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Big Data Analytics Proposal – The Fresh Market

 

Sheshadri Rana

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Georgetown University

 

 

 

 

 

 

 

Table of Contents

Executive Summary
3
Organization Overview
3
Need for Big
Data Analytics
4
Industry
Analysis
5
How Big Data
Analytics capabilities can help The Fresh Market  
6
Steps to Analytics 
8
Roadmap to
Big Data Analytics capabilities
10
Project
Planning and Execution
11
Risks to
Implement Big Data Analytics Project
12
Data
Visualization
12
Conclusion
and Call to Action
13
References
14
 
 

 

 

 

Executive Summary

The Fresh Market is going through
really tough times as the performance has been almost stagnant, there is a high
churn rate of customers and competitors are pushing downward pressures on
prices through cost cutting and using big data analytics.

At present the company is competing
with specialty food retailers such as Whole Foods, Trader Joe’s and other big
food chains such as Wal-Mart, Kroger etc. To compete effectively with players
that are extremely bigger than it is – TFM will need to use big data analytics
to bring in more operational efficiencies and predict better consumer behavior
to reduce the churn rate.

The following functions where big
data can significantly add value to TFM are – customer analytics, marketing
analytics, store operations analytics, merchandising planning and optimization,
and web analytics.

TFM can execute the big data
analytics implementation in three stages – starting from what it has at
present, adding capturing, storing, processing tools at the second stage to
predict customer behavior. At the third stage it can use analytics to prescribe
solutions such as personalized offers, most fuel efficient routes and better
supply chain planning.

Organization Overview

The Fresh Market Inc., founded in
Greensboro, North Carolina in 1982, is a specialty grocery retailer. It focuses
on offering various perishable products such as fresh fruits and vegetables,
seafood, fresh meat, produce, deli, floral, bakery, and other semi prepared and
prepared foods. The company also sells traditional nonperishable categories
such as household groceries, bulk coffee, tea, candy, beer and wine.

The retail chain strives to deliver
high quality fresh products with a clear focus on freshness and organic to its
customers. It targets premium customers who are health conscious and
knowledgeable about the food contents they eat. The company regularly gets very
high ratings on its excellent customer service.

In 2016 The Fresh Market was bought by Apollo Global
Management Inc. for 1.4 billion USD. According to 10-K statement of Apollo
Global Management, The Fresh Market is bought by Apollo Private Equity
Holdings, a part of Apollo Global Management Inc., as opportunistic buyout in
the field of Consumer and Retail segment. The buyout was resulted from a less
than encouraging performance and distressed balance sheet.

            Post
buyout, the firm closed down number of its stores to streamline costs and laid
off number of employees who were working there for a long time. At present the
company has left with only 170 stores in 24 states primarily in Midwest,
Southeast, Mid-Atlantic and Northeast.

            The company
is competing with specialty food retailers such as Whole Foods, Traders Joe and
other big food chains such as Wal-Mart, Kroger etc.

Need for Big Data
Analytics

Over the last 5 years, The Fresh
Market has come under increasing competitive pressure as the industry it
operates in is full of global behemoths. In 2016 the sales turnover of the
company was 1.85 billion USD and it was growing at 5.9% . TFM had 184 stores
and recorded average sales per square foot of 501 USD. Since then the company
was bought by a private equity firm, closed number of stores and laid of
hundreds of store associates and managers.

The latest report suggests the
situation has not improved by much and Moody’s have downgraded it to Caa1 (very
high credit risk) with a negative outlook. According to Moody’s the performance
has not changed much over the last years and performance is below expectations
because the change efforts have not increased footfall in the stores.

Industry Analysis

Competition among Existing Players

Most of the big retailers are
trying to implement big data analytics capabilities because retail industry is
an extremely competitive industry with players operating on laser thin margins.
The rivalry among the existing players are extremely intense and most players
are competing to turnover ratio rather than profit margins. TFM is competing
with players such as Walmart which has a profit margin of 4% but a turnover of
more than 450 billion USD. Other grocery specialty players such as Trader Joe’s
and Whole Foods have higher margins than TFM. The competition has become even
more intense with the acquisition of Whole Foods by Amazon, the world’s biggest
online retailer.

Bargaining Power of Buyers

 As there are numerous options to choose,
buyers have a very strong bargaining power in grocery retailing industry. It is
one of the reasons why customers are constantly bombarded with offers.

Bargaining Power of Suppliers

Big retailers hold extremely strong
control over the supply chain partners and retailers like Wal-Mart has been
using its size to drive down the prices further. This has created additional
problems for retailers like TFM as they can extract the same price as Wal-Mart
because they are smaller in size.

Threat of New Entrants

There is a constant stream of small
retailers joining the retail industry. But it is extremely difficult to build a
big business from scratch. The threat of new players is not significant because
it requires a lot of investment into real estate and inventory to enter the
industry.

