As Forrester Research calls this, we are now in “The Age of the Customer”, where customers – and not brands – are the ones that actually shape the business strategy.

In other words, customers have clear expectations from both their in-person and digital experiences and they do not care how easy or hard is for a business to provide them.

If they do not see an immediate value from visiting your site, the competitor is just a tap away.

In this age where businesses must truly understand customers’ behavior in order to succeed (or even survive), it is crucial to get the most out of your collected customer data.

Getting better analytics from your customers can help you better modify your products to fit customers’ needs, focus and adjust your marketing efforts to what works best with your niche and ultimately be one step (or several steps) ahead of your competitors.

But where do you even start?

We have gathered seven important steps you need to follow to make the most out of your data collection procedures and avoid misfortunate events.


A very common mistake many professionals make is that they collect data with only a single objective in mind. After they have reached their original goals, the collected data cannot be repurposed anywhere else.

When you structure your data collection with only one objective in mind, the collected data cannot be repurposed for a different analysis.

At the same time, all the time and resources spent during the data collection process are going to waste, because the whole procedure must be repeated when a new need occurs.

A long-term analytics strategy ensures that the collected data is treated as a reusable asset.

Rather than collecting data to solve only one immediate problem, you are ensuring that your stored data can be used for multiple purposes whenever needed.

In addition, with a long-term analytics strategy in place, your data can be used by various departments of your organization – from marketing to product management – ensuring that your resources are not wasted. In other words, you can have your cake and eat it too.

At the same time, having long-term data collection strategies in place can be your hidden asset against your competitors. Companies that understand customers’ buying patterns over longer periods can generate key insights versus their competitors.

Of course, creating a data strategy is not an easy task no matter your company’s size. Luckily, there are many available resources to guide you, like this 4-step guide by Digital Transformation Pro on how to build a data strategy or the 3-step guide to building a long-term big data analytics strategy by BMC.

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The core idea behind Lean Analytics is that by knowing what kind of business you are and what stage you are at, you can track and optimize the one metric that matters to your business, starting right now.

While this methodology was designed with startup businesses in mind, every company can benefit from learning and also implementing it.

The main goal of following this methodology is to quickly go through production-experiment-new production cycle in order to maximize learning and reduce costs in a very short amount of time.

The final result is an agile company that can more effectively push new and successful products in the market.

A very good resource is Lean Analytics: Use Data to Build a Better Startup Faster by Alistair Croll and Benjamin Yoskovitz. The book is part of the ‘Lean Startup’ series started by Eric Ries. In a nutshell, Lean Analytics focuses on the ‘measure’ portion of the Build-Measure-Learn cycle. However, let’s see this in more detail.

Types of Metrics

The professional that wants to implement Lean Analytics needs to understand the clear distinction between the various types of metrics. In short, there are two types:

  • Qualitative, and
  • Quantitative.

Qualitative metrics include everything that can be measured in simple numbers. That is the actual verbal feedback that you receive from your customers and leads. Such data usually involve customer interviews but can be also extracted by simpler methods like a friendly email. This feedback is difficult to be translated into measurable data, but the insights it provides is highly valuable and can assist you when interpreting quantitative data.

Quantitative metrics include all data that have to do with numbers. This can be the number of people that abandoned their cart if you are an e-commerce business, the number of site visitors, or simply just this quarter’s sales. They are powerful if data are collected correctly, but they should not be your only metric.

The rule of thumb when doing research is that you use qualitative metrics to find what you should analyze and ask, and then use quantitative metrics to prove your hypotheses true or wrong. What does this mean for you? Each department of your organization needs to comply with this mindset since every department can collect a different type of data. For example, your customer support agents can store customers’ feedback and assist your analysts that set up quantitative research.

The Lean Analytics Framework

Another interesting part of Lean Analytics is the Lean Analytics Framework. This model can help you understand what business you are in and what your current stage is.

To assess your type, you need to answer the following questions:

  • How do your customers buy and use your product?
  • What is their main reason for buying from you?
  • What is the budget of your typical customer?

