Often expressed as 1:1, one-to-one marketing implies a more personal approach to customer relationship building by narrowing broad communications down to individualized experiences.
Once a lofty idea that felt out of reach, this is marketing’s ‘new normal’ as technology has ushered in the ability to tailor experiences for customers based on a variety of information now ripe for data activation. Now, along with basic details about a consumer’s demographics and even geolocation, complex profiles built off of things like affinities, interests, and past behaviors can be used to isolate and customize experiences in as much detail as desired.
Advantages and Disadvantages of One-to-One Marketing
This level of micro-segmentation involves a granular understanding of a visitor’s real interests along with their current position in the customer journey in order to segment and target by individual behavioral interactions. Based on real-time and past activity, machine-learning algorithms can recognize previously unknown connections, and respond to them in a way that raises customer satisfaction and revenue.
For example, an airline knowing when and where (email, push notification, SMS, etc.) to alert a passenger about upcoming check-in or boarding deadlines, tips on what to pack, how to prepare, flight delays, upgrade options, and so on.
Though, this type of hyper-personalization can be hard to attain — in the case of a marketer who does not have access to a robust enough dataset in order to tailor individual content, users may receive incoherent or irrelevant messages. Common issues of data fragmentation from various point solutions used to personalize experiences by channel make true 1:1 marketing near impossible when a unified approach and single view of the customer is necessary.
Should a company comply with privacy rules and regulation around customer data, there are no real disadvantages to delivering highly contextual communications with precision, there is only improper execution and limitations on the part of the technology you employ to power your one-to-one marketing efforts.
Taking The Right Approach from One-to-Many to One-to-One
There’s a strong connection between data, segmentation, and personalization. Getting one-to-one marketing right is much easier when there’s a solid understanding of the characteristics of the segments it is based on. A common practice is to first discover and analyze the most valuable and profitable visitor segmentation and only then personalize. A simple pyramid-like structure can portray the facilitated dialogue between the three elements:
Pyramid Base: Data Collection – The key to both segmenting and personalizing experiences is having robust customer data. The richer your data, the better the understanding of your audience, and the better you can segment and personalize experiences based on that data.
Mid-level: Segmentation – Once you’ve collected enough valuable data, you can start analyzing it, looking for interesting correlations between groups.
Top Level: Personalization – Real-time, one-to-one personalization can only be achieved if you have enough data, and if you know who your audience is and what your most valuable segments are.
And considering today’s eCommerce environment where less than 5% of a site’s visitors are now driving more than 90% of revenue, marketers can no longer personalize for a large segment of users. Instead, one-to-one marketing should be optimized to emphasize conversions from that crucial 5% of visitors who contribute most to their site’s KPIs.
One-to-One Marketing Examples
Displaying “popular items” is the most common product recommendation strategy — but our analysis of more than 50 million shoppers shows that serving each user with personalized recommendations is the best strategy for maximizing revenue.
The best recommendation engines are able to assess what’s known about a particular visitor, and deploy the most appropriate strategy based on data availability, context, user behavior and so on to solicit engagement.
According to research conducted by Google, 85% of people are more likely to shop from brands that offer personalized discounts and exclusive offers.
Online retail brands can cater to customer needs on a personal level and maximizes purchase likelihood by displaying highly-incentivized notifications and overlays in real-time, based on the user’s intent, demographics, preferred product attributes, on-site interactions, past behavior and other data collected.
Customers expect to find what they are looking for as fast as possible. In fact, 80% of visitors search for a product once they hit a retailer’s site.
By delivering highly personalized search results based on customer behavior and general trends, you can ensure they find the items most relevant to them and with accuracy.