Leveraging Data Analytics in Retail · Leveraging Data Analytics in Retail ay 22 Current Scenario 3...

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Leveraging Data Analytics in Retail | May 2020

Leveraging Data Analyticsin Retail

May 2020

Leveraging Data Analytics in Retail

wwwsganalyticscom2


Content

Introduction

Current scenario


Retailerrsquos budgets toward innovation and technology

Case examples of major retailersrsquo successeswith big data

Wading through the COVID-19 pandemic

Conclusion

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9

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Retail has undergone digital transformation involving almost every business function Being multichannel has become a norm as opposed to a differentiating factor However now the competitive edge lies in data science and an insights-led approach to stay relevant Amazonrsquos ldquosubscribe and saverdquo strategy not only ensures higher loyalty but also helps in building customer profiles and segments Retailers today have access to many more sources of data than five years ago Easy access to transaction data customer profiles and social media metrics presents a plethora of opportunities for them to up their game against industry giants Data analytics is no longer at the disposal of only large-sized companies even independent or small-scale retailers are willing to invest in solutions that can optimize prices predict change in demand and increase returns on marketing dollars

Introduction

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Leveraging Data Analytics in RetailSGArsquos recent study of over 60 retailers across retail segments in the US revealed that 67 retailers believe that they are ahead of competition in using data analytics This indicates both an active usage of data solutions to improve business and a high interest in looking for the next best thing in retail data analytics

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6732

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Better than our competitors At par with our competitors Lagging behind our competitors

Data Analyticsin Retail

Fig 1

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The distinction between traditional brick and mortar and online retail is gradually become null and void direct-to-consumer giants are moving toward increasing their physical presence (eg Amazon Go Kylie Cosmetics etc) and vice versa Analyzing customerrsquos shopping behavior with the help of POS data ecommerce data social media activity and loyalty data is helping in the transition for most retailers from a single channel to multichannel But how much data is too much Many companies feel ldquodata fatiguerdquo with over a third having reported facing a lack of skilled resources for analyzing data in a useful manner (SGA study 2020)

Too much unstructured data

Limited resource who can interpret the output of analytics

Difficulty in localizing data insights (for assorments)

Inability to analyze large amount of data

Lack of systemsprocesses to ensure data quality

Lack of secured customer data infrastructure

Data silos

38

35

30

30

27

23

20

Fig 2

Fig 3

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Data Silos Difficulty in measuring ROIfrom investments in technology

solutionsInsufficient IT budgets

Other data-related challenges mentioned include

20 17

Top 5 data related challenges of retailers

Other challenges

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In retail data analytics is becoming an indispensable part of customer engagement initiatives pricing and supply chain Mapping customerrsquos journey through different channels is key to maximizing return on marketing spends A customerrsquos expectation today is a seamless shopping experience whether they choose to try products in-store and make the purchase online afterwards or buy online and pick up from the store later In order to enhance the in-store experience targeted promotions based on a customerrsquos profile can greatly enhance chances of a conversion

Around halfof the retailers are currently using data analytics solutions to derive customer insights

In order to fuel an effective pricing strategy advanced analytics in setting and changing prices based on seasonality promotional impact and competitorrsquos prices is needed either at the category or zonal level Price is still a driving factor for most purchases and retailers need pricing analytics not just for promptly responding to market changes but also to manage inventory better

43 of retailers are currently using data analytics in

pricing while 38 are likely to start using it in the next 12ndash24 months

With the growing complexity of the retail supply chain owing to the advent of numerous channels of purchase and sourcing of products the need to maintain efficiency at every point is becoming crucial for retailers Preventing out-of-stocks is important to minimize missed sales opportunities Data analytics helps to bring the different components of the supply chain together and ensure better customer experience Real-time tracking of delivery vehicles warehouse management and scaling product distribution requires the implementation of supply chain analytics solutions Particularly for small-to medium-scale e-retailers integrating customer data into the supply chain platform can add that extra level of personalization that can lead to greater customer loyalty

80 of retailers are either currently using or are

planning to use data analytics in the supply chain

Predictive analytics is the way forward for most retailers to prevent losses from excess inventory in low demand and focus on product categories with high demand This has become an important tool in most important aspects of the retail business such as pricing targeted promotions customer behavior analysis supply chain etc Predictive analytics has also helped in engaging with customers in a more personalized way Combining customer insights and transactional data retailers can move toward achieving greater customer loyalty and satisfaction

Over 50of retailers are already using predictive analytics in one or more business functions

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Current Scenario

3

The SGA retailer study reveals that in the current scenario over a third of retailers have centralized data analytics teams in-house One in four retailers has a data analytics team embedded within each individual business function There is a greater preference for in-house teams as opposed to hiring contractors or external experts for the function of data analytics Retailers realize that unification of data from different systems is the key to achieving data-driven insights and a holistic view of the business Moreover there is more room to tailor the solution requirements according to the needs of the user with an in-house data analytics team

