Google analytics-centis digital

Do you monitor your Google Analytics? In addition, do you make decisions based on your Google Analytics data so as to grow your nutraceuticals brand?

Data. If anyone would have told me when I was just at 25 years of age that data is fun, I would probably smack them in the face.

Seriously now, most supplements business owners we talk to hate the word “data” and start to phase off.

Let me tell you that today’s data are more important than anything. We created this guide to show you the methodology for doing analytics and data in your nutraceuticals business, as well as which metrics matter the most in order to make sense and take well-informed, profitable decisions.

But first, let’s talk about why data and analytics are so important, not just for nutraceuticals but to any business.

Mastering Analytics & Data

Before being an expert in reading and analyzing data, you need to understand just two, very important principles:

  • Your Data Need To Have A Purpose. Whatever data you gather must help you answer questions and make decisions that take you a step further.
  • The Essence Of Marketing Is Hypothesizing. Testing this hypothesis leads to uncover questions that result in strategies. It’s the process of transforming raw data into business decisions.

It’s easy to get lost in the sea of data when there are so many sources to draw from and sometimes getting different data for the same metric.

Where do you focus? How to compare the data from different sources?

Principle #1: Your Data Must Have A Purpose

In order to understand data first, you must think of having a full-funnel.

The marketing funnel is a method that makes it easy to visualize customer acquisition in marketing:

At the top of the funnel, your nutraceuticals brand marketing generates awareness and attracts new visitors to your website.

Some of these new leads will show interest and will evaluate your nutraceuticals, and a percentage of them will go on to become customers.

To work for analytics and data, this marketing funnel needs to be tweaked.

The model below is a tweaked funnel that not only maps the stages of a customer’s journey, but it also lists the metrics that should be measured at each stage.

With this approach, except for the classic 3-stages funnel we also need to decide what happens after someone becomes a customer.

So the “secret” is not to look at all your data at once. You assign different metrics to each stage of the funnel.

Whilst I understand that it is important to measure your nutraceuticals business only by its bottom-line numbers (your sales), the proper, analytical thing to do is to measure its performance at every phase. This is how you will be identifying if your funnel is underperforming, finding different strategies to address problems and finding ways to convert visitors to customers easier.

Let’s start by identifying the funnel metrics you need for each stage of your customers’ journey.

Categorizing Data by The Stage of the Funnel

TOFU (Top of Funnel)

In case you don’t know yet, at this stage your goal should be new, fresh visitors.

When selecting the proper metric for this stage, you should ask yourself whether it provides you with data that you can act upon about new, first time visitors.

Some examples of TOFU metrics are:

  • Impressions by channel
  • Engagement metrics
  • Pageviews/Unique Pageviews
    Retention metrics

MOFU (Middle of Funnel)

When speaking for MOFU, at this stage, you have already been visited by the first fresh new visitors that never heard of you. So at this stage, they know you a bit, and your objective is to convert them into subscribers, or low-level entry customers.

What metrics could you use here? Well, you should be trying to find metrics that define whether someone took some sort of action on your website.

The term action could be defined as:

  • People subscribing
  • People filling out a form
  • People following you in social media

A few examples of MOFU metrics are:

  • New vs. Returning Visitors
  • Referral Traffic
  • Social Engagements
  • Average Read Percentage
  • Completion Rate
  • Click-Through Rates (CTR)
  • Email Sign-ups
  • Whitepaper Downloads
  • Webinar Participants

BOFU (Bottom of Funnel)

This stage is all about converting leads into customers.

This stage’s metric should be all about conversion insights.

Some BOFU metrics are:

  • Whitepaper Downloads
  • Lead Generation Forms
  • Sales Conversion Rate
  • Transactions

How many people clicked or purchased as a result of your communication? This tells you which offers are working and what kind of offers you should make to new customers.

What happens after you convert someone?

Simple – you maintain the relationship.

At this stage, your goal is to maintain customer satisfaction. You want to increase retention, customer lifetime value, bring more ROI to your traffic.

How satisfied are your customers? This is the main question you should be asking when measuring this metric.

Please note that these data are not on Google Analytics. You will get that data from various sources such as shares, comments and more. When someone leaves a good review, this tells us how well you are helping people reach their goals.

How To Sort Data Per Their Type

We’ve just reviewed TOFU, MOFU, and BOFU metrics, which is a way of sorting metrics by the stages of your funnel. But there’s another way to sort metrics, and that’s by the type of information they provide.

