Customer data ethics has made its first appearance on the Gartner Hype Cycle for Digital Marketing 2020 as it has moved into the Innovation Trigger phase of the Hype Cycle, according to Gartner.
Customer data ethics focuses on aligning business practices with moral and ethical policies that reflect a company’s values.
Marketing operations where customer data ethics will be particularly important include: mobile marketing and advertising targeting, marketing campaign design, customer segmentation, passive data collection, and customer service and loyalty programs. However, Gartner expects customer data ethics to take at least 10 years to reach full adoption.
“The need for customer data ethics arises from two factors – concentrated market power of a few digital tech giants controlling massive amounts of customer data and consumers’ deep seated concerns about how their data is collected and used,” says Mike McGuire, vice-president analyst in Gartner’s Marketing practice.
“In five to 10 years demand for the ethical treatment of customer data will intensify as consumer trust decreases. To win back consumer trust, marketers must talk about customer data ethics and demonstrate, in transparent ways, their commitment to be more than legally compliant.”
Among the 21 technologies represented in the Gartner Hype Cycle for Digital Marketing, 2020, customer data ethics and four other technologies have the capability to transform how marketers respond to changing conditions.
Real-time marketing describes an organization’s ability to interpret and respond to opportunities within time frames that provide business advantage by using tools, technologies and processes that capture, monitor, analyze and act on information in real time.
Companies that adopt real-time marketing tools and techniques across their larger value chain will outperform competitors in operations and their ability to deliver more rapid and relevant offers to customers.
Artificial Intelligence (AI) for Marketing
The hype around AI for marketing has moved past the Peak of Inflated Expectations and is now in the Trough of Disillusionment.
AI techniques are finding their way into multiple marketing systems of record. However, AI for marketing presents a long, steep learning curve. Marketers must overcome multiple challenges, including data availability and team skills gaps, before mastering.
Personalisation engines are commonly used by marketing, digital commerce, merchandising and customer experience teams to optimize content and campaigns; commerce experiences and recommendations.
They can also be used for interactions across customer touchpoints like call centers, chat and digital kiosks.
Brands and retailers newer to personalisation should pilot personalisation using existing resources (data, talent, technology, content) to prove results and justify budget.
Location intelligence for marketing enables marketing leaders to manage and make available correct information about the physical locations under their control to search engines, app publishers, review sites and other social media.
Location intelligence also includes technologies that enable marketers to assemble consumers’ location histories (typically via mobile apps) to refine customers’ profiles to deliver more relevant engagements or offers.
Despite its potential, the promise of location intelligence is hampered by the tension between consumers and brands over how consumers’ location data is used.