Consumers are growing more aware — and increasingly uneasy – about data collecting in their daily lives. In fact, you’re probably one of those consumers on your day off; according to a survey, 97% of consumers are somewhat or very concerned about securing their personal data. This broad apprehension about the collecting and use of consumer data is set to disrupt the marketing world as we know it.

What is data privacy in the digital marketing world?

The ability of customers to control how their data is used by companies and other organizations is known as data privacy. Only a few types of information, including demographic and financial information, may be disclosed. Although these facts assist marketers in producing more individualized experiences, many customers are increasingly worried that their privacy is being compromised.

Fortunately, there are choices available for data-driven marketers who wish to preserve data privacy while hyper-targeting their customers.

Why is data privacy important for marketers today?

Marketing teams have been particularly hard-hit by recent revisions to data protection laws. Nearly one in five marketers say that privacy compliance across all marketing platforms is their top issue, according to a new study. It makes sense why, given how badly this growing emphasis on privacy affects a marketer’s capacity to gather and analyze data quickly and even renders some of their earlier marketing technology investments outdated. In actuality, 73% of marketers worry that privacy issues may harm their analytics efforts.

However, analytics is only part of the equation. Marketers are the guardians and custodians of a company’s brand image, and neglecting privacy concerns, whether by choice or necessity, may have a negative influence on their brand value. According to studies, exploiting user data can have disastrous effects for organizations, with almost 8 out of 10 customers preferring to abandon a brand if their data is utilized without their knowledge. Marketers are now caught between a rock and a hard place, with consumer opinions on one hand and analytical prowess on the other.

The impact on data-driven attribution models

For years, marketers have become accustomed to utilizing massive amounts of data to construct marketing campaigns and target them to the ideal demographic. However, as people take their privacy more seriously, this river of data is slowing to a trickle. While most marketers can still conduct business as usual with less data, many fear that it’s just a matter of time before that steady supply of data becomes erratic.

As a result, many marketers are looking to the past for inspiration. Historically, marketing has produced suggestions based on aggregate data, most notably through Marketing Mix Modeling (MMM). This type of study integrated aggregate data from media exposures with key performance indicators (KPIs) like sales conversions, store visits, or online visits. Unfortunately, many marketers who adopt these strategies today will find that they fall well short of models that follow people on a personal basis.

How marketers can move forward

Marketers abandoned analytical methodologies like Marketing Mix Modeling for a reason: they desired a system that could provide more accurate insights. Although data privacy limits make obtaining a granular picture of their data difficult for marketers, it is not impossible with the right attribution models and marketing analytics technique. Modern businesses seek a marketing analytics solution that can use whatever data is available to ensure that they do not lose key insights and that their current marketing framework does not disintegrate.

They seek a solution that does not require a large quantity of data to function but can nevertheless process vast amounts of data when needed. Furthermore, even if not provided massive amounts of data, a future-proof system must be able to discriminate between causation and correlation. Combining these characteristics can help your company keep up with altering consumer sentiments.