We categorise metrics into two distinct … well, categories:

The Key metrics – the metrics that tell you how well your marketing, your efforts, and your sales are doing. How to understand if a metric is key? Simple. Just look at it and you will instantly see how you are performing.

Deep metrics answer big questions. These metrics are more granular and help you understand what’s going on in specific areas of your business.

Typically, you use both types of metrics together, not one or the other. If key metrics tell you things are going well, you use deep metrics to help you understand why, so you can replicate your success.

Let’s see an example comparison of how key and deep metrics work:

How metrics can improve your vitamins e-commerce website:

When you want to see what is your average order value in Google analytics, this tells you in one go, that in our example above, is 64GBP.

However, you will never understand why and how your average order value is this amount unless you dig deeper and look to find which actual orders made this average.

This opens up a whole array of possibilities and more questions – optimizing your sales funnel, or activating promos, bundles, upsells in order to push the order value higher.

Understanding how well your nutraceuticals or supplements business is doing, as how well things are performing or under-performing is the first step towards data analysis for your nutraceuticals brand.

The next thing you need to do is start using metrics for problem-solving.

How to Use Google Analytics Metrics to Solve Problems

As you can only see your data on a Google Analytics dashboard, your job is getting harder, as it is your job, as a digital marketer at your nutraceuticals brand, to turn these data into something meaningful (i.e. a decision).

At Centis Digital, we use a process that allows us to hypothesize and then test (marketing is always a hypothesis and a test).

Let’s back up a little.

So to understand this process, you obtain data from your Google Analytics dashboard, where the stage of the funnel you want to analyse.

You then see the data and compare them with your metrics. Are they good? Have they surpassed our expectations or did they let us down? Ask questions to get to the truth. When you ask questions you hypothesize, in terms like “If we want bigger orders at ur supplements website, will adding THIS supplement or THAT bundle of vitamins at the order page, increase the orders?”

So you make hypotheses about what might happen if you could impact any of those numbers. Then you test.

The days of guessing are over. You no longer make decisions based on what you FEEL or you THINK it might work, but about what the data is telling you.

Here is How To Reviewing Key Metrics That Create Questions

Getting to the truth is understanding enough about what’s happening, order to ask the right questions, one at a time. When you don’t know what you should be asking, your metrics will make you ask questions that lead to the truth.

Here is a step by step process:

Step 1. Review Your Key Metrics. Start by focusing on the areas that are underperforming or over performing. Usually, the average order value, bounce rate or time on site are where we focus on. These metrics will definitely create a series of questions like:

  • Why our order value just doubled this week? What happened?
  • Our new Google Ads campaign is bringing more people, but we see an increase in bounces. What’s happening?
  • Why people spend less time on our blog than last month? What seems to be the problem?
  • What’s causing increases in newsletter subscriber conversions?

Step 2. Hypothesize About Why This Is Happening. Try to make predictions. If need it to be, place bets with your team (we do this as part of international team fun and keep everyone engaged). For example, we hypothesized for a client that sells a range of beauty supplements and we made three guesses:

  • The Free Product trial is a better offer than the €1 trial because it converts more visitors across every traffic channel.
  • The Free Product trial returns higher subscriptions cancellations than that of the €1 trial members
  • Why people cancel more at the free trial than at the €1 trial?

Several times more than many, there isn’t just a single reason for the problem you’re seeing. Many factors may play a role to whatever this is what you are trying to understand. Hypothesizing more than once will lead you to better assumptions, thus better questions thus the results you need.

Step 3. Use Deep Metrics to Test These Hypotheses. At this step, you will need more detailed data to figure out what’s causing the problem you’re trying to understand.

You don’t normally dig that deep for this kind of data, but you know it there for your research and analysis.

To answer the question above, a cohort analysis maybe what we were looking for. We organized different cohorts on how we could group the customers that purchased either via the €1 trial or the free product, and we included cancellation date, what they paid in their lifespan, how long they were active, and more.

We came to the conclusion that people paying the €1 trial were valuing their purchases better, than those who didn’t pay at all.

In Conclusion

When you evaluate data, you must take into consideration all the above factors, digging deep into what not showing at the first glance.  If you don’t, you’re probably going to assume and hypothesize based on false data, and your conclusion won’t be valid.

What does history tell you to expect? Check data from different months, weeks or days (and years) – only this way you can understand trends and typical behaviour among your nutraceuticals customers.

What were the changes outside your control which influencers your metrics? Did technology changed (i.e. a change in Google’s algorithm that caused a drop in rankings)?

Think through the changes you’ve made internally that might have affected your numbers and then hypothesize, and make decisions.