In today’s uber-competitive digital environment, buyers, and particularly millennial buyers, have come to expect some level of personalization in their website interactions.
While the fine line between personalization and privacy invasion is constantly being (re)defined, the following are clear: some level of personalization is accepted, if not expected, by today’s online community; personalization is an effective way to increase sales; and personalization is a key differentiator in customer retention. With these truths in mind, let us look at six different personalization approaches that need to be considered as you develop your digital strategy.
The first personalization approach is buyer preferences. This is a relatively simple approach in which you solicit information directly from the customer and apply these preferences to their digital experience. These are such things as whether you prefer an aisle seat or window seat when you travel, your preferred shipping and/or payment methods, etc. Users are very familiar and at ease with these approaches, to the point that they are expected as part of any customer account.
The second approach is channel and communication, which is really an extension of buyer preferences because it is also data offered/entered by the customer, but it focuses on both when and how the customer prefers to be notified. For example, a customer may wish to be notified about order updates. In addition, you may allow the customer to choose to be notified only when certain types of order updates occur (e.g., backorder). You also may give the customer the option of multiple communication channels for the notifications, such as email, text, direct contact, etc. Of course, the customer may prefer different channels for different communications since a credit card charge event may be more important to the customer than an order shipped event.
The third approach is about the products and the relationships between them. It is at this level that you, the merchant, begin to take the lead, and so this requires more effort on your part. The classic examples of this are the grouping of products that typically are purchased together (e.g., child’s toy and batteries, or a software package and support). This level can be as simple as the previous example, but this can be taken to a much higher level of complexity by using various data resources to build complex models to identify relationships that aren’t necessarily obvious. There was once a vendor that suggested medical supplies to go along with the purchase of a heavy ceiling fixture because they learned that many customers were getting injured trying to install the fixture.
The fourth approach involves location. In this approach you use the customer’s location to enhance the user experience. The classic examples of this are the recommendation of stores/restaurants based on your location, but many stores use this approach in their mobile apps to help you find items in their store, leading you directly to the aisle and shelf location where your desired item is located.
The fifth approach focuses on content. This is really another step up in the complexity ladder as this approach requires that you really start to understand the characteristics of the individual customer that may not be explicitly offered by the customer themself and/or may not be readily available via the technology (e.g., location). And, at this level, you will generally start pulling in data from more and more sources, some internal and some external, and you may start to look for indirect relationships between various “attributes” towards which the customer exhibits tendencies; and based upon all of this data you begin suggesting content and/or products that the customer themself may not have considered otherwise.
The sixth, and final, approach is customer knowledge. This might be termed the “Holy Grail” and rightfully so. It is the culmination of all the other approaches at a very high level of robustness. Here you begin to develop a 360 degree perspective of the customer, pulling in data from many sources and likely complex machine learning and/or AI types of technology. Only a small percentage of digital platforms will ever reach a high level of maturity in this regard given the effort, cost, and technology required to achieve it.
Now that we’ve discussed the personalization approaches, it is important to understand that these personalization approaches are not independent of one another, nor are they totally dependent upon one another. You will need to determine which of them you want to apply; or, more importantly, which of them you reasonably can apply given your particular products/services and digital maturity model; and this, truly, is where the challenge lies.
Rich digital experiences have significantly raised your customers’ expectations. Create relevant, individualized interaction with your customers that secure long-term relationships. CoStrategix can help with digital commerce, subscription commerce and personalization strategies that use AI-driven, machine learning-based approaches integrating with your product platforms to drive up-sell and cross-sell recommendations – We’d love to chat with you.