analytics data.
With most tools, you have to pull in Google Analytics from multiple API requests and build that into a single relational data set. With Data Studio, that is all done for you. All you have to do is connect to Google Analytics, and your data is modeled. This is really amazing if you are working with a lot of web analytics data.
The second integration that helps us a lot is Google BigQuery. Data Studio has a very simple way to connect to data in BigQuery, and this has been a powerful tool for us to really make the most of the application.
For instance, BigQuery allows you to leverage SQL and to create views, so we have created some complex models in BigQuery and then pulled in a view where the data has already been distilled down to a single table.
This is really useful when we need to build customized attribution models for our clients. We can pull in different resources, like Google Analytics and CRMs like Salesforce, to create linear and even more complex models customized to the client’s business.
Overall, we love working with clients that are using Data Studio, because it means stakeholders are looking at the data. As a data-driven company, that makes us very happy.
If you’re interested in learning more about how to use an attribution model for effective reporting in Google Data Studio, please get in touch!
As we’re kicking off a new year, it’s a good opportunity to review your organization’s marketing goals and optimization strategies. If you’re still struggling with knowing how to prioritize the transition to becoming a data-driven marketer, here are six basic steps you should aim to accomplish this year:
Here’s to a productive and measurable 2020!
The Hedy & Hopp team was excited to attend HCIC (Healthcare Internet Conference) in Orlando. Jenny Bristow, Hedy & Hopp’s CEO, co-presented in a 3-hour pre-conference training session with Annie Haarmann from Ascension. The duo conducted training on best practices for hospital systems to better leverage data in their marketing decision-making process, while still complying with data regulations such as HIPAA.
The St. Louis Business Journal named us the #1 fastest-growing company in the St. Louis region, based on our revenues from 2016 to 2018. The rankings were announced at the event, and we were thrilled when #2 was unveiled and our name hadn’t yet been called.
Check out the video from the big announcement on Facebook: www.facebook.com/watch/?v=500140067382582
We’re so thankful to our clients for the continued support that helped us achieve this milestone! Read the full write-up, published by the St. Louis Business Journal, for more information.
Google Analytics has a lot of metrics and dimensions tracked right out of the box. However, it is still a blunt tool. In order to sharpen Google Analytics, you will need to add custom data. This will help you match your tracking capabilities to the unique needs of your particular website (or the needs of your clients’ websites).
Google’s Digital Marketing Evangelist, Avinash Kaushik, might have put it best when he said, “All data in aggregate is ‘crap.’ Segment absolutely everything.” And in order to segment as much as possible, you need to use custom data. There is an unlimited amount of custom data you could create, in theory, but in this post, we’re going to be talking about the two most common custom data types you should be leveraging in Google Analytics: goals and events.
The custom data type that just about every Google Analytics user will add first is goals. In Google Analytics, goals are actions that users complete on a website and are then collected as conversion metrics.
Goals are built into a lot of different reports, so you can see how different dimensions affect goal completions and goal rates. You can see how different sources and campaigns affect goals, or you can see how different landing pages affect goals. Goals also have their own report in the Conversions reports. You can even compare different attribution models. Lastly, goals can be shared with Google Ads, so you can use one source to track both Google Ads and Google Analytics activity.
There are two limitations to goals to keep in mind. First, you can only create 20 goals for a view, ever. You can have goals in different views for a single property, but the data cannot be combined in the default Google Analytics. You would need to pull the goal conversions into a third-party tool like Looker, Power BI, or Data Studio to see them all together.
The second limitation is that you cannot delete goals. You can rename a goal, and change the rules, but it will still maintain the historical data from its earlier iterations. This can be a problem if you change a goal but try to compare historical trends for that goal. It is a very bad idea to change a goal that has been collecting data.
Goals should be named in a consistent manner and with the assumption that they will be used in a third-party system. It is a good idea to identify where the goal came from when it is pulled into a third-party tool. That being said, you can pull additional metrics such as region, device type, or other custom dimensions, in order to help segment goals.
Assigning a value to goals is extremely useful when trying to determine your return on ad spend, and also when trying to see how different pages and paths are affecting your overall revenue. For example, if you have a lead submission goal, you should be able to pull a report from your customer database to determine the customer lifetime value (CLV) for customers that came from website lead submissions. Then get the rate of how many lead submissions you receive a year and divide by how many become customers to get your conversion rate. Then take your CLV and multiply by conversion rate to get the average value of your lead submission goal.
Ideally, the value is set in Google Tag Manager, so you can dynamically change it if your CLV or conversion rate changes over time.
