Blog Post:Data is the single most important aspect of digital marketing. If you doubt this, consider that the goal of digital marketing is to understand your customers and prospects so completely you can predict their needs and meet them in real time. The only way to achieve this is through the data you collect about your buyers and prospects — who they are, what they do and the likelihood of their next purchase. Data helps form a comprehensive picture of your customers to target your programs, create better marketing opportunities and increase customer satisfaction. None of this is possible without a complete, comprehensive approach to data and data programs. Getting Started: Avoid Data Paralysis Chances are you already have access to a host of data about your customers and prospects — probably an overwhelming amount. It’s important not to let the sheer magnitude of available data have a paralyzing affect. Knowing how to use data in your digital marketing programs can become overwhelming pretty quickly, particularly if you don’t have data scientists on board and don’t have access to true data analytics. I’m a believer in the crawl-walk-run approach to almost any really big project or transformation. Select one program as a pilot keep expectations small and of course fail fast and often. My team, for example, has run small propensity tests with a couple vendors to assess their capabilities and better understand the data impacts on our team. By starting small, we set realistic goals —goals we were able to achieve. The Resource Challenge When building an analytics team, one of the biggest challenges is pulling together the necessary data resources, talent and experience. You probably don’t have five open job positions and a million dollars for data and analytics vendors, so get clever about using the resources on hand. Recently we rebuilt our web analytics team at VMware. We brought on board new leadership and several contractors, but our data needs continued to outweigh the bandwidth of the team, so we looked across the company at other groups with analytics teams. Our corporate operations team has data scientists providing data analytics for the sales team. With that group, we formed a virtual data group bringing together people from different data disciplines who are essentially doing a lot of the same things: collecting and managing data about prospects, customers and products; running data-driven systems; and figuring how to better use data in their line of business. Requirement First, Vendors Second Our virtual data team has been enormously successful, but there have been some additional data challenges when it comes to vendors. When working with data vendors, you need to have a firm grasp on what you’re trying to get out of your data. If you're a little bit uncertain or you haven't come up with a tight set of requirements, you may end up with results that are difficult to interpret and even meaningless. It's nearly impossible to build programs around results that aren't clear and concise and don’t give you good significant insights into buyer behavior. Right now, propensity modeling is every marketer’s focus. Marketers want to better understand the propensity of their customers to take a certain action or respond to a certain offer. We also want to be more targeted in the way we use our customer databases, as well as information about prospects who have never talked to us. However, if you don't really understand your data, haven’t nailed down your database technology and don’t have your reporting built up, you’re not ready for propensity vendors. Know Where You’re Going and Understand Your Limits In addition to understanding your data, knowing where you’re trying to go with it and putting the key pieces of technology in place, you need to have an understanding of what's feasible within your organizational structure. For example, you might have to deal with the fact that you don't have exactly the right talent, or the ROI isn't there to invest with yet another vendor. Don’t be afraid to reset your expectations. Be realistic about what it is you're trying to achieve and how much you really should be investing. Take a hard look at the resources and the structure you've got, and decide if you’re willing to come up with creative solutions that might not include hiring a new group or launching an entirely new technology platform. Author: Date Created:May 6, 2015 Date Published: Headline:Data: The Heart of Digital Marketing Social Counts: Keywords: Publisher:Adobe Image:https://blogs.adobe.com/digitalmarketing/wp-content/uploads/2014/12/504481745-e1418135570269.jpg

Data is the single most important aspect of digital marketing. If you doubt this, consider that the goal of digital marketing is to understand your customers and prospects so completely you can predict their needs and meet them in real time. The only way to achieve this is through the data you collect about your buyers and prospects — who they are, what they do and the likelihood of their next purchase.

Data helps form a comprehensive picture of your customers to target your programs, create better marketing opportunities and increase customer satisfaction. None of this is possible without a complete, comprehensive approach to data and data programs.

Getting Started: Avoid Data Paralysis

Chances are you already have access to a host of data about your customers and prospects — probably an overwhelming amount. It’s important not to let the sheer magnitude of available data have a paralyzing affect. Knowing how to use data in your digital marketing programs can become overwhelming pretty quickly, particularly if you don’t have data scientists on board and don’t have access to true data analytics.

I’m a believer in the crawl-walk-run approach to almost any really big project or transformation. Select one program as a pilot keep expectations small and of course fail fast and often. My team, for example, has run small propensity tests with a couple vendors to assess their capabilities and better understand the data impacts on our team. By starting small, we set realistic goals —goals we were able to achieve.

The Resource Challenge

When building an analytics team, one of the biggest challenges is pulling together the necessary data resources, talent and experience. You probably don’t have five open job positions and a million dollars for data and analytics vendors, so get clever about using the resources on hand.

Recently we rebuilt our web analytics team at VMware. We brought on board new leadership and several contractors, but our data needs continued to outweigh the bandwidth of the team, so we looked across the company at other groups with analytics teams. Our corporate operations team has data scientists providing data analytics for the sales team. With that group, we formed a virtual data group bringing together people from different data disciplines who are essentially doing a lot of the same things: collecting and managing data about prospects, customers and products; running data-driven systems; and figuring how to better use data in their line of business.

Requirement First, Vendors Second

Our virtual data team has been enormously successful, but there have been some additional data challenges when it comes to vendors. When working with data vendors, you need to have a firm grasp on what you’re trying to get out of your data. If you’re a little bit uncertain or you haven’t come up with a tight set of requirements, you may end up with results that are difficult to interpret and even meaningless. It’s nearly impossible to build programs around results that aren’t clear and concise and don’t give you good significant insights into buyer behavior.

Right now, propensity modeling is every marketer’s focus. Marketers want to better understand the propensity of their customers to take a certain action or respond to a certain offer. We also want to be more targeted in the way we use our customer databases, as well as information about prospects who have never talked to us. However, if you don’t really understand your data, haven’t nailed down your database technology and don’t have your reporting built up, you’re not ready for propensity vendors.

Know Where You’re Going and Understand Your Limits

In addition to understanding your data, knowing where you’re trying to go with it and putting the key pieces of technology in place, you need to have an understanding of what’s feasible within your organizational structure.

For example, you might have to deal with the fact that you don’t have exactly the right talent, or the ROI isn’t there to invest with yet another vendor. Don’t be afraid to reset your expectations. Be realistic about what it is you’re trying to achieve and how much you really should be investing. Take a hard look at the resources and the structure you’ve got, and decide if you’re willing to come up with creative solutions that might not include hiring a new group or launching an entirely new technology platform.