Understanding Customer Needs with a Purpose and Data

This data was originally featured in the October 11, 2023 newsletter found here: https://www.trustinsights.ai/blog/2023/10/inbox-insights-october-11-2023-understanding-customer-needs-with-purpose-and-data/.

One of the biggest challenges with the fractured social media landscape we face as marketers is what to do about previously reliable public data. For more than 15 years, marketers and the general public relied – perhaps too heavily – on the social graph that Twitter provided. With the current changes in management and population, Twitter’s audience has become something of a diaspora. A good number of people have stayed with the new platform, X, while others have migrated to Mastodon, Blue Sky, or Threads as direct successors, plus networks like LinkedIn as alternatives. Meanwhile, social media behaviors themselves have changed; people have chosen to move out of the public eye to private communities such as Slack, Discord, Guild, and many others.

What’s a marketer to do when the audience fractures like this into so many pieces? How do we adapt our use of data to determine what’s hot and what’s not when the collective public water cooler is scattered across the Internet?

First, let’s think through what our purpose is for collecting the data. What did we do with it? What would we like to do with it now? Once you’re clear on what you want to do, you can make decisions about what data you need and where you might get it. For example, suppose you want to gather sentiment about your company’s products and services so that you can judge how well the market has received your product.

Once you’ve established your purpose, take a look at the data still available to you. Does that data meet your needs? Using the example of sentiment analysis, if we previously relied on Twitter for that data, does X have it still? What about organic search data, or YouTube data?

One of the easiest ways to gather this data correctly is to simply ask your audience. Ask them buying signal questions, such as “When you want to learn more about a product or service, where do you go?” With today’s generative AI tools, it’s now much simpler to reduce free-form responses in surveys to organized, quantitative data that you can analyze.

For example, we ask you at the beginning of the month in this newsletter about your intent to recommend us to a colleague. We extract and analyze that data so that we know how you feel in general about us – and that data comes directly from you, no social network needed.

If you don’t have access to solid first party data, then one of your strategic imperatives for the coming year should be to build a pool of reliable first party data. This means creating a community of some kind like the Analytics for Marketers Slack community, or an email list like this one that you can ask survey questions to. Put some ad spend behind your community building; think about what a single survey costs when commissioned with a market research firm, and put that budget towards growing a community. If you do it well, it’ll pay multiples on your investment compared to a single one-off research project.

There’s no easy substitute for the loss of Twitter data, but there are viable alternatives once you build the necessary infrastructure and relationships with your audience.