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A few years ago, adding someone’s name to an email message was considered high-tech and deeply personalized. Today, that practice is standard if not antiquated. Big Data, machine learning, and AI are top buzzwords on everyone’s lips in the world of marketing personalization. But these terms don’t have to be intimidating for low-tech marketing organizations and they don’t have to be expensive for small businesses. Here’s what you need to know about data-driven personalization in your marketing efforts.
What Is Data-Driven Personalization?
Put simply, data-driven personalization is the process of segmenting audiences and making marketing decisions based on facts and information, rather than best practices or historic choices. At face value, that concept isn’t complex at all, but grows more advanced when you add data elements and smart tools to do the personalizations for you.
Here are a few common terms that you will encounter in data-driven personalization:
- Big Data: extremely large data sets collected on a variety of factors ranging from customer demographics to personal preferences. This is too much data for any one analyst (or team of analysts) to sort through and make sense of.
- Artificial Intelligence: bots that perform tasks that humans normally would. For marketing, this means analyzing big data and making decisions based off of it.
- Machine Learning: the ability for robots to create their own rules and make decisions based off of their experiences, rather than algorithms programmed by developers.
For example, an AI tool might look at swaths of big data and determine that the best time to send an email to customers is at 10 am on a Tuesday. A machine learning AI tool would study open rates over time, and adjust the send time based off what it finds.
Either way, machines are determining which times are most effective to generate email opens or sales and scheduling the emails instead of humans. This takes the burden off of marketing teams who would otherwise have to study the data and schedule the emails themselves.
Why Is Data-Driven Marketing More Effective?
Data-driven personalization in marketing allows brands to focus on their customer’s needs and intent. Not all customers have the same goals and needs, which can be challenging for marketers broadcasting the same message. Instead, data-driven personalization identifies customer needs and provides solutions for them. A business might have a dozen different messages or solutions based on a customer’s journey or demographics.
Data-driven digital marketing is also flexible. It allows brands to change that they do and how they do it with just a few clicks. If you don’t see the results you want, turn to data to understand why and change your ways. You can also test different ideas and determine their success based on clear analytics.
What Personalization Strategies Do Most Marketers Use?
You don’t need MIT-league developers to implement data-driven personalization in your marketing strategy. In all likelihood, you might already use data to make your marketing decisions and automatically personalize your content in some way or another.
Business Insider shared a survey of more than 220 marketing professionals about the types of data-driven marketing personalization they plan to use in 2017. The top tactics used last year include:
- Email message personalization
- Targeted landing pages
- Contact data segmentation
- Triggered email campaigns
- Retargeted advertising
- Lead generation and collection
Instead of mass promotions where you try to reach as many people as possible, data-driven marketing allows you to reach the best people possible (those who are most likely to buy) and reach them in the best-possible manner.
How Can Data-Driven Automation Improve My Workflow?
Along with making your sales and marketing tactics more effective, data-driven marketing tools can free up your time and make your sales workflow more effective. AI doesn’t necessarily take away the creative aspects of marketing, but rather replaces the menial tasks that clog up your day.
For example, some AI tools vet potential leads to see which ones are likely to become sales. Instead of giving your sales team 1,000 leads to sift through, the tool might present them with 200 highly-qualified leads that are already in the middle of the sales funnel, along with suggestions for what products to sell to them. This makes the sales team more effective by increasing their successful calls and conversion rate.
You don’t have to tap into advanced machine learning tools for effective data-driven marketing personalization, but you do have to base your decisions off of concrete information and not intuition. AI and machine learning can’t solve your problems, but they can boost your metrics and make your workflow a little easier.
Craig Smith is the founder & CEO of Trinity Insight, a leading eCommerce consulting agency.
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