Data is more crucial than ever to marketing success. Companies are striving to utilize the latest analytics to drive improvements in all aspects of operations.
The majority of consumers are willing to provide data under the right conditions. A 2018 survey by Acxiom found that 58 percent of Americans are willing to provide some data to a company if they know what they’ll get in return. Combined with the increasing use of artificial intelligence for analysis, we should anticipate a steady increase in business insight and data-driven decisions.
Getting the Most Out of Data
If you’re not yet using AI, you should start. According to Demandbase, back in 2016, 80 percent of B2B marketers believed that AI would have a significant role in the industry by 2020.
However, not all companies are fully maximizing an AI approach to data collection and analysis. Only 26 percent of marketers are confident in using AI, and just 10 percent have implemented AI into their strategies at this point.
AI opens up new possibilities for marketers. For example, retailers can use AI to analyze weekly SKU performance data, allowing stores to optimize their promotions for better results. Additionally, AI can analyze customers’ behavior in real time, prompting automated systems to respond rapidly enough to positively influence consumer behavior. This has important applications not only in retail but also in banking, telecommunications, entertainment, and many other areas.
Key Data Strategies
For these, and other reasons, making the most of your data is crucial for enhancing overall performance. The following three strategies should guide your approach to data and AI integration.
1. Assign challenging tasks to your team.
AI can deliver a customized user experience with speed and precision that cannot be achieved by human customer service reps. This allows human marketers to focus on long-term strategy and designing customer experiences. According to Norm Johnston, global CEO and chief digital officer of Mindshare, “The rapid evolution of AI in media will enable our people to focus on innovation and intelligence rather than repetition and reports.”
2. Incorporate data collection into your product.
When designing your product, consider ways to collect data from the end user, whether it’s a physical object or an application. Some websites, for instance, require visitors to sign up and share personal data, such as connecting a LinkedIn or Facebook account, to make full use of their sites.
Remember that data isn’t only consumer behavioral data. AI machines also collect data like speech patterns, critical for making iterative improvements to speech recognition software. CMOs use speech recognition products such as Alexa and Siri to enhance the customer experience. For instance, Hyundai recently teamed up with Google to integrate the Google Home voice assistant into its luxury Genesis brand. Drivers can start and lock their cars and request navigational help, all without pressing any buttons.
3. Ensure your data is clean.
While it’s tempting to throw as much data as possible at AI machines, quality is often more important than quantity. When data is missing, incomplete, or dirty, there won’t be much value from your AI. Marketers that make an effort to clean the data they collect will have significant advantages in the marketplace.
By adopting these strategies, marketers can expect encouraging returns in the years to come.