The answers to the above questions will guide to the business model that suits you best. Having said that it is also possible to combine business models. For example, you can be a SaaS company that can be accessed through a free mobile app (such as Facebook).

In short, Lean Analytics helps plug in the missing gap in the Build-Measure-Learn cycle. While there is a lot of literature on how to build and what to build, there isn’t enough on figuring out whether you are meeting expectations or not, and here is where this methodology comes handy.

If you are a visual person, many video resources can give you an idea of what lean analytics mean. The book’s author Ben Yoskovitz has even teamed up with General Assembly to produce a one-hour 101 Lean Analytics session you can watch online.


In the battle of tackling the collected data, the modern professional can find great assistance in the various analytics software solutions.

With so many tools available, it can be difficult to know which analytics solution to use. Let’s take a look at some of the most popular ones, according to G2 Crowd:

Google Analytics

Google Analytics is the king of digital analytics. The platform allows you to measure sales and conversions, but also provides powerful insights into how visitors use a website, how they arrived on your site, and ultimately what you can do to make them stay longer.


This behavioral analytics and engagement platform is specially tailored for digital marketers and product teams. Its users express that they love its ability to take consumers on a “bunny trail” through the website, allowing the professional to control their pathway and see the most relevant content to them.

Adobe Analytics

If you want a holistic view of your business, Adobe Analytics turn customer interactions into actionable insights. With intuitive and interactive dashboards and reports, and a great user interface, the professional can access real-time information that can be used to identify both problems and opportunities.


Another great customer experience and data management platform is Exponea Experience Cloud. Among the easiest to use, the platform features AI-powered engagement automation, but at the same time helps to improve cross-department collaboration and customer centricity. It is best suited for online retail companies and fashion brand leaders that are on a fast-growing track.


In addition, a very easy-to-use platform, GoSquared allows businesses to communicate with their customers through analytics, CRM, live chat, and marketing automation. If you are looking for an all-in-one kind of solution, this might be a great fit.

What’s Right For You

The above selection is by no means the only tools you should choose from. Your budget and industry should be your main criteria when selecting which solutions to try out. While some tools can fit multiple business scenarios, there are many solutions specially tailored for different industries.

Another factor that should be considered is the software’s ability to integrate with the solutions you already have in place. For example, it is important that your analytics tool integrates with your CRM software, so customer and visitor data can flow freely and automatically between tools.

At the same time, you also need to keep in mind what kind of analysis you want to perform.

Forbes has created a short overview of the best analytics techniques every manager should know, and it is worth looking at it before buying.

If you want to gain a deeper understanding of the above techniques, Bernard’s Marr book Data Strategy analyzes them in more detail, while showing how we can profit from big data, analytics and the internet of things.

Do They Comply with GDPR?

Your last step should be to check if your analytics solution is GDPR compliant.

The 25th of May is approaching and eventually your clients may ask about your GDPR status.

One of its main requirements is to make sure that all the third-party services a company is using are GDPR compliant too.

That, of course, is not just something you just ask and wait timidly for a positive reply.

A contract between you and the service may be required and actually advised by your lawyer. If the solution where you hold customers’ data faces a breach, you will be reliable to your clients.


If your work involves the internet, you must have already heard about the General Data Privacy Regulation (GDPR). If you haven’t heard about GDPR at all, start with this introductory webinar by Workable called “What you need to know about GDPR”.

In a nutshell, the aim of the GDPR is to protect all EU citizens from privacy and data breaches, especially since we live in an increasingly data-driven world that is vastly different from the time in which the 1995 directive was first established.

While this law was first introduced in 2016, the key-date is May 25th, 2018, at which time the non-compliant organizations will start receiving heavy fines. If you think that you are invulnerable, keep in mind that in past cases related to processed personal data, it only required just a single unhappy person filing a report, to make the Dara Protection Authority start digging.

Under the new law, organizations that breach GDPR can be fined up to 4% of annual global turnover or €20 Million (whichever is greater). This is the maximum fine that can be imposed for the most serious infringements e.g. not having sufficient customer consent to process data.

Reading this you understand that this law will have a huge effect on how digital marketers work with data.

