For today’s marketers, personalization is both the gold standard and a white whale: there is a general consensus that it is worth pursuing, due to the increased customer engagement and conversion rates it generates, but marketing teams often wonder how to achieve this. Populating an email template with a customer’s first name is no longer enough – those generic emails can still be valuable to customers if their content is relevant and delivered at the right time, but if calling your customer by name is also personal as your message gets through, maybe it’s time to get to know them a little better.
I know what you’re thinking. Heidi, my brand is a little too big to take all of our customers for coffee. Of course it is ! And while I’m sure customers would appreciate a free coffee date with their favorite businesses (okay, maybe not), there’s an easier way. Enter: data science. Those two words, data and science, may actually make some of you shiver. I’m right-brain dominant, Heidi. The last time I thought about science was when I was asked to take a science course in college. Well, never fear, data science doesn’t have to be that complicated. Here are some ways you and your team can start a data science project and deliver more relevant and personalized communications to your customers.
Marketing Technology News: Decentralized identity solutions are key to advancing Web3
Set your intentions
Often when people hear of “data science” they turn to complex predictive models, rapid testing platforms, etc. Yes, once your customer data is in good shape, there are sophisticated AI-powered models that can generate very accurate insights, but let’s not rush it. It is essential to frame any marketing science project with simple questions. These could include: “What time of the day/week/month do my customers want to hear from me?” or “Which email channels do my customers use most frequently?” or “What kind of content is best to offer my customers after their first purchase?”
With a simple, customer-focused question at the heart of your data science project, you can extract targeted insights your team can leverage to improve marketing personalization efforts. From there, you can gradually eliminate customer friction points; once you have determined the best channel to reach a segment of your customers, for example, you can consider timing, content, etc.
Simplicity is a common theme here, and for good reason: if your brand has never used data science, starting small will provide you with a foundation on which to build more sophisticated models. Once you’ve decided what you want to know about your customers, start with some simple A/B testing to unlock early results.
If you’re wondering which messaging tone best captures your customers’ attention, create two different options and send a random segment of your customer base first and another segment second. From there, figure out which option resonates the most with customers (which one got the most clicks? Conversions?), and here’s your winner for that round. Then run the test again, and again. If a particular version of a message consistently outperforms or underperforms others, then your testing has taught you something. I should mention here that, in some cases, A/B tests like this will let you know that a particular message, or activity on a particular channel, outperforms the others – but only by a small percentage. Don’t let those small margins fool you: if you have hundreds of thousands of customers, a 1% increase in conversions translates to a significant increase in sales.
As your data matures, improve your testing
At some point, your team will have accumulated a considerable amount of data through simple testing. You’ll start to realize you’ve reached this point when your colleagues run out of cognitive space to understand the next 50 data points on a certain customer segment. Using the data you have, you can implement more sophisticated strategies to deliver relevant and personalized messages to your customers:
- Similar models can tell you which attributes of a customer are highly correlated with desired customer behaviors (i.e. making a purchase or renewing a subscription). You can then tailor your marketing messages to be relevant based on these attributes and test different versions of a particular message to see which sticks.
- Triggers are automated, timed campaigns that align with when a customer is most likely to reach a specific point in the buyer’s journey. In your initial manual data collection, you may start to see trends at particular times when customers are more likely to convert. Triggering campaigns can take advantage of AI predictive models to automatically reach customers at those crucial times, when your marketing messages are most relevant and appreciated.
Marketing Technology News: JThree things to keep in mind when it comes to marketing attribution
Above all else, I want to stress the importance of putting the customer first – that’s why you’re even considering a data science project, so use your powers for good. The customer should be at the center of your personalization efforts; the more value you can deliver to them through thoughtful marketing campaigns, the more brand loyalty they will have. Leverage all the data they have voluntarily provided to you, as well as their purchase or request history to reach them on the channels they prefer, at the right time, with the right type of messaging. Data science can help pave the way to the white whale that is personalization.