When it comes to marketing, data-driven strategies are a must to understand who truly needs your product. From customer health scores to time on page, data dictates the success of a marketing strategy. However, among the strategies that take the most work is customer segmentation.
Customer segmentation involves having comprehensive data across your customer base, transforming it, and parsing it. Machine learning makes the entire process so much simpler these days. Here’s how machine learning builds better, more strategic marketing.
What is Customer Segmentation?
Customer segmentation is the separation of your customers into multiple groups. The factor usually varies, but it mostly follows a combination of categories, giving you a chance to market to each group effectively.
The customer segments you create should be based on shared characteristics in the most basic sense. These can include basic demographics, purchasing history, or digital behavior. This helps you focus your marketing budget on the most customers who need it.
If you’re still on the fence about whether your marketing tactics are sound, take the time to segment your customers. By doing so, you can better understand the individuals who visit your website and why they specifically do.
You can design an integrated omnichannel marketing plan that focuses on the right market with that information. Segmenting customers into different groups, for example, can help you target a narrow market and increase your conversion rates.
You’d have to be an expert in statistics and data analysis to do this in the past. Any savvy marketer could make their categories. But now, technology makes it much more manageable.
How Does Machine Learning Help?
Naturally, you’d prefer to spend as little time as possible on creating useful buyer personas. After all, you’re running a business, not running a data science company.
That’s where traditional analytics tools and artificial intelligence come in. The tools you use should be able to automate a lot of the processes involved. Automation eliminates the need to do repetitive tasks that a machine could otherwise do, such as an assembly line.
Humans are notoriously bad at dividing people into groups because the distinctions may not be clear or concrete. As a result, many people fall outside of your ideal group, including those whose financial status hurts your business.
The automation of customer data gathering and analysis is one of the most effective uses of AI. Instead of spending time on things like generating actionable insights, you can focus on what you’ve always done: managing customer relationships. This allows you to better communicate with your base and develop your brand.
The benefits of doing so are numerous. Real-time insights are a powerful tool, especially for marketers. If a customer interacts with your channels at a particular time of day, you can trigger a specific action. What’s important here is that you do so with real-time insight.
With integrated software that uses algorithms, you can determine what content is appealing to which types of buyers. With this information, you can better decide what content to produce and where to market it.
By collecting, analyzing, and acting on data about individual users, you can develop each person’s profile and what they might want. Over time, this creates an invaluable database that saves you time and money in the future.
To get the best results, effective usage of your data is a must and an accurate set of information should give you the best results. Any information you assign should be based on facts. Additionally, you must understand that the algorithms you create are merely a guide. Features and criteria are designed keeping in mind that the criteria could change.
How Do You Create a Better Algorithm?
The success of your machine-learning strategy counts on the accuracy of your data. Unfortunately, you can’t just dump data into your system and assume it will yield results. Here are some of the most crucial aspects to create and build to get a better picture of what you’re looking for.
These systems streamline your data analysis using complex algorithms and automated learning tools. They give you the most accurate information about your audience. Any insights gained from those who have already bought from you can be applied to others.
As you’ve gathered more and more sales or client records, patterns should start to emerge. Categorizing and storing that data is half the battle. Now, you want to leverage that data.
Going back to our earlier example, you might have noticed that some of your customers have certain buying behaviors. Their purchases are more predictable. For instance, some clients buy more products from new lines. Some purchase more regularly than others.
These are called “behavioral” characteristics, and you easily observe them. Other behavioral observations might be knowing whether your client has children, is expecting, or is newly married. These will help you create precise segmentation within your data.
Utilize Customer Profiles
While there’s no “one size fits all” profile, knowing more about your target demographic can help. For example, if most of your customers are men between 30 and 40, then that’s where you can start. Characterize your ideal client so that you can target them more effectively. That narrows down your workload and makes it easier to deliver the right content at the right time.
Instead of relying on marketers to make their own categories, the data itself can do the work. How? It’s through applied analytics and machine learning. Data-driven strategies are possible thanks to decisive algorithms that sort, sift and apply trends to the data you already have.
Identifying and categorizing these individuals is a good start, but it’s not enough. A well-thought-out user profile can tell you a lot, such as their complete needs and how likely they are to buy from you.
For example, you can classify your subscribers into a couple of different categories. Perhaps you have one type who has a lower average order value and is into social media sites. Another type is into fashion blogs, but buys less frequently.
After creating a profile and viewing it over time, you can determine which ones are sold more, which ones don’t click as often, and which need more help. This personalization is then part of a targeted strategy that targets your prospects where they are.
You can, therefore, schedule your content accordingly so that it is specific to their individual needs. All these are possible through machine learning, simplifying the process.
The Bottom Line
Ultimately, employing the right tools is the key to this new wave of successful, personalized marketing strategies. Building your algorithm, or having them made for you, is easier than ever.
By leveraging the power of predictive analytics, you can create more relevant products and campaigns that aren’t just aligned with what your customers want but are precisely what they need. Machine learning can offer fantastic benefits that marketers can take advantage of.
by Luke Craig