Data Transformation eCommerce

Marketing Optimization with Unified Data

author Colin Van Dyke February 25, 2020

Modern marketing tools are getting increased attention from organizations that wish to maximize the potential of their customer data. While automation efforts focus mostly on predictive analytics and modern algorithms, the magic happens one layer below, at the data layer. Today we look at the data storage model that makes it all possible: unified data. 

As opposed to siloed data, unified data means storing all of an organization’s data in its raw form, regardless of the data source. Both structured and unstructured data are stored in a single location and become available for predictive analytics.

The implications for sales and marketing teams are non-negligible: unified data provides a single access point to ever-increasing amounts of customer information. Marketing automation tools, CRM systems, and campaign monitoring platforms all become connected at the data layer. When user-generated information (web & mobile activity, sensor data, etc.) is added to the mix, we can say that unified data provides the full 360-degree customer view.

Below are some actual examples of how unified data supports advanced marketing processes. And how that enables your organization to cut down on costs and increase revenue.

Let’s start with customer segmentation. Figuring out who your customers are is one of the main prerequisites of a successful marketing strategy. In this sense, it is mandatory to analyze data from all customer touchpoints. Isolated demographic and location data used in traditional segmentation models are simply not enough anymore since they do not lead to actionable insights. Instead, all customer data must be included in the segmentation process. Emails, call center interactions, webchats, transaction logs, and other available sources. 

A unified data architecture handles heterogeneous data sources and makes the data readily available for modern predictive tools. Automated, ML-based segmentation is a proven way to cut down manual processing costs and remove the need for labor-intensive tasks. 

Other benefits of ML segmentation include the potential to scale on-demand, removing the human bias and dealing with an unlimited number and size of segments. Such capabilities would be unimaginable in a traditional approach, based on siloed data warehouses and human processing. But when the data is unified and predictive technology is employed, there are no limits to how you can let your customer data do the work for you. 

Knowing about the main customer groups and their characteristics, it is then relevant to understand their behavior. Is it possible to predict how they will interact with your business in the future? Customer LifeTime Value (CLTV) is one of the metrics indicating how much revenue will be derived from a customer and helps marketers identify the most valuable prospects. 

Traditional Model vs. Machine Learning Model

Unified data enables accurate predictions of CLTV. Unlike traditional RFM models (that have been used in marketing for more than 3 decades) that only model CLTV based on historical transactional data, machine learning approaches extract predictive insights from customer data itself. Website analytics of your customer’s online behavior, data originating from social media interactions or sensor data of how your products are being used are all data sources that can be aggregated together in a unified architecture. When data is unified and analyzed together with the transactional data, more reliable CLTV estimates are obtained. 

Graph shows basic historical data of how customers have spent their money.
Traditional, are limited to probabilistic models that look at RFM values, representing Recency, Frequency & Monetary values of only past transactional data. 

A Machine Learning Model enables your marketing team to plan a precise marketing strategy and only target the most valuable customers. The accuracy of the prediction becomes a guarantee for well-invested resources and reduces the time potentially wasted with unsuccessful leads.

Machine Learning CLTV estimation models can integrate heterogeneous data sources. This chart shows how when you use a Machine Learning Model you have much more information on each customer, not just what they previously bought, but who they are, what people like them typically want. With this information, you can predict how much you can earn from them, not just right now but over a lifetime of engagement. Heterogeneous customer data originates from, you guessed it, from a unified data storage!
Machine Learning CLTV estimation models can integrate heterogeneous data sources. Unifying website analytics, social media, sensor data and much more.

The above chart shows that when you use a Machine Learning Model with heterogeneous data you have much more information on each customer from multiple sources like website analytics, social media, sensor data, and it's all integrated. This system knows not just what the customers previously bought, but who they are, what people like them typically want, how likely they are to buy, what they will buy next, etc. With this information, you can predict how much you can earn from them, not just right now but over a lifetime of engagement. Heterogeneous customer data originates from, you guessed it, unified data storage! 

In both academia and industry, machine learning CLTV models are overtaking the traditional, probabilistic models. This is yet another example of how unified data architectures play a crucial role in the development of modern predictive tools.

With the understanding of customer types and accurate models of their behavior in the future, you are now ready to set up a targeted marketing campaign. But what is the right content for each of your customers? And which of the plethora of channels (web/social/mobile/email/SMS) is more suitable for engaging with them? And when is the most suitable time to get in touch?

Once again, unified data to the rescue!

The same big data used for segmentation and behavior modeling can be repurposed to create personalized messages that customers are more likely to react to. Moreover, quick access and real-time inference are now possible and that opens a whole world of possibilities since timing is a crucial aspect in customer interaction all throughout the purchase consideration cycle.

The possibility to extract relevant ads and provide speed responses to customer interactions can shorten lead-to-cash cycles. Employing unified data and predictive technology also translates to less time and fewer resources for building personalized interactions with your customers. Such a strategic advantage is crucial for a successful marketing strategy.

We have seen how unified data and predictive technology enable cheaper segmentation efforts, more accurate behavior modeling and quicker inference for targeted marketing campaigns. 

However, these are only a few examples of marketing and sales processes that can be optimized as part of a modern, data-focused strategy. Customer retention, lead generation & scoring, demand forecasting, and many others are also susceptible to improvement by modern predictive technologies.

It all starts with a unified data solution and making sure your customer data is properly aggregated.

Contact us today to unify your data.

See our Case Study on Marketing Optimization for a fortune 100 Energy company:

Blue Orange Digital showcases its Marketing Optimization platform built for a Fortune 100 Energy Company.
Blue Orange Digital showcases its Marketing Optimization platform built for a Fortune 100 Energy Company.
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