For running a successful business, brands must take data-driven decisions. According to the S&P Global Market Intelligence Study, 96% of people stated the importance of data utilization in the decision making process. Rather than relying upon your assumptions and luck, brands use relevant customer insights and data to make relevant and smart decisions.
36% of marketers stated that data helps them in connecting with their target audience. Data-driven decision making is used in every business. It also helps in predicting new trends so that you stay ahead of your competitors.
In this blog, we’ll learn why data-driven decision making is important, how to implement it, and some of the examples of how brands have made successful decisions based on the data available.
Why is Data-Driven Decision Making in Marketing Important?
Data-driven decision making is a process of using data to make smart decisions. The data helps in making strategic and well-thought decisions. Monitor and analyze data from various sources such as market trends, customer behaviour, etc, so that marketers can understand the latest trends, customer behaviour, and optimize marketing strategies and campaigns.
It eliminates the process of guesswork and subjectivity. Making a data-driven decision helps in avoiding biases and making a rational and objective decision based on facts and data. This helps in gaining better outcomes and more success to the brand.
Also by analysing data in customer behaviour, marketers can optimize their budget and utilize their resources more optimally by spending them on channels which are preferred by their target audience. It also helps in creating more targeted campaigns. This helps in increasing engagement and conversion rate. By studying customer behavior and interest, you can provide a more personalized experience to the customers. Which will boost customer loyalty and trust.
How to implement a data-driven approach?
Here are six steps for creating a data-driven approach
- Goal: Firstly set a clear goal. What do you want to achieve, is it more leads? Increase in traffic? Etc. Whatever your goal is, it needs to be well defined and well communicated between your team.
- Strategizing: Whatever your goal is, it is important to plan or strategize a way to achieve it. Focus on the area where you think data-driven decisions will enhance your business. For example, your goal is to get more leads by building an email list. In such a scenario, you can create a lead magnet, such as a case study, etc, to increase the email subscription.
- Data: There are two types of data, qualitative and quantitative. Qualitative data is more subjective and nonnumerical data, whereas quantitative data is objective and numerical data. In order to fulfill your goal, you must know which data you want to collect.
- Data Collection: Once you have decided which data you want to collect, you must know how to collect this data. Is your company capable of collecting data or need external support? Make sure the source of your data is reliable. Also consider the sample size and method of sampling. Data coming from one source is uni-dimensional with limited scope. It is important to have more than one source for better and accurate results. It is advisable to use at least five sources of data. Where three of these sources are external. However, it is important to have common variables, while working on multiple sources.
- Analysis: Analyze all the collected data smartly and sharply. If you have the resources and capability to analyze the data, then you’ll be able to make accurate conclusions from it. However, if your brand lacks it, you should hire a professional for analyzing the data.
Decision Making: Once you have collected and analyzed the data, it is important to make a smart and profitable decision. In order to do so, you must transform the data into actionable insights. You must communicate the true meaning and value of the data to every member of the brand in order to achieve the best results. Every brand collects the data, but it is presentation and timing that can make a difference.
Classic examples of data-driven decision making
According to a report, highly data-driven brands are three times more likely to witness a significant impact and improvement in decision making compared to those who had less reliance on the data. Here are examples of brands who have made successful data-driven decisions.
- Walmart: When they were preparing emergency merchandise for Hurricane in France in 2004. The executives wanted to know the type of merchandise they should stock before the hurricane. They searched for the records of past purchases from other Walmart stores under similar circumstances, where they sorted a terabyte of customer history to decide which type of products should be sent to Florida. It was found at the time of natural disasters. Americans prefer strawberry pop-tarts and beer. They created a huge profit by anticipating demand since most of the products were sold out.
- Amazon: Amazon uses data from customers’ past purchase history, which is paired with behavioural analytics techniques, to help in generating accurate product recommendations for the customers. These recommendations are implemented across different touch points in the shopping experience. For example, if a customer orders a laptop, then Amazon can recommend a laptop bag at checkout or via email after a few days of purchase.
- Netflix: OTT industry is becoming increasingly competitive, hence it is important to enhance user experience and gain a competitive advantage. Netflix studied different metrics such as watch time, location, date, type of serials and movies viewed, medium of using the app, also whether a user resumes a movie after a pause, etc. With the help of this data, they created a recommendation algorithm to improve the watching time of the viewers. After implementation, they realized that 80% of users followed their recommendations. It improved their retention rate.
- Southwest Airlines: They analyzed customer data to understand which new services will be most popular with the customers. By analyzing the customer behaviour and data, they deliver to different segments of customers the best rate for their needs and provide a smooth user experience. As a result, their customer base and loyalty increased.
- Washirika 3 oaks: It is a leading construction firm in South Africa which provides all-in-one solutions in the commercial construction sector. They suffered losses due to lack of real-time control over the progress of the project. To make a change, the firm invested in construction cost management software to eliminate manual data collection through automation and gain real-time useful information and data. It enhances the financial and operational efficiency of the firm. It also improved the turnover of the firm by applying construction cost control strategies.
Data helps in gaining useful information about the brand and target audience. It helps in taking strategic and well-thought decisions which are backed by the data. However, data analytics require selecting the correct source and strategy. Other than this, marketers have to deal with data challenges of studying correctly, data can help in taking powerful decisions. Data collection is equally important to analyzing the data. Lumia 360 can help you in analyzing the data and drawing meaningful conclusions. We also offer digital solutions to small and medium enterprises to enhance their growth rate.
Read Also: The Ultimate Guide to Building an Email List
