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TDWI Upside - Where Data Means Business

Monetizing the Digital Consumer Through Data

Leverage these three best practices to monetize your customer data and stay ahead of your competition.

Online consumers can leave a wide footprint of likes, dislikes, and preferences. Understanding how to best monetize this information is a challenge. At the core, business managers make two types of decisions about their customers: those related to revenue-generating strategies and those related to cost containment or reduction. When it comes to growing revenue, the most effective strategy to optimize resources is not always apparent, nor is the process for how to leverage data in making decisions.

We are already flooded with data and the explosion of information is only accelerating. You only have to observe how much of your time each day is consumed by email, online searches, text messages, blog posts, Facebook, and YouTube postings to see the impact of this information explosion. It is estimated that human knowledge is doubling every 13 months. According to IBM, the expansion of the Internet of Things will eventually lead to the doubling of knowledge every 12 hours!

For Further Reading:

Customer Data Profiling: The Precision Problem

Improve Customer Service and Intelligence with MDM and Data Virtualization

Predictive Analytics Can Shape Customer Behavior

Organizations and executives that are able to distill this data into revenue-generating strategies will have an advantage over their competition in understanding and engaging their digital consumer. This capability is available regardless of the type of enterprise, whether you are primarily a brick-and-mortar company or an online-based organization. We have identified three key areas to focus your efforts to drive value from your data:

1. Operationalize Your Data

The focus of this effort is to gain a deeper understanding of your customer.

One of the biggest concerns with any analytics is the quality of the data. If there is a significant amount of inconsistent, incomplete, or dirty data, it may be difficult to perform enough meaningful data analysis to drive strategy. For example, if you are inconsistently capturing the gender of your consumer, it may be difficult to develop strategies for female-targeted products. Understanding the health of your data will be one of the first steps you will need to take.

You also need to comprehend the variety of available data. Piecing together the footprints of your consumer into a clear journey is a difficult task. Data may reside in a variety of locations, including point-of-sale systems, ERP applications, CRM tools, and e-commerce systems. Pulling this data together to create a cohesive view of the customer is a challenge, but fortunately there are several available tools that make this task much easier, from master data management tools to big data tools.

2. Segment Your Target Market

Look for techniques in consumer analytics that permit targeting customers while preserving privacy, such as developing target personas and look-alike audiences. These leverage analytics based on customer lifetime value, loyalty programs, segmentation, propensity models, and customer communities, all common tools of the trade in data science.

One of the easiest analytics techniques to implement is customer segmentation. The basic premise of segmentation is that by grouping your customers into the right segments, you can target a specific message to drive a lift in sales due to the increased relevance of your message to the specific target group.

A consumer marketing data set typically has many characteristics useful for segmentation. For example, the attributes in a demographic segmentation may include: geography, age, income, education, and ethnicity. If your organization does not already use segmentation in sales and marketing, you can use this method to find underserved segments that can be monetized.

For organizations already leveraging segmentation, there may be additional opportunities to further carve out commercially viable subsegments to drive an increase in revenue. Applying segmentation in this fashion allows you to target products and services to consumers more likely to make a purchase.

One word of caution: many companies that matured in the offline world make the mistake of concentrating on the highest-valued customers who tend to be older with more disposable income and a longer track record. This can be a mistake if the majority of people that shop at your online store are younger. You can develop strategies to move your high-value customers to your online channel, but you should also improve your targeting for younger audiences to drive additional value to this segment -- and therefore drive additional revenue.

3. Consider ROI and Confidence Level when Evaluating Opportunities

Many businesses calculate ROI for marketing activities that require an investment, but rarely calculate a confidence level. A confidence or probability level is the likelihood of an event occurring; the higher the probability, the more likely the event will happen.

Making a business decision that has a 95 percent probability of driving 15 million in revenue is different from a business decision that has a 50 percent probability of driving 60 million in revenue. Which option to choose depends on the organization's tolerance for risk and its overall business strategy.

Learning how to develop confidence levels is part of the discipline of decision theory. Data science helps you turn information into actionable insights, and decision theory helps you structure the decision process to guide a person to the correct decision. Decision theory, along with behavioral economics, is focused on understanding the components of the decision process to explain why we make the choices we do. It provides a systematic way to consider trade-offs among attributes that helps us make better decisions.

When considering various monetization strategies for your digital consumer, adding a confidence level adds perspective to help you make a better decision. The ability to weigh the probability of success changes the dynamics of your decision.

Targeting Success

Your digital consumer is one of your most important assets. Applying your organizational data to better segment and target products and services to these consumers drives additional revenue for your organization. Once you have a set of strategies, weighing the economic trade-off between the options will help you make the best decision. Leverage these best practices to monetize your data and stay ahead of the competition.

About the Authors

Andrew Roman Wells is the CEO of Aspirent, a management consulting firm focused on analytics. He is the co-author (with Kathy Williams Chiang) of Monetizing Your Data: A Guide to Turning Data into Profit-Driving Strategies and Solutions.

Kathy Williams Chiang is VP, business insights at Wunderman Data Management. She is the co-author (with Andrew Roman Wells) of Monetizing Your Data: A Guide to Turning Data into Profit-Driving Strategies and Solutions.

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