Omnichannel Behaviour:
Definitions and Covariables

Raquel Chocarro, Mónica Cortiñas, Margarita Elorz

September, 2017

1. Motivation

Complete development of online channels:

  • Nearly full penetration of online channels in companies
    • e.g. in Europe (2014) 80% of companies were selling online using their own websites or apps (internal channels) (Flash Eurobarometer 2016)
  • Consumers use a variety of tools in order to access these channels
    • e.g. Spain: in 2016, 35% of population (16-74 years) has made a purchase online in the last 3 months (INE, 2016)
    • e.g. Spain in 2015, 46% of consumers purchasing online say their buying behavior is influenced by reviews, and 31% compared prices using mobile in store (PwC, 2016)

In this context outburst in the use of the term "omnichannel"

Not only professional press

But, what exactly does "omnichannel" mean?

  • What does "omnichannel management" mean?
  • What does "omnichannel behavior" mean?

Omnichannel management

The synergetic management of the numerous available channels and customer touchpoints, in such a way that the customer experience across channels and the performance over channels is optimized
(Verhoef, Kannan, Inman, 2015)

Omnichannel customers: Unsolved questions

  • Which customers are "omni-channel customers" and which are not?
  • What does it means "omnichannel behavior"?
  • What type of customers are omni-channel customers?

The need for a segmentation

  • Customers do what they want in a wide variety of ways in every purchase
  • Can we provide a sensible segmentation useful for companies in understanding their customer base behavior?
  • We need to provide unambiguous rules for the classification

Outline

  • 1. Motivation
  • 2. Conceptual Framework
  • 3. Empirical Illustration
  • 4. Conclusions and Implications

2. Conceptual Framework

1. Focus: One company studying its customers

  • Manufacturer or retailer
  • Two channels: brick and mortar and online store
  • What type of customers are omni-channel customers?

3. The demand of distribution

Distribution Services are the main channel outputs (Keh 1997; Betancourt et al 2007)

The multichannel operation of companies is based on the need to attend different demands of DS:

  • Accessibility
  • Information
  • Assortment: breadth and depth
  • Assurance of product delivery (time and form)
  • Ambiance

4. The key role of separability

ICT have brought a dramatic change: ICT allow for the separability of all DS in time and space (Betancourt et al 2016)

Separability makes it possible for customers to combine DS from different company’s channels:

5. Examples:

  • A customer purchases products at the company’s retail store after getting information about the products in the company’s web site (or vice versa)
  • A customer purchases products at the company’s web site and asks the product to be delivered to one retail store located close to her/his office
  • A customer never purchases products online and never visits the company’s website
  • ...

6. Definitions:

We define:

  • A customer: Someone that has bought from one of the company's channel during a given period of analysis.
  • A user: A company’s customer that interacts with the company (use the DS provided by the company) through either or both channels during the period of analysis.

7. Segmentation:

As a result:

  • 1. A customer of only one channel: Monochannel customer
  • 2. A user of only one channel: Monochannel user
  • 3. A customer of only one channel and user of both: Partially omnichannel customer
  • 4. A customer of both channels and, consequently, also user of both: Complete omnichannel customer

8. Graphically:

3. Empirical Application

1. Objective: Identification of the segments in the multichannel operation of a company with two channels (web and store)

  • Predict how customers self-select into omni and non-omni customers on the basis of their attitudes and characteristics
  • Predict how customers self-select into complete an incomplete blenders of DS on the basis of their perceptions of the distribution services offered by both channels as well as other variables

2. The data

  • 450 valid responses from customers of a fast fashion retailer company within a panel of online consumers
  • Measures of shopping behavior and evaluation of DS within the last year
  • Customers were asked to evaluate the different components of every DS at the store and at the web
  • Customers were asked about the use of DS of the alternative channel when purchasing in a channel (service blending)
  • Other variables: shopping behavior and attitudes, channel policies and general consumer characteristics

3. Empirical Application: Segmentation

4. Segmentation Analysis

5. Estimation: Bivariate probit

\(y_i=x_i β+u_i\) (1)

Where \(y_i=1\) when the customer has visited both channels and \(x_i\) are measures of attitudes and characteristics

\(s_i=z_i β+v_i\) (2)

Where \(s_i=1\) when the customer has purchased in both channels and \(z_i\) are measures of distribution services, channel policies, attitudes and characteristics

5. Results: Bivariate probit (I)

 

User (1:users)

Coef.

