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.
Your website holds a ton of valuable data about your users, customers, and potential leads. But in order to do any meaningful analysis with all your tracking data, you first have to collect it successfully.
In this post, we’re going to go over step-by-step instructions for how to set up Custom Alerts in Google Analytics (GA). Custom Alerts help marketing and analytics teams identify problems quickly, so they can address problems in the early stages. The basic Custom Alert we provide here will notify you the next day if your tracking is compromised (or your site is down).
Important Note: Custom Alerts must be created by each GA user who wants access to the diagnostic report provided after an Alert is fired.
This type of Alert is very common, as it applies to just about every site. The Alert will fire when fewer than 5 sessions are recorded to your site in a given day. We chose the number 5 because on a busy site a few sessions might still be recorded even if tracking isn’t working properly. If your site has a high variance of user sessions, you might raise or lower that number accordingly. For our purposes here, when daily site sessions drop below 5, we’re assuming either the tracking implemented has broken, or the site is down.
Here are step-by-step instructions for how to set up this Alert:
An example of a text alert:
The email alert:
And the diagnostic report:
Setting up a standard Custom Alert like this one is a simple way to ensure the consistency and accuracy of your tracking so that you can monitor your site’s traffic.
In addition to simple, standard Alerts like this one, you can use GA’s Custom Alerts to set up more complex warning systems to monitor your website’s marketing strategy. For example, you might want to know if the conversion rate for a certain key touchpoint falls below a particular threshold during a given week. We’ll cover the process for that in a later post.
If you’re interested in learning more about how you can leverage Google Analytics to boost your marketing analytics capabilities and improve your digital return, get in touch!
The term “predictive analytics” refers to the use of:
In order to predict the likelihood of future outcomes based on past results.
In marketing, as well as in business more broadly, we make predictions all the time. Take, for instance, the process of lead scoring. When a prospective lead completes a certain action, like signing up for a newsletter, then we say this lead is more qualified and we prioritize them. When we do this, we’re predicting that they’re more likely to convert down the line. As marketers, we know that predictions like these are enormously helpful in moving customers through a sales funnel.
But we also know that marketing and business decisions in the real world are usually not so simple. In a complex digital ecosystem, it’s possible to track a wide variety of metrics. We can track sign-ups, clicks, scroll depth, etc. However, we can’t unearth all the possible insights from this data without the help of analytics. Using predictive analytics, such as propensity modeling, digital marketers are now able to score leads in a highly detailed and granular way. According to Forrester Research, “Predictive-scoring adds a scientific, mathematical dimension to conventional prioritization methods that rely on experimentation and iteration.”
And predictive analytics can be used in lots of ways beyond just lead scoring. This article from Emerj identifies other areas where predictive analytics are being used to produce valuable insights:
What sets predictive analytics apart from other methods of forecasting is the use of sophisticated mathematical techniques, like regression analysis, Bayesian analysis, or network analysis. You can combine these techniques with machine learning to discover insights or inefficiencies that would be impossible for a human analyst, or even a team of analysts, to recognize.
As with any sort of analytics, though, the power is in the data. The greater the quantity of high-quality data you collect, the more accurate and actionable your insights will be. When it comes to making smarter, better-informed business decisions, there’s always room for growth.
If you’re looking for cutting-edge ways to leverage your data using predictive analytics, get in touch.
84% failure rate.
That’s steep. No matter what you’re measuring, failing 84% of the time is a hard pill to swallow. Believe it or not, though, just two short years ago that was the estimated failure rate for digital transformation.
Digital transformation – it’s a buzzword that executives can’t get out of their heads. It’s often used in conjunction with terms like “innovation,” “automation,” “disruption,” etc. For companies hoping to increase efficiency by streamlining repeatable tasks typically handled by entry-level employees, digital transformation can drive significant improvements for both your capacity and bottom line.
Changes in various industries are forcing many organizations to look for ways to turn to digital before it’s too late. Everywhere we look, another company is unveiling some “innovative” idea and flipping a traditional business model on its head. Honestly, though, what’s too late?
It’s almost as if there is an imaginary timeclock that is pressuring businesses to transform or die. That’s some serious pressure. But – think about it – is every business ready to ramp up digitally? Is every business prepared with the infrastructure and systems to support such quick movement? No. Forbes published an article earlier this year that outlined the key failure points for businesses that rush into digital transformation just because it’s the buzzword of the week. The exact pieces of the business that organizational leaders are hoping to improve upon are lost – money, productivity and time. How this can happen in organizations that are otherwise savvy about business dealings? Lack of proper planning.
How do you get past the “buzzword” and plan for a digital transformation project? First of all, there are two important things wrong with that question:
Changing your mind on the above two points can shift your mindset from being focused on the trendiness of buzzwords to looking more at digital transformation as a tool for business growth and productivity.
Digital transformation is a shift in your organization’s mindset. There are no hard start and stop dates. While there will be key initiatives and milestones, this is a culture change for your organization. That said, as you make the move into digital, you can divide the things you want to tackle into mini-projects with key objectives to hit as you go.
