Product sales forecasting using online reviews and historicalsales data: A method combining the bass model and

sentiment analysis

Zhi-Ping Fan, Yu-Jie Che, Zhen-Yu Chen

Department of Information Management and Decision Sciences, School of Business Administration, Northeastern University, Shenyang 110167, China

Journal of Business Research 74 (2017) 90–100 [SSCI]

Speaker Yejin Kim

Date 5th September 2017

1

NEMO English Seminar

• Introduction

• Theoretical Background

• Mathematical Background

• Methology

• Data and results

• Conclusion

2

Contents

• Key idea

- Online reviews have a significant influence on product sales.

• Background.

- advertisements and the mass media, among other factors influence consumers' purchasing decisions.

- Word of mouth (WOM) is considered one of the most important factors influencing the purchasing decisions of consumers.

• Propose of paper

- We propose a bass Model considering the sentiments expressed in the content of online reviews For Sales forecasting

3

Introduction

• Product sales forecasting based on online review data

- Asur and Humberman (2010) : the sentiments extracted from the chatter of Twitter.com about movie improve box office sales forecasting

• Product sales forecasting using the Bass model

-Dellarocas et al. (2007) : developed a Bass model based on the revenue forecasting model using online ratings and the number of posted reviews

• Difference

- we extract the sentiment index from the content of online reviews, rather than ratings, and use it to extend the Bass and Norton model.

4

Theoretical Background

• Bass Model

(1)

(2)

(3)

• Bass Model differential equation

5

Mathematical Background

m -- the potential market (the ultimate number of adopters)p -- coefficient of innovation, External influenceq -- coefficient of imitation, internal influence f(t) -- the portion of M that adopts at time t.F(t) -- the portion of M that have adopted by time t

S(t) -- cumulative adopters (or adoptions) at t

• Bass & North Model

- Considered with next generation.

- Each generation has its potential market

• ith generation’s cumulative fraction

of adopters in time period t

6

Mathematical Background

𝑚𝑖 -- potential for the ith generation𝜏𝑖 -- the time at which ith generation is introduced𝑆𝐼 𝑡 -- Sales of the ith generation in time period t

• Step 1. Data collection and Preprocessing

-Collection Period : 2007.07 – 2014.12

-Collection Review site : Car Website ‘Bitauto’ of china

7

Methodlogy

Product Collection Period Time Period(per 3Month) Total Review

Elantra 2007.07-2014.12 30 1407

Elantra-1 2008.04-2014.12 27 2524

Elantra-y 2012.08-2014.12 10 368

• Step 2. Sentiment Extraction & Model Building

> Sentiment Extraction - NB(naïve bayes) algorithm

• Used Sentiment dictionary : CNKI sentiment dictionary

• Sentiment Categories : Ci with i∈{+,−} → negative, positive

8

Methodlogy

Wt -- sentiment index in time period t Wtk -- the percentage of words corresponding to category i Of the emotion words in the kth reviewDk -- -- set of emotional words in kth review i – positive or negative categoriest – time periodh – the number of reviews in the time periodc – positive or negative categories value

• Step 2. Sentiment Extraction & Model Building

> Focecasting model – Extended Bass model

- the cumulative sales by the end of time period t

- a function of the online review

sentiment index in the bass-emotion model

9

Methodlogy

=𝑞0𝑒𝛾𝑤𝑡

1 + ൘𝑞0(𝑒𝛾𝑤𝑡 − 1)𝑞𝑚

𝑞0 -- minimum of q𝑞𝑚 -- maximum of q𝛾 -- constant that controls the steepness of the S-curve

= proportional increase of the q in one unit of time (Verhulst, logistics equation)

𝑃 𝑡 =𝑝(0)𝑒𝑟𝑡

1 + 𝑝(0)(𝑒𝑟𝑡 − 1)/𝐾

• Step 2. Sentiment Extraction & Model Building

> Focecasting model – Extended Norton model

- the cumulative sales by the end of time period t

- a function of the online review

sentiment index in the Norton-emotion model

10

Methodlogy

𝑞𝑖0 -- minimum of qi

𝑞𝑖𝑚 -- maximum of qi

𝛾𝑖 -- constant that controls the steepness of the S-curve ith generation. = proportional increase of the qi in one unit of time

- Parameter Result

- Forecasting results

- Forecasting data

11

Data and results - Bass-emotion Model

R-square RMSE

0.9987 1.4910

- Parameter Result

- Statistics of Norton-emotion model

12

Data and results – Norton-emotion Model

- Forecasting results

13

Data and results – Norton-emotion Model

- Forecasting data

14

Data and results – comparison of MAPE

15

Conclusion

• a forecasting model that combines the Bass/Norton model and sentiment analysis techniques is proposed.

• this paper extends the Bass model by analyzing sentiments expressed in online reviews

• the proposed models exhibit lower forecasting errors than the comparative models

• Since m is a constant, The range of the predicted value is limited.

• Step 1. Data collection

16

Appendix – Step 1. Data collection

• Step 1. Data collection

17

Appendix – Step 1. Data collection

Collectable Sale Period

Collectable Review Period

Both Collectable Period

Elantra 2006.04-2014.12

2007.07-2015.02

2007.07-2014.12

Elantra-1 2008.04-2014.12

2008.04-2015.02

2008.04-2014.12

Elantra-y 2012.08-2014.12

2012.04-2015.03

2012.08-2014.12