IoT Data Analytics in Retail: Framework and Implementation

Jānis Grabis, Kristina Jegorova and Krišjānis Pinka

Institute of Information Technology, Faculty of Computer Science and Information Technology,

Riga Technical University, Kalku Street 1, LV-1658, Riga, Latvia

Keywords: Customer Experience, Ambient Conditions, IoT Analytics.

Abstract: IoT data analytics has many potential applications in the retail industry. However, relations among ambient

conditions at stores as measured by IoT devices and sales performance are not well understood. This paper

explores sensory and sales data provided by a large retail chain to quantify the impact of air quality,

temperature, humidity and lighting on customer behaviour. It has been determined that the air quality and

humidity have a significant impact and temperature appears to have a non-linear effect on customer behaviour.

The data analysis findings are used to configure an IoT data analytics platform. The platform is used to

monitor the ambient conditions in retail stores, to evaluate a need for improving the conditions as well as to

enact improvement by passing them over to a building management system.

1 INTRODUCTION

Customer experience is a paramount to the retail

industry. It has many dimensions such as sensorial,

affective, physical, social and cognitive (Lemon and

Verhoef 2016). In the case of brick and mortar

retailing, vendors have to make any effort to retain

customers and to compete with on-line shopping

(Misra et al. 2017). The customers should be offered

a comfortable and enjoyable environment. Modern

computing and data processing capabilities provide

opportunities for measuring and improving customer

experience. Internet of Things (IoT) is one the

technologies allowing to measure conditions at

retailing facilities and data analytics processes these

measurements to elaborate solutions for improving

the customer experience. IoT helps businesses to

harness and process data to improve operations and

increase customer satisfaction (Shrikanth 2016).

Automation opportunities of IoT help service

industry in reducing costs and improving customer

service. IoT enables the concept of constant

connectivity to provide complete picture of on-going

retailing processes (Berthiaume (2019).

However, the current trends indicate that the retail

industry lags other industries in usage of the IoT

technologies (Shanhong 2018) what could be caused

by lack of understanding of relations between

customer experience and environmental conditions as

measured using IoT devices. That requires empirical

investigations analysing sensory information in

relation to sales data and customer behaviour (Ben-

Daya 2019). Additionally, deployment of IoT devices

and supporting data analytical solutions is a complex

endeavorment and requires sophisticated technological

platforms (Weyrich and Ebert 2016). The solution

should be setup-up according to the results of data

analysis and continuously operated to monitor condi-

tions at retailing facilities and to enact improvements.

The objective of this paper is to empirically test

relations between environmental conditions in a retail

store and customer behaviour as well as to outline a

technological solution for deploying IoT data

analytics. The paper considers data analysis case

study using data provided by a large retailing

company. The data set and problem description are

made available by the European Data Incubator

program. Statistical data analysis is performed what

yields rules for setting up a system used to control the

environmental conditions. An IoT data analytical

platform is proposed for hosting these rules as well as

for monitoring the current environmental conditions.

The contributions of the paper are practical

quantification of relations among the environmental

conditions and customer behaviour and sales

performance as well as a proposal for implementing the

results of data analysis.

The rest of the paper is organized as follows.

Section 2 discussed applications of IoT in retail and

customer experience dimensions. Empirical data

analysis is reported in Section 3. Section 4 outlines a

solution for implementing the results of IoT data

analytics. Section 5 concludes.

Grabis, J., Jegorova, K. and Pinka, K.

IoT Data Analytics in Retail: Framework and Implementation.

DOI: 10.5220/0010133700930100

In Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL 2020), pages 93-100

ISBN: 978-989-758-476-3

2 FRAMEWORK

To better understand value of using IoT analytics in

retail, customer experience dimensions are analysed.

A brief literature review is conducted on IoT

applications in retail.