The way you handle everything will change, which is why it’s important to think through a proper digital transformation plan for your organization.
We hate to admit it, but you won’t find all of the answers in this article. Ultimately, it’s up to your team (and partners you’re working with) to determine what that plan looks like. Here are a few questions business leaders can use to help guide the discussion:
Certainly, there are more questions that will come up and need to be addressed, but the above are good conversation starters.
After you’ve figured out what to tackle, you’ll need to determine exactly how you plan to do it. Depending on what’s important to your business, some changes could be as simple as automating data collection and dashboard generation, while other changes could be as complex as launching a new online ordering experience that allows customers to buy groceries and other household goods to pick up without leaving the comfort of their car (Target and Walmart, here’s looking at you).
Of course, a plan is not realized until there is a goal. For anything you plan to change, there needs to be a hard and fast goal by which you measure effectiveness. Our team is a huge fan of SMART goals. You may already know about them, but you can learn more here just in case. Whatever the number is, set something you can measure and timebox it. If you take the data collection automation example above, you could set a goal like this: With the automation, I want to have an extra 16 hours per month that I can dedicate to reviewing TPS reports by the end of January (smile). If you are able to dedicate 16 hours or more (without increasing your work time), that initiative was a success.
Once you’ve determined the area of your business you want to change and set goals/plans, you need to tell everyone. Everyone. Don’t leave anyone out. This evolution will impact everyone in your organization, and everyone needs to be prepared to handle the change. In order to do that, they need to have a clear understanding of what’s changing, why, and how it will impact the company and their role.
If your CEO doesn’t believe in your digital transformation plan, you’re unlikely to get much traction. The same goes for the rest of the C-Suite. There must be buy-in coming from the top down in order to be successful in your digital transformation. You’ll be more focused and with the blessing of leadership, you’re more likely to get approvals on smaller digital transformation initiatives.
If you’re looking to develop a digital transformation strategy for your organization, Hedy & Hopp can help. We’re a digital transformation hybrid agency that is led by data. Get in touch today!
We were recently announced as an honorable mention recipient of PR Daily’s 2018 Content Marketing Awards in the Video category for our work with St. Louis Children’s Hospital.
Ragan Communications and PR Daily run 11 awards programs each year, including the PR Daily Awards, the Employee Communications Awards and the Video and Visual Awards. Judged by globally regarded experts and featuring multiple categories, these programs honor the top work in communications, PR, marketing and media.
PR Daily’s Content Marketing Awards celebrates teams, organizations and consultants who have redefined the field with their groundbreaking work. As an honorable mention recipient in the program, we’ve joined an elite group of past recipients, including PCL Construction, IBM, Cambia Health Solutions, Sunrise Banks, Michigan Medicine, the American Red Cross and The Coca-Cola Company.
Our organization was chosen from a wide pool of entries to receive an honorable mention in the Video category for the impact of its MomDocs campaign.
“Out of many outstanding submissions, we found [Hedy & Hopp]’s work to truly set a new standard of excellence for all practitioners. We congratulate them and look forward to seeing their future successes in this field,” said Justine Figueroa, senior marketing coordinator for awards programs at Ragan Communications.
Ragan Communications operates two of the top news and information sites for the PR and corporate communications industries: Ragan.com and PRDaily.com. Together, these daily news sites attract more than 700,000 global visitors monthly. The Chicago-based company also covers the health care industry on HealthCareCommunication.com. Ragan is the leading provider of conferences and online training for PR, media relations and corporate communications professionals. The company also produces RaganTraining.com and PR Daily’s PR University, the industry’s leading video and online education portals.
St. Louis, MO – Jenny Bristow, CEO of Hedy & Hopp, has been honored with a silver-level Stevie® Awards for Women in Business. Bristow was recognized in the “Female Entrepreneur of the Year – Business Services – 11-2,500 Employees” category. The awards were handed out at a ceremony in New York City on Nov. 16.
The Stevie Awards were created in 2002 to honor the achievements and positive contributions of organizations and working professionals worldwide. The Women in Business category recognizes the achievements of women executives, entrepreneurs, and the organizations they run worldwide.
“I am so proud and humbled to be recognized with a prestigious award focused on entrepreneurialism,” Bristow says. “I’ve been an entrepreneur from a young age, and I have poured that spirit into everything we do at [Hedy & Hopp].”
Thanks to that approach, Hedy & Hopp has exploded with growth over the last two years, growing from 4 employees to over 30. Along the way, the company has been recognized for marketing innovation, rapid growth, and client successes.
So, you and your team have decided to go all in on data analytics. You’ve started collecting data, and you were able to get Google Data Studio up and running. Now you have a plethora of pie charts, time series, and tables. They look fantastic and everyone exchanges high fives. Analytics is about to change your company. But then…nothing happens. A few months go by, and your beautiful dashboards are collecting dust. You’re not sure how the dashboards are supposed to make your business more effective. You think to yourself, “Google Data Studio sucks!” The reality is, though, a platform by itself won’t instantly allow you to achieve all your goals. But what are you missing?
