At any given moment you can scroll through your inbox and see the impact of personalization at work. Some companies get it right. Some companies get it very wrong. The best personalization approach will tailor experiences to the unique preferences and needs of your audience, which will significantly improve engagement, boost conversions, and foster customer loyalty.
Data is the key to informed decision-making, and digital analytics plays a pivotal role in unlocking those insights and driving success. Integrating personalization into your experimentation plan can be a powerful way to refine and optimize your customer experience. Hannah Alexander, Associate Director of Decision Science, shares the steps to effectively integrate personalization in your testing practice and meet the ever-evolving needs of your customers.
The first step in integrating personalization is to establish a baseline understanding of your business:
Conduct a baseline analysis to gain an understanding of who your customer is and how they’re currently interacting with your client touch-points.
Leveraging Cluster Analysis
Conducting cluster analysis allows you to gain insights into your audience by identifying distinct groups within your dataset. These groups can help you understand your audience’s behaviors, preferences, and characteristics. Cluster analysis employs mathematical algorithms to uncover natural clusters or groups of data points that share similarities with each other while differing from other groups.
This analysis can be applied to various types of data, including numerical, categorical, or mixed data. In the context of personalization, some data science model considerations might include:
Cluster analysis enables you to test various marketing strategies or messages tailored to different audience segments. It also allows you to measure the effectiveness of these strategies and gauge audience responses.
For example, we might uncover the following groups within a client dataset:
You can also create rich customer profiles by surveying your audience. A customer profile is a data-driven document that describes your current customers. Profiles are based on surveys that gather purchasing behaviors, pain points, psychographic data, and demographics. A customer profile can help you find segments of customers with common traits so you can target them in your marketing campaigns.
Market basket analysis can be used to better understand common purchase combinations. Purchase history is analyzed to reveal product groupings as well as products that are frequently purchased together. Once conducting market basket analysis, you can learn how to strategically market items alongside each other to encourage incremental sales.
For example, this market basket analysis identified a common association between bread and milk, which is expected given their status as grocery essentials. As we dig deeper into the data, we discover the second most common grouping is bread, diapers, beer, and eggs. Is there an opportunity to optimize product placements, marketing strategies, or coupon offerings based on this purchasing pattern?
Personalization is a powerful way to enhance your conversion rate optimization (CRO) strategy. By leveraging the insights into your customers and visitor interactions, you can build an engaging experience that pushes them to complete a specific action, such as clicking a call-to-action or purchasing a product.
CRO Baseline Analysis
Data is the backbone for measuring the success of your CRO strategy. To understand your current performance metrics, you first need to establish a baseline. When conducting a baseline CRO analysis, the following questions should be considered:
Once you answer these questions, you can develop a marketing plan to better understand customer behavior. Customer journey mapping highlights customer experiences with your brand across all touchpoints, which helps you gain insight into common pain points and allows you to better optimize and personalize the customer experience.
It’s important to determine the touchpoints in the customer purchase journey that drive acquisition, improve conversion rates, and influence CLTV. Equally important is recognizing the touchpoints that negatively impact metrics like exits and unsubscribes. With this information, you can identify what matters most to your audience and begin to think about how you can tailor the customer experience to their needs.
With a clear understanding of buyer behavior, you can begin to test the content and functionality that’s optimal for your segments at each stage of their customer journey.
Formulating Your Hypothesis
It’s important to approach your personalization as an iterative series of experiments based on a data-driven hypothesis. A well-defined hypothesis sets your personalization up for success. After mapping your goals, you can start to make informed theories as to how you can optimize the customer journeys to meet those goals.
Take a look at your conversion funnel, acquisition touchpoints, and marketing plan. Do you have any theories about what is or isn’t working? Do you have ideas for optimizing your current customer experience? Drawing from your analysis, identify areas where personalization can enhance the user experience, particularly those aspects that have the most significant impact on the chosen conversion goal.
With your data-based problem defined, it’s time to construct your hypothesis. A robust experimental hypothesis follows an ‘if-then’ format to establish causality between the variables. Your hypothesis format should read: IF {a specific change (X) occurs on the site}, THEN {a particular customer metric (Y) will exhibit positive improvement}.
Your assumptions about optimal audience segments and messaging for those segments are just assumptions until subjected to testing. A structured process is necessary to determine which insights are valid for your target audience.
A/B testing leverages objective data to evaluate the effectiveness of different personalized experiences, determining whether the changes made lead to statistically proven improvements in user engagement, conversion rates, and other key metrics.
A/B testing enables you to optimize via personalization for different audience segments. By testing variations on specific user segments, you can tailor experiences to meet the needs of each group and determine what resonates most. It also allows for an iterative approach to personalization. Insights gained from A/B testing inform future audience segments and personalized messaging, while insights derived from personalization experimentation informs future A/B testing, and so on. You can continually refine and enhance personalized experiences based on the results of ongoing tests, which leads to continuous improvement and optimization.
The landscape of data-driven personalization services is diverse and constantly evolving, encompassing a range of software and platform solutions. Businesses can leverage these tools to harness customer data, automate personalization efforts, and deliver tailored experiences that resonate with individual consumers, ultimately leading to improved engagement, conversion rates, and customer satisfaction.
Concord has worked across a variety of MarTech stacks, implementing and building personalization programs with leading tools such as Adobe Target, Optimizely, Certona, Braze, and Tealium. Our experts can empower your team to build custom, CRM-driven segments for personalization that don’t require you to rebuild your MarTech tack from scratch.
Successful personalization requires a mix of consumer behavior knowledge, A/B testing, UX skills, and personalization expertise. By following the processes above, your business can:
Concord’s personalization and optimization expertise and data analysis strategies equip us to help businesses from all industries with their personalization efforts. Contact us today if you need assistance incorporating personalization into your testing strategy.
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