“Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.
Today’s column is written by Jeff Puzenski, an executive at Infinitive.
“I can’t trust the data.”
“Using the new system slows me down.”
“We’re different, so this system won’t work for us.”
Those who work with digital advertising technology have no doubt heard these phrases many times. They’re typically spoken after large implementation or integration projects, by users who struggle to adopt new tools. In the eyes of most of these users, it’s almost always the technology’s fault.
But is it really? Technology either creates new user needs – virtual reality is one example – or chases existing user needs, which is most common in the dynamic ad technology space. Rarely do we find technology solving our exact needs.
Another flaw: garbage in, garbage out. No tool can magically turn bad data inputs into good outputs. Therefore, users must understand the importance of quality data on the front end, in terms of its benefits for the entire process.
Technology training most often focuses on the “what” and the “how,” leaving out the “who” and “why.” Oftentimes, users, across the digital advertising order-to-cash life cycle, are unaware of the roles and responsibilities of others in the supply chain. Even worse, many do not fully grasp the impact of their actions on others. For example, media planners may not understand how spending a few more minutes on a task may save the billing team a few hours during invoicing, which is a net benefit to the overall organization.
There is also a general failure to recognize industry standards. Today’s technology is built on industry standards for automation, data sharing and reporting. It may feel like the technology is imposing a process or way of working on a team. But in most cases, these processes offer publishers immediate-term performance improvement opportunities – provided they inform users about the upside for the business and ensure they know how to use new tools and processes correctly.
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Finally, the unfortunate but all-too-common shortfall: pennywise thinking. Sometimes tight budgets trump compelling value propositions in technology selection decisions. For instance, the big and expensive ad tech players often have integration hooks that facilitate automation and seamless data sharing. That’s one reason why they cost more. But if a cheaper software package requires the hiring of an additional “swivel seat” resource or duplicate data entry by different departments, the cost savings may disappear. And the risk of getting bad data somewhere in the system will lead users to say they can’t trust the technology.
How To Boost User Adoption: Clarity, Communication And Role-Based Training
Building a strong business case is a great way to start moving past these barriers. Breaking down the specific ways new technology will generate more revenue or make back-office processes more efficient takes time and effort. But this step helps clarify both ROI models and specific business requirements. It’s also critical that the users understand “what is in it for them,” including the expected benefits that will come with consistent and accurate usage.
Educating everyone involved in the digital advertising ecosystem about the relationship between individual tasks and big-picture processes is also key to reducing the high cost of confusion that results when workers don’t know how their actions affect other groups.
Consider a sales rep working with an advertiser who needs to drive awareness of a new product to a particular demographic. Unless that campaign objective is fully understood by all parties in the life cycle, accurate campaign delivery may fall short or downstream reporting functions may deliver general reach metrics, which is of little use to the client. Too many sales staff consider such vital information to be optional so they don’t enter it. Better data in this case helps boost client satisfaction, minimizes rework and can avoid make-goods.
Similarly, billing and finance can only produce timely and accurate invoices if they have complete and accurate data. “Finance will figure it out” is not a strategy for process optimization. “Flat-rate,” “bill on delivery” and “campaign” are examples of inputs that save time and hassle in the long run, even if they require a bit more time and attention to detail at the point of initial data entry. Ideally, customer data should be exactly replicated across all systems, from CRM to OMS (order management system) to billing and accounting.
It’s hard to overstate the need for training, communication or organizational change management. I know that in the face of intense pressure to deliver ASAP, training can feel like a luxury. But think of it this way: Saying you don’t have time to invest in training is like saying you don’t have time to achieve ROI. When people understand why changes are being made, what will change for them and how to use new tools, they are much more likely to accept those changes and modify their behaviors.
Post-Launch To-Dos
Even after launch, there are steps to take to ensure new tools and processes really take hold. Measuring usage and compliance helps ensure that bad habits, such as manual workarounds and secret spreadsheets, don’t return. By monitoring, you may find new issues that need addressing or opportunities to improve processes. Often, these highly granular steps, such as adding steps to the order insertion process for video pre-roll, can yield surprisingly significant results.
There is a lot to mastering end-user adoption. But the next time you hear some or all of the user base blaming the technology, just ask yourself if you have enabled the technology to deliver the value you hoped for.
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