Principles of e-Marketing  © Otilia OTLACAN

 

CHAPTER 6:

Elements of Planning (part 2)

 

Part 2 – E-Metrics

 

 

Chap ter highlights:
- establishing the measurement process
- key data in e-metrics
- primary measures
- additional measures
- tools and techniques of evaluating e-metrics

We have a website. What shall we do about it? It sounds a little ridiculous asking that, what shall we do about a site? The point is, your company decided to go online and built a website with some purpose – that of attracting new customers and help retain the existing ones. But how do we know if the target audience reaches our site? And from them, how many will take an action, will become buyers and not just visiting surfers?

 

The necessary steps in order to evaluate our online presence would be:

As far as the process is involved, we might confront several barriers, coming especially from inside the organizations. Typical barriers are usually represented by: top management with no priority for performance measurement, unclear responsibilities, insufficient resources, employee motivation, lack of quality data.

In order to overpass these issues, we need to structure the measurement process, so that it will be easier to have it integrated within the organizational. The 4 stages of establishing the e-measurement process are:

The process is a cyclic one, therefore, it must be performed continuously in order to deliver best results. After the 4th stage is completed, a new cycle must begin, following the same stages.

There are several metrics we can use to determine the structure of the website’s audience, thus providing the proper support for making e-marketing decisions that will improve our audience.

 

5.1. Key Data in e-Metrics

 

Prior to proceed with the study and use of e-metrics (e-metrics: tools and techniques used to collect metrics from a digital business environment), we should first establish what data we must collect in order to have the decision support for the company’s online activities.

Chaffey (2001) proposed a framework made up of the following 5 diagnostic categories:

 a. Business contribution
How does Internet marketing contribute to the bottom line? What is the online revenue contribution (direct and indirect), costs and profitability?

 b. Marketing outcomes
How many marketing outcomes are achieved online? For example, what proportion of leads, sales, service contacts occur online? How effective is online marketing at acquiring, converting and retaining customers?

 c. Customer satisfaction:
What are the customers’ opinions of the online experience and how does this affect their loyalty?

 d. Customer behavior (web analytics):
This assesses how different customer segments interact with web site content and assesses how the actions they take are influenced by usability, design, content, promotions and services.

 e. Site promotion
How effective are the different promotional tools such as search engines, e-mail, direct marketing and advertising at driving quality traffic to the web site? Measures include attraction efficiency, referrer efficiency, cost of acquisition, reach and the integration between tools.

 

5.2. Primary measures of visitor activity

The customer behavior can be studied from the data on website visitor activity. The most widely used primary measures are page impressions, visitor sessions and unique visitors.

These key metrics are always reported to a time interval, such as a year, month, week, day.

Page impressions: a page impression is defined as a one page view by an individual visitor. The most comprehensive usage of page impressions is the study of “clickstream” – the succession of the pages a visitor viewed, a collection of page impressions – which helps to understand the behavior of the online buyer.

Visi tor sessions: a visitor session consists in the series of page requests made by an individual visitor, without exceeding 30 consecutive minutes of inactivity.

Uniq ue visitors: defined as the number of individual visitors to the site within the reporting time period (it is also highly desirable to measure repeat visitors).

You might note many sites having a “hit” counter. A “hit” is not a reliable indicator of the activity on a website, because it does not express only the number of people viewing a page, but it also records as hits each graphic and file included in a page.  Therefore, professionals do not use it anymore as a metric, since it induces a false sensation of high traffic on the page.

 

5.3. Additional key ratios

We can obtain valuable information by studying the primary measures of the online activity on our site, and many businesses can rely on this information when designing or improving their e-marketing strategy. Still, it is often of great use to calculate additional metrics, in order to have a deeper understanding of our online customers’ behavior.

The following additional ratios offer a perspective on the qualitative aspect of a website’s activity, rather than a quantitative perspective (offered by the basic e-metrics presented above):

Aver age page views per visitor: is calculated as a ratio page impressions/visitor sessions. The indicator gives an indication of duration of visit – the more pages viewed, the longer the duration. It is a better indication of content viewing than average visit time since this is skewed by visitors who keep a page loaded after they have finished reading it.

Aver age visit frequency: is calculated as a ratio visitor sessions/unique visits and indicates the average number of visits within a given time period.