2.1 Dimensions

The customer experience and behaviour are

influenced by many factors. The theoretical analysis

of existing research is conducted to identify these

factors with focus on suitability of IoT technologies

for addressing these factors. The customer experience

dimensions are summarized in Table 1. They concern

various aspect of customer journey and some of them

Table 1: Dimensions of customer experience.

Author Dimensions

Parasuraman

(1988)

Reliability, Responsiveness, Assurance,

Empathy, Tangibility

Schmitt (1999)

Sensory (sense), Affective (feel),

Cognitive (think), Physical (act), Social-

identity (relate) experiences

Wolfinbarger

(2003)

Website design, Fulfilment/Reliability,

Security/ Privacy, Customer service

Parasuraman et

al. (2005)

Efficiency, Fulfilment, System

availability, Privacy

Fornerino

(2008)

Sensorial, Affective, Physical/ Behavioral,

Social, Cognitive

Gentile et al.

(2007)

Sensorial component, Emotional

component, Cognitive component,

Pragmatic component, Lifestyle

component, Relational component.

Verhoef et al.

(2009)

Social environment, Service interface,

(Retail) Store atmosphere, Assortment,

Price and promotions (including loyalty

programs), CEs in an alternative channel,

Retail brand, Past customer experience.

Lemke et al.

(2011)

Communication encounter, Service

encounter, Usage encounter

Klaus &

Maklan (2013

Product experience, Outcome focus,

Moments of truth, Peace of mind

Kim and Choi

2013

Service outcome quality, Interaction

quality, Peer-to-peer quality

Handayani

2019

Accessibility, Competence, Customer

recognition, Willingness to help, Personal

treatment, Problem solving, Fulfilment o

promises, Value for time

are within retailer’s control (e.g., service interface,

retail atmosphere, assortment, price) whole others are

outside of the retailer’s control (e.g., influence of

others, purpose of shopping) (Verhoef et al. 2009).

The identified dimensions are categorized 10 groups

(Figure 1 shows the number of research works

considering the dimensions belonging to a category).

The most frequently considered dimensions are in

affective, sensory, customer service and

fulfilment/reliability. Some of these elements can be

controlled by retailers. Traditional product

presentation now has less impact than sensations as

what customers see, feel, hear and touch (Arineli and

Quintella 2015). Bagdare (2015) observes that

customers’ mood and behaviour depend on many

elements like - music, lights, colours, displays,

fragrances, soft and cozy ambience. Thus, the

dimensions “affective”, and “sensory” are more

important in customer experience analysis.

Consumers perceive shopping as a mode of

relaxation, free-time activity or a habit. New

expectations and needs have been created from

changes in people's lifestyle and increased comfort

Sathish and Venkatesakumar (2011). These

observations confirm that ambient or environmental

conditions are very significant aspects of the affective

and sensory dimensions of customer experience. IoT

analytics is well suited to measure and interpret of

these conditions.

2.2 IoT in Retailing

IoT technologies have found many applications in

retailing. The industry survey shows that 37% of food

and grocery companies already experiment with IoT

technology or have successfully initiated IoT services

or products and further 58% are planning to expand

their utilization of the technology (Irish 2017). IoT

helps to improve both internal operations and

customer facing processes. Patil (2017) identifies a

number of operational benefits including

personalization, dynamic pricing, inventory tracking

and monitoring, and recommendations. Energy

efficient smart thermostats and lighting are also

mentioned. Sensors provide real-time stock

information what is used to improve demand

forecasts and optimize inventories (Kolassa 2019).

IoT improves monitoring and control by coding and

tracking objects (Madakam et al. 2015). That allows

companies to become more efficient, accelerate

processes, decrease errors and avoid theft (Gaur et al.

2017). The real-time data provided by sensors allows

stakeholders to make better operational decisions

(Balaji and Roy 2017).

