As you’ve probably experienced, the process of sorting data into target customer segments is a lot harder than it sounds. It’s pretty common that creating a segment of 80% of your customers takes the same amount of time as a segment of 1% of your customers. That’s time that could be invested in generating content and experiences that require human creativity.
On top of that, segmenting implies that your customers can only wear one hat or tag. In reality, you could use a ton of tags to label your best customers. When customers fall into multiple segments, you may end up spamming them. And if you’re not using data effectively, you may be presenting stale and ineffective experiences — which can get old in a hurry.
In fashion, for example, SKUs aren’t consistent; they depend on external trends and impacts (like the weather or what a famous influencer is wearing), which change every year. It’s best to decide which products and inspirational articles a person should receive rather than throwing marketing spaghetti at the wall and seeing what sticks.
The sheer scope of personalization and the amount of data to sort through can be overwhelming for even the most experienced of data scientists. So how are marketers — who have enough work to do in the day to day — supposed to keep up? And more importantly, how can marketers achieve the holy trinity of understanding dynamic preferences, selecting real-time relevant content, and executing this at scale?
Making Your Personalization Process Smarter
Here are some tips to help make personalization a little more manageable so you can provide the right experiences to your customers:
1. Determine your goals.
Look for metrics you’re aiming to encourage. Start with the finish line — what does your first-place podium look like?
For Retail:
⬡ Is it 2% more checkouts?
⬡ Or higher sales post-tax by $200K?
⬡ Or to promote products with better margins?
For Consumer Goods:
⬡ Is it to increase recipe views?
⬡ Or drive twice as many visits in 30 days?
⬡ Or focus on registrations to increase your sign-up rate?
2. Approach contextually.
Segmentation, curation, and data activation should all come together to provide context around your customers’ preferences. As an example, let’s look at someone’s adult beverage preferences.
Around the holidays, Diane orders bottles of Scotch whiskey to give as gifts. When the weather is pleasant in San Francisco, though, she orders beers to enjoy outside. On Wednesdays and Thursdays, however, Diane searches for white wine to drink after a long week of work. An effective approach to personalization would take into account all of Diane’s preferences.
Think about how you can make your various tools and data silos come together for easier experimentation.
3. Think scalably.
A powerful AI platform makes personalization more intelligent. It can reduce the number of segmentations, curations, and activations of data — saving you valuable time and effort. Breinify, for example, easily activates data from different areas by connecting various tools and platforms. That way, you don’t lose important customer data that sits across your marketing tech stack.
Breinify’s AI provides digital personalization at scale. Instead of saying, “Here’s a set of data; do something with it,” we focus on goals and move backward with the data you have to offer solutions from day one. With Breinify, you can go from weeks of curation to seconds. And unlike other enterprise-grade platforms, we’re a lightweight and turnkey solution that can launch in fewer than 14 days.
Instead of spending hours muddling through data sets to create target customer segments, you can focus on what you do best: creating unique experiences for your customers.
Request a demo with Breinify to see how we can save you time and improve your digital personalization at scale.