Customer Intent Analytics

Posted on Aug 14, 2009 by in customer experience | 0 comments

Many e-commerce companies have thousands of statistical reports, down from the number of page views and up to the sales trends, but how can they know if the purchase intent of their customers is satisfied properly? And how can they learn to do it better?

While the fundamental goal of most web analytics systems is to reports the KPIs ({en:Key Performance Indicator}) having the most effect on sales, these KPIs all tend to be usage driven instead of satisfaction driven.

The CEM (Customer Experience Management) consulting companies tends to supplement this problem by polluting the life of the customers by asking them to answer countless surveys, but fail to deliver precise and actionable conclusions, especially on the per case basis.

How can you capture your customer intents in order to see how well your product offering is adapted to it and presented properly?


Understanding the key parameters of customer intents for products which are not easy to find or not in the database is the key to ensure the satisfaction of all the significant purchase intents.

Statistical information like product page views and purchase counts give valuable information about the products matching the most common customers’ intents of purchase, but they do not provide any information about why the purchase happened, or help identifying missing product lines or products which are hard to find.

As the entire structure of e-commerce web sites is build around its existing content, the only real source of information of unsatisfied intents are the search logs, but it would be a mistake to consider them as a perfect representation, notably for the following reasons:

1. Intents are flexible; search queries are not:

Customers traditionally have a certain flexibility in the definition of their intents, which is not well adapted to the rigidity of the search query. As a result, the search queries tend to be either to general or too specific compared to the real intent.

For example, a customer searches first for “shampoo”, while he is looking for a shampoo of a high class brand, and will then do a new search with “shampoo l’oréal” due to too big amount of results provided by the first trial.

2. Intents are subjective; search queries are not:

Only the most objective parts of the customer intent are usually expressed, the customer will then evaluate if products matching his intent are in the list or not, but will never inform the system of what the intent really was (whether it is matched or not).

For example, a customer searches for “shampoo”, while he actually intent in buying an expensive shampoo for his wife. Only the objective part remains.

3. Intents are personal; search queries are not:

An intent is very comparable to a motivation: there are many different intents bringing to the same product, but for different reasons. Due to the impossibility to express an intent freely as a search query, only the people able to formalize objective and impersonal search queries will use the search tools enough to provide statistically relevant information.

For example, a customer who is looking for a shampoo to protect his sensitive hair and likes ones with tropical fruit smell will probably not know how to formalize his search query, but if he saw a commercial of it and remember the product name, he will simply type it.

Another critical problem of analysing and understanding the customer intent only with search logs and category access statistics is to identify the unsatisfied intent. First, many intent will not be expressed (as discussed above), and the only simple case which can be managed easily is the identification of the search queries providing no results, however, this approach has a very limited potential, partially for the following reasons:

Many searches provide results, but many do not include any relevant result

As the amount of textual information about products (description, features, reviews, comments, etc.) tends to become more and more important, it starts to be increasingly uncommon to face the zero results page. To some degree, one could argue that it is better, as showing a list of items which are at least partially relevant is better than no list at all. However, it is not the same from the side of the web analytics where we would like to differentiate the cases of success and failures.

For example, a customer searches for “l’oréal conditioner for sensitive hair”, but none of the products of the database matches this intent. The search will stil provide results with L’oréal conditioners as the others words can be found from the textual descriptions and the customer reviews (even if in a negative sense, e.g.: “not for sensitive hair”). It is then almost impossible for the company to identify that this request was not properly answered, even if it provided results.

Unfortunately, differentiating the searches bringing sales from the others cannot help either, as the one bringing sales are very clean, but not helpful, and the others tends to be extremely noisy and most of the time unusable (as many search queries providing valuable results will be there as well).

In this post, I try to demonstrate that the need of a new understanding level of the search queries is not only beneficial for the end-customer (as discussed in prior and probably future posts), but is absolutely necessary to report and analyse customer intents properly.

In this process, two components are critical for the success:

1.    Have a technology which is able to capture the customer intent in a structured way
2.    Educate the customers so they know they can express their intent freely and should expect the web site to answer them better over time due to the intent analysis and optimization outcomes

PS: All the examples are coming from a current case I am working on (, so don’t worry, it’s not that I am trying to sell shampoo, it’s just my current work which is a good source of examples and a motivation for this post 😉    Send article as PDF   

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