Data analytics for inbound marketing: what metrics to look at and why

At Cyberclick we spend our lives highlighting the importance of data analytics in inbound marketing .

We believe that it is fundamental to be able to measure what is happening in all our campaigns and that this measurement should be behind the strategy and the decisions we make from the beginning. buy youtube views

But accumulating data without rhyme or reason is not useful either. Today we can measure almost everything, but that does not mean that everything interests us. We must avoid falling into “paralysis by analysis” and focus on obtaining the most important information and applying it to our strategy.

Therefore, in this article we will see why data analytics is important in inbound marketing , what metrics we should be analyzing and how to do it effectively. We start!

Why do we need data analytics in our inbound marketing?

  • To take better advantage of the investment . If we measure the results of our campaigns accurately, we can know where money is being spent and whether we are recovering the investment or not. We can also establish which are the most effective actions and which are not worthwhile. Thus, we will progressively adjust our budget and be more and more profitable.
  • To really know our potential customers . In the world of inbound marketing many times we create campaigns based on a mix of buyer people , good practices, intuitions … but until we start them, we can not really know what works and what does not. But we can not see the reactions of our users, and often their feedback is limited to a few comments. Therefore, the way to really communicate with them, to know what they like and what they do not, is to analyze the data about their behavior.
  • To continuously improve our campaigns . The continuous improvement and learning (with its corresponding dose of errors!) Is one of the maxims to advance inbound marketing. Through controlled experiments and metric analysis, we can see which tactics work best with each target and adapting our campaigns to incorporate them.
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  • To detect errors . Although we follow the best practices when creating our website and other online marketing materials , there are always things that escape us: a slightly optimized form, a video that takes time to load, a button that does not work well from mobile … When we start data analytics, it often happens that we see clearly that a page does not work as well as it should. From there, we can analyze what is happening and correct the error that is negatively affecting the results.
  • To communicate with clients, bosses and colleagues . Having regular data analytics reports is a very valuable tool for reporting what is happening in our campaigns. Thus, it will be much easier for us to explain to the client what the budget really is for, to decide with the boss the next steps to follow or to comment to the colleagues of other departments what we need from them.  

The inbound marketing metrics you need to measure

As you know, inbound marketing campaigns are usually divided into three phases: TOFU or top of the funnel, MOFU or middle of the funnel and BOFU or bottom of the funnel . Each of them has a series of associated metrics that will help us know what is happening. Let’s see which are the most important in each stage.

TOFU phase

In the phase , the user is in the early stages of the process: he has just recognized that he has a need and plans to look for solutions.

  • : any inbound marketing strategy is based on getting users to visit our website. But the raw number of visits is only part of the story. To really understand the web traffic, we have to analyze the number of sessions, the unique users, the page views, the duration of the sessions, the bounce … All this will help us to understand how visitors behave on our website and if we must take some measure to improve the quality of the social networks : although it is not easy to link engagement metrics in social networks to business results, that does not mean that we have to lose sight of them. Social media is a great channel to distribute our content and attract new users, and their health status is measured through impressions, clicks and user reactions.
  • Inbound links : this is another “secondary” metric but important to evaluate the results of our SEO in Inbound Marketing . The incoming links not only attract visitors who click on them, but are like a vote of confidence from other websites that helps us improve our organic positioning in Google.
  • Conversion of traffic to leads : when a user leaves us their data, it becomes a lead and advances to the next stages of the conversion funnel. In fact, we could say that the main objective of the websites within an inbound marketing strategy is to generate leads . But not all leads are the same: we have to distinguish between leads that we can discard, those that are qualified for marketing and those that are qualified for sales.

Phase MOFU

Here the user is considering different ways to solve their need, among which is our brand.

  • Quality and conversion ratios of the leads : here we will study in detail what is the proportion of each type of leads and, above all, how they are progressing from one state to another. That is, how many of the users who leave us their data go on to become a qualified contact for marketing and how many of these become qualified contacts at the same time so that the sales team starts working with them. Thus, we will progress step by step in the conversion funnel until we have the users ready to buy.
  • Email marketing metrics .  Within email marketing there are many different types of campaigns, each with its own metrics and with different roles within the conversion funnel. But I have decided to place email marketing within the MOFU phase because of the great importance it has in the lead nurturing strategies. By sending regular publications to the contact base, we can turn the leads into qualified leads for marketing and sales and guiding them on the path to conversion. If this process does not work as it should, maybe it’s time to take a look at our segmentation strategy.

