Big Data is a term used in almost every company these days but what does it really mean? I think it means different things to different people.  As a marketer it means having the ability to learn more about your customers through online behavior.  It means, to marketing, a way in which to accumulate information and leverage it for stronger more rewarding engagements with their customers.

And there are a lot of vendors out there that claim to provide the missing piece to the puzzle needed to make that customer engagement highly valuable.  The big data vendors not only claim this about our existing prospects and customers today but through predictive analytics you can determine other likely candidates with a high propensity to buy.  This is great stuff and true in theory but how well does it work?  What kind of insight can you really get?  Is your marketing organization ready to leverage this information to justify the investment.  Ultimately, have you put the cart before the horse with your data strategy by jumping into bed with the big data giants?

Questions I have no doubt asked myself over the course of the last 5 years while taking in the rhetoric and buying into the big data dream.  The reality, though, is this……

Big Data solutions are not for everyone.  They are not for the ABM focused marketing organizations.  They not for niche markets and certainly not for marketers looking to fill specific persona gaps.  I say this because when we look to these big houses to append and enrich we expect in the end that our data will be clean, marketable and ultimately easier to report on (due to the normalization process).

Having put my all my eggs in the automation of data append and optimization basket I have learned the following:

  1. Match rates on your data can be sketchy.  If you have a niche data base I guarantee your match rate will be as low as 10 -15%.  The automated big data vendors will try to broaden the match criteria to uplift the match rate to market standards but the reality is that there simply isn’t a match to be had.
  2. Of those records that are matched – the information is inconsistent and inaccurate.
    • Global companies register various names under the SIC  and NAIC.  This means normalization of company will not occur.
    • Grouping contacts by LOB or Vertical is also inaccurate.  One individual registering under one LOB category and another under something different.And in the rare case you do have a match you categorize them for your marketing and sales strategies in a different bucket – something that is more meaningful to you as a marketer, that resonates with your persona story telling – not necessarily to industry classifications.
    • Out of date information…..this is a biggie.  The match rate is there, the information is being appended but wait….it’s the wrong information.  That job title or that location or phone number is not correct.  So ultimately you are marketing to someone who possibly may not be relevant to your environment at all.
    • Override good data – when you do large big data append processes you assume the information is the most current but in actuality your current data is likely more closely tied to the individual you are nurturing.  If you use MAP, standardized forms, and a single data repository you have received information from the individual themselves via form fill or event engagement.  This information is marketing gold.
  3. It isn’t a replacement for a data team nor is it a 1 stop fix.  Data is dynamic and ever changing so the management and review of the information is critical as an ongoing effort.  You can’t deploy a big data solution for $60k/year and walk way assuming all your data management issues are gone.  This is a myth shared among many within marketing.  The reality is data needs human interaction to validate as it pertains to your business and the relevancy of the campaigns that you are working on.

What do I suggest?

  1. Have a team of data specialists that comb your marketing contacts and engagements on a regular basis.  If supported by a tool like Marketo, where you can run smart lists and data pulls based on certain missing information you can prioritize and build a process that is reliable and ongoing.
  2. Compliment your “Human Data Strategy” with some automation.  Work with your team to evaluate potential options that could bring you specific information or reduce the man hours needed to comb and clean the data.  Yes – there is a place for these big data vendors.
  3. Do regular smallish data audits that quickly allow you to see where there may be some inconsistencies or gaps that require filling.
  4. Do extensive yearly evaluations of your data including; a gap analysis against your ABM accounts and target personas, your data growth – how many new names have you acquired, what does the regional breakdown of these data points mean, and determine the overall health of your data (measure by normalization standards, optimization, and email deliverability).
  5. Plan for heavy data management investment.  I come from a world where we will spend $100K on a campaign and then leverage old or irrelevant data to execute.  That is putting the cart before the horse.  Spend money on good data management so you can better understand your brand penetration, contribute to sales pipeline effectively and be prepared for the next big campaign launch coming down the pipe.

Some parting words…….Put data management first and in front of all good campaign planning and execution.  Be aligned to your ABM accounts, share contacts between sales and marketing, and provide reporting on these contacts that offers insight into their buying stage.  And whatever you do – send relevant and meaningful content to these people frequently through inbound or outbound channels.

If you’d like to learn more about your data journey, please contact us below!

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