How Algorithms are Revolutionizing the Way Marketers Segment Consumers

Segmentation has long been a critical component in constructing effective marketing communications. But with the increases in privacy legislation and consumers’ continued embrace of anti-tracking technologies from companies like Apple, traditional methods of segmentation are becoming difficult, if not impossible, to apply in digital channels.How can one effectively and consistently apply segmented and targeted messaging across a wide range of customer targets and needs states when who the customer is and what their need states are is increasingly challenging to know or maintain with any consistency?

Segmentation Provides Structure and Efficiency

Before we delve into how segmentation has changed and what we must know to continue to use it successfully, it is important to remember why we use segmentation in the first place.

Marketing segmentation involves dividing a target market into smaller groups of consumers who have similar needs, interests, or characteristics. By doing so, marketers can create more targeted and effective campaigns tailored to each segment’s specific needs and preferences and, as a result, generally increase the effectiveness of their communications.

In addition to simply making marketing efforts more manageable and efficient, marketing segmentation delivers several additional benefits:

1. Better customer insights. Segmentation can lead to better understanding of one’s customers by analyzing distinct segments based on their needs, interests, and behaviors. This approach can lead to insights that drive better communications but can also be a source of product development ideas.
2. Improved targeting. Better understanding allows companies to target their marketing efforts more effectively. By understanding the unique characteristics of each segment, companies can refine their messaging, promotions, and product offerings to resonate with them, leading to higher conversion rates and better ROI.
3. Increased customer attention. In today’s crowded marketplace, increasing the relevancy of communications is essential to being noticed. Segmentation allows for more relevant – and therefore more noticed – communications.
4. Enhanced competitiveness. By effectively segmenting the market, companies can gain a competitive advantage by delivering more tailored and effective marketing campaigns based on unique insights about their specific customers. This can help to differentiate the brand from competitors and increase market share.
5. More efficient use of resources. By targeting specific segments, companies can avoid wasting resources on inefficient marketing efforts that are unlikely to resonate with certain groups of customers. This can lead to more efficient use of marketing resources, lower customer acquisition costs, and higher return on investments.

What is Changing

In recent years there has been a proliferation of new privacy laws and technology that are limiting the effectiveness of many marketing segmentation strategies. These include:

1. Data collection limitations. With the introduction of new privacy laws, companies are now required to collect and use consumer data in a more limited way. This is particularly true of certain protected classes involving housing, healthcare, consumer finance, and age-related demographics, where the collection and use of certain types of data is prohibited outright or subject to very restrictive standards.
2. Consent requirements. New privacy laws, such as the EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), require companies to obtain explicit consent from consumers before collecting and using their data for marketing purposes. This has limited the amount of data that is available and the composition of the available data as many consumers choose to opt-out. For example, according to Flurry, a mobile analytics company, as many as 96% of consumers chose to opt out of tracking on Apple devices.
3. Limits on ad targeting. Increasingly, the major platforms like Google and Meta are rolling back are limiting many of their targeting options, particularly within protected classes, but increasingly across a range of topics.
4. Increased demand for privacy. Consumers are becoming more aware of their privacy rights and are demanding more control over their personal data.

The combined effect of privacy legislation, consumer attitudes, and technology advancements that limit unwanted tracking and targeting, is that the traditional marketing segmentation approach of predefining distinct segments using data and targeting them explicitly is no longer effective within digital channels.

But, how is this possible you say? I was told my 1^st party data was going to enable me to continue to execute my segmentation strategies on platforms like Facebook and Google. In theory, this is correct. You can “match” your customers with the IDs these platforms have using your 1^st party data. Assuming permissions have been granted all around, email addresses match, and the technical feat of mapping device IDs and cookies together has been solved, congratulations – you will be lucky if you match 25% of your records.

The reality is, only companies with the largest 1^st party data assets combined with robust permissions will have the data sufficient to conduct large-scale segmentation-based strategies based on a traditional, pre-defined, data-based segment approach.

