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

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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 essential to be able to measure what is happening in all our campaigns and that this measurement should be behind the strategy and decisions we make from the beginning. But accumulating data without rhyme or reason does not help us either. Today we can measure almost everything, but that does not mean that we are interested in everything. We must avoid falling into “analysis paralysis” and focus on obtaining the most important information and applying it to our strategy.

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

Why do we need data analytics in our inbound marketing?

  • To better take advantage of the investment . If we measure the results of our campaigns accurately, we can know where money is being spent and if we are recovering the investment or not. We can also establish which are the most effective actions and which are not worth it. Thus, we will progressively adjust our budget and become increasingly profitable.
  • To really know our potential clients . In the world of inbound marketing, many times we create campaigns based on a mix of  buyer personas , good practices, intuitions … but until we start them, we can’t really know what works and what doesn’t. But we cannot Buy Uruguay Mobile Phone Number Database see the reactions of our users, and many times their feedback is limited to a few comments. Therefore, the way to really communicate with them, to find out what they like and what they don’t like, is to analyze the data about their behavior.
  • To continually improve our campaigns . Continuous improvement and learning (with its corresponding dose of mistakes!) Is one of the maxims to advance in inbound marketing. Through controlled experiments and metric analysis, we can see which tactics work best with each target and adapt our campaigns to incorporate them.
  • 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 poorly optimized form, a video that takes time to load, a button that does not work well from mobile phones … When we start the data analytics, many times it happens that we clearly see 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 to inform about what is happening in our campaigns. Thus, it will be much easier for us to explain to the client what the budget is really intended for, decide with the boss the next steps to follow or tell colleagues from other departments what we need from them.
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The inbound marketing metrics you need to measure

As you already 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 at each stage.

TOFU phase

In the TOFU phase  , the user is in the early stages of the process: he has just recognized that he has a need and considers finding solutions.

  • Traffic : every inbound marketing strategy starts from getting users to visit our website. But the raw number of visits is only part of the story. To really understand 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 action to improve the quality of visits.
  • Social media engagement: Although it is not easy to link social media engagement metrics to business results, that doesn’t mean 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 . Incoming links not only attract visitors who click on them, but they are like a vote of confidence from other websites that helps us improve our organic positioning in Google.

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  • Conversion of traffic to leads : when a user leaves us their data, they become a lead and progress to the next phases 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.

MOFU phase

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

  • Quality and conversion rates of 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 become a qualified contact for marketing and how many of these become at the same time qualified contacts for the sales team to start working with them. Thus, we will advance step by step through the conversion funnel until we have 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 due to the great importance it has in lead nurturing strategies . By sending regular publications to the contact base, we can gradually convert leads into qualified leads for marketing and sales and guide them on the path to conversion. If this process isn’t working the way it should, maybe it’s time to take a look at our targeting strategy.
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BOFU phase

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

  • Acquisition cost : once we have achieved that the user becomes a customer, we will be able to know what the cost has been. To do this, we will divide the investment in the campaign by the number of clients obtained. Cost per acquisition is one of the most crucial metrics for the profitability of our marketing, so it is a good idea to always be aware and look for ways to keep it as low as possible.
  • Increase in sales : here we measure whether we have achieved the star objective of all brands, that is, “sell more”. With a good data analytics strategy, we can see the entire customer journey from first contact to sale and know if our digital marketing campaigns are really helping to increase sales.
  • ROI : closely linked to the previous two, this is the metric that tells us if we have managed to recover the invested budget. Here we can find a lot of useful Phone Number List information by analyzing the ROI of each channel or even each ad separately, to see which ones have performed better and redistribute our budget based on the results.
  • Customer lifetime value : this is the metric that allows us to know if acquiring new customers is “expensive” or “cheap”, since it tells us how much we are going to earn on average for each customer. To calculate it, we need to know the average amount of a purchase and the number of times the user purchases while he is a brand customer.

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, we are going to 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 it. If you measure the wrong data or interpret it incorrectly, you are drawing the wrong conclusions and deviating from the way forward. So ask yourself what you want to achieve. This can be a general problem, but you need to be able to link it to a specific KPI, such as leads, sales, or 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.”

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2) Set goals based on data

Now that you are clear about what the problem is and what you want to achieve, you need a  concrete and quantifiable objective  to determine if you are achieving it or not. Goals and benchmarks (for example, the average conversion rate to leads in your industry or in previous campaigns) give context to the data and help us interpret it. We can set goals within a margin of error, for example, setting an affordable first goal, a more ambitious one, 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.

Continuing with the example from before, we can analyze the data of our  landing pages . Thus, we see that one of them attracts a large amount of traffic but the conversion ratio is only 1%. When compared to the rest of the site, we see that the best landing pages on our website get 5% conversions, so we decided to focus on improving the ratio of this particular landing page instead of the web traffic in general. Based on these figures, we can set a minimum goal of doubling the conversion ratio to 2% and an ideal goal of reaching 6%.

3) Collect data

In this phase,  precision  is essential. To reach the right conclusion and put the right measures in place, 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 IT team, to make sure we have all the plugins, pixels, tracking codes and other tools installed. We also need to have an analytics platform (such as Google Analytics ) that allows us to analyze the data we are obtaining.

4) Make informed decisions

Based on the information we have collected, it is time to start  making changes . The most efficient way to work is to formulate hypotheses and do experiments using  A / B or multivariate 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, we are going to test the changes one by one using A / B tests.

In the first test, we launched a version of the landing with the shortened form and in another we kept the original, directing half the traffic to each of them. We see that the version with the shorter form manages to increase the conversion rate up to 2.5%, so we stick with it. Next, we tested a version of the new landing with video and another without video. Once again, we see that we were right, since the landing with the shorter form and with video has managed to increase the conversion rate to 4%. Goal achieved!

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