There are many applications of data analytics to the business field of marketing. You are probably most familiar with the application of data analytics to marketing. This is due to the widespread use of analytics for marketing on the internet.
Data is at the center of personalization, which means a personalized experience. Companies are using data to tailor their marketing of products and service to individuals. One could argue that personalization is at the heart of modern marketing. It is no longer just about advertising a one-size-fits-all message. It is about improving the customer experience and providing value. Some people like this, but others feel that personalization is a bit invasive. The company must find the right balance.
Ideally, marketing is a form of two-way communication. We typically think of marketing as a company communicating information about their products and services. This is important, but it is extremely limited if this is only one-way communication. What does the customer want? What has been their experience with your product or service? Information also needs to flow from the customer back to the company. This feedback loop allows the company to assess what’s working and what isn’t. Modern marketing is about optimizing this two-way communication by using data to improve the customer experience.
An example of personalization is the recommendation system (algorithm) on services like Netflix and Amazon. Recommendation systems provide a recommended product or service based on data, which is a great example of the insights provided by data analytics. If the provider of the service knows what you’ve liked before, it can recommend other items that are likely to be of interest to you. Beyond this step, if the provider can find similarities between you and other users, it can use data from other users to make recommendations for you. Showing “what’s popular” is a way of informing the user about what other users find valuable. Recommendations can also use data based on your location (“allow app to use your location”?). For instance, Starbucks can use local temperature to determine whether to recommend a hot or cold beverage. The recommendation is based on an estimated probability of what the customer is likely to want. In other words, data on consumers helps predict consumer behavior. Every social media platform uses its own unique recommendation algorithm to decide what to show in the user’s feed. For instance, the algorithm used by TikTok is different than the algorithm used by Instagram. This will influence how people use the platform and whether they like it or not.
A marketing channel is the medium through which the message is being delivered to potential customers. In the history of marketing, channels like newspaper, radio, and TV were very important for reaching mass audiences. Today, the digital channels are most important. This can include a website, advertisements on internet platforms, email, etc. One of the key benefits of the internet as a marketing channel is the ability to observe consumer behavior. Traditionally, businesses tracked people’s behavior by interviewing or surveying them. This provided a rough measure of marketing effectiveness. Today, the internet provides a more precise way to track responses to marketing through views, clicks, purchases, etc. In other words, the internet produces data that can be analyzed to improve marketing performance.
A marketing campaign is a particular initiative used by a company to market their product or service. Data analytics can be very useful for evaluating the effectiveness of a marketing campaign by tracking users through various touch points.
A marketing funnel represents the stages of a marketing campaign that reaches a wide number of potential customers at the beginning stage and then gradually moves a smaller number of users through the stages of consideration to the point of purchase. The funnel moves customers through the customer journey. It is referred to as a funnel, because the opening part is wide (reaching the most potential customers) and moves them to the point of sale (a smaller number of core customers). An attribution model can then attribute the success of the campaign to the various stages from first touch to last touch. This process again reflects a customer journey and the goal of the marketing is to help support the customer experience through this journey. Analytics identifies where the experience can be improved.
Businesses use data analytics in marketing to improve all of the following:
- Customer acquisition
- Customer loyalty
- Customer revenue
Using data effectively can increase customer acquisition, loyalty, and revenue. These are stages in the customer journey and data helps the company know how to move the customer further along in this process. Acquisition is the first purchase, loyalty is continuing to purchase, and revenue is the amount being purchased.
Segmentation is the process of grouping data. In marketing, this is often used for grouping customers by similar preferences. Many marketing campaigns are focused on a particular segment of potential customers.
More and better customer data also means better product and service design. Data creates a feedback loop by which a company can learn customer preferences. This is a form of marketing by which the firm learns from the customer. This is the future of marketing based on data analytics.