Top Ten Demand Generation FAILS (Part 10) The Missing Link!

Last week we looked into the secrets to reporting, and we’ve got it properly lined up before we launch our campaign. We’re going to report on MQLs generated by territory for our new Wombat preventer SaaS software. We’ll need to consider both outbound sources and inbound sources from media, such as Wombat Weekly and The Wombat Report. Just one problem: we don’t have Australian contacts in our database, not to mention critical opt-in data. And the only market for Wombat Preventers in is Australia.

Incomplete and incorrect data account for many, if not most, of the problems marketers have automating campaigns.  We’ve mentioned before that data is one of the critical interdependencies required in the demand gen process. Data is the fuel that helps us make critical decisions. Bad data equals bad decisions.

Over the years, I’ve seen a lot of databases and, as you can guess, in 100% of the cases they contained significant levels of incomplete and incorrect data. The best I’ve ever seen contained 45% incorrect or incomplete records. You don’t want to hear the worst. Having said that, also in 100% of the cases, we were able to identify the problems and make significant improvements to the data. How? Let’s look at some things you can do to improve your data quality starting today.

Identify your requirements.
Where have we heard that before? Right, everywhere! Without an objective, there isn’t any way to know whether or not your data is right, wrong, or incomplete. How do you intend to use your marketing data? Make a list in the form of questions you need to pose to your database, and include all the decisions you will need to make. This list will include questions like:

What is the Contact’s vertical market?

Where is the Contact located?

Is the Contact a current customer?

How many employees in the Contact’s company?

These segmentation questions help you form the basis to understand what information needs to be accurately recorded in a Contact’s record in order to extract these answers.

What decision will I make based on this information?
This is a great way to determine how you need to record field-level information, often referred to as field standardization. There is no universal answer to this question, because every organization uses unique rules in their business. You will need to determine this in relation to every question posed in the requirements section above. Let’s look at a pretty universal requirement, “Where is the Contact located?”

This is almost always (if not, you need to consider why not) composed of several fields used in conjunction with each other, such as Address, City, State and Zip. Some businesses also use telephone area code to determine physical location. Your database may use different field names, but they are going to be similar. What decision will you make based on this information?

Are you determining sales territories for lead routing? Do you execute location-based marketing programs? Do you sell different products or services in different geographies? Are you a global marketing organization and need to consider email compliance in various countries? Does your marketing message need to be localized to consider different cultural implications?

The answers to these questions can range from very simple to extremely complex, depending on how the decisions intersect. In many cases, you will be making multiple decisions based on location and, if so, do these decisions have conflicting data requirements? For instance, are your sales territories (lead routing) divided by area codes while your market localization is divided by metropolitan area?

Where are the holes?

Now that we know what decisions we need to make based on our data, we can begin to identify the holes. Those holes always manifest in on of two ways: incorrect data or incomplete data. In our example above, we need to understand major metropolitan areas so we can create location-based marketing messages. Perhaps we have affiliate marketing programs with the local sports teams. If so, how does our system know that St. Charles is a suburb of St. Louis? Do we need a zip code lookup table, or do we need a field with a specific MMA value to determine our target market? In this case, we need to add a missing data element to make up for the incomplete data preventing us from making a decision.

Our use case also requires us to route leads by area code, because that’s the way our sales territories are divided. Since we know from previous {Demand Gen Brief} posts our data degrades at about 25% per year, our phone numbers can go bad at the same rate. People move, change jobs and retire all the time, so we need to keep our data fresh, so your sales reps don’t follow up on a hot lead only to find out John doesn’t work in that department any more. (Cue the snotty email from your sales rep.)

Data governance and regular hygiene are the answer here. Whether it is in the form of data plugins that constantly update your database, or in the form of quarterly refresh imports, your data needs a regular cleansing so it doesn’t start smelling like last week’s garbage.

To take this particular use case a step further, how will you act on the area code data? Does your MAP platform have the ability to use part of a field to perform programmatic decision steps? Can it use CONTAINS or STARTS WITH as operators in decision blocks? If not, you may need to consider how to extract area code into a separate field that is actionable in your system!

Data is the missing link most often overlooked when building a demand gen strategy.  Don’t make the mistake of not designing your database to very specifically answer the critical questions you outlined above. If not, you may be relegated to considering specious Bigfoot sightings to find your Demand Gen missing link!

Notes:

You can’t properly design and build your database without clear requirements.

Be specific when asking yourself, “What decisions will I make based on this data?”

Find the two holes in your data: incomplete and incorrect data.

Don’t let this happen to you! We’ve now reviewed the Top Ten Demand Generation FAILS, and I hope I’ve presented actionable solutions to those fails. Next week, we’re going to switch gears and look at why Sales thinks your “qualified Leads” stink. Do they? Really?