Last, it is good practice to categorize macro and micro conversions. Macro conversions are activities that are directly tied to the success of an organization. Lead submissions, e-commerce transactions, and account creations could all be possible macro conversions.
The other key activities on the site that should be tracked are micro conversions. These could include newsletter sign-ups, whitepaper downloads, and job application submissions. They are activities that have some level of correlation with macro conversions.
Macro and micro conversions generally work best when your macro conversions are 1-2 lagging metrics on your site (like first-time purchases) and the micro conversions are leading metrics that can help you improve your macro conversion rate. However, micro conversions may not always be something you can directly influence and therefore may not be the best metrics to measure the day-to-day success of your team.
Macro vs micro lines can become blurry, especially for non-profit organizations, so a well-planned strategy can help a lot in determining what should be considered a macro conversion.
The second-most common custom data type in GA is events. Before Google Tag Manager, events were difficult to set up and were not commonly used. In fact, Google Analytics comes with zero events out of the box.
Events are dimensions where you can assign three values to an activity on the site — an event category, action and label. Events are generally set up in Google Tag Manager and are designed to capture user activities that are not pageviews. This includes playing videos, clicking on interactive elements, and AJAX forms that don’t fire a new pageview.
Events have three attributes: category, action and label. These are normally used as a funnel which gets more specific as you go from category to label.
We recommend that categories and actions are mutually exclusive nominal data and the fewer categories you have the better. In our approach, labels do not need to be mutually exclusive, and they can stack infinitely. Labels should be used as a way to search for data, so it is good to have a system for labels. We often store dynamic information about an action in a label like Click Classes, Click ID, and Container ID, which can then help us segment data like if a user clicked the same link from different spots on the page, or how the change of the click text affected conversions over time.
In short, Google Analytics goals are used to track conversions on your website. If goals are set up correctly, they can help you make important business decisions, like the ones that affect your bottom line. Remember, though, that you can only create 20 goals per view, and once created you cannot delete a goal, and you should not change a goal after it starts collecting data. So be careful in setting up your goals and before you do so, make sure your high-level strategy is solidified.
Events in Google Analytics are used to track a wide variety of non-pageview events on your site. Events have three attributes — category, action and label — and these can be used to organize and segment your data on a more granular level.
We hope this post provided a good overview of the two most common custom data types in Google Analytics: goals and events. Once you have these mastered, you may find that you have more complex data that does not fit into goals or events. In that case, you will likely have to define your own custom metrics and dimensions in Google Analytics. More on that in a later post.
If you feel like there’s still untapped potential in your website’s data get in touch!
One of the most fulfilling parts of my role at Hedy & Hopp is helping senior marketing leaders successfully integrate analytics and business intelligence (BI) into their decision-making process. While a successful integration is more about training and culture than it is about software and platform solutions (more on that another day), one of the first things we discuss is terminology. For marketing leaders with more traditional backgrounds (think communications, branding, marketing or PR), analytics, data, and BI are all very new and very technical. Leaders are often hesitant to ask basic questions in a larger setting, so we have these discussions in a more intimate 1:1 setting where they can really dig in and get their questions answered.
So what is one of the most foundational questions I’m asked? It’s to simply define the difference between reports, analytics, and business intelligence. Our Director of Analytics has a great way to differentiate between reports and analytics, so I’m going to steal it from him.
Imagine you’re driving a car and you get a speeding ticket. The ticket is similar to a report. It’s simply a snapshot in time. You were going X miles per hour on this road, at this time. Your speedometer and dashboard, on the other hand, is representative of analytics. It’s the real-time feedback of what’s happening — your speed, gas levels, engine temperature, etc. So, if you’re a marketer and you’re asking for a report, you’re asking for a snapshot in time of performance, for a website, a campaign, etc. Accessing the analytics, however, can give you real-time information and enable you to learn more about what is happening right now.
Business Intelligence is a term that is used incorrectly more often than not. BI is when multiple data points are combined to find insights within a comprehensive database. BI isn’t just multiple reports or analytics from different sources placed side by side. Instead, all of the data from the different sources (think email, Facebook ads, and e-commerce sales info) is combined into one database so you can see front-to-end performance using a key indicator, such as an individual’s email address. This allows you to see not only that someone bought something from your website because they found you yesterday via a Facebook ad with a specific headline, but you can also run lifetime value reports over time to see how that customer continued to buy. This is the key difference between the insights a platform like Google Analytics (or even Data Studio) can provide compared to Power BI or another BI platform. Correctly merging data sets to see a complete picture of performance takes time and expertise, but allows for much deeper insights.