Thanks to a bestselling book and a hit Netflix series, Marie Kondo and her ethos of cleaning, organizing and “sparking joy” has become an international phenomenon. However, the power of decluttering isn’t limited to homes. Businesses can also embrace the KonMari method to emphasize the assets that make their brand shine.
Organizations today are positively swimming in data. In 2019, it’s estimated mobile data traffic will reach 190 exabytes (19 billion gigabytes) a month, and climb to 405 by 2022. In 2018, cloud computing traffic in North America hit 3.8 zettabytes a year. To reach their full potential, brands need to clean up their data act.
Walmart, for example, is building the world’s biggest private cloud to crunch more than 2.5 petabytes of information an hour, including in-store sales and online transactions. If an item at the store is underperforming, analysts can judge if the error is due to a pricing mistake, customer sentiments, seasonal buying patterns, or another issue entirely. Walmart is looking for correlations between a problem or a positive improvement and the actual data to create a better customer experience.
Finding those correlations is not possible if data is unstructured, unorganized or in a state of chaos. Every marketer aspires to develop actionable insights from data—the hard part is understanding how to generate and store enough of the right data so intelligent insights are produced, and then adjusting marketing decisions accordingly.
Consider these four tips for applying the KonMari principles of Marie Kondo to your data strategy.
Use clean data
Organizations should aim to generate and store as much data as possible, as it pertains to every aspect of the business that can affect a campaign. It’s important to proactively build the data, rather than trying to add data after a campaign launch, but it’s also important to remember the adage of “garbage in and garbage out.” Marketers have to identify what is essential and what can be tossed out.
Insights are only as good as the underlying data. If the data entered into the analytics engine is not well organized or non-essential, then even the best data scientist won’t be able to extract value. Marketers need to make sure data has been thoroughly scrubbed before it goes into the data warehouse. It’s like putting a rumpled, dirty, or torn shirt into a drawer—why put anything in there that isn’t clean and purposeful? Organization of the data on the front end will pay dividends later.
Introduce pixel tracking analytics
The philosophy of Marie Kondo includes making the most of the resources at your disposal and ensuring everything has its place. When it comes to digital assets, marketers should leverage their company’s website as a data generating vehicle, as well as a marketing tool and channel for sales. IT and marketing teams can work together to introduce pixel tracking, which places capabilities on the websites used by a company, including mobile and microsites. Social media information can also be tracked and analyzed using pixel tracking to understand how Facebook ads are performing.
Pixel tracking provides user device data, so marketers can better understand where sales are coming from and monitor trends over time. Collecting enough data makes it possible to create behavioral categories for consumers to find correlations between certain behaviors and buying decisions across segments. Brands can then present content to customers in targeted ways that enhance their experience.
Model, model, model
Part of what led to the success of Marie Kondo was her ability create a universal system that anyone can use to declutter. At the same time, as her Netflix series demonstrated, the best results are achieved by taking the system and customizing it to a particular person, family or set of needs. The more information she has about a client, the better equipped she is to understand what strategies or organizational systems are most likely to succeed. Adapting is key. The same is true for marketers as they develop campaigns and figure out how to have the greatest impact. This is where statistical and mixed-media modeling can come into play.
For TV campaigns, there are many data points marketers can use to construct statistical modeling of campaign performance, including stations, airing size, demographic information, timeframes, second-screen activity and Nielsen weighting points. Of course, accurate models require clean and granular data from all the channels. Most of the modeling work will take place after a campaign using regression analysis, cluster analysis and logistic regression, among other techniques. These models allow marketers to adjust the campaign to match actual results.
With mixed-media modeling, analyzing sales and response data helps marketers to properly weigh each channel to weed out the underperformers. In short, it enables them to declutter and thus direct more budget to the channels that are outperforming. The process of identifying the best channels will become more refined over time, as data improves a marketer’s ability to connect information and future results. In today’s highly fragmented digital media environment, modeling helps marketers select the right channels that deliver real measurable results.
Target specific demographics
Along the same lines, marketers can make the most of their digital assets by focusing their attention on specific demographics. Big Data can help provide marketers with precise direction on where consumers live, the devices they use, their search habits, and other behavioral metrics. Armed with this information, marketers can yield more ROI from their digital media and TV placements. Big data makes the marketers and campaigns more intelligent and allows for granular messaging, which could differ significantly from one channel to the next.