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Kate O’NeillCEO & Founding Partner[meta]marketer |
Awareness of Web site experience is finally becoming a mainstream marketing issue. But all the talk about optimization tends to focus on a single metric: conversion rate. While conversion rate is certainly important, it is, after all, a very simplistic ratio; it overlooks masses of deep meaning that can be ferreted out from visitors’ interactions with your site.
For that matter, taking any metric in isolation obscures a world of potentially discoverable insight: motivations, needs, problems waiting to be solved, etc. Getting to the heart of those insights requires taking a more in-depth look at both converting and nonconverting interactions so you can have a better understanding of how to carry those interactions—those relationships—further.
The barrier to achieving success with this approach lies in striking the right balance between qualitative and quantitative analysis. Whereas analysis is often thought to be a cold, objective science, a truly balanced approach must be holistic and multifaceted. It means practicing bean-counting along with navel-gazing, and incorporating a quasi-psychological and sociological process, yet remaining accountable to the scientific method: In a standoff between gut and data, data must be allowed to win. Yet as long as a case can be made for it, gut must be allowed to continually challenge your data.
Whatever the balance your organization needs to strike, the goal is, in reality, an optimized customer experience, not conversion. Customer experience yields more than conversion—it yields loyalty. Customers who experience messaging and site flow that resonates with their motivations and expectations tend to feel heard and understood, and customers who feel understood tend to be more loyal than those who are simply converting because converting was more convenient and efficient than starting their search over again.
It should be clear that this is not simply empty-headed nonsense about getting back to basics or running your $500-million business as if it were a neighborhood shop. It’s a disciplined approach to maximizing profit by understanding that metrics represent people. Customers and potential customers have motivations that are behind both their delighted purchases and disappointed purchases (both of which look like success to the business), as well as their near-purchases and nonpurchases (both of which look like failure to the business). The better you understand those motivations, the more opportunity you have to accommodate them, and the more you accommodate them, the more likely you are to generate incremental sales customer satisfaction, and to garner loyalty at the lowest possible cost. That’s not feel-good fluff; it’s marketing at its fullest realization, and it’s darn good sense.
How Do You Optimize For Customer Experience?
We have to begin with how we quantify customer experience because you can’t optimize what you can’t quantify. So how do you quantify something as esoteric as customer experience?
A basic but widely accepted model for deriving customer-experience metrics is:
1. Business need: incremental sales
2. Intended Web action(s): drive visitors through site to cart
3. Success metric: order place
4. Financial result: revenue
This is all well and good—and it sometimes accidentally leads to an improved customer experience. But alongside this path there’s a parallel path from the customer’s perspective. It looks something like this:
1. Customer need: acquire product or service as painlessly as possible
2. Necessary Web actions: find item and buy
3. Success metric: task done
4. Experience result: satisfaction level
Where empathy is often overlooked is in making the leap from business need to intended user action. What is commonly called “optimization” is often anything but: It is opportunistic and short-sighted, and disregards the potential for longer term profitable relationships in the name of near-term revenue.
Amazon.com, for example, is infamous for its automatic cross-promotions that appear in what, for most people, are one-time purchase items. Take, for example, blenders. “You bought a blender. Here are some other blenders you might like” is a laughable example of metrics-oriented nonoptimization that only annoys the customer and diminishes the sense of satisfaction that might have arisen from a successful purchase.
In short, optimizing the online experience is largely about fine-tuning relevance. Relevance in online experience is about respecting your customers enough to show them only the most meaningful content, the stuff most likely to resonate with their needs insofar as you can deduce them from context based on:
• source (where they came to your site from)
• acquisition campaign
• current visit pathing
• responsiveness to messaging
• logged-in user information
• whatever else is available to you
Next: The five steps of empathetic optimization.