Not surprisingly the study reveals that over half the retailers still use excel spreadsheets for their daily MIS reporting activities Independent retailers in particular still have a long way to go in grassroots adoption of tools for daily or weekly reporting activities However a considerate proportion of big-box retailers have adopted high-end cloud-computing tools such as Microsoft Azure and Amazon Web Services While full-scale adoption of AI in day-to-day operations is a long road ahead retailers are currently working on strategies to incorporate some elements into aspects of customer service such as chatbots or in-store shopping assistants (LoweBot)

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IT budgets for companies across industries in North America and Europe are expected to increase in 2020

Some industry studies reveal that AI-driven initiatives are expected to grow three times by the year 2021 Retailers are increasingly realizing that there is an urgent need to upgrade or replace legacy systems that are not compatible in a fast-paced business environment driven by evolving customer preferences

The SGA retailer study reveals that over 50 of the retailers have an average IT budget of 13 for the coming year which is 5 above the industry benchmark of 8 However the study also reveals that over 40 of IT budget is allocated toward maintaining existing systems leaving a small proportion for innovation and RampD


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Technology Budget Data Analytics

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Case examples of major retailersrsquo successes with big data

5

Macyrsquos realized in 2010 that they needed more than excel spreadsheets to gather customer insights and implement marketing campaigns that would yield better returns Using transactional data customer data ecommerce data and social media Macyrsquos was able to build a robust in-house solution that would allow them to dynamically change prices of all their SKUs This real-time product pricing strategy enabled higher conversion rates and provided valuable customer behavior insights to the retailer Additionally Macyrsquos also implemented SAPrsquos infinite Insight Solution in 2014 to raise their online sales With predictive algorithms in place Macyrsquos was able to record an increase in sales of about 8ndash12 within just a few weeks

Shopper marketers are constantly looking for the next best thing to get more personal with their campaigns to get better results and predictive analytics solutions offer exactly that Target has used customer data analytics to identify certain specific consumer demographics such as parents-to-be or new parents to send personalized coupons Target Circle is a new brand loyalty initiative that not only offers rewards on every purchase but also provides the option of donating rewards for a cause that the customer believes in Building customer loyalty through data analytics is currently being implemented by over 40 of the retailers (SGA study 2020)

Despite being criticized for poor customer service in recent times Walmart has left no stone unturned in using predictive analytics and big data to increase efficiency in their stores The retailer established a ldquoData Cafeacuterdquo that built algorithms to solve issues of out-of-stock long checkout queues and manage store associates by analyzing several billion rows of data Several customer polls reveal that Walmart continues to rank higher above Amazon and Target across consumer income groups in terms of retailer loyalty

While small-and medium-sized retailers are developing strategies to adopt a more data-driven approach to marketing supply chain and merchandising bigger players have already tried and tested several technology initiatives and seen significant results

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wwwsganalyticscom Leveraging Data Analytics in Retail | May 2020

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In the first quarter of 2020 an unprecedented challenge has presented itself to almost every industry In the US retailers have witnessed short term windfall gains led by stockpiling and panic buying of daily essentials by customers However most of them are well aware of the massive operational challenges that lie ahead due to shortage of staff lesser store hours supply chain pressures manufacturing lags etc The global health crisis has revealed the significance of predictive modeling to forecast demand and of the need to have a dynamic supply chain solution in place Till the end of the pandemic there is no set demand-supply model that can be put in place considering the high levels of uncertainty on every aspect of the business This calls for flexibility in solutions for retailers or suppliers merchandising for instance needs to undergo a complete transformation at least in this period of time Some initial consumer research also predicts an overall change in the way people shop in the US with a large proportion of shoppers making the shift to online grocery in order to avoid crowded stores Even the casual mall visit by the average urban American shopper is likely to reduce in frequency post the pandemic

With these many unknowns research insights and analytics can come to the rescue of all major stakeholders in the retail industry For solution providers there is a great scope to incorporate software design flexibility to ensure smooth functioning for retailers At the same time many suppliers will need to gauge consumer sentiment and deploy revamped promotional campaigns for the post COVID-19 market Even though measuring the total impact of the pandemic will only be possible after the first complete cycle retailers can brace themselves for the fast-changing circumstances with the help of the right business intelligence efforts Optimizing assortment increased mobile ordering through supplier supported applications maintaining customer relationships and confidence are some of the immediate steps that retailers have had to take since the crisis began A holistic view of the market taking into account forecasts from government authorities customer sentiments updates from other regions travel advisories can help mitigate potential losses that are likely to arise in the weeks to come due to the coronavirus impact

With sourcing difficulties from China many of the major retailers like Target and Walmart are facing supply shortages In order to avoid increasing customer traffic for essentials in-store retailers have to quickly adapt to changes and implement curbside pickup or buy-online-pickup-in-store on a larger scale Focusing on reaching the customers through their current most preferred channel is crucial hence retailers are directing their efforts to gain shopper insights and feedback Since these are unique circumstances there are no clear answers and the best approach for every retailer is to stay responsive under volatile circumstances


