Impacts of Web Analytics to E-Commerce Strategies

Table of Contents

Today World Wide Web has changed the ways of doing business and firms are expanding their operations onto web to access markets from whole the world (Barua et al, 2004). It is argued that firms can gain competitive advantages through effective online operations (Porter 2001). For instance Dell added US $5 million to its revenue through its effective online ordering system (Attaran, 2004). A number of authors have confirmed the significance payoffs of business strategies predominately based on web technologies (Devaraj & Kohli 2000; Devaraj et al. 2007; Laudon & Laudon 2006:98 etc). This is because an increasing number of internet users are evidenced especially for the last decade. Currently more than 52 million people are using internet in UK that is 61.8% of UK population as compared to 15.8 million in 2000. Moreover, according to National statics, (2010) more than 50 percent of individuals from UK has registered themselves for online purchase transactions while this statistic is expected to increase to 75% in near future. However, firms who do not have enough knowledge that how well their web sites are performing unable to optimize their online operations. So, it is important to learn the ways to which firms can optimize their online operations to gain competitive advantages in this respect. One of the modern techniques that can be used to optimize firms’ online operations is web analytics (Kaushik, 2007). The purpose of current is also to explore the relationship between the web analytics and E-commerce strategies predominately based on web technologies.

2. What is Web Analytics?

The concept of web analytics is highly industrial in nature and growing rapidly especially for the last decade (Malacinski et al., 2001, Newcomb, 2004). Web analytics can be defined as science and art of tracking the visitors’ actions on a web site through analyzing web traffic in order to improve the visitors’ experience to use that web site (Kent et al., 2011). The term web analytics is described in term of science because it contains the statistical data and methodology while the term art is used because that statistical data is analyzed through various tools and techniques to optimize online operations. Similarly, web analytics association (2011) has also defined the term web analytics with the words of “Web analytics is the objective tracking, collection, measurement, reporting, and analysis of quantitative Internet data to optimize websites and web marketing initiatives”. So, the basic purpose of web analytics is to know about the success of web site developed and to improve the visitors’ experience to use it. In short web analytics endeavor to track and analyze the web strategies in order to respond accordingly. For instance managers can correlate number of visits with some particular group of sightseer to confine their market segmentation. On the other hand measuring the success of web site is a difficult thing to do. Success of a web site can be measured into various ways depends on the objectives to which it is developed such as competitive advantages, effective operations, customers’ loyalty etc. For instance for a web site developed to flow of information, number of visitors is important. Nonetheless, it is more appropriate for a commercial web site to measure its success through number of purchase.

3. Web Analytics and E-Commerce:

Inan (2002) argued that in current scenario web sites are viewed as business channel rather than only a tool for advertisement. This implies that virtual online operations are also critical as physical operations and it is important to construct effective web strategies as well. However, one can do so through measuring web site performances such as number of clicks or visitors or number of online purchases. This will also provide the opportunity to optimize web site to get required results. Such measurements can be made through web analytics. A number of software packages are available that provide the opportunity to view the web site traffic. One can perform various measurement tasks through these software packages that allow developing strategies accordingly. Cutler and Sterne (2000) suggest some basic measurement tasks that can be use to optimize online operations. For instance the number of visitors or web site traffic allows predicting future growth. Moreover, such measurement of visitors’ click also allows handling web traffic for smooth online operations.

Another implication of web analytics is to segment the targeted market. Managers can differentiate various groups of visitors on the basis of their behavior towards exploring that web site. This will also allow constructing the market segmentation to target potential customers. Smith (1956) argued that a heterogeneous market is a set of various homogeneous sub groups having some common characteristics. Market segmentation is a process to define those homogeneous sub groups. Haley, (1968) argued that it is useful to divide the whole market into small segments that contain similar characteristics and needs and wants as it will allow to construct strategies for each group accordingly. In case of web sites, it is important for managers to provide right customer with right information that can be done through correlating number of clicks with some particular groups of visitors. Cutler and Sterne (2000) also argued that web analytics also provides the opportunity to assess the effectiveness web strategies through evaluating visitors’ response. These strategies can be related to some online initiative or redesigning processes for improvement. For instance web analytics allows evaluating the effectiveness of redesigning web site through visitors’ response and mangers can devise further web based plans accordingly. Similarly, it is important to evaluate the effectiveness of new developed shopping processes. For instance new developed shopping process will be effective if it reduces the transaction time and ensure security as compared to previous process. Similarly, managers can assess the usefulness of online campaigns. Without measuring such effectiveness it is not possible to construct strategies accordingly. Moreover, one can also compare such online campaigns with off line promotions for further decision making. Cutler and Sterne (2000) also suggest that web analytics can be used to monitor external referrers. In many cases some web partners are associated with that web site. Moreover, most of the visitors use search engines to track their required results. However, web analytics identifies the affiliated partners and search engines that cause high profits. At last Cutler and Sterne (2000) argued that web analytics also identifies the unvisited or poorly performing sections of the web site that should redesign.

