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Tuesday, April 15, 2014

Applying the Alternative Response Hierarchy Models to Predict E-Commerce Success in Southeast Asia

The strong economic growth in Southeast Asia has led to a continuous improvement in Internet infrastructure making it the next hotspot for e-commerce ventures. The abundance of young, tech-savvy consumers is an appealing aspect of the Southeast Asian market. Roughly half of the region’s 600 million people are under 30 and increasingly moving to cities, which helps explain why the region has some of the highest levels of digital engagement in the world. And high Facebook and Twitter usage provides e-commerce companies with a fertile channel for connecting with potential customers.

However, the rapid increase in digital engagement did not produce a relative growth in Internet shopping. The total e-commerce transactions the past decade is still relatively small compared to more advanced economies. The level of trust in using the Internet for shopping is still weak because Southeast Asian consumers have yet to develop the maturity in buying products or services online. Comparing advanced economies to Southeast Asia, trust in Internet shopping is high. The high trust index is driven by four antecedents of which can be used to predict future buying behavior in Southeast Asian markets.

Lee and Turban (2001) proposed a theoretical model for investigating the four main antecedent influences on consumer trust in Internet shopping, a major form of business-to-consumer e-commerce: trustworthiness of the Internet merchant, trustworthiness of the Internet as a shopping medium, infrastructural (contextual) factors (e.g. security, third-party certification), and other factors (e.g. company size, demographic variables).

They suggested that the antecedent variables are moderated by the individual consumer’s degree of trust propensity, which reflects personality traits, culture, and experience. Based on the research model, a comprehensive set of hypotheses is formulated and a methodology for testing them is outlined.

Some of the hypotheses are tested empirically to demonstrate the applicability of the theoretical model. The findings indicate that merchant integrity is a major positive determinant of consumer trust in Internet shopping, and that its effect is moderated by the individual consumer’s trust propensity.

As seen in recent studies, Southeast Asia is the next big market for Internet shopping platforms. The slow but increasing growth of e-commerce transactions in the past is an indicator of adoption and increasing consumer trust. However, as more online stores opens, consumers are presented with several websites that are similar in nature but provides different experiences from discovery to purchasing an offer. This experience is a predictor for future online transactions – whether to continue buying online or abandon Internet shopping and revert to the physical retail stores.
In most studies, experience in the online context is synonymous to the entire digital sales funnel. It is the entire user experience from discovering the offer through online ads, increasing their knowledge by familiarizing themselves with the website, until the consumer reaches the end of the sales cycle and decides to buy the offer. Most marketers also use this linear formula to improve their digital marketing campaigns and expect incremental sales growth by replicating the offline retail experience in an online setting. What most marketers forget is that online buying behavior does not follow the traditional consumer response processes.

Product tangibility is what’s lacking in the Internet shopping process hence forces the consumer to respond to the experience differently compared to the traditional retail experience. And if the consumer behaves differently, a different approach is needed to further investigate what are the key drivers for buying online.

Michael Ray (1974) has developed a model of information processing that identifies three alternative orderings of the three stages on perceived product differentiation and product involvement. These alternative response hierarchies are the standard learning, low-involvement, and dissonance/attribution models (Figure 1)



Figure 1


Ray’s study show that not all consumers follow the same purchase patterns and their response process varies based on their level of involvement. A change in consumer attitude, in this case the act of purchasing an item, changes depending on what type of product or services is being bought and under what circumstances. Adding the Internet as a medium complicates this process and highly influences the consumer attitude. Ergo, applying the alternative response process in understanding Internet shopping can easily predict e-commerce success. E-commerce succes is defined as total sales revenue and repeat spending on the online store.

The DeLone and McLean IS (Information System) Success Model is an existing success-measurement framework that has found wide application since its publication in 1992. It is a comprehensive framework for measuring the performance of information systems. With the emergence of new business models and e-commerce platforms, an updated version of the model can be applied to e-commerce success measurement (Figure 2).


Figure 2


The new and updated model is based on the empirical and theoretical contributions of researchers who have tested and discussed the original model. They suggested that the updated model is composed of six dimensions - System Quality, Information Quality, Service Quality, Use, User Satisfaction, and Net Benefits. Based on these independent researchers, they stressed the importance of measuring the possible interactions among the success dimensions in order to isolate the effects of independent variables on one or more of them.

Viewing the DeLone & McLean IS Success Model from both a process perspective and a variance perspective can be useful in identifying and understanding these interactions. Drawing from the information system and marketing literature over recent years, the six dimensions of the updated DeLone and McLean model comprise a parsimonious framework for organizing the various e-commerce success metrics identified in the literature.


