Undoubtedly, AI algorithms can help build truly transformational marketing solutions. However, as I consistently emphasize, the challenging part of the journey to build these capabilities is not the analytics portion. It is making sure that the underlying data foundation is optimal. This becomes much more challenging to build a comprehensive marketing analytics capability. The varied nature of data sources and data points needed adds to this difficulty level. This three-part article will explore examples of different types of data sources that need to be available in an underlying data architecture. Please note that it should be an obvious assumption that customer consent must be obtained to collect these data points.

Before we start going through those data sources, it is critical to know that there can be so many sources, based on your portfolio requirement, that one can write a book that covers all those data sources. This article will cover only the key categories of data sources.

Point of Sale (POS) and Retail Data

One of the most widely used and valuable data sources, this data can provide some critical insights like:

• Where does the customer buy the product
• When does he buy the product
• Demographics of the customer
• Price history
• Payment methods
• Which other products get bought with the specific product

This data is most widely used, and most organizations may already be able to tap into this data near real-time. Recommendation engines are an example of an analytics method leveraging this data type. In a separate article, we will discuss how AI algorithms can help generate more excellent value from this data type.

Ecommerce sales/Online Sales Data

The explosion in online sales means that the volume of online sales data is rapidly catching up with brick-and-mortar sales data. Though the attributes of this data type are similar to retail data, this is a separate category since you need to capture this data separately and then embed it in your data architecture accordingly. Examples of insights that can be generated leveraging this type of data are:

• Number of times an online ad was viewed
• The lag time between ad views and purchases
• Additional products bought or considered while purchasing a specific product
• Online navigation path of the customer
• Cross-channel behavior
• Performance of online campaigns (words, images, metaphors etc.)

In an Omnichannel context, this data must be paired with the retail data to build the complete picture. Together, these two data sets are powerful. However, the key is ensuring that the data architecture absorbs, prepares, and presents them for analytics optimally.

Social Media data

For decades, organizations scrambled to devise ways to obtain customer feedback and input. Social media allows them to attain those insights they have been seeking for decades if they know how to leverage that data. Social media activities like tweets, reactions, reviews, and comments can be analyzed to generate information about current and potential buyers. In addition to the data generated through activities, our online profiles on social media can reveal additional details about us, the customers. This profile demographic can allow algorithms to fine-tune marketing attributes like marketing language, images, contexts, and price.

While data from social media can provide standalone insights as well, the critical aspect here is to strategize, before you even start designing a data architecture, on how you will pair this with other data sources, and why.

Loyalty Cards

We all possess at least one loyalty card in some form, whether a plain supermarket loyalty card or a retail chain co-branded credit card. In tandem with the POS data discussed above, the data collected through these cards present a much deeper picture of our behavior as a consumer. The aspect that makes the data from the loyalty card powerful is that the company does not have to organize the data to build a picture of your behavior as a buyer. The card data captures your behavior across categories and enables the provider to build an understanding of your behavior, proclivities, personality, and price sensitivity.

While it may seem repetitive at this point, I should again emphasize that the true power of this data source, like all other data sources being discussed here, comes from pairing it with other data sources.

Financial data

This type of data source includes economic data points like:

• Income (personal)
• Household income
• credit status,
• Tax data,

This data type may also include more indirect, deduced financial information that may interest marketing professionals. This data type generally combines with demographic data to build a more comprehensive picture of the individual consumer.

In the second part of this article series, we will discuss the following data sources:

• Demographic data
• Economic index data
• Google CPC data
• Stock market data
• Digital media consumption data

The second part of this article will be published on 10/13/2023.