All the above discussion of web analytics is confined to the basic metrics like number of visitors of purchases. However, today such basic measurement tasks are viewed inadequately while evaluating the success of that web site as it can produce misleading results. For instance the high number of frames within a web site or spidering of that web site can produce high numbers of hits of visits (Buresh, 2003). Similarly, Kilpatrick, (2002) and Whitecross, (2002) also document some of examples that illustrate that how such basic web analytics metrics can represent misleading results. This implies that basic web analytics metrics are inadequate to evaluate success of web site and to construct strategies accordingly (Phippen et al., 2004). One of the reasons behind this inadequacy is increasing number of online customers. It has become a lot more difficult to find loyal customers over web. Today customers can easily approach to national or even international markets in search of goods and services that increase the customer empowerment. This is why organizations are following customer oriented web strategies. Inan, (2002) provides the same results as 56% of firms interviewed consider customers’ intimacy as one of top three priorities. This implies that customers’ are the key to success of online operations. So, it is important to explore that how customers interact and engage with web site and develop customer oriented web strategies rather than the strategies based on organization. Subsequent part of the study will explore some of implications of advance web analytics.

3. Advanced Web Analytics:

Aberdeen Group (2000) defines advanced web analytics as “Monitoring and reporting of web site usage so that enterprises can better understand the complex interactions between web site visitor actions and web site offers, as well as leverage insight to optimize the site for increased customer loyalty and sales”. Moreover, Whitecross, (2002) argued that scope of optimizing web site is not confined to attracting new customers but also the management of current customers as well that how customers can be converted to loyal customers. So, the main concern of advanced web analytics is to increase customers’ loyalty and profits through improving visitors’ experience to use web site. Advanced web analytics is not just about to collect web site data but integrating such statistical data with other information to construct strategies accordingly. Advanced web analytics concentrates to methodology as compared to basic web analytics matrices that focus to simple measurements such as number of clicks or purchases. Two implications of advanced web analytics are analysis of customers’ life cycle and consumers’ behavior through formula based on the information of web site usage.

3.1 Customer Life Cycle Analysis:

This section endeavors to integrate the success of web strategy with customer life cycle theory. Customer life cycle explores the relationship between visitor and the web site. The analysis provides the opportunity to assess the customers’ information at the each step of customer life cycle (Imhoff et al., 2000). The analysis explores the numbers of customers at each stage of life cycle and the cost that firm bear when holds these customers to next stage or dropout. Reiner, (2000) provided a frame work that analyze customer life cycle. His framework can be explained through a hypothetical example. Suppose a firm spends $25,000 to newly market campaign and 1000000 impressions are served with the click-through rate of 0.5%. In this way number of click through will be 5000 (1000000*0.005). Then the cost per acquisition can be measured through following formulae.

Cost per acquisition = Advertising and promotional cost / Number of click-through

In this way the cost per acquisition of above example will be $5 per visitor. This statistic can be used to compare with the benefits of new market campaign. For instance suppose this increased click-through rate increase sales revenue by $35,000 then benefits per visitor can be measure through the ratio of additional sales revenue divided by number of click-through. The benefits per visitor will be $7 (35000/5000). This indicates the positive impacts in term of profits of newly developed marketing campaign over web.

Similarly, Reiner, (2000) also provide useful metrics to investigate the rate of customers lost. The phenomenon can also be explained through a hypothetical example. Suppose there are 2000 subscribers while current month added 200 additional subscribers. However, 50 customers also unsubscribed from the web site. This makes total number of current subscribers as 2150. One can measure the churn rate from following formula.

Churn = Customers lost / Total customer base

In above example the churn rate will be 2.3% (50 / 2150). This indicates that the 2.3% of total customers were dropout for current month. High rate of dropout indicates the need to optimize web operations. However, to do so it is imperative to explore the customers’ behavior to find actual problem that cause to such dropouts.

3.2 Customer Behavior Analysis:

Though it is useful to explore the number of dropout of visitors of a web site but the most important thing is to know the factors that cause such high number of abandon. Inan, (2002) argued that there are three different factors that affect customers’ behavior to abandon the use of a web site. These factors are content inappropriateness, ineffective design and performance deficiency. For instance Cutler and Sterne, (2000) have described content appropriateness in term of stickiness and relevance. Stickiness shows the effectiveness of the content used in gaining visitors’ attentions while relevancy demonstrates the extent to which web site provide relevant information to the user. In order to measure the stickiness Cutler and Sterne, (2000) define the metrics of stickiness factor.

