The importance of clean data in your sales and marketing programs

More fun for data week on the Funnelholic. Remember, we are deep diving on the topic on a webinar this Wednesday, Sep 18 2013 at 9am: The Impact of Dirty Data on Marketing ROI and How to Avoid It. Join us because the issue is critical.

Today’s post is from Justin Gray, the CEO of LeadMD. Why Justin? Because they have done 100′s or marketing programs and have found a consistent theme — dirty data can destroy the best intentions in marketing. He pounds his fist on the table on this issue. Oh and he is really smart and a great writer so I am honored to have him tackle the topic.

Before you read on, enjoy this photo from emilydickinsonridesabmx.

data cleanse, dirty data

You have been big proponents of maintaining a clean, up-to-date database, tell us why you have developed this stance?

Content gets a pretty big head by being termed the “king” when it comes to marketing – but really data is the one and true ruler. The funniest metric that marketers measure is number of leads. When you start talking to marketers about their databases, you can see the pride welling up in their eyes – that is, until you start to dive in to some of the finer points like freshness and duplication. The fact of the matter is most marketers are grossly misinformed in regards to the validity of their databases.

In 2012, 65 percent of LeadMD’s clients saw turnover in the “core” marketing department – core meaning director level or higher. Think about that metric for a moment – the transient nature of marketing executives is absolutely rampant – and it’s not isolated to marketers. IT, executive management, operations – we see the same issues there. We can speculate why until we are blue in the face, but the fact remains – things are moving and changing faster than many marketers are prepared to keep up with and the fact that turnover is happening so often also contributes to this.

A marketing team gets a data strategy in place just in time for the entire lot to be dislocated to other positions. Did they document the process before they left? Probably not, which of course leaves the new team to put the pieces together or begin the process of bringing in vendors they’ve worked with in the past. It’s become a never-ending cycle that we are seeing over and over again. Therefore, like most things at LeadMD, our processes are born out of common pains. We try to solve things at our org in ways that we can replicate and scale across out 1,000+ client customer base – which presents unique challenges in and of itself. The common issues we see every day and a need to fundamentally solve for those issues formed our stance for not only clean and standardized data but also a sustainable data waterfall process. 

What are the likely causes of data issues?

Again, the pace of change is really at the heart of data issues. People open new email accounts like changing their shoes. The latest social media craze literally pops up overnight and with all of these changes comes the changing preference for communication so even if we do have the correct email address for a prospect – do they really want to communicate via that channel? Because of this, we need to gather MORE data about our prospects as well as capture their communication preferences and honor them.

Beneath this surface need, we also must create uniformity between records so if we denote data one way for one record, we can count on that same structure when we view the next record. Even when we apply this to something as simple as phone numbers, we see issues in many client CRMs. Sales departments love to record data – just often not in the place or manner in which marketing would like them to. This doesn’t seem like a huge deal until marketing conducts an SMS campaign and the phone numbers all have a mix of mobile and landline data. No, you start to see the importance of data hygiene and also the small nuances that can create a huge bird’s nest.  I see the number one cause of dirty data being turnover in sales and marketing while maintaining tools that are not governed by a process. It truly becomes a free for all – and it’s all bad.

What effect does bad data have on sales and marketing programs?

Bad data kills marketing effectiveness. Period. The catch 22 here is frankly that most attempts to gather good data also kill effectiveness. Those 20 question forms aren’t helping anyone reach your message or build trust in your solution. So the question really becomes less of “does dirty data cost us money?” and more what the proper solution for this is. Problem identification around data is almost a moot point, as it literally comes pre-packed with data storage (i.e. CRM) – the moment we house data it begins the degradation process. So, then, what is the answer? We still have not found one holistic data holy grail – instead we focus on layering a number of solutions which, in a complimentary manner, allow us to achieve our goals for data quality and marketing effectiveness.

What are your best practices for keeping an organization’s data clean?

So in true blog format I’ve managed to dance around the solution all the way up until the end – but I really do feel it’s necessary to highlight the fact that data woes really do run much deeper than most marketers believe or are willing to admit. As G.I. Joe once told me, knowing is half the battle. The other half begins with outlining what a true data solution consists of, and at LeadMD this means a four-pronged approach:

  1. Standardization
  2. De-Duplication
  3. Capture & hygiene
  4. Append

First, standardization – or how you intend to format data and how you will maintain that formatting. We do this though the formation of rules that standardize fields like titles (i.e. VP of Marketing to Vice President of Marketing) and other somewhat subjective items into nice and neat buckets of data that we can easily score and campaign against. This is just fundamental scalability in practice and it’s something every organization should have a solution in place for. We recommend this happen at every data entry point and in an automated scenario.

