A restaurant chain approached a Tier 1 mobile service provider to help launch a campaign sending messages to consumers’ mobile phones, encouraging people to download the restaurant’s free app. The messages were sent to large audience segments, created on the basis of standard demographic information, such as device type and age group of opt-in subscribers. However, the ad campaign was costly, as it was being sent to millions of mobile users, and was yielding poor results – about .6 app downloads per every 100 messages sent. The restaurant chain challenged the mobile operator to improve the conversion rate, agreeing to pay more for each download if the operator could better target their customers.

The marketing team of a leading Mobile Service Provider had a plan to offer advertising agencies target market insights to optimize their advertising ROI. They planned to gather subscriber analytics including demographic, geographic and behavioral statistics in an anonymous format, and offer this information to current and prospective advertising customers. However, due to the size of the network and volume of data that needed to be analyzed, the option of rolling out a deep packet inspection solution was extremely cost prohibitive and could not be done within the timeframe needed. The complexity of correlating content data with subscriber demographics and geographical location, put the marketing team’s initiative beyond reach.

The corporation, a leading Multiple-System Operator in North America, was facing challenges with delayed problem resolution which was affecting customer satisfaction. The Operations Team was unable to identify the root of the problem and rapidly distinguish between customer premise problems and headend CMTS or video server issues. This led to unnecessary dispatch of technicians to homes, which often turned out to be a headend problem instead of an individual set-top box problem. This resulted in customer frustration and a negative impact on their Net Promoter Score.

A European Multiple-System Operator (MSO) was struggling to rapidly distinguish between issues caused by customer premise devices and headend equipment. This delayed the MSO’s ability to find the root cause of problems and subsequently resolve the issues. With the cost of a truck roll in Germany running about 60 to 70 euros and the handling of incoming customer service calls running about 5 to 10 euros each, the provider hoped to reduce customer service costs and improve customer satisfaction at the same time.

The marketing team of a Mobile Network Operator (MNO) was struggling to build rich customer profiles to present relevant offers and ads to their subscribers. They needed to match individual preferences and browsing behaviors with subscriber IDs. Additionally, they needed to accurately categorize website URLs viewed with accuracy levels of 80% or greater. However, they were unable to achieve this with the software tools they had in place.