ORDL analyse the large volumes of data that are collected from server access logs, referrer logs and user registration or survey data

We use this information to increase client conversion rates, improve retention and repeat purchase rates, and elevate customer satisfaction levels.

ORDL use a variety of web mining techniques to identify user access patterns in a wide range of data to help clients determine:

  • up and cross-selling opportunities between products
  • the effectiveness of marketing campaigns
  • how best to structure the site in order to maximise web presence
  • how to improve communication to customer segments
  • how to target specific segments

We use sophisticated pattern discovery techniques to mine knowledge from web site data.  For example, we can identify association rules and sequential patterns from server access logs.  The visualisation and interpretation of this information will determine the most traversed paths through your website, leading to recommendations on how best to optimise its structure to maximise conversion.

ORDL can help determine how your site is connecting with visitors.  If the average time that people spend on the site is small or the average visitor only visits one or two pages, it may indicate a problem.  Your site may be attracting the wrong traffic, with visitors abandoning the site when they realise it isn’t what they want.  Some visitors may be confused by site navigation and decide to look elsewhere or certain areas of your site may be receiving too much or too little attention.  Whatever the problem, ORDL will identify it and help you measure how effective your solution is.

One of the most exciting developments is the coupling of offline data, such as demographics and lifestyle information, to the individual site user.  This is achieved via name and address matching using data collected at registration.  This means that ORDL can not only identify site usage and conversion patterns, but also build up a detailed pen portrait of the user segments.  For example, we can identify users’ demographics (age, income, marital status, geographical location, etc) as well as lifestyle preferences (preferred holiday location, newspaper readership, hobbies, shopping habits, etc).  This additional information provides incredible depth and understanding of the customers in each segment, resulting in greatly improved targeting and increased conversion.