Analyzing changes in web analytics metrics

A computer-readable storage device having stored thereon instructions, which, when executed by one or more data processing apparatus, cause the one or more data processing apparatus to perform operations comprising:

• obtaining, by the one or more data processing apparatus, from a data store, and over a network, visitor data collected from one or more client devices accessing, over the network, one or more remote servers that manage websites visited by the one or more client devices;
• identifying, by the one or more data processing apparatus, a change in a web analytics metric for a given website over a period of time, the web analytics metric being based at least in part on the visitor data associated with the one or more client devices accessing, over the network, the one or more remote servers to visit the given website during the period of time;
• computing, by the one or more data processing apparatus, a respective segment contribution score for each of a plurality of segments of the web analytics metric, wherein each of the plurality of segments of the web analytics metric is defined by a respective set of one or more attribute-value pairs such that values of the segment are determined only from visits to the given website that have attribute values that satisfy each of the attribute-value pairs that define the segment, and wherein computing the respective segment contribution score for each of the plurality of segments comprises:
• determining a first comparison between (i) a value of the web analytics metric at an earliest time in the period of time and (ii) a value of the segment at the earliest time in the period of time,
• determining a second comparison between (iii) a value of the web analytics metric at a latest time in the period of time and (iv) a value of the segment at the latest time in the period of time, and
• computing the respective segment contribution score for each of the plurality of segments of the web analytics metric from the first comparison and the second comparison;
• and
• identifying, by the one or more data processing apparatus, one or more of the plurality of segments as contributing to the change in the web analytics metric based on the respective segment contribution scores,
• wherein the web analytics metric is an additive metric and wherein the respective segment contribution score for each of the plurality of segments is equal to:
• VSt2/Vt2−VSt1/Vt1, or
• k*(VSt2−Vt2)WSt2−(VSt1−Vt1)WSt1, and
• wherein Vt1 is the value of the web analytics metric at the earliest time, Vt2 is the value of the web analytics metric at the latest time, VSt1 is the value of the segment at the earliest time, VSt2 is the value of the segment at the latest time, WSt1 is a value of a weight time series of the segment at the earliest time, WSt2 is a value of the weight time series of the segment at the latest time, and k is a normalization constant.

A system comprising:

• one or more data processing apparatus;
• and
• a computer-readable storage device having stored thereon instructions that, when executed by the one or more data processing apparatus, cause the one or more data processing apparatus to perform operations comprising:
• obtaining, by the one or more data processing apparatus, from a data store, and over a network, visitor data collected from one or more client devices accessing, over the network, one or more remote servers that manage websites visited by the one or more client devices;
• identifying, by the one or more data processing apparatus, a change in a web analytics metric for a given website over a period of time, the web analytics metric being based at least in part on the visitor data associated with the one or more client devices accessing, over the network, the one or more remote servers to visit the given website during the period of time;
• computing, by the one or more data processing apparatus, a respective segment contribution score for each of a plurality of segments of the web analytics metric, wherein each of the plurality of segments of the web analytics metric is defined by a respective set of one or more attribute-value pairs such that values of the segment are determined only from visits to the given website that have attribute values that satisfy each of the attribute-value pairs that define the segment, and wherein computing the respective segment contribution score for each of the plurality of segments comprises:
• determining a first comparison between (i) a value of the web analytics metric at an earliest time in the period of time and (ii) a value of the segment at the earliest time in the period of time,
• determining a second comparison between (iii) a value of the web analytics metric at a latest time in the period of time and (iv) a value of the segment at the latest time in the period of time, and
• computing the respective segment contribution score for each of the plurality of segments of the web analytics metric from the first comparison and the second comparison;
• and
• identifying, by the one or more data processing apparatus, one or more of the plurality of segments as contributing to the change in the web analytics metric based on the respective segment contribution scores,
• wherein the web analytics metric is an additive metric and wherein the respective segment contribution score for each of the plurality of segments is equal to:
• VSt2/Vt2−VSt1/Vt1, or
• k*(VSt2−Vt2)WSt2−(VSt1−Vt1)WSt1, and
• wherein Vt1 is the value of the web analytics metric at the earliest time, Vt2 is the value of the web analytics metric at the latest time, VSt1 is the value of the segment at the earliest time, VSt2 is the value of the segment at the latest time, WSt1 is a value of a weight time series of the segment at the earliest time, WSt2 is a value of the weight time series of the segment at the latest time, and k is a normalization constant.

A method comprising:

• obtaining, by one or more data processing apparatus, from a data store, and over a network, visitor data collected from one or more client devices accessing, over the network, one or more remote servers that manage websites visited by the one or more client devices;
• identifying, by the one or more data processing apparatus, a change in a web analytics metric for a given website over a period of time, the web analytics metric being based at least in part on the visitor data associated with the one or more client devices accessing, over the network, the one or more remote servers to visit the given website during the period of time;
• computing, by the one or more data processing apparatus, a respective segment contribution score for each of a plurality of segments of the web analytics metric, wherein the web analytics metric is an additive metric, and wherein the respective segment contribution score for each of the plurality of segments is equal to:
• VSt2/Vt2−VSt1/Vt1,
• wherein Vt1 is a value of the web analytics metric at an earliest time in the period of time, Vt2 is a value of the web analytics metric at a latest time in the period of time, VSt1 is a value of the segment at the earliest time, and VSt2 is a value of the segment at the latest time;
• and
• identifying, by the one or more data processing apparatus, one or more of the plurality of segments as contributing to the change in the web analytics metric based on the respective segment contribution scores.

A method comprising:

• obtaining by one or more data processing apparatus, from a data store, and over a network, visitor data collected from one or more client devices accessing, over the network, one or more remote servers that manage websites visited by the one or more client devices;
• identifying, by the one or more data processing apparatus, a change in a web analytics metric for a given website over a period of time, the web analytics metric being based at least in part on the visitor data associated with the one or more client devices accessing, over the network, the one or more remote servers to visit the given website during the period of time;
• computing, by the one or more data processing apparatus, a respective segment contribution score for each of a plurality of segments of the web analytics metric, wherein the web analytics metric is a ratio metric, and wherein the respective segment contribution score for each of the plurality of segments is equal to:
• k*(VSt2−Vt2)WSt2−(VSt1−Vt1)WSt1,
• wherein Vt1 is a value of the web analytics metric at an earliest time in the period of time, Vt2 is a value of the web analytics metric at a latest time in the period of time, VSt1 is a value of the segment at the earliest time, VSt2 is a value of the segment at the latest time, WSt1 is a value of a weight time series of the segment at the earliest time, WSt2 is a value of the weight time series of the segment at the latest time, and k is a normalization constant;
• and
• identifying, by the one or more data processing apparatus, one or more of the plurality of segments as contributing to the change in the web analytics metric based on the respective segment contribution scores.