Eyes in Front of Your Head
This post’s title will probably make more sense when you read part 2, so you’re just going to have to trust me on this one. (Oh the suspense: it’s killing you!)
As you develop your marketing automation system into the shiny and perfect platform that it is, one element that is often left out of the mix is data management. You focus so much time on scoring and sourcing and the lifecycle and nurturing that you neglect to think about keeping the data clean. In turn, this clean data ensures that the processes you put into place function as flawlessly as you anticipate. In contrast, a hodgepodge of data and unstandardized field values will only wreak havoc on your system, plunging you into the agonizing exercise called troubleshooting. Dun dun duuuunnnn!
A few key areas that need immediate data management attention are country, state, lead source, and possibly job title and industry. Country and state are usually the biggest offenders of variable data formats, as there are multiple ways to correctly refer to each. For example, let’s take the United States. If you allow your leads to fill out a form with an open text field, you will most certainly get a list of variations and typos that include data like, “US, USA, U.S. ,U.S.A, United States, The United States, America, United States of America, The Land of the Free and the Home of the Brave, ‘Murica…”. I could go on, but I believe you get the point.
There are many reasons why having this many variations is detrimental to your database. First of all, lead assignment rules usually rely on geography, and many CRM platforms have field validation in place to ensure a predictable data set. If your CRM assignment rules are expecting “USA” and your lead comes into the system with “That Landmass South of Canada”, not only will it likely fail to sync to the CRM, but the lead will never get assigned to the sales rep.
Secondary to seamless data integration between your Marketo and CRM platforms is email targeting. Let’s say you need to send an email to your US-based customers. The last thing you want to do is waste your time locating or guessing all the variations and typos of USA. If you know that every single record has a standard value, you can easily locate them, email them, and then head out early for happy hour. I mean…doctor’s appointment…
The same logic should apply to state values, as assignment rules in the CRM are often built around state-based territory assignment.
Outside of geographical data values, fields like job title and industry can be important to standardize, too. You are likely scoring on job title (both from a job function and job level perspective), so having predictable data can ensure that you’re not missing valuable demographic score values to your prospects. The same can apply to industry, especially if that is part of your firmographic target profile.
Last, but absolutely not least, we come to lead source. Lead source is an area that has a shocking amount of disparate data. As part of your overall approach to Marketo and marketing automation, lead source should be considered one of the most critical data points to standardize. Highly inconsistent lead source data results in unreadable reports that provide no insight into where records are coming from, their quality, and where to focus next year’s marketing dollars.
To mitigate this, define a finite set of places that you are obtaining leads, taking into consideration that sales and partners are contributing to the database, too. Then, build an intelligent mechanism to capture and populate only those lead sources for any and all new records in your database.
While this all may sound somewhat daunting, it’s actually pretty straightforward to implement good data management practices. First, consider all the sources of input for data: form fills, list imports, your CRM, and in some cases webhooks or via the API. Controlling the data at the point of insertion into your database will eliminate 99% of the workload.
Change text fields on a form to pick lists. This will prevent junk data, typos and random variations of data to be entered, such as “Untied States”. You can even do this with job title and industry, defining a standard set of values and populating it in a list for your leads to select from.
Mandate a list import template. This will require some effort on your part and your Marketo users’ parts, but will be worth it in the end. Educating and enforcing the data practices will ensure that all lists being imported are scrubbed and formatted correctly so that all data being imported is standardized.
One of the biggest downfalls to data integrity is the lack of effort put into scrubbing a list. It’s no secret that no one wants to spend their time reworking data in Excel, but it’s not something that can be shirked without some painful consequences on the system.
Coordinate with your sales ops and CRM platform. Ensure that your marketing automation system adheres to the particular data formats that your CRM platform expects, and vice versa. Inform the sales ops team that Marketo will be providing standardized data to the CRM and therefore expects the same in return. Since the two systems sync bidirectionally, ask for their help in ensuring data comes into Marketo normalized from the CRM side, too.
Lastly, when you’ve plugged most of the data insertion gaps that you can identify, it’s time to accept that things will slip through the cracks. To catch those slippery little devils, you’ll want to set up a washing machine campaign inside of Marketo. Its function is to identify any non-standard value and map it to the correct data format. For example, you could say “if lead is created and country is United States, change data value to USA”.
Predicting all points of incoming data and staying ahead of the dirty data curve will give you eyes in the “front” of your head, but what about your blind spots? That’s where the next post in this series, “Eyes in the back of your head,” will come in!