While you are allowed to store customers’ data upon claiming a legitimate interest, the field is more blurred when we are talking about visitors’ data. While bothering your website’s visitors with consent forms may skyrocket bounce rates, not having sufficient customer consent and easy withdrawal methods may cost you…well, your business.

Having said that, there is no need to despair yet, but start preparing instead. Most marketers are not at all ready yet, so if even if you are starting now, you are still ahead of many other professionals.

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If you are based outside of the EU, you may think that compliance it is not necessary for you, but you could not be further the truth. Even if a single piece of information is in anyway related to an EU citizen, the law applies to you too.

A great resource on this is the another webinar by Workable, specially tailored for US companies is “A GDPR rundown for US companies”.

Mailjet’s Head of Legal has also published the process they followed in order to become GDPR compliant and it can help you make a sense of the steps you should follow next. Of course, the above tips cannot substitute legal advice, where an attorney actually applies the law to your specific needs and guides your steps. Take the above legal information as your aid to get a general understanding of GDPR.


When spring comes, we clean our homes and declutter our closets. Wouldn’t it be great if we also apply this logic to our stored data?

Data are not everlasting. If you are saving your customer data as you save everything else, it is likely that much of the customers’ data you have collected has already gone dark.

Unused or ‘dark data’, can cost you more and more money, since you constantly need to buy more storage.

Also, it may mean that more time is wasted, as it gets harder to access the file you are looking for. Believe it or not, outdated data can even cost you even billions of dollars every year.

So, just like your annual spring clean, it is also important to declutter your database.

Keep also in mind, that under GDPR, identifying what data exists, where it sits, and whether it should be there at all, are now basic requirements to be compliant. If you are not sure when to even start, check this 5-step guide by Marketo.


Customer data must be treated and managed as an asset.

Since more and more data is being collected and processed, the new data need to be aggregated with data collected during previous collection procedures, like your last campaign, your last sales cycle and so on.

This is called data threading, and in simpler words allows you to piece together various customer data collection efforts into a single threaded digital binder.

Improving your analytics methods also means being agile. Customer needs and preferences are changing and you and your methods need to adapt. To make sure that you do not fall behind, your data should always be adjusting to reflect what is happening right now with your customers.

But how can you be agile with data? If you are not sure where to start, Gauthier Vasseur from Stanford University discusses “Being Agile – Adapting to Data” in a simple, yet informative way:

Of course, improving your methods is not a linear process. Remembering what you learned before with Lean Analytics, you make the most out of data when your processes follow this cycle:

Metrics > Hypothesis > Experiment > Act > New Metrics

In other words, your metrics guide your next act, which results in new metrics. Being versatile and adjustable helps you continuously improving and making the most out of a limited budget.

If the above seems too complex to follow, you can always refer to this “Decalogue” by the Corporation of National & Community Service.


As was mentioned at the beginning of the article, many professionals do not have a clear objective related to data collection. Many times data are collected because it is what expected (you never know when they will be useful).

The truth is that your main objective should shape your data collection methods. As a business, the ultimate goal is usually (or should be) to make your customers happy.

Therefore, it is highly important to read data with an unbiased eye and be open to what your customers tell you. Embrace them for what they do and not for what you think they are doing.

With that in mind, understand that your data collection process or analytics, in general, should never hinder your customers’ experience when doing business with you. If you are afraid that this may be your case, you need to start implementing changes now.


Getting the most out of your collected customer data is your key to truly understanding customer behavior and ultimately winning against your competitors.

For those who are not yet convinced that the economy of the future will be based on data and how well you can analyze them, it is worth listening to World Bank’s President’s speech at the Mobile World Congress.

According to Jim Yong Kim, the World Bank’s goal is to help exploit the big data that will result from the extensive implementation of Internet of Things, in order to achieve new economic growth prospects.

It looks that the various economic organization and the market itself can provide the professional that wants to tap into data with all the necessary tools and methods to use it and analyze it effectively.

In this article, we covered only some of the available strategies that can be used to extract the most value from your collected data. If you are interested in learning more, it is advised to review some of the provided resources throughout the article.

Moreover, remember, data is powerful only when collected, stored and used wisely.

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