So, if the traditional approach to segmentation is no longer working in digital media, yet we need the benefits of segmentation like efficiency and increased message relevancy more than ever, what should a marketer do?

Creative-driven Segmentation: A New Approach to Segmentation

Creative-driven segmentation uses many of the same techniques as traditional segmentation.  Similar to traditional segmentation, we start by defining our target customer through deep research. We can tap into many of the big data sets to get insights and learnings about the consumers we want to target. We focus on things like values, behaviors, and beliefs that will help us create more relevant communications.

Instead of breaking these communications down into Byzantine messaging matrices that will be used to push out hyper-targeted communications, today we need to translate our segmentation into a series of creative executions designed to “draw out” the desired segments.  We are looking at creative that will create “hand raisers” among the less targeted audiences we are being forced to content with on all of the major digital media platforms.

From here, the algorithm works to “find” the target audience, or ‘segment’, using a response to our creative as its signal. The creative is used as “bait” to draw out the audience via response, which in turn feeds the algorithm, helping it find even more of the same type of responders. This is why in privacy-restricted categories like housing, where we cannot target income, geography, or age, we deliver media into the 90th percentile for things like age, income, and creditworthiness regarding audience composition using this very approach. Not only is this method privacy-compliant, but it’s also more effective than traditional segmentation approaches which are challenged by scale associated with 1^st party data assets and match rates.

Approaching Creative-driven Segmentation

Creative-driven segmentation is a back-to-basic marketing approach. No magic bullets, and no quick fixes, but it does deliver solid results with an undeniable upside — you’ll collect your own solid data set which you can use to build your own algorithms to grow and improve your marketing data analytics for a stronger, more efficient program. Also, you are leaning into the algorithmic decision-making that is behind all of the major digital platforms and, rather than fighting them, seeking to fine-tune them to fit your strategies.

1. Start with the customer. Define your segments or use preexisting ones.
2. Research and deep insights. Dig deeper into what, why, when, where, and how of consumer decision-making. Direct research or the use of aggregated consumer insight platforms can help here, but you need to plan to focus and invest here as the area of creative insights is the key differentiator in many cases.
3. Test your creative. Utilize a testing construct but understand that algorithms create worlds in which it is not what works best, A or B, but what combination of delivering A and B will yield the best results.
4. Validate. Assuming you are doing acquisition, back-test your approach. How well is your “creative bait” working to bring in the right kind of customer? You can fine-tune your approach over time by analyzing this data.

Legislation and technology have dealt a definitive blow to those looking for an easy answer to marketing segmentation. But for those of us willing to put in the work and rely on our marketing foundations and leverage new technology to our advantage, segmentation still has a very valid place in the marketing tool chest.

Algorithms are revolutionizing the way marketers segment consumers, enabling them to better understand and target their audience. By using advanced algorithms, marketers can quickly and accurately segment consumers based on their preferences, interests, and behaviors. This allows them to create more effective marketing campaigns that are tailored to the needs of their target audience. Algorithms also enable marketers to gain valuable insights into consumer behavior, allowing them to optimize their campaigns and maximize their ROI. By leveraging the power of algorithms, marketers can gain a better understanding of their customers and create more effective marketing strategies.

Understanding Consumer Behavior Through Algorithmic Segmentation

Understanding consumer behavior is a key factor in any successful business. Algorithmic segmentation is a powerful tool that can help businesses better understand their customers and tailor their marketing strategies accordingly.

Algorithmic segmentation is a process of analyzing consumer data to identify patterns and trends in consumer behavior. This data can include customer demographics, purchase history, online activity, and more. By using algorithms to analyze this data, businesses can gain valuable insights into how their customers interact with their products and services.

Algorithmic segmentation can help businesses better understand their customers and target their marketing efforts more effectively. For example, businesses can use algorithmic segmentation to identify customer segments that are more likely to purchase a certain product or service. This allows businesses to tailor their marketing campaigns to these segments, increasing the likelihood of success.