If we tried to answer that question for everyone at the same time this would be a much longer article. Instead, we’re going to review some key questions you need to ask your internal team before you begin the process of looking for a Business Intelligence (BI) platform. Then, we’ll dive into the pros and cons of the three most popular BI platforms on the market, while recognizing that there’s not necessarily going to be an easy, one-size-fits-all solution. We’ll start with Google Data Studio (which isn’t actually so bad, of course) and we’ll work our way up to a more comprehensive Power BI platform, which is what our team often uses.
One last thing: we’ll be using the term “Business Intelligence platform” as something of a catchall, with the intention of looking at three rather different products. At the end of the day, Business Intelligence is a bit of a nebulous term (in fact, at Hedy & Hopp, we changed the name of our Business Intelligence team to the Decision Science team, because we felt like that name was more descriptive). Business Intelligence is all about leveraging data to make smarter decisions. A platform can help you accomplish that, but it’s not a magic wand. In data collection, we talk about “garbage in, garbage out,” meaning if your data collection isn’t set up properly, the data you collect won’t be useful. In the same way, you can collect all the pristine, useful data in the world, but you still have to put that data to use. That’s where Business Intelligence platforms come in. Ultimately though, it will be up to you and your team, with help from a BI platform (or several), to turn your data into actionable insights.
A key decision your team must first make is gaining alignment on the key performance indicators you’ll want to track with the BI platform. (Hopefully, you identified objectives and KPIs before you even started collecting data). So, before we get down to the nitty-gritty of comparing BI platforms, here are some key questions you need to take into account:
Some other important questions to consider:
Okay, now let’s take a look at a few of the different BI platforms out there.
It was actually our Decision Science team that recommended the title for this post. They were kidding…mostly.
Google Data Studio is great for those just getting started with BI and looking to get their feet wet in reporting and analytics. If you’re reading this, chances are you might have already played around a little with Google Data Studio.
Pros:
Cons:
Conclusion: Data Studio is an excellent (and free) way to get started organizing and visualizing your data from Google Analytics and certain other channels, but it lacks connections to some important data sources, like Facebook, and it doesn’t offer many customization options. Depending on your needs and your resources, you might want to consider something more comprehensive down the road.
Here’s a joke from our Decision Science team: “The third most popular feature in software development is “Export to Excel.” Number one and number two are “OK” and “Cancel.”
Hilarious, right? Here’s the thing: tons of people use Excel but many aren’t aware of the full range of its BI capabilities.
After getting started with Google Data Studio and getting a feel for how BI and data analysis might be able to help you in your digital marketing efforts, or whatever job you’re attempting to accomplish, you might find yourself wanting to try out a more powerful platform, one that will allow you to connect to a wider variety of data channels, blend your data to better identify patterns and trends, and create more customized visualizations.
The good news is you might be able to accomplish all these things using a platform that is probably already sitting on your desktop: Excel. As long as you’re running it on a PC and not a Mac, you can leverage the free, built-in features Power Query and Power Pivot to transform Excel into a robust BI platform.
Pros:
Cons:
Conclusion: Excel offers a ton of capabilities, particularly if you’re running Power Query or Power Pivot on Windows. However, while Excel provides a wider range of functionality, it isn’t as intuitive or user-friendly as Google Data Studio.
If you’re serious about Business Intelligence, and you’ve determined that you need a platform that can combine custom visualizations, integrated data connectors, and complex programming capabilities, you might be ready to consider a premium BI platform such as Microsoft’s Power BI.
Pros:
Cons:
Conclusion: Power BI offers an impressive array of customization, visualization, and functionality. However, if you don’t have at least one member of your team (and/or a partner like Hedy & Hopp) devoted to implementing and managing BI, it might be hard to draw value from this platform or other high-powered, premium BI platforms.
The hard truth is that implementing BI is a difficult process. Somewhere between 70 and 80 percent of businesses fail when they try to implement BI initiatives.* Some implementations are unsuccessful because companies try to use the wrong platform, but implementations are just as likely to fail because they lack company-wide buy-in, or because companies attempt to implement too quickly, forgetting to take a “crawl-walk-run” approach.
All that said, there is a reason why there’s so much buzz around BI. As difficult as it can be to implement, the potential of BI to help any company is immense. Remember, at its core, BI means leveraging data to make better decisions—ensuring your company is guided by hard data instead of guesswork.
The key to making BI work for your company isn’t just about choosing the right platform. It’s even more important to find the right people who can leverage your data in the right ways. Whether you’re just getting started, learning to walk, or ready to run when it comes to BI, working with the right team is crucial. This could mean investing in an in-house data team or partnering with an agency.
At Hedy & Hopp, our Decision Science team has experience with every aspect of Business Intelligence, from developing customized dashboards and creating advanced visualizations to identifying inefficiencies in marketing strategy. If you are seeking help with Business Intelligence, Decision Science, or turning your company’s data into actionable insights, get in touch.
*https://www.cio.com/article/3221430/business-intelligence/4-reasons-most-companies-fail-at-business-intelligence.html