Std. Err.

z

P>z

 

Cons

0.367

0.389

0.940

0.346

A1

Attitude (Innovativeness)

0.424

0.161

2.640

0.008***

A2

Experience with Zara

0.076

0.041

1.860

0.063*

A3_S

Share Offline

-0.001

0.037

-0.020

0.985

A3_W

Share Online

0.086

0.033

2.640

0.008***

A4

Cost Time (S+W)

0.075

0.054

1.400

0.161

C1

Gender (Male)

-0.602

0.165

-3.640

0.000***

C2

Age

-0.017

0.007

-2.380

0.017**

C3

Income (1-10)

0.080

0.041

1.940

0.053*

C4

Distance (km).

-0.004

0.003

-1.270

0.205

6. Results: Bivariate probit (II)

 

Blending (1:blender)

Coef.

Std. Err.

z

P>z

 

Cons

-3.486

0.805

-4.330

0.000

S1_S

AccLocation (S)

0.075

0.046

1.640

0.101

S1_W

AccLocation (W)

0.035

0.058

0.600

0.551

S2_S

Information (S)

0.147

0.087

1.690

0.091*

S2_W

Information (W)

-0.178

0.095

-1.880

0.060*

S3_S

Assortment (S)

0.055

0.070

0.790

0.432

S3_W

Assortment (W)

-0.121

0.079

-1.540

0.124

S4F_S

Assurance Form (S)

-0.038

0.089

-0.430

0.667

S4F_W

Assurance Form (W)

0.085

0.086

0.980

0.325

S4T_S

Assurance Time (S)

-0.151

0.069

-2.190

0.029**

S4T_W

Assurance Time (W)***

0.512

0.126

4.080

0.000

S5_S

Ambiance (S)

0.037

0.072

0.510

0.610

S5_W

Ambiance (W)

0.063

0.094

0.670

0.504

P1_S

Access (S)

0.056

0.050

1.110

0.267

P1_W

Acces (W)

-0.031

0.066

-0.470

0.641

6. Results: Bivariate probit (III)

 

Blending (1:blender)

Coef.

Std. Err.

z

P>z

P1_S

Access (S)

0.056

0.050

1.110

0.267

P1_W

Acces (W)

-0.031

0.066

-0.470

0.641

P2_S

Return Policies (S)

-0.076

0.090

-0.840

0.399

P2_W

Return Policies (W)

0.033

0.074

0.440

0.657

P3_S

Payment (S)

-0.183

0.100

-1.830

0.068*

P3_W

Payment (W)

0.036

0.075

0.480

0.633

P4_W

Information Privacy and Security (W)

0.911

0.141

6.480

0.000***

P5

Price

0.006

0.056

0.110

0.912

P6_W

Sending fees

-0.045

0.057

-0.790

0.431

A1

Attitude (Innovativeness)

0.392

0.173

2.270

0.023**

A2

Experience with the brand

0.075

0.044

1.730

0.084*

A3_S

Share Offline

0.059

0.040

1.470

0.142

A3_W

Share Online

0.013

0.037

0.360

0.718

C1

Gender (Male)

-0.198

0.182

-1.090

0.275

C2

Age

-0.011

0.009

-1.220

0.224

 

 Rho

       

 

/athrho

1.700

0.799

2.130

0.033**

 

rho

0.935

0.100

   

 

4. Concluding remarks

Conclusion and Implications:

  • Conceptual definition of omnichannel behavior with managerial and research implications
  • Empirical application for fast fashion retailer: assurance in time and security and privacy policy are key drivers of omnichannel behavior