Algorithmic segmentation can also help businesses identify new opportunities. By analyzing customer data, businesses can uncover new markets or customer segments that they may not have previously considered. This can open up new opportunities for businesses to expand their reach and increase their customer base.

Finally, algorithmic segmentation can help businesses better understand their competitors. By analyzing customer data, businesses can gain insights into their competitors’ strategies and customer segments. This can help businesses develop more effective strategies to compete in the marketplace.

Overall, algorithmic segmentation is a powerful tool that can help businesses better understand their customers and tailor their marketing strategies accordingly. By using algorithms to analyze customer data, businesses can gain valuable insights into how their customers interact with their products and services, identify new opportunities, and gain insights into their competitors. Algorithmic segmentation can be a powerful tool for businesses looking to increase their success in the marketplace.

Leveraging Algorithms to Target the Right Consumers

Leveraging algorithms to target the right consumers is a powerful tool for businesses. Algorithms are used to identify the best customers for a company’s products or services, allowing businesses to maximize their marketing efforts and ensure they are reaching the right people.

Algorithms are able to analyze large amounts of data to identify patterns and trends in consumer behavior. This data can include demographics, purchasing habits, and other information about a customer’s lifestyle. By leveraging algorithms, businesses can create more targeted campaigns and tailor their marketing messages to the right consumers.

Algorithms can also be used to identify potential customers who may be interested in a company’s products or services. By analyzing data from a variety of sources, algorithms can identify people who are likely to be interested in a company’s offerings. This allows businesses to reach out to potential customers and increase their chances of making a sale.

Algorithms can also be used to identify customers who are likely to be loyal to a company. By analyzing customer data, algorithms can identify customers who are likely to purchase from a company multiple times. This allows businesses to focus their marketing efforts on customers who are likely to remain loyal to the company.

Finally, algorithms can be used to optimize a company’s pricing strategy. By analyzing customer data, algorithms can identify the optimal price point for a product or service. This allows businesses to maximize their profits by charging the right price for their products or services.

Overall, leveraging algorithms to target the right consumers is a powerful tool for businesses. By analyzing customer data, businesses can create more targeted campaigns, identify potential customers, and optimize their pricing strategies. By leveraging algorithms, businesses can ensure they are reaching the right people and maximizing their profits.

Utilizing Machine Learning to Personalize Marketing Strategies

Machine learning has revolutionized the way marketers personalize their strategies. By utilizing machine learning, marketers can quickly analyze large amounts of data and identify patterns that can be used to create more effective marketing campaigns.

Machine learning enables marketers to create personalized marketing strategies that are tailored to the individual customer. By analyzing customer data, marketers can gain insights into customer behavior and preferences. This data can then be used to create marketing campaigns that are more likely to be successful. For example, machine learning can be used to identify customer segments that are more likely to respond to certain types of messages or offers.

Machine learning can also be used to create more effective targeting strategies. By analyzing customer data, marketers can identify the most effective channels and audiences for their campaigns. This can help marketers reach the right people with the right message at the right time.

Machine learning can also be used to optimize marketing campaigns in real-time. By analyzing customer data, marketers can quickly identify which elements of their campaigns are working and which are not. This allows marketers to make adjustments to their campaigns on the fly in order to maximize their return on investment.

Finally, machine learning can be used to automate marketing tasks. By utilizing machine learning algorithms, marketers can automate mundane tasks such as creating customer segments, targeting campaigns, and optimizing campaigns. This can save marketers time and money, allowing them to focus on more important tasks.

Overall, machine learning is a powerful tool that can be used to create more effective and personalized marketing strategies. By utilizing machine learning, marketers can quickly analyze customer data and create campaigns that are more likely to be successful. This can help marketers reach the right people with the right message at the right time, while also saving time and money.