Brands recognize that success in today’s data-driven world depends on better use of marketing insight. But as more and more aim to bring campaigns, reporting, and data management in-house, agencies face the growing need for reinvention. Keeping pace with rising demands and delivering enhanced value is going to mean upping their own data game.
The role of agencies has gradually transformed. The growing availability of advanced marketing tech and data analytics solutions has allowed brands to take ownership of their marketing efforts, fuelling a major evolution of traditional dynamics.
Agencies now need to be more than just slick advertising engines. As the number of digital channels and platforms continues to multiply, brands want help coordinating fragmented performance, harnessing the massive amount of data they collect, and executing more agile and effective marketing strategies.
For agencies looking to build big data capabilities and meet these higher expectations, here are our three top tips:
How can agencies use data to deliver more value to clients?
Silos are out of season (they were never in)
Although changes to the marketing landscape have been incremental, the race to tackle each fresh innovation has led to major inefficiencies. Brands and agencies have often introduced unintentional divisions, a.k.a. silos, in the wake of each development. Agencies are acutely aware of what this looks like on the client side. Disconnects between marketing and analytics teams, for example, mean concerns about data accuracy aren’t shared and put activity at risk of misfiring. But what about agencies’ own silos?
The challenge: some agencies continue to organize departments in isolated verticals and operate separate data handling teams. This impacts their ability to deliver comprehensive data and reports when brands want agencies to make their lives easier.
The solution: to enhance value, agencies must provide complete performance visibility. One vital starting point is creating closer integration of internal structures, for instance by assigning a dedicated operations specialist to connect data pipelines and practices. This enables agencies to run holistic analysis that gives clients a complete picture of cross-channel performance, while also improving the accessibility of crucial data across every agency function.
To build or to buy: that is the tech question
Technology is integral for agencies to sharpen their data edge and create the right setup for well-aligned teams and data ops experts to thrive.
What that should involve is simple: adopting tech that streamlines the ETL (extract, transform and load) process by automatically collating multi-source data and turning it into a normalized and harmonized pool. This enables agencies to:
- Prepare data for further analysis and activation; essentially establishing a single source of truth
- Eradicate errors caused by manual data wrangling
- Keep pace with brand requirements for fast reporting, especially if tools aggregate new data daily and present up-to-date insight through holistic dashboards.
The complicated part is deciding whether to build or buy the technology. Where heavyweights such as WPP have opted to beef up owned data systems, doing so isn’t the best choice for every agency. Many have set off down this path with big ambitions, only to find the task of building numerous API connectors, forging robust internal pipelines, and translating data into usable visualizations goes beyond their current resources. Moreover, many also find plowing through software iterations takes their attention away from what matters — powering client success.
This makes it crucial to weigh up the pros and cons. Agencies need to carefully consider if taking the purchase route could ultimately allow them to offer a better quality of service. The goal is to drive more value using data without losing their brand centricity.
Hone skills fit for the data-driven world
As Gartner’s analytical ascendancy model highlights, honing data maturity is a lengthy journey. Tools that enable agencies to transition from manual chaos to automated insight generation can do much to accelerate their progress, but they aren’t a quick fix. If teams don’t have essential skills and knowledge, they won’t get far.
This isn’t to say that creative designers will need to re-train as coders and data scientists, but teams must have the ability to wield data effectively. This requires a firm grounding of tech and data proficiency, including an understanding of:
- What’s going on under the hood of orchestration systems
- The type of data being gathered
- How to use sophisticated data analysis to optimize real-time outcomes for clients.
Conducting an in-depth review of existing capabilities and upskilling people is essential — not only for plugging gaps in expertise that hamper efficiency but also enabling employees to realize their full potential. In turn, this will further bolster organizational maturity.
When individuals are confident with handling intelligent tech and extracting reliable insights, they’re more likely to feel comfortable with making data-driven decisions and moving into higher levels of data mastery. This could include leveraging forward-looking data to determine the next best move for clients and embracing incoming intelligence as a strategic revenue driver. In other words, it equips them to act as proactive and valuable consultants for brands.
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