We live in an age of data. From buying a latte or liking a Facebook post to applying for a car loan or Googling the closest restaurant, just about everything we do creates or transmits a record of information. Companies are awash in this data, and while some utilize it to personalize their advertisements and recommendations or improve their products and services, most do not have a clear-cut strategy for monetizing the data at their disposal.
However, by deploying proven data monetization best practices, companies have the potential to avoid commoditization, achieve diversification, limit production costs, create unique products, unlock new markets, and/or maximize value offerings to customers and shareholders.
To help companies get up to speed on these best practices, I have crafted a three-part series that serves as a primer on data monetization. Future installments of this series will cover tips for indirect data monetization and tips for direct data monetization, but below, I set the stage by exploring three of the most common benefits companies achieve by leveraging data monetization strategies.
Defining Data Monetization
Before diving into specific strategies, it is worth taking a moment to clearly delineate what “data monetization” does and does not mean.
In short, data monetization is a strategic process through which companies leverage their data to increase revenue, reduce costs, and/or increase profits. Data monetization does not relate solely to individual techniques like business intelligence or technology enablement, nor does it provide an avenue through which to bring about large-scale technological change. Rather, it consists of a clear, focused strategic approach to converting exploitable data into increased revenues and profits.
Data monetization can take one of two distinct forms: indirect or direct. I will tease out the differences in greater detail in future installments of this series, but for now, suffice it to say that indirect data monetization efforts are comprised of internally focused strategic initiatives and direct data monetization efforts are comprised of externally focused strategic initiatives.
For companies that are just starting to formalize their approaches to data monetization, indirect data monetization efforts often represent the easiest first step, as most companies can execute these strategies without significantly altering their business model or organizational structure. Some of the most common benefits of these types of data monetization strategies include:
1. Enhanced Existing Goods and Services
This type of data monetization initiative is best illustrated by a brief case study. Capital One understood that while its customers were worried about credit card fraud, they found reading itemized credit card statements tedious. This was a direct result of the way the statements displayed merchants’ processed names. As a solution, Capital One created a database of merchant logos that could be pulled into customers’ statements and displayed alongside the corresponding transactions. Having a merchant logo next to a transaction record made it much easier for customers to identify the merchant, remember the transaction, and confirm the transaction’s validity.
Capital One’s willingness to document and resolve customers’ pain points can be taken as a model for all companies looking to enhance their existing goods and services through the use of data. Product owners typically obtain data regarding which features customers use, which features they do not use, and where product usage roadblocks are likely occurring. By working in conjunction with these product owners, business leaders can utilize this data to rapidly identify problems and create solutions.
2. Optimized Sales and Marketing Efforts
This type of data monetization initiative requires strong strategic leadership, robust data science skills, and a firm commitment to giving a variety of stakeholders access to a number of disparate data sources.
For many companies, customer contact information lives in one system, customer transaction histories live in another system, and financial/payment data lives in a third system. In order to create predictive models that optimize sales and marketing efforts, data scientists need to be able to synthesize all this data without encountering significant data access obstacles. Provided the conditions of possibility for such synthesis are in place, it is relatively easy to generate customer segmentation models or propensity to purchase models, as well as to perform key feature identification and extraction.
Armed with these insights, sales and marketing teams can improve the accuracy of their sales forecasts, enhance operational efficiencies, and prevent competitor acquisition of existing customers. Many teams have even unlocked new markets for their products based on cohort analyses.
3. Reduced Costs and Improved Decision-Making
Most companies possess large volumes of data on their suppliers, materials pricing, and production processes. They also have a wealth of data on customer demand profiles, transaction histories, and general market trends. While business intelligence reports can help companies leverage this data to improve their decision-making on a tactical level, companies also have a strategic need to utilize the full breadth of data at their disposal to properly evaluate the broader production environment.
While, in the 20th century, decision-making often boiled down to experienced individuals making tactical judgements based on current and historical reports augmented with basic business intelligence, we are currently seeing a shift toward the widespread use of predictive and prescriptive analytics and sophisticated data modeling. By implementing highly accurate forecasting methods rooted in these advanced applications of data science, companies can meet their strategic needs and significantly reduce both production and inventory costs.
The Need for Data Monetization Is Clear
Ultimately, whether it is through the types of data monetization strategies outlined above or the direct data monetization strategies I will cover in the third installment of this series, companies stand to gain a great deal from leveraging the data to which they already have access.
Thanks to the emergence of advanced tools and methodologies like distributed computing, data science, and cutting-edge visualization applications, data has become an asset class of unparalleled importance — one that no company can afford to ignore any longer.