Katie Wah, Senior Director of Product Marketing, Analytic Partners
It’s a challenging time for marketers. Brands are getting hit hard by inflation and other macroeconomic factors, and marketing teams are under increasing pressure to cut costs. There’s a greater push to bring marketing functions (such as ad buying, creative, or marketing measurement) in-house to own it end-to-end, streamline processes, and minimize overhead.
While each brand’s situation is different, marketing analytics is critical within a B2C/B2B marketing department. How else does a brand know if they’re making the right investments to drive growth? Bringing it entirely in-house has some important considerations though, which may cause you to think twice about whether it’s truly more cost-efficient.
Here’s 7 hidden costs brands have experienced once they brought marketing measurement in-house
- 1. Resource skillset
Hiring the right skillset is key to a successful in-house marketing measurement program. The problem is that very few candidates have both data science and marketing expertise.
Many marketing analytics teams have subject matter expertise in marketing and media, storytelling, and the context of the overall business. However, they seldom have a background in data science to build and automate analytic processes at scale.
Then there’s data scientists. Don’t get me wrong, in-house data scientists bring significant value. They are skilled in statistics and advanced analytics; they pressure test methodologies and validate the analytics. They design internal tests for learning, re-application and validation. They also create automated processes and programming to speed up analysis. However, data scientists often have limited knowledge of how marketing and media works, lack specific training on marketing measurement methods, and lack strong storytelling capabilities.
The magic is combining the two together and finding these unicorns is near-impossible! Marketing scientists (as we like to call them) help piece together both aspects: statistical modeling and advanced analytics with marketing and media expertise, to help drive results. This is a very specialized field, which makes it hard to find and hire internally, delaying, or even halting, the hiring process. The result is stagnant marketing measurement programs and missed opportunities to drive incremental revenue.
2. Ability to scale
Most businesses are impacted by seasonality, category demand, competition, and other non-controllable external factors. This means that marketing measurement may need to scale up and down at different points in the year based on that brand’s unique business attributes. It’s difficult to scale up or down (hire or fire) with internal resources. It reduces the flexibility of your measurement program which means missed opportunities for planning and optimization of multimillion-dollar marketing and media budgets.
- 3. Access, store, prepare data for analytics
Marketing measurement requires realms of data to accurately model decisions. It requires very granular data across a variety of first- and third-party data sources, which need to be updated constantly and consistently. There’s granular data from CRM’s, CDP’s, paid social channels, paid search, D2C and in-store, retail partners, campaign data, direct mail, affiliate marketing, billboards, sponsorships & events, and more. Plus, when new channels such as CTV advertising and platforms such as TikTok arise, teams need a plan in place to partner and collect insights from billions of data points to deduce what’s working. Besides these key marketing and sales sources, there are also external data sources that need to be set up and integrated such as financial data, community mobility data, category trends data, weather data, CPI data, unemployment data, consumer sentiment, inflation data, and more. Acquiring this data and ensuring data integrity is only the first step. The next is building a modern data infrastructure to ingest, separate, store, compute, govern, and visualize the data. The field of data architecture has changed dramatically over the past 10 years. It’s no longer just creating a database and storing it in a data warehouse like AWS. There’s been a shift to the data cloud to democratize access to data, speed up time to deployment, reduce time spent on data transfer, and easier compliance and auditing. This often requires additional data engineering resources in-house to work in close collaboration with the IT and security teams to set up and manage.
- 4. Comparison and benchmarks
It’s crucial to compare results to industry benchmarks to evaluate how a marketing program is doing, especially in comparison to similar companies. While there may be standard ROMI or ROI benchmarks across industries, it may not match a firm’s specific or unique business assumptions such as the impact of competitors, or category growth contraction. All internal and external factors should be incorporated in benchmarks, but in-housing would mean most comparison and benchmark data would most likely be limited to the brand’s own experience and perspectives month-over-month, year-over-year. This leads to report-card analytics and reduces innovation as it’s more challenging to measure the future impact of new channels or programs that the brand hasn’t tried before.
- 5. Credibility bias within the organization
Marketing measurement is successful when all internal stakeholders are bought into the program and utilize these insights in their decision-making. It’s difficult to overcome challenges with organizational adoption with in-house teams because the internal perception could be that the analysis is biased given internal silos, politics, etc. But Marketers at brands who do not adopt marketing measurement are also the first teams to experience budget cuts and brands that do not adopt marketing measurement and revert to gut-level decision-making, attribution or last-click analytics, are leaving millions of dollars of incremental revenue on the table. Organizational buy-in is essential.
- 6. Constant R&D and disruption
The world of marketing and media measurement is constantly changing. Browsers are experiencing constant updates, third-party cookies are depreciated, and device identifiers are changing. There are global and local regulatory changes such as GDPR and CCPA on how data can be collected. User privacy controls are changing how advertisers can utilize user-level data for targeting. Plus, with advancements in machine learning and AI, there are opportunities to capitalize on new technical possibilities. As a result, the additional time and resources required for in-house marketing teams to stay abreast of changes and opportunities is difficult to achieve.
- 7. Regional challenges
The marketing and media ecosystem is very different in different regions. Data privacy laws vastly differ in Europe to the U.S. Popular media and marketing channels widely differ from China to the U.K. Having expertise in each of these regions is crucial in fine-tuning marketing measurement programs for individual markets to deliver analytics that will aid in decision-making. A single in-house team, often located in one geography, is unlikely to be able to meet this need.
There’s a lot of complexity around building a marketing measurement program and choosing to bring it in-house can bring in unexpected costs. Marketers have a lot of tough decisions to make this year. Expectations from the C-suite are growing and it’s going to be harder to meet revenue targets amidst headwinds and rapid economic changes. As they evaluate your options, they need to consider what will make the biggest impact on their organization? What will drive adoption of a marketing measurement program? Adoption is critical for a marketing measurement program to work and to drive incremental results. We hope this list will help kickstart the conversation internally.