Gathering Data and Analytics (and How to Use Them)

We’ve briefly mentioned what metrics to watch with different kinds of digital marketing, but we haven’t gone deep into how to use these analytics and data-points after you’ve collected them.  Turning analytics and data into actionable intel can be very difficult to master, which can be overwhelming. But, without the data and analytics, you cannot hope to understand which areas of your marketing are succeeding, and which are underperforming.

It’s said that there are two kinds of data:  too much, and not enough. So how do you decide what’s useful?  How do you figure out what these metrics mean, and how can you use them to create a plan of action to improve your business?  In this article, we will discuss three methods of a well-executed analytics and data strategy.

Categorizing data by Type

Before we get into how to use your data, we need to discuss the two different types of data.  No, it’s not “too much” or “not enough”, they’re more concrete than that.

Key metrics are data points that dictate overall health.  They’re the broad strokes, to figure out quickly if your business is doing well or not.

Drill-Down metrics are more granular, and answer specific questions.  They help you understand why things are or aren’t working.

Give Your Data a Job

You should know the reason you are gathering each piece of data.  To understand data easily, think of the sales funnel. The sales funnel is an easy way to visualize customer acquisition, but we can easily modify it to work for data collection and analysis.  Traditionally, the sales funnel has three stages–top of the funnel, which raises awareness of your business and brand, middle of the funnel, which helps the prospect evaluate their choices between you and the competition, and bottom of the funnel, which converts prospects into customers.  

With analytics, we don’t stop there.  We need to add a fourth phase, the post-conversion phase, to focus on customer retention and ascension.  In other words, how do you keep customers coming back again and again?

Top of Funnel (TOFU)

The goal of top of funnel marketing is new visitors to your website, so your metrics should focus on that.  A good metric here would be the number of new visits to your website. If you’re running ads to boost awareness, you should see spikes in new views to your website.  If you’ve been advertising on a billboard with your URL on it, you should see spikes in traffic from that geographical area.

Middle of Funnel (MOFU)

The goal of middle of funnel is to convert new visitors into leads.  Are you getting new followers on your social media? Are people signing up for your email list?  Call to action (CTA) clicks, or clicks on a banner on your website telling the person to do something, beit follow/subscribe, purchase, or just learn more, are great ways to get these metrics.

Bottom of Funnel (BOFU)

Bottom of the funnel is for converting prospects into customers.  What is the conversion percentage, or percentage of viewers to buyers, of a particular ad?  What insight does this give me about how well a particular ad is working?

Post-Conversion (Retention and Monetization)

You’ve got a sale, now is the time to focus on retention, so that you will have a repeat sale.  The goals here are to keep your customer satisfied with their purchase, increase membership and traffic ROI (return on investment), and customer lifetime value.  These metrics, unlike in the other phases, aren’t automatically gathered and compiled, so they’re a bit harder to track. Subscriber numbers, positive reviews, and social mentions are all metrics that tell us how healthy the product is, and show what we can do to drive traffic.  This is especially critical for a membership-based product because retention is what drives profits.

Use Metrics to Solve Problems

An analyst’s job is to take raw data, interpret it, and come out with actionable steps to improve your business.  This process works similar to the scientific method, where you start with a question/hypothesis, test your theory, and come out with answers.  But what questions should you be asking?

Reviewing Key Metrics to Inspire Questions

Start by reviewing your key metrics.  What is your traffic on your blog posts?  Is there a post or set of posts that are doing exceptionally better than others?  Why? Is one author receiving more hits than others? Maybe you’re getting new subscribers daily, but your overall subscriber count is stagnant; why do you think that is?  Why are people unsubscribing? These are all key metric-related questions, because they focus on the broadest possible data.

Next, come up with hypotheses or theories about why your data is the way it is.  Is [blogger]’s writing style more approachable than other authors on your site? Are people only interested in your trial offer, and unsubscribing after that offer is up?  Are people restarting their trial after it’s up, inflating the conversion rate? You want to have as many hypotheses as possible (aim for 5-7), because there may be more than one answer.  Try to get as complete a picture as possible to maximize your success.

After you have your hypotheses, it’s time to test them.  Use your drill-down metrics to test your hypotheses and see which one(s) rings true.  Drill-down data is more granular, so you can get very specific to figure out exactly what’s causing your issue(s).  This data isn’t always looked at every day, but is easily accessible for when you need it.

Now that you’ve tested your hypotheses and hopefully have answers, take action!  What are you going to do with this new insight? If you’ve determined [blogger] gets more interaction because of their conversational writing style, consider telling your other contributors to adopt a similar tone in their posts.  If you’ve found that people don’t continue to purchase your program after the trial and instead start it over with a different email, try tweaking how many times a particular IP address can download the trial, or what’s included in the trial.

Contextualizing Your Data to Account for the Unmeasurable

Having context for your data is important so you know how to use it, as well as gauge its validity.  If you don’t consider factors like a promotion going on, competition, or technological issues, your data can be skewed and give you false results.  When speaking of context, there are four ways to think about data:

Historical Context

Historically, how does this data collected today compare to the same data in the past?  For example, are there spikes and dips during certain points in the year?

External Context

Outside of what you can control, what was going on?  Was there new competition? Was new technology released?  Did Google change its algorithm? These are all things that you did not do, but can still impact your business in drastic ways.

Internal Context

The opposite of external context, because you have control.  Have you made changes to your marketing strategy? Did you update your website?  Did you release a new product or product line?

Contextual Context

This may seem redundant, but it’s not.  Bias is something that happens, and it’s important to take that into account when looking at your collected data. How are you getting this data? What is it looking at?

Making Data Actionable

It’s time to put your data to use.  Use the three principles to understand how your business is performing, by turning raw numbers into actionable tasks. Assign roles to your data so you know what they’re measuring, and at what stage of the funnel. From there you can make hypotheses and test them.

Still have questions? We can help! Contact us here for more information.

Gathering Data and Analytics (and How to Use Them)
Gathering Data and Analytics (and How to Use Them)

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