IN4PL 2020 - International Conference on Innovative Intelligent Industrial Production and Logistics

IoT-connected smart labels provide means for

identification of products as well as for providing

additionally information (Fernandez-Carames and

Fraga-Lamas 2018a). This information can be

combined with personalization and recommendation

services to enrich shopping experience with pervasive

displays and smart things (Longo 2013). IoT provides

means to engage customer throughout the product

life-cycle (Fernandez-Carames and Fraga-Lamas

2018b). For example, smart textiles can communicate

with smartphones to process biometric information.

The life-cycle support requires an adequate IoT

architecture ensuring efficient and secure data

processing.

Figure 1: Categorization of customer experience

dimensions and their frequency of mentioning in research

works.

To summarize, retailers use IoT for inventory

management, product tracking, equipment control

and customer engagement. From the customer

perspective, the existing research focuses on

customer service, customer experience and fulfilment

dimensions. However, there is little work on using

IoT in relation to the impact of ambient or

environmental conditions on customer behaviour in

retailing. Therefore, this paper focuses on the sensory

dimension and the impact of environmental

conditions on the customer behaviour. That is

conceptually represented as an IDEF0 activity in

Figure 2. An IoT platform is used to harness sensory

measurements of the environmental conditions.

These are used by a Building Management Systems

(BMS) to alter environmental controls and to improve

the environmental conditions at a retail store. That

should result in improved sales performance. In order

to achieve that, relations among the sales performance

and the sensor measurements should be understood to

properly configure BMS.

Sales

Sensor

measurements

BMS

IoT

platform

Sales

performance

Figure 2: The IDEF0 model of the sales activity.

3 DATA ANALYSIS

Empirical data are used to investigate relationships

among the environmental conditions, customer

behaviour and sales performance. The data are

provided by a large retail chain (more than 2000

stores and 30.000 employees), which have

accumulated sensor measurements in their stores are

well as sales data (EDI 2019). The data are gathered

over the period from February 25th, 2019 till March

3rd, 2019 with store’s operating hours 8:00 am to

10:00 pm. The data come from a single store and

contain more than 60 000 purchase lines or registered

transactions. The purchase lines belong to more than

7000 purchase orders. Over 150 000 sensor

measurements are available for each sensor. The

sensor data are not recorded strictly at the specific

time intervals and there are missing data.

The company aims to interpret the effect of the

ambient conditions of the stores in customer

behaviour (EDI 2019). The main question for the

analysis is about the effect of lighting conditions,

temperature and humidity on the customer basket

size. That involves determining thresholds for

unfavourable ambient conditions. The company also

expects to have a technological solution in the form

of a decision support system that can analyse the IoT

data along with the transactions in the store. The

sensor data provided include measurements of air

quality (higher values correspond to worse air

quality), humidity, lighting and temperature. These

can be used to control the affective and sensory

aspects of customer experience. All customer

transactions are recorded and the following sales

performance measurements are considered in this

investigation:

• Number of items (N)– number of different

products purchased by a customer in one store

visit (i.e., number of items in shopping basket);

• Weight of purchases (W) – weight of all products

purchased by a customer in one store visit;

IoT Data Analytics in Retail: Framework and Implementation

• Quantity of items (Q) – quantity of items of all

products (summed across all types of products)

purchased by a customer in one store visit.

Figure 3: The average number of items N in the shopping

basket by hour.

The value of transactions is not a part of the data

set and names of products are not known. It is

assumed that the measures indirectly characterize

sales performance (i.e., a large number of items

implies better sales performance) and customer

behaviour (e.g., their willingness to pick-up more

items and carry more weight).