Repe at visit percentage: is calculated as the percentage of visitors who are repeat visitors within a given time period.

 

 

5.4. Tools and techniques of evaluating e-metrics

Most methods of collecting primary e-metrics data refer to the customer behavior and they are very important for any e-marketer.

The main issue raised is the difficulty of gathering such data, because it requires a quite high level of IT knowledge that most marketers miss. Therefore, it is crucial for the marketers to cooperate closely with their webmaster or with the web analyst, and try to understand and be aware of how these methods are used.

The main techniques of collecting data from websites are:

Server-based log file analysis

A log file is a feature of web servers that records all requests for web pages including the page requested, and the time and source of the enquiry. Since log files quickly grow with the number of visitors, we will find it impossible to process and analyze the log file ourselves, manually, and we will need to buy specific software summarize the data. Server-based log file data can be also audited, we would usually request an audit for advertising purposes.

When interpreting log file data care should be taken since there may be major sources of undercounting or overcounting as we will see in the following table (Chaffey, 2001):

 

Sources of undercounting

Sources of overcounting
Caching in user’s web browsers (when a user accesses a previously accessed file, it is loaded from the user’s cache on their PC) Frames (A user viewing a framed page with three frames will be recorded as three page impressions on a server-based system)
Caching on proxy servers (proxy servers are used within organisations or ISPs to reduce Internet traffic by storing copies of frequently used pages) Spiders and robots (Traversing of a site by spiders from different search engines is recorded as page impressions, they can be excluded, but time consuming)
Firewalls (These do not usually exclude page impressions, but they usually assign a single IP address for the user of the page, rather than referring to an individuals PC) Executable files (These can also be recorded as hits or page impressions unless excluded)
Dynamically generated pages, generated ‘on the fly’ are difficult to assess with server-based log files.
Table 1 – Inaccuracies caused by server-based log-file analysis

Browser-based analysis.

This method is not actually a new one, but one that appeared to address the problems mentioned above, referring to undercounting and overcounting of the server-based log-file analysis.

It consists in recording the number of accesses to a web page, by using a short script inserted into the page. The script records a hit every time a page is loaded into the user’s browser.

Pane l data.

Interne t panel data is collected in a similar way to home-based TV panels. Panel members agree to have software installed on their PC that sends data that is collected by the monitoring organization.

The accuracy of such method is questioned because no organization can control if the members of the panel are representative for the whole population. People who accept having their online activities under supervision might not act like the majority of the collectivity and it might cause inaccuracy.

ISP data.

The ISP data method is the newest, initiated by an Australian company (Hitwise). The method consists in daily analysis of a site’s visitors, retaining the ISP the visitors belong to. In the end, the data collected from each ISP taken into account are aggregated and will help calculate a ranking compared to other sites (usually competitors’ sites).

The method is useful especially for the purpose of relating a company’s online activity to that of its competitors.

More advanced analysis approaches can be used to gain a better perspective upon customer behavior. Some of the new such techniques are:

Asse ssing customer satisfaction

Custome r satisfaction analysis offer important information to the e-marketer, because it serves as a support for further decisions of how to improve the organization’s online presence.

A useful comprehensive presentation of collecting such data is the next table, compiled by Chaffey (2001).

Technique for assessing customer opinions

Strengths

Weaknesses

1. Outcome data e.g. enquiries, customer service e-mails

 

Records marketing outcomes Difficulty of integrating data with other methods of data collection when collected manually or in other information systems

2. Online questionnaires. Customers are prompted randomly - every nth customer or after customer activity or by e-mail

Can record customer satisfaction and profiles.

Relatively cheap to create and analyse

Difficulty of recruiting respondents who complete accurately

Sample bias – tend to be advocates or terrorists

3. Online focus groups

Synchronous recording.

Relatively cheap to create

Difficult to moderate and co-ordinate.

No visual cues as from offline focus groups

4. Mystery shoppers. Example customers are recruited to evaluate the site

Structured tests give detailed feedback

Also tests integration with other channels such as e-mail and phone.

Relatively expensive

Sample must be representative

Table 2 - Comparison of different techniques for assessing online customer satisfaction

 

 

Keywords: e-metrics, page impression, visitor session, unique visitors, average page views, average visit frequency, repeat visit percentage, server-based log-file analysis, browser-based analysis, panel data, ISP data.

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