In a data-governed market with reduced profit margins and growing inflation effects, retailers are striving to connect with their customers on a deeper level and create sharper and more accurate promotions that resonate with their needs. The key to ending the struggle? Data-driven targeted promotions.
The good news is that retailers are moving away from the traditional approach of creating standard discounts for everyone. However, hyper-personalization is still out of reach for most of them due to their lack of understanding of consumer behavior. In this blog, we will explore the concept of targeted promotions, taking a look at the data-driven methods and techniques that allow retailers to better understand their customer’s path to purchase and find them at every intersection with the right promotion at the right time.
In this blog, we will answer these questions:
Targeted promotions transcend the traditional and simple notion of offering standard discounts to the masses, as they involve tailoring these offers to specific customers based on a segmentation plan that takes into account their needs, preferences, and buying behaviors. The targeting process consists of following multiple approaches to entice these customers to make their purchasing decisions across various touchpoints and channels, drawing their attention using relatable deals that answer their exact needs.
Retailers use targeted promotions to influence consumers’ emotional and rational decision-making, resonating with their audience by making them feel valued and understood and fostering positive brand perceptions. Additionally, this type of promotion reduces shoppers’ decision fatigue by effortlessly guiding them to pick the products that align with their profile, making it easier for them to purchase. In short, highlighting the product a customer might be interested in incentivizes them to convert, shortening their purchase path, saving time and effort, and boosting the campaign’s ROI.
According to Forbes, “About 91% of consumers are more encouraged to purchase when a brand personalizes its communication with them”. This indicates that leveraging data to create better targeted promotions relevant to consumers leads to a win-win outcome, with happy consumers who feel heard and understood, a significant uplift in conversion rates, average order value (AOV), and customer lifetime value (CLV).
The shift from one-size-fits-all promotion planning and execution to tailored campaigns with targeted promotions that are narrowed down to the single user level requires the use and analysis of large chunks of data, and optimizing decisions through continuous monitoring of real-time insights. Such a feat is too complex to achieve manually, as it requires the integration of capable and data-driven tools with built-in features that leverage AI & ML to make informed decisions.
The new technologies made different promotion targeting strategies applicable and manageable, including:
Retailers collect and coordinate behavioral data in real-time (products browsed, transactional data, carted items, search keywords, visitor frequency, content viewed, average order size, etc) across multiple channels and feed them to their trained AI models. Then, these models identify customers and segment them into different categories (lapsed or disengaged shoppers, low or high spenders, etc) and then generate insights and recommendations on how to target them with the right combination of content and products at the right time.
It’s all about trying to take customers from your local competitors, leveraging geo-conquesting to win over local customers based on price, convenience, quality, and overall value. As its name suggests, location-based targeting gathers data from shoppers’ location tags and journeys on various e-platforms to track them and better segment them based on where they are, enabling retailers to reach consumers based on qualifiers like proximity to a store, events happening in their region, and more.
When permitted by users, AI-powered models can provide deeper insights into the shoppers’ habits, offering personalized recommendations based on their real-time location. This enables notifying customers about available deals in nearby stores. Machine learning can be utilized to create virtual fences around certain areas, enabling the execution of a location-based marketing technique known as geo-fencing. Additionally, retailers can leverage this data and invest in some PPC advertising campaigns and Local-SEO strategies. The result? Targeted promotions that align with the consumer’s purchase intent, with discounted items that are available in a close-by store.
By analyzing the context in which a shopper encounters an advertisement for a certain deal, AI-trained models provide valuable insights into the circumstances that surround the shopper-ad intersection (the content of a web page, the time of day, the weather, and even current events). Then, retailers can leverage this data to identify the ads that are relevant to the content the consumer is already consuming and decide where and when to place it, placing their targeted promotions at the most optimal position.
As a result, hyper-relevant targeted promotions can be delivered based on the consumer’s interests and needs, like demonstrating a vitamin water ad to the consumer while they’re reading a blog about intensive workouts around the time they go to the gym. This leads to a positive user experience for customers, boosting their engagement rate and leading to more conversions and increased revenue.
This integrated approach is adopted by retailers to find consumers at every intersection of their shopping journey (both in-store and online) and target them with personalized ads. First, an AI-powered model would analyze all consumer data (purchases, browsing behavior, social media interactions, loyalty programs) to identify patterns and preferences, then the ML would help create personalized displays with special offers and discounts that align with what they might be looking for. By doing that, retailers can find customers in their preferred channel, armed with targeted promotions that have better chances to convert.
For better results, retailers often resort to attribution modeling, where they determine how to credit various marketing activities for conversions. It creates a framework that makes understanding the factors that influence buying decisions easier by considering every channel a customer interacts with during his journey and identifying the final touchpoints that led to making these decisions. The ML-powered models then assign more weight to touchpoints closer to conversion, helping retailers to understand what works best and what needs more work to improve their targeting strategies.
Now that we understand the impact of Artificial Intelligence and Machine Learning on your promotion effectiveness and your targeting process, let’s explore how you can leverage these modern technologies and analytics to create hyper-targeted promotions for your retail business:
During promotion planning, retailers must segment their customer base by relying on various demographic, psychographic, geographic, and behavioral options. By integrating a dedicated data-driven promotion management solution with your current data sources (browsing history, shopping journeys, consumer cart, loyalty programs), they can easily segment their customers for easier targeted promotions execution.
This step is inclusive, as it falls within any promotional plan. Retailers must define their aims and objectives and the reasons behind launching the campaign, whether it’s increasing sales of a specific item, clearing surplus inventory, attracting new customers, or retaining existing ones.
After mapping out your strategy, the next step would be choosing the right promotion management solution. Detailed customer segmentation, real-time insights and recommendations, campaign performance tracking, and other optimization enablers are the features you must look for in a capable promotion management solution like Cognira’s PromoAI.
Identify the promotion medium type based on different simulations and scenarios to gain a comprehensive view of their performance. Then, utilize these AI-powered insights to create targeted promotions and personalized plans for your target audience segments, resonating with their needs and aligning with what they are looking for or might need.
Track the performance of your targeted promotions according to the pre-defined KPIs. By looking into the current status of your promotions according to your chronological plan, you can make real-time adjustments and modifications that allow more accurate targeting, smarter promotion personalization, and eventually better results.
Promotion effectiveness can be enhanced by creating a win-win situation for both retailers and consumers. By leveraging Artificial Intelligence and Machine Learning, retailers can analyze large chunks of data and gain valuable insights that can be turned into actionable promotion targeting plans, whereas the consumers find it time-saving and enjoyable to encounter an advertisement or display for a deal regarding an item they were looking for. In that manner, modern technology allows unblurring fragmented and sparse data from multiple sources and analyzes it effectively, resulting in targeted promotions, better conversion rates, and increased order and customer lifetime value.
A data-driven promotion targeting approach can only be achieved through a capable promotion management solution, ensuring that the retailers have a centralized environment to analyze data, make segmentation decisions, execute targeting strategies, and make adjustments whenever necessary to make the best out of the promotions.
When leveraged efficiently, a data-driven promotion management solution allows retailers to unlock a new level of customer engagement and loyalty, enables even more sophisticated targeting capabilities, and ensures they can ride the wave of hyper-targeted promotions and adapt to the ever-evolving landscape of consumer behavior.
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