How much time do you spend with dirty data? Are you even thinking about the time lost trying to clean it up? What would you do if you could have spotless data in real time? Cory Treffiletti looks for answers.
In this era of big data for marketing, these are questions we’ve started to ask, but the answers aren’t forthcoming.
The problem is real. Think about how much time you spend on menial tasks like:
- De-duping email lists
- Managing opt-in, opt-out and bounces in your CRM
- Aggregating performance data for your weekly ad reports
Just playing in Excel on a weekly basis, trying to determine how your ad campaigns are performing sucks away hours and hours of the day. A few years ago, my business partners and I worked at a company called Track Simple. We came up with the concept of ‘Get Your Tuesdays Back’ as a means of showing agency folks how they could gain back as much as 20% of their entire time simply by using this software.
Track Simple doesn’t exist anymore, but the problems still do — and I think they’re worse, with no immediate end in sight. Most agency folks are focused on creating efficiencies in media buying using programmatic tools, but the back end of reporting, performance dashboards and cleaning up the data from both an inbound and outbound perspective still create challenges. Whoever solves those challenges will be creating a massive opportunity.
I would estimate as much as 30% of time in our industry is wasted on dirty data. For an industry that is spending upwards of $50 billion in the US alone, that amounts to a pretty hefty sum of money.
There are specialties trying to help with this issue, like attribution, data visualisation and data enrichment.
Attribution is key, one area where reporting efficiency makes sense, because if you can attribute customer interactions and conversions to differing media touch points, it goes without saying that you are aggregating together all those touch points into a single report.
The challenge here has always been about weighted value of the media and the problems that arise around pacing and getting clean data from each source. Pulling data in from DSPs, site optimisation tools, Google, Facebook, your ad server and more is not as simple as it sounds. Over-delivery creates challenges as does fraud, viewability and other non-standardised issues. If you can come to agreement on a single model for attribution, and you can stick to it, then aggregating data for reports and using that data for optimisation can prove valuable.
Data visualisation tools are abundant in this age of Big Data and can be useful. Traditional marketers have started to explore these tools, and I see this as a huge growth area for the coming year. Agencies have a unique opportunity here to partner with these companies and craft exclusive partnerships that will help their customers see the data and make sense of it, which would reallocate agency teams from report creation to actual work.
Data enrichment is a new area, which seems to represent a unique opportunity. How does one clean up CRM and other data on an ongoing basis? How do agencies and marketers make sure the data they have is accurate and up to date? Just keeping up with your CRM opt-ins and opt-outs in a more effective manner can create efficiency. In the ad business, efficiency is equivalent to money: Saving time literally saves money.
Dirty data is a not a new problem, but its impact is much more significant now than it was just five years ago. As we evolve our respective businesses to be data-driven, the old adage of “garbage in, garbage out” is even more applicable. What is your data strategy, and how are you tackling these issues in your daily business?
Cory Treffiletti is vice president of strategy for the Oracle Marketing Cloud, and is a founder, author, marketer and evangelist. This post was first published by MediaPost.com and is republished with the kind permission of the author.
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