As companies become more proactive and effective in targeting across traditional direct channels, it’s time to start focusing on customizing interactions online, seeking to gain a better understanding of each website visitor and making the most out of each visit. Website Visitor Relationship Management (VRM) follows the path of CRM, mimicking the use of similar analytical models and lifecycle management principles…
According to a study by Forrester, 75% of companies consider web analytics vital for their business. Yet, most of these companies rely on standard and static reports, with minimal focus on proactive management of visitor relations and experience. To gain an optimal return on online investments, companies should start concentrating on Visitor Relationship Management (VRM), as they did around better managing customers via CRM back in the 1990s.
VRM has similar objectives as CRM, though applied from an online perspective – example objectives include the acquisition of high potential visitors to the site in a low cost manner, retention of visitors on the site until a conversion happens, having customers come back and visit new content on a regular basis, etc.
VRM provides the ability to customize online interactions at an individual level, increasing relevance for the visitor while maximizing return on each visit. At its most generic definition, VRM tackles business problems across the whole visitor lifecycle:
Decreasing cost of visitor acquisition, by identifying the most effective traffic sources for each visitor segment
Improving and customizing site design, by understanding behavior and preferences of different visitor segments
Increasing site stickiness, by identifying content and issues causing site abandonment by segment
Increasing pages per visit, by proactively promoting the most relevant content to individual visitors
Maximizing online ROI, by identifying and acting on visitors with the highest conversion likelihood
VRM requires extensive use and analysis of visitor data. For this purpose, we recommend companies conduct five key analyses (at the outset, to get things started – additional analyses can be conducted down the road), which would then be used for site content and experience customization.
1. Acquisition Optimization: As the variety of traffic sources continues to increase, it is becoming more crucial and difficult to optimize investments in them. The value of a very fine-tuned Google search phrase or LinkedIn Ads target profile could be easily worth hundreds of times its cost, while the generic use of such traffic sources could bring in visitors for all the wrong reasons.
A common blunder in measuring effectiveness of traffic sources is blind focus on cost per acquired visitor, discarding whether that visitor could or can be converted, or whether he / she is in the target market to begin with. In order to get the most out of their digital marketing budgets, companies need to analyze the “lifetime value” generated from each unique visitor acquired (lifetime value is relatively straight-forward for e-commerce sites, with direct financial value from sales the determinant; off-line sales triggered through an initial unique visit may be very difficult to assess).
An Example of a Traffic Profitability Analysis, by Search Phrase
2. Visitor Segmentation: To be able to customize for and take action on its website visitors, companies need to actually understand the differences between them (i.e. how frequently does each visitor come to the site, what is the depth and breadth of the visits, what type of content is the most interested in, etc.). As in traditional analytical CRM activities, this necessitates a segmentation of visitors, based on their profile (if registered) and visit behavior.
An Example of a Basic Visit Behavior Segmentation Output
These segments can then be used for monitoring site performance in a more specific manner, as well as defining strategies and actions specific to each, such as:
Identifying whether your loyal visitors are losing their interest, and if so, ensuring that new content that is in line with their needs is available and visible to them
Understanding why frequent visitors do not become paying customers on an e-commerce site (i.e. whether they are landing from competitor sites for only price comparison) and taking actions to uplift the conversion rate
Analyzing the triggers which make an offer seeker land on the page for the first time, and how to utilize those traffic sources better
3. Next Best Content: As companies invest more in microsites, blogs and content in general, the chance of visitors landing on content irrelevant for them or not accessing the most valuable pages from their perspective at all increases significantly (not to mention a diminishing rate of return on content). Although a well-designed site hierarchy and menu system can be of assistance here, visitors need more refined guidance in larger sites. “Next Best Content” analysis identifies the pages or microsites that would be the most interesting for each visitor and visit, facilitating targeted promotion of that content. In other words, it is the application of Next Best Activity analysis in VRM.
An Example of a Next Best Content Promotion
Such analysis requires the use of clickstream and path data along with visitor profile and visit statistics, building a predictive model for the page that would have the highest click rate for each visit. Then, companies may choose to optimize time spent on the site by promoting the most relevant content or optimize conversion likelihood by promoting the content that would be of interest and take the visitor to a conversion page via the shortest path.
4. Conversion Propensity: Especially for those sites with highly specific conversion targets (i.e. customer registration, online sale, newsletter subscription, etc.), conversion propensity models predict which of the visitors and visits are most likely to serve these targets. Predicting the likelihood of conversion for each visitor, companies can proactively and efficiently target visitors while they are on the website, via the utilization of online chat tools, customized pop-up offers, etc.
An Example of Conversion Propensity Modeling
5. Site Abandonment Prediction: Another application of common analytical CRM models in VRM is prediction of site abandonment during each visit. Based on the visitor profile, as well as the current visit experience (i.e. 404 errors experienced, searched content not available, delayed page loads), each visited page can become the last for a visitor. Identification of what causes site abandonment and which visitor is likely to leave the site can provide valuable insights in site design, performance management and content driving visitor loyalty.
An Example of Site Abandonment Prediction Based on Experience Analysis
Through the utilization of site abandonment prediction, targeted visitors can be caught before they abandon the site, through customized offers being made immediately, or through a representative intervening via a chat tool. Such actions can help in increasing conversion rates around the desired action.
Once companies turn their websites into relationship management platforms through the use of VRM, the next step would be the use of web data for additional purposes (i.e. as an early indicator in demand forecasting based on traffic to specific product or promotion pages). E-commerce sites, on the other hand, need to bring VRM and CRM together, incorporating visitor and customer analytics to enable a more comprehensive understanding of their portfolio.