The world is becoming extremely conscious nowadays in their presence online. Algorithms are getting smarter and smarter and more personal as time goes by, the reason why people are a little skeptic to give their personal data on certain circumstances. Gaining customer data is one valuable information a marketer could ever do. It is said that customers usually share personal data when something is offered to them in exchange such as discounts, special offers, or loyalty points and premiums.
For organizations and businesses lurking around the online world gathering customer data, it must be a requirement that they process and churn data into the optimum use. Nowadays, with the algorithms getting smarter and smarter, gaining basic customer data is more than just collecting name, address and birthday. For a data to be considered useful, it must deeply be personal to a consumer.
Below are some data types that must be considered by marketers and strategists when customer data is being collected:
1. Make Everything Personal
To be honest, we all know that personalization is not something new in the world of marketing. But it has changed all throughout time. Overall, data gathering should be practiced with great transparency. Let’s take a certain organization for example, the giant music streaming app called Spotify.
Spotify launched a campaign called Spotify Wrapped, where it collects the users streaming data in the app and summarizes the entire year for a listener. They do this annually. The campaign is highly personalized that users and listeners flood social media with their annual summaries of music streamed. The campaign that Spotify did is very effective since the information they collected to their listeners has been available and open to the public. They just found a creative way to repurpose the information, and make their platform more pleasing to the market. This is a very nice way of personalization.
2. Eliminate Obstacles
Customers are the ones paying whatever their purchase is, so it is understandable that they want autonomy in their buying journey. Every decision that they make when buying needs to be personally coming from them, their ideas, emotions and experiences. They need to eliminate obstacles and distractions when making a decision. It is indeed the job of businesses to eliminate the obstacles for the customer. Paving the way and making it easy and very convenient for them to reach the end of the purchasing process.
3. Establish Customer Preference Center
A customer preference center is something like a collection of gathered customer data that is processed by marketers in order to make intelligent and more personal offers to the customer in the future. Establishing a preference center is a very logical way to practice personalization. Businesses in the clothing industry are doing this by asking data from the customer about their favorite color, brand and style. Later on, a customer will receive a style recommendation from the brand, making it a personalized offer that elevates the experience of the buyer.
Types of Data
For marketers to have effective personalization strategies, data collected from the customer must be analyzed properly. It is vital that one must know the types of data being collected in order to process it efficiently.
1. Quantitative Data –
quantitative data is the type of data that enables a marketer to know and understand customer behavior, transaction making process, and their usual reactions with the brand and the business. Anything that happens and takes place between the customer and the business is considered quantitative data. Examples include website activity, social network activity, transactional information and customer services information.
2. Qualitative Data –
qualitative data is the kind of data that is usually collected using surveys and questionnaires. This is done in order to effectively identify a customer’s attitude, purchasing motivator, and even opinions. Some of the information that can be collected includes customer opinion, motivations, attitude on buying and reaction done after the purchase.
3. Descriptive data –
descriptive data is the type of data that provides more context compared to numerical or quantitative data, and more ideas compared to qualitative data. This type of data is being used to help in making business decisions. Some examples of descriptive data includes lifestyle information like car model, pet owned and type of property owned. Others include habits, online hobbies, career details and educational level.
4. First Party Data –
first party data is the type of data that is accumulated from the customers and prospects. It is relevant since it is gained from actual customers of an organization. It presents relevant experiences of a customer. Data usually comes from an app or website, data from your organization’s CRM, social media data and subscription data collected.
5. Second Part Data –
this type of data is the one that is coming from a source other than your own data mining tools. It is necessary to have this type of data in order to have a greater scale that allows more detailed data analysis on your customer’s behaviors. Such data are usually social media data, customer surveys and questionnaires and activity of consumers on your website.
6. Third Party Data –
third party data is the same as second party data, when it comes to where the source comes from. Third party data usually comes from outside sources but the difference between the two is that the sources are not the original owners of the data. So basically, a third party data is second party data obtained by a company, making you the third owner of the collected information. Third party data is said to be not always relevant and useful but they can fill the gaps and provide quality in certain circumstances.
Conclusion:
In conclusion, the landscape of data collection for hyper-personalized marketing is evolving rapidly, driven by the increasing awareness and sensitivity of individuals towards their online presence. As algorithms become more sophisticated, marketers face the challenge of obtaining valuable customer data while addressing privacy concerns and skepticism.
Effective data analysis is crucial for organizations navigating the digital realm. Beyond basic information, the depth of personalization is key to unlocking the full potential of collected data. Spotify’s annual campaign, Spotify Wrapped, exemplifies a creative and transparent approach to personalization, turning user data into a personalized experience that resonates with the audience.
Marketers must embrace strategies that prioritize personalization, and this involves eliminating obstacles in the customer journey, ensuring autonomy for customers in their decision-making process, and establishing customer preference centers. The latter allows businesses to intelligently utilize gathered data for personalized offers, enhancing the overall customer experience, a concept exemplified by the innovative techniques employed in “coinflip marketing.”
Understanding the types of data is vital for crafting effective personalization strategies. Quantitative data provides insights into customer behavior and transactions, while qualitative data delves into attitudes and motivations. Descriptive data offers context and aids in decision-making, while first-party, second-party, and third-party data contribute different perspectives, allowing for a comprehensive understanding of customer preferences and behaviors.
As businesses tread the path of hyper-personalized marketing, they must strike a delicate balance between data collection and respecting privacy. Transparency in data practices, coupled with innovative and respectful personalization strategies, will be instrumental in building trust with customers. In a world where data is abundant, the ability to harness it responsibly and meaningfully, as demonstrated in “coinflip marketing,” will define the success of hyper-personalized marketing endeavors.
Frequently Asked Question:
1. What is hyper-personalization, and why is it crucial in data-driven marketing?
Hyper-personalization involves tailoring marketing efforts to an individual’s specific preferences, behaviors, and needs. In the context of data-driven marketing, it ensures that collected data goes beyond basic information like names and addresses. The example of Spotify Wrapped demonstrates how businesses can creatively utilize personalization to engage and delight users.
2. How can businesses eliminate obstacles for customers in their buying journey, and why is customer autonomy important?
Customer autonomy is key in the purchasing process, and businesses must pave the way for a seamless and convenient buying experience. This involves understanding customer decision-making, emotions, and experiences. Learn how businesses can prioritize customer needs and eliminate obstacles to create a personalized and satisfying buying journey.
3. What is a Customer Preference Center, and how does it contribute to effective personalization strategies?
Customer Preference Center is a collection of customer data used by marketers to make intelligent and personalized offers in the future. Discover how businesses, especially in the clothing industry, leverage this approach by gathering data on customer preferences such as favorite colors, brands, and styles. Learn how this practice enhances the overall buyer experience.
