The digital age has brought with it a new era of marketing, one in which businesses must adapt to the ever-changing landscape of the internet. It’s no longer enough to have a website and a few social media accounts. To be successful, businesses must now engage in digital marketing, which often requires careful data analysis.
Data analysis has enabled organizations to segment customers, build user personas, and target consumers at the right touchpoint. That is why data analysis is a critical part of digital marketing. By understanding customer behavior and preferences, businesses can make more informed decisions about allocating their resources. Additionally, data-driven analysis can help companies to identify opportunities and optimize their strategies.
Defining data analysis
Data analysis is the process of organizing, cleansing, and transforming data in order to make it useful for businesses and organizations. Data analysts use a variety of tools and techniques to extract information from data sets, including statistical analysis, machine learning, and data visualization. This process is an essential component of business intelligence, as it helps organizations make better decisions by providing them with insights into their customers, operations, and marketing efforts. Additionally, data analysis can be used to improve current business processes and to develop new ones.
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Benefits of data analysis
Data in digital marketing refers to massive sets of unstructured data derived from multiple sources that need to be processed to provide actionable insights to a company. In itself, structured data is already a huge benefit of data analysis, however, there are some specific business areas that leverage the use of this information.
Data analysis is the process of modeling raw data to discover useful insights, suggesting conclusions, and supporting decision-making. The combination of digital marketing and data analysis provides marketers the ability to collect customer data at scale, analyze it to better understand customer needs and wants, and then use those insights to create more targeted and effective marketing campaigns.
1. Developing marketing campaigns
Data analysis enables companies to better understand customers’ needs and wants by developing powerful insights. One example of applying data analysis to the construction of marketing campaigns is the understanding of consumer behavior and consequently the development of different buying personas. An understanding of customer behavior is essential when developing a marketing plan and it can be sourced through data. Information like purchasing patterns, demographics, or favorite products can all be sourced through data collected by cookie files. These files collect information about website visitors’ activities as they browse the internet – meanwhile, this data generates personalized data that companies can use.
2. Tracking digital performance
Marketing performance metrics help you understand how your marketing efforts are contributing to your digital success and goal meeting. Collecting and measuring customer data can help make strategic decisions to improve overall business performance. This can be done through the measurement and tracking of specific KPIs like customer acquisition, growth, and retention. A good digital performance results in an enhanced user experience and better resource allocation.
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3. Better customer experiences
In today’s competitive and massified market, personalization plays a big role in customer experience. Personalization now extends to the whole customer experience, which means that every touchpoint the customer has with a brand is personalized. These personalized actions are not targeted just for the alike customer, but for the customer as an individual. Through AI mechanisms, and data collection it is possible to provide product suggestions, offers, and communications that are uniquely relevant to an individual.
Customer experience leaders in the retail space have provided their shareholders with returns that are three times higher than the returns generated by retailers with low customer satisfaction scores.
According to McKinsey
4. Customer segmentation
It is essential that every business knows and understands its customers’ wants and needs. For this organizations often build target personas – figurative alike customer – that represents their ideal customer, and most notably with the same pain points. Customers can be segmented based on different characteristics such as location, purchase history, website navigation, payment methods, and so on. All this information can be sourced through a collection of data and it is even possible to determine the value of each customer group. This can then influence marketing strategies and how resources are allocated.
Real-life applications of data analysis in digital marketing
Nowadays almost every company takes advantage of consumer data, here are some examples of how some organizations leverage data analysis.
1. Netflix
Netflix’s recommendation feature is based on data collected on viewer history, similar interests, or even customers with similar view history on the platform. This enables the feed of every client to be different and personalized for each individual. In the end, this feature is a win-win for both company and the customer – the customer has a unique and differentiated experience as the company keeps the user subscribed month after month.
2. Spotify
Similar to Netflix, Spotify recommends music and podcasts based on listening history. As a result, it provides a unique feed with new and interesting music for the user to discover. Playlists are curated for every user based on their listening history and on artists the platform knows the user enjoys. This keeps the user engaged with the platform and with a fresh feed, designed uniquely for them.
3. Amazon
Amazon is the biggest retailer in the world, as such, they collect massive amounts of data every day. This data is processed to figure out how customers spend their money on an individual product. All this information is being collected to expand customer relations, offer better product recommendations and improve overall customer experience.
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How to implement data-driven marketing
When thinking about implementing data-driven marketing in a company, its main concern must be finding people with the right skills for the job. This process can represent hiring data analysts (or data scientists), building an entire team/department or resorting to outside partners. The choice between each of these options depends on the company’s needs and possibilities. If the choice is to hire new people, then data analysts are what you should look for in the market. Airbnb is actually a great example of this. Since its early days, when the team was only seven people, they hired a data scientist to help them grow the business.
However if you want to start making data-driven decisions in your business, besides people with right skills, you need that everyone in the business adopt a mindset that includes data in their daily activities. Only by doing this can you turn data into a strategic asset and build actionable insights. Educating your team about how data can be useful in daily tasks and decisions is a great way to make them embrace data and analytical thinking.
