Data-driven Marketing: how and why to switch to it

They say that you can still meet marketers who wince at the abundance of numbers, formulas, tables and diagrams. Say, this big data of yours is killing the soul of marketing! Okay, Grandpa, go to bed, and in the meantime we'll talk about data-driven marketing, which is already becoming a basic thing in business.
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Data-driven marketing is marketing based on the analysis of large amounts of information about all business processes, mainly about consumers. For large companies working online, this has long been a must-have. But even for offline businesses, the need to work with big data is becoming more and more obvious.

What can data analysis do?

Of course, creativity and qualitative research have not gone away from marketing. On the contrary, with the transition to data-driven marketing, they reach a higher level. The magic of numbers gives the marketer a sharper vision. Without data analysis, even the most creative and fumbling marketer essentially puts forward hypotheses at random: it didn't work out — well, let's try something else. How will data analysis help here?

With the help of data analysis, a business gets a detailed portrait of its client. This allows him to focus on working with those who are interested in the product, and not waste resources on those who definitely do not need it.

The analysis helps to identify problem areas. Let's say where and why the money goes.

Switching to data-driven enables businesses to see and understand how different factors affect each other. This allows you to determine the cause of any event, which means to manage the situation.

To summarize, data-driven marketing gives businesses an understanding of what they need to do to earn more.

When to start?

Many believe that data collection and analysis is for large companies. Yes, for very young companies, the transition to data-driven is likely to be inefficient. There is little data, it will be difficult to draw any valuable conclusion from them.

But the situation "it's too early for us" changes to "we overslept everything" suddenly. When it comes time to analyze the data, it may turn out that there is no data. Therefore, it is necessary to start collecting data as early as possible. Then, when you grow up, you will already have accumulated an array of information.

This is especially true for startups that have already found funding — at least there will be something to report to investors.

What data should I collect?

A set of metrics for tracking is formed based directly on the tasks (ingenious, isn't it?). Concentrate on the key parameters on which the company's KPI is tied. It is necessary to collect data on factors that accurately or at least hypothetically affect these parameters.

No need to collect data just in case. Data-driven marketing should help you optimize your business, and not spend all your resources on collecting useless information. Before you start tracking any metric, you need to ask the question: "Will this help me understand something?"

To distinguish useless information from useful information, you need to introduce the concepts of vanity metrics and quality metrics into your work. Vanity metrics are indicators that look beautiful, but do not directly affect business objectives. These include the number of subscribers and coverage in social networks, the number of site visits, app installations, registered users, leads, and so on.


Someone subscribed to you on social networks — it's not a fact that he reads you. Downloaded the application — not the fact that he uses it. People visit your site — it's not a fact that they buy something. This also includes the number of goods sold — in pursuit of this indicator, you can completely kill the margin.

You can keep an eye on vanity metrics, but you need to consider them only with reference to quality metrics that directly follow from your effectiveness.

Examples of basic quality metrics:

  • average check;
  • number of active clients;
  • the cost of each order and each attracted customer;
  • the profit that the client brings for the entire period of cooperation (LTV);
  • profit from each active client per year (ARPU);
  • share of advertising expenses and return of marketing expenses (ROMI);
  • conversion;
  • customer Retention Rate (Retention Rate).

Also, of course, you need to collect data on income, expenses, profits, and the size of the marketing budget — all companies need to do this.

How to save money?

A common argument against switching to data-driven is expensive. Indeed, cool tools for collecting, storing and analyzing data can cost a lot of money, and not for every company such costs will be justified. But a good analyst can give answers to difficult questions without expensive software. There are a lot of free tools out there.

As the company grows and, accordingly, the volume of information, a greater degree of automation of data collection and analysis will probably be required, for which you will have to turn to paid services. But at first, free tools will be quite enough.

What is the profit?

Proper data processing shows which parameters need to be "tweaked" in order to earn more. This saves time on decision-making and reduces the necessary marketing budget. The analysis reduces the probability of error and, consequently, loss of money.

Let's say you sell hygiene products and want to increase sales. It would seem that an obvious and logical move is to give a discount, say, on toothpaste. What could possibly go wrong?

Well, perhaps most of those who have received information about the discount already constantly take only your toothpaste. That is, the promotion will lead to the fact that they will simply bring you less money. Therefore, it makes sense to offer this segment of the audience to use a mouthwash or dental floss of the same brand, and it is better to distribute promo codes with a discount on toothpaste to a new audience.