After data pre-processing (e.g., standardizing data

recording frequency for the sensors and treatment of

missing data), the statistical analysis is carried out to

identify relationships among the environmental

conditions as measured by the sensors and sales

performance and customer behaviour. It is identified

that the shopping behaviour depends on the hour of

the day (Figure 3). It can be observed that the

purchases are relatively stable from 9:00 to 16:00 and

they increase significantly from 17:00 to 22:00. The

number of transactions increases gradually

throughout the day till 18:00 and then gradually

decreases. The environmental conditions also vary

significantly depending on the hour. However, every

environmental indicator has a different pattern. The

air quality is the best around the noon and deteriorates

in the afternoon as the busiest shopping hours

approach (Figure 4). The pattern suggests that the air

quality controls are only partially aligned with the

customer behaviour. The lighting measurements have

very distinctive spike at 13:00 and have significantly

lower value from 16:00 and on.

In order to analyse relationships among the

customer behaviour and the environmental

conditions, the ANOVA analysis is conducted. The

linear model is considered is:





∗

          





where s refers to the sensor group, j refers to the

individual measurement, 



∗

ln



 logarithmic

transformation of N to reduce data skewness, 



the random noise. air, light, humidity and temp refer

to air quality, lighting, humidity and temperature

sensor measurements, respectively. The results

(Table 2) confirm that the hour of the day

significantly affects the customer behaviour. The air

quality and humidity are the most significant factors

of the sensory factors. The lighting is a statistically

significant impact at the 5% significance level. The

temperature appears not to have a significant impact

on the customer behaviour. Similar results were also

obtained for the weight of purchases and quantity of

items.

Figure 4: The impact of time on environmental conditions:

the average air quality by hour (upper pane) and the average

lighting by hour (lower pane).

To reduce noisiness and to improve

interpretability of the relations, the sensor

measurements are factorized in quintiles of equal

number of observations. The number of items

depending on the category of sensor measurements is

reported in Figure 5. The figure suggests that the

number of items purchased decreases if the air quality

is low. The number items purchased is by

approximately 50% smaller in the 5th quintile than in

the 2nd quintile. The relative decline of the number

of items purchased in the 1st quintile (the best air

quality) can be explained by interactions between the

air quality and hour of the day factors. The number of

items purchased is relatively stable according to the

IN4PL 2020 - International Conference on Innovative Intelligent Industrial Production and Logistics

Figure 5: The average number of items N according to the quintile of sensory measurements.

humidity factor in all quintiles but the fifth. Improved

lighting gradually increases the number of items

purchased. The ANOVA analysis showed that the

temperature is not a significant factor although Figure

4 suggests that there are non-linear relation customers

not liking either too cold or hot conditions. The

number of items purchased is the largest if

temperature is in the fourth quintile. These

observations are useful to formulate rules for

managing environmental conditions to be used in the

IoT platform. Figure 6 illustrates dependence of

weight W of purchases on the environmental

conditions. It confirms that the performance measures

N, W and Q used to represent the customer behaviour

follow the similar pattern.

Table 2: The ANOVA analysis of N* according to hour and

sensory measurements.

Sensor DF Sum

Mean

value

Air 1 60 60.1 59.13 0.000

Light 1 4 4.2 4.138 0.042

Humidity 1 85 84.8 83.41 0.000

Temp 1 0 0.3 0.301 0.583

Hour 1 435 434.9 427.9 0.000

Residuals 7182 7299 1 1

4 IMPLEMENTATION

A suitable technological solution is used to

implement the findings about the relations between

customer behaviour and the environmental

conditions. The solution is a platform integrating data

from IoT devices and other data sources, evaluating a

need to improve the environmental conditions and

invoking a building management system to enact the

improvements. It is adopted from previous studies on

context aware and adaptive systems (Kampars and

Grabis 2018).

Component of the IoT data analytics platform is

shown in Figure 7. Stream processing units K

are

responsible for receiving raw data from data

providers (1) and handling internal data streams. The

archiving jobs store the data in persistence storage

and evaluation jobs use the raw data to evaluate the

environmental conditions and the evaluation results

are sent to internal stream processing (4), where they

are forwarded for evaluation by triggering jobs used

to invoke improvement actions (6). If triggering

conditions are met (7), an improvement action is

generated and posted to BMS (8,9). All potentially

computationally intensive tasks are executed in

dedicated containers in a cluster to ensure high

performance. SP is implemented using Apache Kafka

streaming platform. Evaluation jobs are built using

Apache Spark big data analytics engine and the

adaption engine is based on Docker containers. The

infrastructure is provided using CloudStack cloud

infrastructure tools.