Second, de-duplication – our old friend. Duplicate data is embarrassing – let’s face it.  We don’t want crap on the database because it makes us look bad and it undermines the quality of our marketing and our sales process. We run a twofold process for ridding our databases of duplicate data. One literally polices data at entry into the database and, based on a set of twenty-two rules we’ve predefined, identifies duplicates and merges them. We’re not just talking email to email comparison – we’re talking robust data key formation based on elements like address, name, email (if present) and, through the formation of a unique data key, combines the suspect records. We then also conduct this monthly via a hands on batch data scrub which is meant to catch outliers or duplicates that simply don’t have enough similar data to be prevented automatically. We really hate duplicates.

Data capture, for us, then means deploying intelligent forms on our Web assets, which standardizes data at the form level. By utilizing “smart forms,” we also cookie site visitors and progressively profile them. By deploying a capture solution we ask fewer demographic questions at the form level and are able to focus more on BANT and psychographics because not only are we enabling our forms to only ask questions to the same visitor once, but we are also appending demographic information automatically based on several backend data sources.

For data entry points that are not form dependent, we follow a similar procedure.  When a record enters our database we immediately append the record with any additional information we have access to through our data relationships so that we can more accurately and comprehensively score and communicate with the prospect. Keep in mind after the appending takes place we are still performing steps one and two on these records. So, after the appending happens, we will again standardize the data and check to ensure that the additional info has not revealed a duplicate record. All of these steps trigger on data change as well as net new creation.

Finally, append. And I know what you are going to ask… I thought we just appended the record right? We did. We term append as the appending of additional contacts at the prospect organization. That’s right – we want to add complimentary contacts that will enable our sale. We have a tendency to land in the middle of an organization at the director level, but in order for us to be truly effective we need to penetrate the organization. This means we need C-level contacts as well as more hands on users of the LeadMD solution. The sooner we start nurturing the organization as a whole, the sooner we land the type of account we love – one in which we are both strategic as well as executional. To do this we need an automated data solution that can add net new records to our database based on the interest on a member of that organization. This is an absolute key for us.

So there you have it – what we consider to be the essentials of a healthy database and one that will instill confidence amongst your internal teams as well as ensure the messaging that represents your organization falls on the ears you want it to, and in the manner they want it to.

 

Don’t forget: Join us for a deep dive into the importance of clean data: Webinar this Wednesday, Sep 18 2013 at 9am: The Impact of Dirty Data on Marketing ROI and How to Avoid It.

Justin Gray is the CEO & Chief Marketing EvangelistLeadMD of LeadMD. He founded the company in 2009 with the vision of transforming traditional “grassroots” marketing efforts through the use of cloud based marketing solutions. Since that time Mr. Gray has emerged as strong voice for Marketing Automation and Conversation Marketing through both industry publications and his Blog, The Marketing Evangelist.

Craig Rosenberg is the Funnelholic and a co-founder of Topo. He loves sales, marketing, and things that drive revenue. Follow him on Google+ or Twitter

 

 

  • Saqib Sherazi

    Really great information that can be applied to a variety of situations. Standardization is definitely important when it comes to data especially in big organizations. If data is not standardized then it may create problems when one department wants access to data but it is messed up.
    Also important not to have duplication. You can have lots of data but if half is duplicated then it may not really be very useful.
    How can this apply to me? We’ll take for example a Facebook fan page. Would you rather have 5 unique fans or one guy namedBob who has 5 separate accounts liking you. First choice is preferable

  • Andrew Koller

    Great blog post. In my experience, organizations are always fighting to increase quantity, while keeping quality at a reasonable space. For example, someone will buy a list of 5000 leads, not knowing the quality of them.
    What are your recommendations for that situation?

    • Ellie Hib

      Really speaks to the old phrase “garbage in, garbage out”. For high quality reach (and to keep your reputation!), keep things clean. Great techniques for keeping things neat without combing through files one by one. Thanks!

    • http://leadmd.com/ Justin Gray

      Andrew – This probably is more of a simplistic response than you were expecting but I would say – don’t do that! I really don’t believe in quantity when it comes to data – it will cost you much more than the cost of the list. First, it skews your metrics – you think you are generating leads when in actuality you are generating headaches. More simply isn’t better, especially when you are creating a resource issue by causing your sales team or BDR team to weed through that crap. Also keep in mind many software and CRM solutions are based on the number of records in your database so now you have sunk costs around technology that rise based on the poor decision to buy data with little to no upside. So, when it comes to buying data – do it sparingly and when you do evaluate the source as it may cost you far more than the list.

      Justin Gray
      @myleadmd | @jgraymatter