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By4 Min Read
March 11, 2021
All brands vie to target customers with intelligent marketing, but a few get it right. Imagine if at the height of the pandemic, after spending a week browsing for a bigger apartment to shift into, you come across your dream house through a Facebook ad—a classic scenario when you’ve been targeted with intelligent marketing. However, the opposite scenario would be receiving the same ad a week after you lost your job, and when you had no interest in looking for a new home.
The last decade has shown us that ‘relevance’ has been the underpinning theme of marketing in the digital-first era. A majority of consumers (88% according to a survey of 1,500 U.S. consumers conducted by Marketing Insider Group) say that personally relevant content improves how they feel about a brand This is also reflected amongst marketing experts. A survey of marketing professionals in North America shows that 62 percent of respondents said improving audience segmentation to enable more precisely targeted messaging was a top priority. Personalizing customer engagement and content for different customer segments improves conversion rates by a whopping 70%, and its impact ripples across the entire consumer lifecycle: from acquisition costs, customer loyalty, engagement levels, average order values to repeat purchases. Segmented marketing can improve your productivity and the effectiveness and ROI of your marketing campaigns overall.
In order to increase the chances of people engaging with content, brands must target the content to the right people in the right way, rather than targeting the entire audience with a luke-warm message. Yet this is a monumental task today since there is extreme noise in the digital world, and an overwhelming amount of data to sift through. Additionally, the rapidly changing trends and events of today make it difficult to truly know who customers are as they evolve.
It isn’t a recent phenomenon for marketers to customize their products and content based on broad static filters, like geographic location, organization, or even income, but this kind of targeting has typically been one-dimensional and doesn’t take into consideration more nuanced, personal-level factors about the person being targeted.
For example, being a t-shirt manufacturing company, you might learn that most of your customers are in the age group 20 to 35, and a significant number of them share an interest in a recently launched Video Game. You may also learn that many of your customers are interested in environmental pages, and have recently liked posts related to environmental causes. With artificial intelligence powering your marketing segmentation, you can personalize engagement for various micro-segments of customers. Different factors determine why a customer is coming back to your business. The same customer can show different kinds of attributes from trip to trip. It is important to understand what is their intent and then personalize their content.
The biggest tech giants – from Facebook to Amazon, to Netflix – have invested billions in getting the right content in front of every user. You may have noticed this when Netflix recommends shows you might like based on your previous viewing habits. Google “auto-fills” suggestions based on searches you’ve previously made. Facebook tailors your news feed based on the items you’ve engaged with.
Collecting customer data to divide them into segments can be done using various tools and processes, based on the brand’s size and requirements.
A traditional method for gathering data is requesting surveys from existing customers—but frequent requests will only serve to irritate customers. Fortunately, retailers can obtain data in less intrusive ways today with digital technology, like capturing how consumers interact with their website, their priorities, interests and preferences. By leveraging a Customer Data Platform, brands can combine first-hand data along with partner data or third-party data from multiple channels.
A typical AI-driven approach to customer segmentation would involve the following steps:
Existing customer personas can be strengthened further by layering attributes like:
The list goes on.
Granular micro-segments help draw out important nuances that improve decision-making across the organization and throughout the customer lifecycle. Based on the capacity for a segment to drive value, brands should tailor products, services, and experiences for top segments.
Typically, a customer’s lifecycle value relies specifically on the frequency and recency of purchases. The idea of using these metrics comes from the RFM analysis. Recency and Frequency are critical behavior parameters. Companies are interested in frequent and recent purchases because frequency affects the client’s lifetime value and recency affects retention. Therefore, these metrics can help us to understand the current phase of the customer’s lifecycle.
Using artificial intelligence to segment your customers offers a number of advantages over traditional manual segmentation.
Customer segmentation is a vital component of brand strategy and the marketing process, no matter the method chosen for segmentation. However, AI/ML-based algorithms create extremely precise and nuanced micro-segments and can take into account behavioral data from all sources and channels. This provides retailers with a scalable way of understanding how their customers interact with the brand and products, without getting inundated with data. Want to target the most engaged customers within a 20-mile radius of an event, or those with more affinity to spend? That’s a breeze with Artificial Intelligence-powered segmentation. Today, the vast amount of consumer data being generated shows no indication of slowing down. This makes AI-powered segmentation the obvious choice for effective marketing heading into the future.
March 11, 2021 | 4 Min Read
All brands vie to target customers with intelligent marketin