In the case study considered, IoT data analysis

yielded that if the air quality deteriorates beyond the

lower boundary of the air quality 5th quintile, it

should be improved (i.e., by powering AC) to avoid

decreasing sales. This results in implemented in the

IoT data analytics platform. The air sensor is one of

the IoT devices providing data.

The platform continuously measures

environmental conditions and compares them with

the threshold. Figure 8 shows the air quality and sales

IoT Data Analytics in Retail: Framework and Implementation

Figure 6: The average weight of purchases W according to the quintile of sensory measurements.

data according to time. It can be observed that

occasionally the air quality exceeds the acceptable

level, which is specified as a lower boundary of the

fifth quintile of the air quality. The analytical suggests

that this deterioration of the air quality leads to

decreased sales. Upon these circumstances, the IoT

data analytics platform should trigger an action to

improve the air quality by BMS. In this case, the

improvement logics is relatively simple while the

platform allows implementation of logics of arbitrary

complexity.

The platform continuously measures

environmental conditions and compares them with

the threshold. Figure 8 shows the air quality and sales

data according to time. It can be observed that

occasionally the air quality exceeds the acceptable

level, which is specified as a lower boundary of the

fifth quintile of the air quality. The analytical suggests

that this deterioration of the air quality leads to

decreased sales. Upon these circumstances, the IoT

data analytics platform should trigger an action to

improve the air quality by BMS. In this case, the

improvement logics is relatively simple while the

platform allows implementation of logics of arbitrary

complexity.

There are various alternatives to the proposed

platform and comprehensive comparison is beyond

the scope of this paper. The main advantages of the

platform are the use of open technologies, ability to

integrate various data providers, decoupling of

information requirements from data supply and

separation of IoT analytics from the core BMS

system. The decoupling allows to setup the system in

various stores in a large chain, where different types

of sensors might be used. The separation allows

delegation of computationally intensive tasks to the

platform without overloading BMS and using the IoT

analytics with various types of BMS as well as other

systems used in customer relationships management.

The platform is horizontally scalable for application

in large retail chains and can benefit from data

exchange among the stores.

Stream processing (SP)

Evaluation of environmental conditions (EEC)

Persistent

storage

Building management system (BMS)

Other

data

sources

...

Adaptation engine (AE)

...

Archiving

jobs

Evaluation

jobs

Trigg ering

jobs

3,5

46 7

IoT

devices

POS data

Figure 7: Components of IoT data analytics platform.

5 CONCLUSION

The empirical data analysis of relation among the

environmental conditions and customer behaviour as

well as sales performance has been conducted. It has

been shown that the results of the analysis could be

IN4PL 2020 - International Conference on Innovative Intelligent Industrial Production and Logistics

Figure 8: The air quality changes and the number of items (N) according to time.

used to configure the IoT data analysis platform for

enactment of improvements of the environmental

conditions. The statistical analysis shows that the

sales performance is significantly affected by the air

quality and humidity. The temperature appears to

have a non-linear impact on the customer behaviour.

The static analysis of historically accumulated data is

performed in the paper. Dynamic adjustment of the

data analytical models is possible as well as

integration of real-time point-of-sales data for

dynamic pricing and personalized recommendations.

The current study uses only already observed data

and does not consider what kind controls have been

applied to alter the environmental conditions and

implementation of the proposed controls is necessary

to check actual impact on customer behaviour and

sales performance.

ACKNOWLEDGMENTS

This research is funded by the Ministry of Education

and Science, Republic of Latvia, project ARTSS,

project No. VPP-COVID-2020/1-0009.

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