That is, if you have detailed data about consumers, you can prepare personalized relevant offers.

Many firms even include the amount of accumulated data in the cost of the company. This may, for example, play a role in the sale of a business. Let's say you sell a pizzeria. It's one thing when a customer receives a kitchen and a moped for delivery. Another thing is when a portrait of the consumer, statistics, feedback, data on the geography of orders, the results of all previously held promotions are added to this — that is, all the necessary information for running this business.

What kind of specialist is needed?

Of course, someone who knows how to analyze data should work in the company. Obviously, he must have skills in marketing statistics, marketing modeling and forecasting. But he will need to work not with a couple of questionnaires from clients, but with such volumes of information that he will have to process it all manually until old age. Therefore, he must also have "technical" competencies.

A good analyst talks to IT specialists without an interpreter and knows how to access databases himself. Therefore, he needs to be able to use SQL, and better yet Python.

The analyst is well-versed in all the above-mentioned services. As for Yandex.Metrica and Google Analytics, then he should be a virtuoso at all.

He knows how to visualize data. Most likely, he has experience with Tableau, Power BI and Qlik. But a good analyst does not just fix and visualize the numbers beautifully. He is able to see the connections between indicators, can explain them and offers solutions to problems. That is, strategic thinking and the ability to defend one's point of view are certainly appreciated here.

How to organize?

If you have a small company, then all the work on data collection and analysis will fall on your marketer. As you grow, you will have a separate specialist analyst. Even further — the whole analytics department.

Keep in mind that using data can be easily deceived. Intentionally or unwittingly, it doesn't matter. For example, sellers bring a report that their sales have increased many times over the past few months. While the director admired the skyrocketing curve on the chart, they have already bulged their pockets and are waiting for the award. But in reality, it may turn out that such growth is seasonal and happens this month every year. And it may even turn out that compared to the same period last year, there is a general drop in indicators, that is, sellers should not be given a premium, but a belt.

Those responsible for data analysis and reporting should be motivated to show a reliable picture. Therefore, in the company's structure, analysts should not depend on departments whose KPIs are tied to tracked metrics. It is best if they report directly to the CEO.

How to interact?

Marketers, financiers, IT specialists and analysts should work in concert. They should have a single data warehouse, and all calculations should be carried out according to a single methodology.

Analysts need to be involved in the discussion of all marketing decisions. For example, before the launch of the campaign, they must necessarily be privy to all the details. At a minimum, they should have the right to ask why they need it when receiving a task. If you set an imperative task for analysts so that they follow the TOR exactly, you risk getting a huge amount of information that does not give you answers to questions.

Understanding the essence of the campaign, its goals and objectives, analysts will be able to choose the optimal set of metrics to evaluate its effectiveness.

Also, if the IT department has conceived some processes related to changing the data architecture, it should discuss these issues with analysts. In general, it will be great if the analytics team has its own developer. This will reduce the likelihood of a situation when, due to the workload of the IT department, it was not possible to organize the collection of some important data in time.

How not to get lost?

There may be not only too little information, but also too much. This is generally a common mistake when switching to data-driven marketing — too many reports.

Often, the manager requires analysts to display a bunch of parameters on dashboards to him in order to be aware of all the affairs in the company. This creates the illusion of control. But only an illusion! When there is too much data, everything merges into a monotonous mess. The manager opens the report, and there are graphs, charts, tables, tables, charts and graphs — it's unclear where to look. Soon he gets the feeling that all this fuss is useless, and he stops looking at dashboards altogether.

Therefore, it is necessary to separate the reporting levels. The content of each dashboard should directly follow from the tasks that the employee solves. The CEO should see indicators that coincide as much as possible with the key KPIs that he and the company as a whole are motivated by. Since he makes strategic decisions, he sees only the main dependencies of the indicators. Top managers see more detailed metrics for their areas of responsibility. They need to see different factors that affect their key indicators. Specialists receive even more detailed reports: all the metrics that matter for their work.

Companies that have switched to data-driven marketing, other things being equal, have an obvious competitive advantage over those who have not yet switched to it. They have a better understanding of the market situation, figure out what needs to be done faster, and therefore grow faster. Therefore, if you haven't done it yet, you should start looking for an analyst.

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