The pursuit of quality marketing data may have started even before Stan Rapp and the late Lester Wunderman were growing up in the Bronx but, it seems, with myriad data sources the problem is getting even worse with brands still struggling to find a solution.
That is according to a report from Marketing Evolution, carried out by Forrester Consulting, which quizzed just over 400 executives with responsibility for marketing performance and measurement at both medium-size and large organisations.
It found that, despite data quality improvements being a high priority, most firms still fail to optimise their data and, in turn, the insights they generate suffer.
Perhaps unsurprisingly, wasted media spend is the most frequently cited repercussion of poor data. Respondents estimated that the equivalent of 20p in every £1 spent by their organisation on marketing in the past year was wasted due to shoddy or incomplete consumer information. This translates to £1m going down the plughole for medium sized firms and a whopping £13.8m for large organisations.
In addition, nearly a third (32%) of respondents’ marketing teams’ time is spent managing data quality, and over a quarter (26%) of their campaigns in the past year were hurt by second-rate info.
Even so, most marketers rate the quality of their own data favourably, with three-fifths (61%) describing their first-party data as “excellent”. However, just 26% and 17% say the same for their second- and third-party data, respectively.
The report claims that to boost the potential of their data, brands should strive to meet a “magnificent seven” quality dimensions:
› Timeliness. Timely data comes from sources that are up to date. Access to faster data enables relevant insights that meet business objectives.
› Completeness. Complete data records are ones where all expected attributes are provided. A complete customer and marketing data set ensures that all behaviours, intentions, permissions, and sentiments are captured for robust analysis, such as understanding channel halo effects or how customers feel about your brand.
› Consistency. Consistent data references a common taxonomy across platforms, channels, and campaigns. Having consistent data for things like campaign codes and customer identifiers can help marketers speed up the data collection process and analyse trends over time, without worrying about data being labelled correctly.
› Relevance. Relevant data directly relates to the analysis being performed. Adding a slew of data into the system will not help solve the business problem if it is not relevant. Relevant data helps answer marketing business problems, address customer behaviour questions, and make day-to-day decisions.
› Transparency. Transparent data refers to data whose sources are easy to trace and identify. Marketers who understand the data nuances from first-party and media sources, such as ad servers, will be able to determine if specific streams of data are necessary for their marketing performance analysis.
› Accuracy. The adage “rubbish in, rubbish out” has never been more relevant in today’s data-rich world. Accurate data reflects the true actions of customers or marketers.
› Representativeness. Representativeness is an important part of targeting and insights; it ensures data collected and used for insights is accurately reflective of the marketplace or an advertiser’s targeted audience.
In conclusion, the report urges brands to take three key actions:
Build data governance policies that are intertwined with the marketing process. Successful data governance policies stay tightly aligned to business objectives. In this context, organisations that are successful at data governance include processes related to planning, budgeting, and business alignment.
Focus on data quality first. Data quality will help marketers determine if they need to update customer information, manage duplicates, validate email addresses, purge fraudulent and bot traffic, and maintain data hygiene through third-party verification services. Focusing on data quality first will have a direct impact on customer and marketing analysis.
Raise the data capture game. Use frontline engagement as an opportunity to request updated and new information to maintain and fill out data that companies are using for analytical models. For example, has a third-party data source changed over time, affecting its overall reliability and quality?
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