Blog post

How to overcome data roadblocks in personalized marketing

By Kit Germeroth

Have you noticed engagement dropping on your marketing campaigns? It’s likely because you’re not targeting your customers effectively. 63% of customers expect personalized experiences when they engage with brands, and most of these customers don’t feel that brands are delivering.

Personalized marketing delivers messaging that’s more appealing to each individual consumer based on aspects like demographics, purchase history, and the preferences of similar cohorts. The logic is simple: people want to see products that they’re interested in.

When you think about why you’re loyal to your favorite brands, it’s likely because they have an understanding of your interests as a consumer, and they provide a great experience. Big companies like Amazon, Netflix, and Spotify keep customers coming back because they’ve nailed the art of personalization.

Regardless of your industry or product, your customers have a wide variety of options at their fingertips, and they expect their experience to be easy and convenient. If you’re serving up generic content or offers that aren’t targeted to their interests or needs, you risk losing them altogether.

One-to-one marketing isn’t just better for your customer. 93% of businesses with an advanced personalization strategy experienced revenue growth in 2019. It also makes it easier to assess marketing ROI; with more targeted campaigns, you can see exactly which efforts are most effective and cut those that aren’t.

Automated marketing software also enables you to immediately follow up with existing customers and suggest recommendations for similar or related products, adding another opportunity to increase revenue and improve retention.

How to get more personal

If you’re starting from scratch, how do you go about setting up a personalized marketing strategy? It all comes down to your data.

Consider the data sources that are most useful in getting to know your customers better. Demographic data, such as gender, location, and age, can be enhanced with consumer preference models from mass surveys to identify trends among similar cohorts or segmentations.

Use this as a foundation that can then be further fleshed out with behavioral data. What does their purchase and browsing history look like? How often do they visit? It’s also important to tie customer data together across different devices to get a comprehensive picture.

All of the data you’ve gathered from various sources should come together to create an in-depth profile that you can use to guide your marketing.

Roadblocks to personalization

Data architecture & quality

A recent study that surveyed more than 600 marketers found that the biggest obstacle to personalization is data architecture. Prior years cited third-party data integration and data quality as the biggest challenges. It’s clear that companies have the data, they just aren’t sure how to organize it to create a successful personalization strategy.

These silos result in companies taking a piecemeal approach to customization; with scattered sources, they’re basing decisions off of incomplete information. If the organizational structure isn’t set up properly, the strategy can’t succeed.

Organizational structure

Aside from the structure of the data itself, implementing a personalization strategy requires an organizational change.

Unfamiliarity with new processes and communication issues can easily cause new initiatives to fail. You could come up with a great personalization strategy, but if the organizational structure isn’t set up correctly, it will never work because your team isn’t working toward the same goal.

Where to start

The first step in setting up the correct data architecture is to ensure you can access and process all of your data on demand. Your data should be accessible by processes and pipelines, supported by your data architecture so information isn’t siloed. You should also establish a data governance framework. This is a continuous process for identifying which data is critical to your business and ensuring it stays at the right level of quality.

Aside from the tools you implement, you also need to set the right organizational structure in place - a data scientist, business lead, and technical product person who all work together to use the results of the analytics. Consider implementing an analytics center of excellence, a strategic concept that streamlines all of the analytics efforts at an organization.

Incorporate change strategy in your overall strategy as well. These initiatives can be more technical, like how to implement a new tool, or more strategic, like changing ways of working or creating new positions.

Once you have the basics established, you can work on scaling your personalization strategy with more advanced machine learning models, which can further guide marketing efforts by showing the probability for certain consumer behavior.

The final step in your personalization strategy is to ensure your data architecture can support reaching your customers in the most effective ways, via email, mobile application, or their other preferred channels.

Take these steps to get to know your customers better, make your marketing efforts more effective, and watch your customer retention flourish.