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ConclusionIn the year ahead the focus for retailers is likely to be building scalable and flexible data science models that can be used to not only to derive actionable insights but also provide solutions to problems in real-time Retailers and CPG manufacturers both realize the importance of sharing data across stakeholders in the value chain in addition to sharing with internal teams Privacy and data regulation will be a major focus area for most organizations that collect sensitive customer data Retailers realize that building trust is important to ensure that customers continue to be willing to share more personal information with them Artificial intelligence efforts are likely to move to the implementation phase wherein employees will need to be trained and systems will need to be upgraded to accommodate the adoption The COVID-19 pandemic is likely to change a lot of rules of the game however the importance of technology in retail and the use of data to build better customer strategies is most likely going to become more important than ever before Overall data science is likely to become a core function of an organization that can fuel growth in revenue and profitability and by becoming the key differentiator in an otherwise brutally competitive industry


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Itrsquos Raining Billionaires In China

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About the Authors

About SG Analytics For over a decade SG Analytics has been one of the leading global research amp analytics firms

New York | Seattle | London | Zuumlrich | Pune | Hyderabad

RESEARCH | ANALYTICS | TECHNOLOGY

For further information please visit our website sganalyticscom

Join the conversation

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Watch and enjoy our corporate videos

Click on the above title to watch the video

THE CEO SPEAK Success Mantra of a Research amp Analytics Firm

We are dedicated to deliver tailored solutions for our global clients Our team of vastly experienced analysts is well versed in providing valuable insight to our clients At SG Analytics we have worked on hundreds of deals for international clients over the years that enable us to understand the client needs

with offices in USA UK Switzerland and India servicing scores of customers across the globe We are the partner of choice for Fortune 500 companies across several sectors We have been recognized as the ldquoBest Employerrdquo by the World HRD Congress in 2018

This document makes descriptive reference to trademarks that may be owned by others The use of such trademarks herein is not an assertion of ownership of such trademarks by SG Analytics (SGA) and is not intended to represent or imply the existence of an association between SGA and the lawful owners of such trademarks Information regarding third-party products services and organizations was obtained from publicly available sources and SGA cannot confirm the accuracy or reliability of such sources or information Its inclusion does not imply an endorsement by or of any third party

Copyright copy 2020 SG Analytics Pvt Ltd

Nayanjyoti Das

Rubica Bhowmick

AVP - Research amp Insights

Senior Analyst - Retail amp Technology

A market intelligence professional with 15+ years of experience in Research amp Insights He has significant exposure in conducting both quantitative and qualitative programs for several global firms across retail CPG and technology sector He has seamlessly executed several market amp competitive intelligence benchmarking consumer insights assignments developed business strategies (GTM expansion and digital) and launched ROI tools amp Innovative indexes

An experienced research solutions professional with over 5 years of experience in retail CPG and technology industries particularly in the US market Her work has ranged from B2B technology vendor benchmarking go-to-market strategy retailer insights to consumer insights research for leading companies Rubica holds a Masterrsquos degree in Economics

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Content

Introduction

Current scenario



Case examples of major retailersrsquo successeswith big data


Conclusion

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4

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11

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Introduction

1

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Fig 1

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Data silos

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27

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20

Fig 2

Fig 3

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20 17


Other challenges

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Current Scenario

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About the Authors






Sharing Insights









Nayanjyoti Das

Rubica Bhowmick





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Introduction

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Fig 1

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Data silos

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Fig 2

Fig 3

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20 17


Other challenges

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Current Scenario

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About the Authors






Sharing Insights









Nayanjyoti Das

Rubica Bhowmick





wwwsganalyticscom4



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Fig 1

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Data silos

38

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Fig 2

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20 17


Other challenges

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Current Scenario

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About the Authors






Sharing Insights









Nayanjyoti Das

Rubica Bhowmick





wwwsganalyticscom5









Data silos

38

35

30

30

27

23

20

Fig 2

Fig 3

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20 17


Other challenges

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Current Scenario

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About the Authors






Sharing Insights









Nayanjyoti Das

Rubica Bhowmick





wwwsganalyticscom6












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Current Scenario

3



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About the Authors






Sharing Insights









Nayanjyoti Das

Rubica Bhowmick





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Current Scenario

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About the Authors






Sharing Insights









Nayanjyoti Das

Rubica Bhowmick





wwwsganalyticscom8






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wwwsganalyticscom12

About the Authors






Sharing Insights









Nayanjyoti Das

Rubica Bhowmick





wwwsganalyticscom9



5





1

3

2

4


wwwsganalyticscom10



6





wwwsganalyticscom11


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wwwsganalyticscom12



wwwsganalyticscom12

About the Authors






Sharing Insights









Nayanjyoti Das

Rubica Bhowmick





wwwsganalyticscom10



6





wwwsganalyticscom11


7



wwwsganalyticscom12



wwwsganalyticscom12

About the Authors






Sharing Insights









Nayanjyoti Das

Rubica Bhowmick





wwwsganalyticscom11


7



wwwsganalyticscom12



wwwsganalyticscom12

About the Authors






Sharing Insights









Nayanjyoti Das

Rubica Bhowmick





wwwsganalyticscom12



wwwsganalyticscom12

About the Authors






Sharing Insights









Nayanjyoti Das

Rubica Bhowmick





Leveraging Data Analytics in Retail · Leveraging Data Analytics in Retail ay 22 Current Scenario 3...

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