Stickiness factor = Time taken spent viewing all pages / Total number of unique visitors

Similarly, they also define relevancy factor to evaluate the effectiveness and relevancy of the contents used in web site. Relevancy factor is measured as below

Relevant factor = No. of content pieces consumed by visitor / No. of available (or expected) content pieces

Despite above two metrics to measure customers’ behavior towards dropout, a huge collection of metrics are available. However, the selection of appropriate method depends on the situation that what you want to measure. Subsequent section will provide a view of case analysis in the context of web analytics.

4. Case Study:

Phippen et al., (2004) provided a case analysis to analyze the effectiveness of web strategies for one of the largest airlines from UK in the context of web analytics. The purpose of this web strategy was to get better the customers’ experience in order to compete with other airlines that reduce their costs through high online bookings. To do so, various reports based on the web site traffic were developed to evaluate the effectiveness of web strategy. These reports are described as below

4.1 Monthly Summary:

Monthly summery provides the basic level information such as number of visitors, page view, mean time of visits or mean number of pages viewed per visit etc as demonstrated by figure 1. In other words monthly summery provide the information about the web site activity for whole the month. Such summery information can be used for decision making. For instance it is found from analysis report that at weekend considerable few visitors use their web site. This was followed by the largest number of visitors to the site in a week, coming on Mondays. Moreover, these statics were true for the period of June 2001 to March 2002. So, it is not a good idea to launch new marketing promotions at Monday due to busy traffic that may lead to crashing the site to impact negatively in customers’ mind. On the other hand monthly summery reports also contain the information about top visited pages. Moreover, top external referral that becomes the cause of visit of that web site is also given. This will allow indentifying the most profitable business partner as well. On the other hand top entry page illustrates the most favorable page that visitors access first while top exit page refers to the web page to which most of visitors exit. If this high exit rate is due to any problem then web master should solve it.

Source: Phippen et al., (2004)

4.2 Monthly Dashboard:

Monthly dashboard provides the view of compiled monthly reports for last 12 months as demonstrated by figure 2. Such analyses allow to compares the web traffic on monthly basis to know the slowest and busiest time of web site. From such analysis one can also predict the demand for some specific month.

Source: Phippen et al., (2004)

4.3 Weakly Dashboard:

Similar to monthly summary report, weekly dashboard also provides the view of summary statistic but for shorter period i.e. weekly basis as demonstrated by figure 3. In practice weekly dashboard is used more frequently as compared to monthly dashboard as it allows analyzing web traffic more frequently. In order to keep web operations more smooth, it is critical to monitor and analyze weekly dashboard frequently. Moreover, above discussed metrics can also be evaluated through using such data to construct strategies accordingly.

Source:Phippen et al., (2004)

Figure 4: Weekly Dashboard Information

Source: Whitecross, (2002)

Moreover, Whitecross, (2002) has also defined some of measures that can be utilize to evaluate some important areas i.e. financial performances, people and relationship, business to business relations and performance of web pages. Figure 4 demonstrates the view of these areas and measures that can be deployed to evaluate their performances to construct strategies accordingly.

4.4 Post Implementation Analysis:

Post implementation reports are most useful implication of web analytics. Such reports allow assessing the success or failure of some campaign over web. A number of metrics can be utilized to do so. For instance number of visits to the page of campaign that further can be compared with other web pages to evaluate its attractiveness. Similarly, click stream analysis can be conducted to evaluate the effectiveness of banner advertisement. Moreover, to evaluate the success of campaign, actual return on investment generated through online bookings can be compared with expected returns. The airline company has also adopted a useful metrics to evaluate the success of new marketing campaign over web. The metrics consists of the ratio of number of online bookings in response of campaign to the number of clicks to that campaign page. This statistics demonstrate the percentage of visitors who like the proposed campaign and book for online bookings.

5. Conclusion:

In conclusion web analytics is one of the modern tools used by managers to optimize their web operations. Web analytics is defined as the science and art that uses statistics related to web traffic for decision making to improve customers’ experience to use that website. Conventional measures of web analytics such as simple number of visitors or purchases can be used to predict future growth, to segment the customers’ group or to assess the effectiveness of some campaign over web. However, simple measures of web analytics can mislead the results due to high number of frames within a web site or spidering of that web site. So, it is important to deploy advance web analytics measures that mainly focus to customer oriented strategies. For instance cost of acquisition and rate losing customers can be used to assess customer life cycle. Similarly, ratio oh stickiness factor and relevant factor can be used to evaluate the content appropriateness while studying customers’ behavior towards abandoning the use of web site. At last case study shows that using monthly summary, monthly dashboard, weekly dashboard and post implementation reports provide useful information about web traffic that managers can use to optimize their web operations.

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Wednesday 19 June 2013

Web Analytics to E-Commerce Strategies

1. Introduction: