Rapleaf Consumer Interest Data within Marketing Automation

If you’re marketing to consumers, understanding their interests, demographics, and location is important if not vital. However, when working online, you are often challenged with the fact that you’re working with little more than an email address. Translating a bare email address to that level of insight requires a database that is specially built for the purpose. That’s where Rapleaf, a provider of demographic and interest data from social media comes in.

Rapleaf works from an email address to parse the social web and return data on an individual’s location, age, gender, interests, and other data. This data is now immediately available via a Cloud Connector from within your marketing automation programs in Eloqua. With the data available, you can then communicate, personalize, nurture, and score accordingly, resulting in a significant boost in your results.

To get started, just snap in the Rapleaf Cloud Connector into your Eloqua instance using the typical way to install a Cloud Connector. You’ll find it on cloudconnectors.eloqua.com, Eloqua’s library of Cloud Connectors, under Contact Data. With the connector installed, you can begin building a marketing automation program to take advantage of the connector.

To do that, add in a step into your program that grabs data from Rapleaf. The data on age, gender, and location is provided for free, and with it, a flag of “Data Available” is provided for any data field that has premium data available for it. Rapleaf will provide you with “API Keys” to access your data, and each key has access to different data fields with different price points per query. Be sure to start with a key that only has access to free data so you can first see if any data is available. If data is available, you can requery for it with a different key that has access.

For each step that queries Rapleaf, select Cloud Connector as your step type, and select Rapleaf Data Append as the connector type. This will give you access to the configuration button that lets you configure the connector.

For configuration, paste in your API key from Rapleaf, and indicate whether you want to store the returned data in contact fields or in an attached custom data object (data card).

With that selected, under the field mappings tab, you can select what fields, on the contact or the custom data object, you would like your data returned into. Note that most of the data fields, even if they appear to be numeric (like Age and Income) are actually delivered as ranges ("35-40"), so they are text fields not numbers.


If data is available, but your Rapleaf API key does not have access to it, you will see a value of “Data Available” returned. You can use this value in guiding decision rules within your marketing automation programs in order to decide whether to requery Rapleaf to get the additional data.




With that set up, you can use the “Run Step” tab to manually run the connector a few times to make sure it all works as intended, and then when you are happy with it, move to the Configuration tab to check off the “Enable Step” checkbox to allow the step to run automatically.

Now, Rapleaf data on demographics, age, interests, and a variety of other fields will flow into your Eloqua marketing database automatically. As soon as it’s there it is immediately available for segmentation, scoring, analysis, and nurturing.

Please keep your feedback coming on this Cloud Connector and any others you’d like to see.

Powerful New Contact Filters

Eloqua's spring release had a number of very interesting new capabilities to explore, and over the next few posts, I'll look at a few of those. The first to look at, and perhaps the most powerful, is the integrated filtering engine.

Defining a target segment based on demographic or firmographic information has been a mainstay of marketing for many years, but over the past few years, the ability to add in the psychographics of online behaviour has added a new dimension to the mix that allows you to capture not just who a person is, but how interested they are, or even what they are interested in.

This has been possible within Eloqua for a long time, but now, the new integrated filtering engine brings demographics, firmographics, and psychographics together in one easy to use interface.

Building Combined Filters

As you always have, click "New Contact Filter" to begin creating a contact filter. Now, the interface shows a palette of options on the right hand side. These options range from data on the contact itself, data on attached custom objects, associated company information, or activity information.



Select a filter option and simply drag it onto the main canvas. A configuration window pops up, giving you the ability to set the details of that filter option. Note that for data field, you can use lists of criteria, such as titles, to match if the contact meets any of the criteria in that list.



To build an integrated filter that includes activity, inactivity, company, or custom data object information, just drag in one of those items as a filter option.

For time related criteria, such as activity, the simplification is taken one step further. Just begin typing the amount of time you have in mind, and the auto-suggest feature will provide you with possible options ranging from hours to months.


Keep dragging the criteria you require onto the canvas until you have created the contact filter that you need. By default the conditions you choose will work together as "ANDs" - all criteria must be met to return the contact - but you can configure this logic (including brackets and more advanced boolean logic) by pressing the "Advanced" button at the bottom of the page.

With your filter complete, you can see all criteria displayed for you in natural text form, giving you a very clear sense of what you are looking at:



And to ensure that you are returning the number of contacts you expected, you can always click the "refresh" button and see exactly how many contacts meet the criteria you have specified.



These new integrated contact filters that combine explicit data on the contact, the company, and any custom data objects with implicit information on an individual's activity are extremely flexible and powerful, while also being very simple to use. I look forward to hearing from you how you have used these filters to better target your audience, and any enhancements you see being of further value.

Sanity Checks and Data Profiling

If you're doing almost anything in marketing, you need good data. Lead scoring, lead routing, personalization, segmentation, and many other processes are only as good as the underlying data. Profiling what you have in terms of data is the first step, as it tells you whether you need to cleanse your data. It may also tell you that your underlying data will not give you the insight you need to execute your scoring or segmentation strategy as you had intended.



The quick and easy way to profile your data is to get a sense of what is in each field. You can do this from any Contact, Prospect or Company Group, and it gives you an overall sense of the consistency and quality of your data.



First, create a group that contains the people you are interested in working with (one technique for defining group membership using overlap is talked about here: http://eloqua.blogspot.com/2008/12/defining-segments-using-group-overlap.html). From there, use the "Field Summary" option in the top menu. This brings up a window where you can choose the field that you wish to profile.



You can profile any field in your data model, and you will see a result that shows the values in that field and the frequency (ie number of contacts) in which that value appears.



Note that you will only be able to profile fields where there is some consistency of data - if there are over 1000 different distinct values, this method will not work.






It's a great habit to get into to do a quick sanity check of your data any time you build a process that relies on it. Data quality is a key foundation for many marketing processes and strategies, and the more you focus on it, the more you will build in processes to ensure that data quality remains high.



If you find that data profiling highlights some data issues, you might find the following useful; a common strategy that many marketers employ in handling their data quality is the Contact Washing Machine described here: http://digitalbodylanguage.blogspot.com/2008/12/contact-washing-machine.html

Leaks in the nurture process

Most marketers I know build their campaigns with a segment in mind to target. Maybe it's geographic, like people in Western Europe, maybe it's verticalized, such as a specific list of industries, maybe company size, etc. The challeng with this, is that there are always cracks in the coverage - people who are not in any targeted segment. These people fall through the cracks of a well-intentioned marketing program.


Here's an interesting experiment - we do this for a lot of clients if they haven't looked at the data themselves, and almost all of them are shocked at the results. How many of the contacts in your database have not received any communication from you in many, many months?


To find out, it's quite simple. Create a new Contact Filter, make it an Inactivity Based filter. Add a criteria for Sent Emails, and select no email in the last 6 months (or whatever timeframe you want to look at). Save it, and look at the results (click "View Contacts In Filter" for the full list if you want to eyeball it and see if there are great prospects there) . I'd be willing to bet it's a higher number than you expected.


Most marketers tend to think that they are overcommunicating, and that might be true for some segments. But there can still be a lot of perfectly good people who connected with you 18 months ago, and have fallen through the cracks.



If you don't have a general, catch-all nurture marketing campaign that is suitable for, and connecting with, all your contacts, you might want to run this analysis to see where you're at. If the list is small, excellent, but if it's larger than you'd expected, have a look at the data to see if there are opportunities you are missing.