Clickstream analysis is necessary to improve navigation, merchandising for e-commerce, conversion and return on investment. But when it comes to understanding and segmenting visitors to optimize individual relationships over time, such data alone do not always give an accurate view of the customer and his intentions. Search data can provide marketers with the customer’s intent and can be incorporated into the mix to better understand and optimize customer value and experiences.
Clickstream data analysis alone can confuse, mislead. Online marketers face new challenges as visitors are more sophisticated in how they search for products and what they expect from a Web site. In the past, visitors often did what we expected them to do. Navigation was predictable. Today, click paths on Web sites seem scattered, and visitors don’t play by the rules. They may have multiple, unrelated interests on a site and follow unexpected paths. When a site owner tries to get her arms around the volume of data that comes from a site, making sense of the information to gain customer insight, individually and collectively, remains the biggest challenge.
It’s impossible to predict what people will do on a site with perfect accuracy, even when their initial intent is clear. How many of us click to a site for one reason, then find ourselves chasing down a new interest we find there? Consider the offline parallel: How many of us walk into a department store and come out with shopping bags filled with items that we didn’t set out to buy? It is unsurprising that this type of behavior crosses over to e-commerce, particularly as more site owners use cross- and upsell strategies as brick-and-mortar storefronts do.
Search data inform customer optimization. Organizations tend to think of their Web site visitors in large groups, evaluating where they go on the site and trying to determine who they are by their behavior. Buckets on a home page representing major product lines offered by a site can serve as a convenient starting point for differentiating customers, and the clickstream analysis may be based on the visitor’s first selection. But some visitors may be interested in more than one thing and become “cross-overs” to another section or bucket. For example, when shopping for a dress recently online, I was distracted by the model’s shoes, leading me on a quest within the Web site for these cute shoes that match a dress I already own.
Perhaps a better starting point for customer analysis is to gauge intent as well as behavior. The search terms that visitors use when looking for a site reveal their initial intentions. This makes search the “anchor point” of their visit. Search terms allow determination of a visitor’s objective when entering and let marketers draw a straight line to the subsequent sale (or other desired action). Rather than look at the clickstream of the site in its entirety, start at the source of the visit and segment by search term and resulting action.
Search data incorporate terms, engines, even offline campaigns. The first category of data to view is the keyword or phrase the visitor used to find the site, either through natural or paid search. Matching search visitors to the sale or conversion point on the site is the first step of the process. The next step is to map the keyword used to the product purchased or other desired action (such as a registration or a lead generated and qualified).
Following visitors from their entry point and matching those visitors and their keywords to specific actions help companies understand what visitors originally wanted when they came to the site, as opposed to where they went and what they did once they got there.
The next slice of data may be the search engine the visitor used, which may suggest demographic details. Each engine’s users have slightly different profiles, which marketers can use when developing personas or adding another layer of data to the customer’s profile.
Another factor is what drove the visitor to search with the keyword. Sometimes the search term, either alone or in combination with Web site data entry, can signify that offline marketing prompted the visit. Television and radio spots can prompt a search, so keywords may include campaign elements that help determine the source of the lead. Tying this information to clickstream data on the Web site yields yet another layer of data for informed marketing decisions.
For example, retail organizations often optimize their search engine positioning by including product comparison content on their sites. If visitors enter a retail site using the keyword “Brand X DVD Player” through natural search, and they consistently end up on a specific content page of the site, the retailer should ensure that this page has call-outs to deals and offers for various DVD players, among them Brand X. However, if it were discovered that people who came to the site with the term “Brand X DVD Player” bought Brand Y, this, too, could inform a marketing strategy.
And in the case of my matching shoes, determining the best product placement on a Web site is not unlike how brick-and-mortar stores capitalize on in-store shopper behavior.
Organizations should include this level of data in the customer’s database profile. Viewing these data in aggregate may be the first step toward lower conversion costs and higher ROI. Keyword and search engine use could become the “bucket” that identifies visitor segments in an organization’s Web analytics data. This approach allows marketing based on intent, which is the anchor point of the visit, following the visitor’s clickstream through to checkout or conversion, and using the data to inform marketing decisions.
