A Definition of Data Modeling
Marketers are relying on data more now than ever before, as data is more readily available to companies and customer analytics solutions are available to companies of all sizes. A recent survey found that Big Data was the third highest priority for US digital marketers in 2015, and marketers have specific perceived benefits of effectively using Big Data. Infogroup also reported that 20% of American marketers planned to greatly increase their marketing budgets for data-related items in 2015.
Data modeling assists marketers in visualizing flow and in supporting marketing strategy development. Models provide marketers with a framework to assess how campaigns are performing in comparison to competitors. Data models also aid marketers in communicating their purpose and objective behind certain strategies.
There are a variety of data modeling approaches to gain insight, and organizations must decide which model is best suited to their company. In order to do this, organizations identify their goals and key performance indicators (KPIs) to get an idea of which kind of marketing data quality matters most to them.
Challenges with Data Modeling
One of the biggest challenges organizations face is in determining the type of marketing data quality that is most important to achieving their goals. Companies struggle when there is a contradiction between the insight the model delivers and the analytical quality of the model itself. In fact, organizations must be aware of the fact that more accurate and complex modeling techniques are less intuitive and provide little insight into behavior. On the other hand, models such as decision tree and linear regression typically are less accurate but are more easily understood by stakeholders.
Organizations also need to recognize that the quality of the data modeling technique depends on the quality of data that is put into it. Essentially, the quality of your data model depends on the quality of your data.
Benefits of Using Big Data and Data Modeling in Marketing
Study after study is finding that data management professionals and marketers are reaping the benefits of effectively using Big Data and data modeling. One poll found that 58% of data management professionals report keeping high-quality customer data increases efficiency, and a majority of them agree that modeling aids them in making better, more informed decisions. Another study found that nearly 40% of marketing and communications executives are generating significant business revenues by acting on their data, and another 37% are taking major steps to gather and analyze the information they have.
Types of Data Modeling Techniques
Deciding which type of data model to use depends on your goals. More common goals for predictive marketing analytics, for instance, include identifying the best target for acquisition within a prospect list, determining the best ways to cross-sell to existing customers, determining opportunities to deep-sell to existing customers, and preventing customer churn.
One of the most widely used data modeling techniques is for attribution, followed closely by data modeling for calculating customer lifetime value. Attribution models are the set of rules that determine how credits for sales and conversions is assigned to touchpoints in customer conversion paths. In addition, organizations are using predictive models and data modeling tools to enhance business processes, enter new markets, drive marketing automation, and predict customer lifetime value. There are many benefits of predictive modeling including obtaining a single view of the customer by focusing on customer data integration, determining promotional effectiveness by channel and by narrowly-defined customer segments, and identifying which customers already maintain a relationship with a marketer in more than one channel.
Further Reading on Data Modeling
For further information on Data Modeling, visit our blog. For your convenience, we have linked to three of our most recent Data Modeling posts below:
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