Building a better data strategy
The good news is it doesn’t have to be this way. Rather than fire out generic lo-fi marketing campaigns in the hope something sticks, organizations can now use artificial intelligence (AI) to better engage customers. This ranges from hyper-personalized promotions and loyalty programs to advanced techniques, such as image extraction, that retrieve metadata and product information from an image of, say, an influencer, then target people who follow them with recommendations and deals to match.
In each case, the result is more bang for your marketing buck and less time wading through irrelevant content for consumers.
Yet, while sharing our personal data through apps, online transactions, and public Wi-Fi connections may now be commonplace, it’s also creating challenges for organizations—especially when it comes to privacy and security. Every successful data strategy therefore should balance better, more personalized experiences with the reassurance that people’s information will be handled safely and responsibly.
Here are five things to consider when building a data strategy that delivers value for your business and your customers:
1. Focus on conversations, not keywords.
Research suggests the average person’s attention span is now eight seconds, down from 12 in the early 2000s. Engaging consumers therefore requires an almost instant connection. The more data you have regarding their preferences and behavior, the more likely you are to make that connection.
What’s more, as the way search operates shifts from keyword density to questions and answers, interactions will become increasingly conversational. It’s therefore a good idea to start preparing for a world in which consumers (or perhaps consumers’ AI proxies) engage with your brand’s own AI solutions to deliver meaningful outcomes.
2. Harness data across multiple touchpoints.
Many organizations still think about data in a process-oriented way. This means their focus is on the individual steps required to get to a particular output, like driving someone to an e-commerce site through a social media ad.
Yet the emergence of new AI tools is supercharging the number of touchpoints your brand has with consumers—from voice and social to conversational agents and chatbots. This, in turn, is democratizing access to information by allowing data from all these touchpoints to be captured and consolidated to build richer consumer personas and profiles. These can then be used to create connected experiences that convert at a higher rate.
3. Look inside and outside.
To create relevant, meaningful connections with consumers, you need to capture data from both inside and outside your organization. By analyzing external factors such as style trends, seasonality, regional variations, and major events, you can begin overlaying them with the information pulled from your own customer behavior data.
This gives a more rounded, up-to-date picture of consumers and can inform everything from your product development roadmap to the type of brand collaborations you engage in.
4. Connect the enterprise.
The most successful enterprises are connecting their technology function and initiatives tightly with the needs of the business and better focusing AI and data investments to value that has been validated. When business groups join forces with IT teams to test out hypotheses and create scientifically backed value estimates, the right projects move forward and the wrong ones fall on the cutting room floor before they become too costly.
This requires your marketing team to collaborate more closely than ever with not just IT but also legal and risk—essentially everyone with a stake in the way information is gathered and managed. It also means continually upskilling employees on product development lifecycle methods and how to apply AI so that cross-functional teams speak the same language and have an understanding of how to work together.
5. Use AI to address privacy concerns.
While some people are happy to share their personal information in exchange for enhanced convenience, the emergence of AI has made others more concerned than ever about data security. This disparity creates a conundrum for businesses that need to successfully engage both groups.
Being transparent and accountable around data privacy and ethics is therefore paramount, but, beyond that, AI offers another possible solution. Synthetic personas and digital twins help you better understand consumers by using AI-generated models to simulate their behavior.
This, in turn, enables you to deliver the personalized experiences people want without asking them to share too much personally identifiable information. It has the added bonus of helping you comply with varying data regulations across regions such as Europe and the U.S.
Unprecedented and exciting opportunities
Of course, we’re still very much at the beginning of what AI could do to shape the future of marketing. The more information we feed it, the smarter and more effective it will become in analyzing, predicting, and responding to the needs of consumers. The speed and accuracy with which it can turn unstructured data into structured insights will only increase, too.
For organizations, the opportunities this offers are unprecedented and exciting. By investing in careful data and model management, leaning into data privacy, and maintaining a relentless focus on customer experience, you can be well-positioned to seize them.
