Slowly but surely, data-driven is taking over the world. The concept of data-driven decisions is increasingly common – decisions based on a large amount of data. Whatever companies are from the point of view of management culture, they somehow use data to make a decision – otherwise, the decisive one would have nothing to rely on. But then there are two ways:
- A business owner hires a manager who has nine years of experience in the same industry and has been in a management position for five years. Thus, the responsibility for intuition, experience, and other professional qualities is placed on the manager’s shoulders.
This approach may occur because a professional can quickly assess the situation, draw conclusions, and propose solutions. It is a great option when there is no time to collect data and conduct brainstorms. But if something goes wrong – and the manager’s decisions do not lead to success, he is replaced and so on.
- The second approach relies entirely on data. In this case, the company creates a department that deals with data and creates an infrastructure (data supply channels, storage locations, software for analytics, dashboards, etc.).
The most crucial thing in this approach is the objectivity of the data. If the system is configured correctly, the data is correct, then the conclusions that the mathematical algorithm makes are accurate and correct.
In case something goes wrong, then you need to find a failure in the system. For example, you have an email database that you formed ten years ago and have never cleared of inappropriate contacts. It is the likely reason for the low conversion rate. Clean the database, check the result, and look for the reason again if it doesn’t help. The debugging process in a data-driven approach is generally a particular kind of torture, but the end justifies the means.
I don’t think it’s worth mentioning which approach most companies prefer to take. Of course, it’s easier to rely on expertise. And the data-driven approach in its classic definition is generally a rarity.
What is data-driven, who uses it, and where
The data-driven approach is when data drives decisions in a company. A data-driven organization is a company that collects, processes data, and uses it on time to improve efficiency in the production and development of new products and to exist in a competitive environment.
The data-driven approach virtually eliminates guesswork from business strategies, providing companies with more accurate insights. Data also significantly impacts marketing and is fundamental to business strategy, advertising, and sales. It helps streamline companies’ operations, allowing employees to work faster and more efficiently, which ultimately has a positive impact on the quality of customer service. Using data to drive its actions, an organization can personalize its messages to leads and customers for a more customer-centric approach. The data-driven approach encompasses many types of data, including machine data, relational data, transactional data, big data, and AI. The data-driven approach contrasts with decision-making, driven by emotion, external pressure, or instinct.
Transitioning a company to a data-driven strategy is a huge step forward in advancing and enriching an organization. There are endless benefits, and while making this massive change to data can be intimidating, the benefits far outweigh the costs and can help a business grow exponentially.
Relying on data has several benefits:
- All critical decisions in management, design, and marketing are based only on numbers and facts.
- Data analysis offers useful information in various areas, both internal and external.
- User behavior forecast based on patterns.
- Improving the effectiveness of advertising campaigns.
- Always up-to-date data and this eliminates the human factor.
- Customer service improvement. In other words, by using a data-driven approach, you get a roadmap for improving customer focus.
But for the data-driven approach to provide high-quality results, everything must be properly configured and connected. At the same time, one should not forget about each company’s uniqueness, and therefore it is worth considering the specifics and characteristics of both the business and the industry.
The approach is used in marketing, UX, sales, advertising, and in general, wherever there is something to collect and analyze. Even the smallest decisions can lead to large deviations in profits. This Google case illustrates how meaningful data can be.
About seven years ago, Google launched ads in Gmail. Similar blue links were already in the search, but in Gmail, they were slightly different and it was decided to conduct testing. The essence of the test was that 1% of users saw links of one color, another 1% – another. As a result, we found out that the shade, which was a little closer to purple, converted much better, we left it. This allows the company to receive 200 million more revenue every year.
The role of data in decision making
Data is the lifeblood of smart companies today. Organizations are leveraging data to become more intelligent, make quicker decisions, and drive business results growth.
The amount of data is continuously growing according to statistics that were cited by the Institute for Business Analysis (IIBA)
Business is becoming more and more data-oriented:
- Leaders of 82% of organizations say they are increasingly using data to implement automated solutions;
- 89% of companies believe that Big Data will revolutionize business operations, just like the Internet once did;
- 79% of companies that do not use big data will lose their positions and maybe on the verge of extinction.
Data is transforming the industry.
Companies that have been collecting and processing data have switched to a new, customer-centered approach to solving their business problems for a long time. This is when their entire business and services are built around customers and data. Such giants as Google, Amazon, and Netflix have been collecting data for a long time to personalize their services to improve customer interaction in the future. Not surprisingly, these companies have relatively high customer satisfaction rates.
How to use a data-driven approach in practice?
It is good if the company’s management is fans of the data-driven culture and want to implement it in their organization. The use of data in decision-making makes this process quite laborious and expensive, which at the beginning of the journey requires the conviction that it makes sense to use just such an approach. If you have confidence in the right choice, then this is not enough.
What should you do first if you just decided to become a data-driven organization? What are the next steps?
Of course, you need data to start using a data-driven approach, but where do you get it? To do this, you build the infrastructure. Then there will be data collection, reporting, and analysis of the results, which will lead to actions, which should lead you to obtain the desired profit.
The first thing you may encounter when building a data-driven decision-making process is that you don’t have enough data, and that’s okay. To start working with data, you need to build an infrastructure for collecting and storing metrics.
In most projects, for backend data (for example, information about customers, their orders), a replica of the production database is used. Here you may face the fact that your software’s internal data structure is not adapted to make the data easy to analyze. In this case, you need experienced developers to set up the structure to collect and analyze data.