Applying the alternative response hierarchies to predict e-commerce success will give insights to the key drivers for a successful online business. Understanding how a consumer develop their attitude towards Internet shopping in Southeast Asia will provide future researchers insights into how to tap this growing economy.
4:36 PM Posted by Nicco Joselito Lopez-Tan (陳里道) 0

Monday, February 24, 2014

Predicting Integration Strategy Effectiveness

As consumers evolve and multichannel agnosticism kicks in, identifying which marketing channels are effective for specific campaigns or promotions are tougher than ever. Specially when top executives are more honed in ensuring positive returns for every marketing initiative.

These days, one marketing channel is not enough but it is also futile to select channels you can't sustain. With 'big data' being the most popular topic in this century, more and more ways are being discovered in measuring integrated campaign effectiveness.

Cross-Channel Trinity Predictive Index P(c)

One approach I developed (still pending for further multivariate testing) is to measure effectiveness through a probability of conversion P(c) index. I am calling it the Cross-Channel Trinity Predictive Index. It is a measure of a relative "ad" exposure frequency of consumers and their probability of conversion.

Conversion in this sense is defined as an activated buyer.

I hypothesize that a consumer can only consume X amount of content from X amount of channels per day. I continue to hypothesize that in any given day, a minimum of 3 core channels are used to get a marketing message across that has high probability of conversion.

The more exposed a consumer in these 3 core channels, the higher the correlated conversion rate.  In essence, Relative Ad Exposure Frequency is directly proportional to probability of conversion.

Relative Frequency ∝ P(c) 

So how do we define Relative Frequency?

Since we are counting exposure of a consumer, we define relative frequency as the total unique consumer who saw your ad over the total Ad impression seen for a specific channel.

For example:

In a digital marketing campaign, a consumer used Facebook as a channel to promote a campaign. It has a corresponding link with tracking codes to identify total unique visitors coming to the final online sales page. Total unique visitors recorded is 100 and Facebook recorded a total ad impression of 1000.

Relative Frequency = 100 / 1000 = .10

So how do we 'predict' cross-channel conversion effectiveness?

The principle behind this approach is based on the random sample spaces and events. That we can isolate a subset of the sample space of a random experiment and describe the new events from a combination of existing events. That by knowing the intersection of two events, we will be able to deduce the possible outcomes that are contained in both events. In this case, the event is the exposure of a single consumer to two channels at the same time.

Channel A  ∩  Channel B 

Going back to my initial hypothesis, in any given day, a consumer is exposed to a minimum of 3 channels. For maximum retention of a message, optimizing these 3 channels will give the highest probability of conversion.

Graphically, the consumer in discussion is intersected in the middle of these 3 channels.

Cross-Channel Trinity Predictive Index P(c)


In order to "predict" the conversion rate among these channels, we apply the concept of Event Intersection using Relative Frequency as the unit of measurement. And based on this statistical concept, computing for the intersection of two events is mainly multiplying the probability per intersection.

Simply said (in the venn diagram shown above):

P(c) = P(RFM1RFM2) x P(RFM2RFM3) x P(RFM3RFM1)

Let's put it into an application:

A marketer decided to use the following 3 channels based on historical data to promote an upcoming Sale. The corresponding count of unique consumers and the ad impression of each channel is displayed below. (assuming the three channels were measured with the same number of days).

CHANNEL                     UNIQUE VISITORS     TOTAL IMPRESSION     RF
1. Facebook                         500                                         1000                        .50

2. Direct Mail Coupon
Reservation via Phone          150                                        200                          .75

3. Youtube Video Ad           950                                        3500                        .27

The Predictive Index for these 3 channels is:

P(c) = P(facebook) x P(Direct Mail) x P(Youtube video ad)
P(c) = P(.5)x P(.75) x P(.27)
P(c) = .10 or 10%

With this volume of exposure, the probability of converting all the consumers who have seen the ad X amount of times is 10%. In this case:

10% of total Unique Visitors = (500 + 150 + 950)10% = 160 converted customers.

The next question is, with this model, is it worth investing in these channels once you've identified your potential return.

Lastly, as you can see, the relative frequency is your key driver. The higher the relative frequency, the higher is the conversion rate.

Caveat Emptor 

This model is still on its testing phase. I've only used this in a minimum sample size in three different countries. Although the results are quite encouraging, this model is still pending for more testing.

I would love people to test this out in their own respective companies and please feel free to message me if you applied it and got the results close to your predicted conversion rate.
10:37 PM Posted by Nicco Joselito Lopez-Tan (陳里道) 0

Monday, January 20, 2014

How to Slice a Business Strategy Pie


Dear readers, I will have a series called "Strategy Pi", learning how to build strategy for your business from proven frameworks. Coming soon!
11:22 PM Posted by Nicco Joselito Lopez-Tan (陳里道) 2