The Dying Breath of ‘You May Also Like’
As the debate goes on as to whether brick-and-mortar stores must also have an online presence if they want to compete in today's digital world, there's one area where traditional brick-and-mortar stores retain an advantage over digital: customer engagement and experience.
We've all had that in-store experience, the one that makes us so loyal as to never even consider the competition. The perfect retail salesperson who takes the time to get to truly know their customers, has an encyclopedic knowledge of the products, and combines the two in a perfect match creating a curated, personalized experience.
The impersonal and often anonymous nature of the internet puts e-commerce stores at a disadvantage when compared to connecting with customers and reaching a level of engagement like that of the perfect in-store experience. In general, online retailers rely on photography and merchandising to provide consumers with choices and convenience. However, as the e-commerce channel evolves, it's becoming possible for online retailers to leverage the piles of data available to personalize their digital stores in a way that creates unique and personalized experiences for every customer. This is an advantage that brick-and-mortar retail doesn't have; physical stores can't be rearranged and customized for every customer, but a website can.
Putting customers first and personalizing web experiences around their preferences represents a fundamental shift in the paradigm of digital engagement. Online retailers looking for sustainable competitive advantage should move away from the traditional product-centric model that dominates the digital space to create meaningful customer-focused experiences. By placing focus on the customer and personalizing each customer's experience based on the unique characteristics of that customer, e-commerce retailers can differentiate themselves by prescribing personalized solutions for customers based on the information that they now have readily available.
Effective prescriptive personalization is reliant upon five key elements:
1. Intimate knowledge of individual customers: In order to match customers with individualized versions of a website that appeals most to them, it's important to know as much as possible about each customer. Online retailers will need to use intrinsic data such as geolocation, search terms, device and browser type, click paths, etc. Additionally, online retailers should consider using explicit data, such as providing visitors with the ability to take a brief survey which can provide valuable insights into building a customer profile.
2. Complete understanding of product offerings: Customer profiles are just one part of the process; retailers should also leverage all the information they have about their products. You'll need to break each product down to its fundamental attributes and assign values and relative ranks across many subjective and objective traits. Note that objective attributes like size, weight, country of manufacture, materials, etc., may already be available to you. You should also use the product experts in your organization to rank subjective factors like style, quality, utility, value, etc.
3. Evaluate products on a relative basis: When assigning values to product attributes, use a relative scale that considers only other items in the same category. The goal of assigning these values is to differentiate similar items from one another, and the scope of that differentiation is limited to items offered within the same product category.
4. Create a process for prescribing attributes for customer characteristics: Use algorithms to map product attributes to customer characteristics. Although this process is technical and tedious, keep in mind that it's intended to solve basic problems. Therefore, a budget-conscious customer who's a heavy user of a product should receive recommendations that have the highest ratios for value, quality and durability. The customer characteristics will provide a set of relevant variables. For example, a customer who prefers a large size, the color red and cotton fabric would have a variable set displaying products that rank the highest on the product side for being large, red and cotton.
5. System to test, validate and improve on assumptions: Initial match criteria is based on assumptions and hypotheses, and some will ultimately prove successful where others will not. Analytic metrics like conversions, satisfaction and engagement time should be used to continually optimize the algorithm. Over time, customer experiences should be measured to improve the assumptions in the algorithms. When enough information becomes available, machine learning becomes possible and the optimization process itself can be largely automated.
Though e-commerce is 20 years old, techniques for improving the personal connection digital retailers have with their customers are still pretty basic. We've made advances using "the wisdom of crowds"-based customer recommendations, however, as e-commerce evolves it will become more and more important for retailers to add their particular expertise and product knowledge to the process to create a better customer experience and a quantum leap ahead of today's "You may also like … "
T.J. Gentle is the president and CEO of SmartFurniture.com.