BOFU phase

Finally, we have the phase , in which the user is ready to buy

  • Acquisition cost : once we have got the user to become a customer, we can know what the cost has been. To do this, we will divide the investment in the campaign between the number of clients obtained. The cost per acquisition is one of the most crucial metrics for the profitability of our marketing, so it is advisable to always be aware and look for ways to make it as low as possible.
  • Sales increase : here we measure if we have achieved the star goal of all the brands, that is, “sell more”. With a good data analytics strategy, we can see the entire trip of the client from the first contact to the sale and know if our digital marketing campaigns are really contributing to increase sales.: very linked to the previous two, this is the metric that tells us if we have managed to recover the budget invested. Here we can find a lot of useful information analyzing the ROI of each channel or even of each ad separately, to see which ones worked better and redistribute our budget based on the results.
  • Value of the customer’s lifetime : this is the metric that lets us know if acquiring new clients is “expensive” or “cheap”, since it tells us how much we will earn on average for each client. To calculate it, we need to know the average amount of a purchase and the number of times the user buys while being a customer of the brand.

How to apply data analytics to your inbound marketing step by step

In the previous section we have seen a lot of metrics that can be useful to measure the results of our inbound marketing, but we lack a framework in which to apply them to improve our results. So, let’s see a simple step-by-step method to improve our campaigns with data analytics.

1) Define a problem

First of all, you need to know what you want to achieve or what problem you need to solve . Only then will you be able to know what data you really need and in what context you have to analyze them. If you measure the wrong data or interpret it incorrectly, you will be coming to wrong conclusions and deviating from the path to follow.

Therefore, ask yourself what you want to achieve. It may be a general problem, but you need to be able to link it to a specific KPI, such as leads, sales or the conversion rate.

For example: “I think my website is not giving the results it should and I would like to get more leads with it”.

2) Set goals based on data

Now that you are clear about the problem and what you want to achieve, you need a concrete and quantifiable objective to determine if you are getting it or not. The objectives and comparisons (for example, the average conversion rate to leads in your sector or in previous campaigns) give context to the data and help us interpret them.

We can establish objectives within a margin of error, for example, to mark us a first objective affordable, one more ambitious and a third that would be the ideal situation. Using these figures as a reference, we will know what and how much we need to improve.

Following the example above, we can analyze the data of our . Thus, we see that one of them attracts a lot of traffic but the conversion ratio is only 1%. When compared to the rest of the site, we see that the best landing page on our website achieves 5% conversions, so we decided to focus on improving the ratio of this particular landing page instead of web traffic in general. Based on these figures, we can establish a minimum objective of doubling the conversion ratio to 2% and an ideal target of reaching 6%.

3) Collect data

In this phase, accuracy is fundamental. To reach the correct conclusion and put in place the appropriate measures, our data analytics tools have to be reliable and give us the data in an easy to interpret format.

For this to happen, we have to work hand in hand with the computer equipment, to make sure that we have installed all the plugins, pixels, tracking codes and other tools. We also need to have an analytics platform ( like Google Analytics ) that allows us to analyze the data we are obtaining.

4) Make informed decisions

Based on the information we have collected, the time has come to start making changes . The most efficient way to work is to formulate hypotheses and make experiments using A / B or multivariable tests .

For example, we can think that the landing page of our example would convert better if it had a shorter form and an explanatory video of the product. To see if this is true, let’s test the changes one by one using A / B tests.

In the first test, we launch a version of the landing with the shortened form and in another we maintain the original, directing half of the traffic to each of them. We see that the version with the shortest form manages to increase the conversion ratio up to 2.5%, so we are left with it.

Next, we tested a version of the new landing with video and another without video. Once again, we see that we were successful, since the landing with the shortest form and with vi