In the beginning, you will need only one database, where the structure of this data will be simple. You will begin to have more complex problems that you want to solve in the future, and then the structure can be improved. So you might need to build a Data Warehouse.
For front-end data (page views, scrolling, clicks, etc.), you can use classic tools like Google Analytics and HotJar to record sessions. Essential functions will be enough to get you started so that you can solve marketing problems.
Once the underlying infrastructure is in place and you start collecting data, it’s essential to ensure that the product evolves in sync with its metrics. In other words, when you want to implement a new function in a product, you must understand what key business indicators this will affect, what changes will occur in the customer journey or backend algorithms. When you have the answers to these questions, you will need to solve how you will collect new data.
It is essential to ensure that the entire team understands the importance of collecting and storing statistics so that there are no disruptions to the system. For example, you lose Google Analytics tracking from different pages, which means that the developers have not been informed about this task’s importance. You must have the necessary shared libraries, QA guidelines, etc.
Analytics and reporting
Data availability does not mean effective use of data. At the stage of data processing, you may face the following tasks:
- Where to get this or that metric, and how to extract it?
- Is the collection set up correctly?
- What report should be made so that you can draw some conclusions?
This work is quite voluminous and requires special skills and knowledge. To cope with such work, you need an analytics department, where professionals will have good knowledge of SQL and understand how and what data you need to make business decisions. Working with data does not seem like an overwhelming task. You can create a module that will collect data and generate reports on different metrics to not waste time on parsing numbers and make decisions quickly. But before proceeding to specific actions, it is necessary to go through the analysis stage.
Analysis and actions in a data-driven process
At this stage, based on the data obtained from the reports, you can form hypotheses about what needs to be improved in the marketing strategy or the future / existing product to increase user engagement, loyalty and ultimately get more profit.
Let’s say you have formed several hypotheses that now need to be tested. Validation is usually done using A / B tests (we give a similar example in the Google case).
Many companies actively use multivariate tests for value propositions, product features, interfaces, and more. A / B testing is widespread, but the bottom line is that it can be used not only to change the color of buttons or redesign the landing page but to change some parts of the customer journey, which in turn can affect some of the leading business indicators.
Correctly conducting such tests is a whole art because the error will be costly in this case. If you need several A / B tests and no more, you should contact the specialists and not create your software for this task. But if you plan regularly to conduct such testing, you have a complicated customer journey; you want to perform complex experiments that have a complex impact. Then you should think about a digital solution that will help you cope with this task quickly and precisely as it will be convenient for you.
It is also worth saying that you will need the analysis just like before proceeding to Actions, during, and after their execution. You need to continually analyze what is happening and how to make the right decision as a result.
When your business starts to grow, and the volume of data grows, it makes sense to think about advanced statistical techniques and applied libraries, commonly called data science.
Data science is not only neural networks and machine learning but also a transition from classical packages like SAS for building logistic regression to self-written tools in PHP or Python, for example, which can significantly save you resources.
Logistic regression and cluster analysis with specific data volumes can be used to segment customers and determine the optimal product or discount strategy individually.
- We are ready to invest in working with data: extraction, storage, analysis, interpretation, visualization, and more.
- We are ready to listen to data. When we need to make a business decision, we stop and say to ourselves – let’s look at the numbers.
- We can understand the data. Indeed, it is easy to draw the wrong conclusion, having in hand all the necessary numbers. Whatever one may say, there are some minimum requirements for analytical thinking of decision-makers to make sense from tables, graphs, and charts.
- We trust and use data when making decisions. When a manager, looking at a prepared analytical report, says that he will do better as experience tells him, and not a report, he is not necessarily wrong. What if the analysts did not consider the seasonality, the results of the upcoming elections, or something else?
Naturally, a company’s data-driven culture is most comfortable to build when the founders of the company are already its carriers. The use of data in decision-making makes this process more time-consuming and expensive. And without a severe conviction that it makes sense to do this and not otherwise, you will not go far.
- Google Analytics, Hotjar, and Google Tag Manager;
- End-to-end analytics services – either collect reports on your own or contact specialists and technologies;
- Data visualization services – Chart.js, ZingChart, Timeline, and others;
- Big Data technologies and programs – here, data-driven decisions are closely intertwined with big data because they are based on the same thing – large amounts of data.
Data-driven must be entrenched in the culture of the company. Leaders and employees must think about analytics every day and turn to it as inspiration. To do this, you need to learn to analyze large amounts of data and interpret them. It forms a data-driven culture, which also determines whether it makes sense or not.
Anticipating the future
In any business, profit and loss are the critical success indicators. Accordingly, the role of various predictive models and their integration into forecasting the future of P&L comes to the fore. Examples of such models are average bills based on customer segmentation data, number of purchases based on refund data, etc.
It is generally very inspiring when there is a toolkit that allows you to assess your feature’s impact on various key business metrics and predict an increase in the company’s revenue.
To develop, maintain, and implement such tools, you will most likely need a financial planning and analysis department, whose task will be to make business decisions even more supported by data, analysis, and modeling.
In the future, for the development of BI infrastructure, you will need to create departments that support it and processes that use it.
To sum up
Data-driven is a highly effective approach. With objective data, you will have the basis for making informed decisions. Data-driven eliminates mistakes and gives the business an edge over competitors. Use it for the good of your business: in management, marketing, and design.