Threat of Substitutes

The entry of Amazon has raised
fresh questions for the brick and mortar retailers. The online channel may not
replace the model but it can certainly squeeze the already thin margins.

Two of the players who are able to survive in an intensely
competitive retail industry are – Tesco and Kroger. Both the players
aggressively use big data analytics to take data driven decisions in managing
business operations. TFM can also use big data analytics to make business
decision to compete in the extremely competitive field.

How Big Data
Analytics capabilities can help The Fresh Market 

The Fresh Market can use big data
analytics across all the areas of business operations.

Customer Analytics

Big data analytics capabilities can
help TFM in better understanding and targeting of its present and potential
customers. It can help the company in doing better segmentation and targeting
of customer using behavioral data. Secondly it can help TFM to run efficient
loyalty card and retention programs. It can also help the company to understand
churn rate and life time value of the company. One of the reasons why Moody’s
downgraded the ratings of TFM is that the company failed to reduce the churn
rate of the customers.

TFM can also develop customer
profile based on the data collected using big data analytics. This will help
the company in better demand forecasting and merchandising planning. It can
also reduce the churn rate which at present is big problem for TFM.

Marketing Analytics

TFM can use big data analytics for
developing cross selling and up selling product categories. It can also use big
data analytics for measuring media campaigns, attributing the costs to specific
marketing efforts, designing marketing mix models and product attribution
model.

Merchandizing and Category Analytics

            Big data
analytics capabilities can help TFM to plan and execute merchandising strategy
and category planning better than what it is doing at present. Good
merchandizing planning can help in reducing wastage, lower the transportation
cost and increase sales per square feet. Big data analytics can also help the
organization in better shelf allocation, lower the stock outs and reduce
products discounts.

Store Operations Analytics

            Big data
analytics will help the company in better understand the fixed costs and other
hidden costs. Analytics can be used for better store optimization and work
force optimization.  

Web / Online Analytics

TFM can use web analytics to
understand the online behavior of its customers. It can use these capabilities
to build recommendation engines that provide personalized offers to each
customer. This can increase the engagement level of the potential customers and
bring them back to the store.

Improving Supply Chain Management using Analytics

            TFM can use
big data analytics to streamline its supply chain management and thus lower the
cost of its product through greater savings. Some of the areas where big data analytics
can effectively contribute to streamline supply chain are –

·      
Product sourcing and inward logistics – If the
company can use analytics to identify the quality of the fresh fruits and
vegetable at farmer or distributor level then it can not only reduce the inward
transportation and reverse logistics but also pilferages.

·      
Building efficient transport routes to reduce
the distance covered by trucks thus reducing overall cost of delivering
products.

·      
Providing prescriptive solutions regarding road
conditions and weather information to truck drivers to reduce supply chain
delays and bottlenecks.

Steps to Analytics

There are four steps to data
analytics – capturing the data, storing the data, processing the data and
analyzing the data. Each of these steps require both IT infrastructure and
human resource expertise.

Capture of the data – The Fresh Market can use
various tools to capture data starting from simple POS transaction to shopper
movement tracking and Wi-Fi signal triangulations. At this stages the devices
will capture both structured data and unstructured data.

Storage of Data – TFM has to decide appropriate hardware
tools to store the data. Some tools such as traditional databases are good for
storing structured data while products like Hadoop are better for unstructured
data.

 Data Processing
– Sensors and tracking devices can capture and store vast amount of data that
require processing before the analysis stage. Some of tools to use at this
stage are – MapReduce, Storm and Tika.

Analysis – For business decisions this is the most
critical stage and TFM managers should start backward from the analysis part.
They should first decide what needs to be analyzed and then go ahead capturing
the data accordingly. Some of the most popular tools for computation are R
& Python. For visualization Tableau is a good tool for a retail
organization as it can make dashboards. 

Roadmap to Big Data
Analytics capabilities

Stage 1 – Using Data to Understand Business 3 months

            Rather than
going all in with resources, TFM should start with using present data to
understand the business better. It will help in analyzing the business
decisions that are been taken by managers at respective level.

            The present
Point of Sale and Inventory data will help the company in understanding
operational efficiency and store operations. The main challenge at this point
is that data scientists can’t extract meaningful data to predict future trends
as there are too few data points.

            Before
making further investments into the big data analytics it should either hire
data experts or build capabilities within by training and development.

Stage 2 – Using Big Data Analytics to Predict Trends 9-12
Months

            To reach
this stage the leadership has to decide first which data points it needs to
understand and predict trends. The prediction process can involve – demand
forecasting, customer behavior prediction, per stores sales forecast based on
weather, economic status and demography of the region.

            To
effectively build a predictive system the company needs to invest in both the
hardware and human resources.

Stage 3 – Prescriptive Analytics

            Once the
organization build capabilities to capture, store, process and analyze large
amount of data it can go ahead with the prescriptive analytics. At this stage
the managers can use data to not only predict but also prescribe solution. For
example data providing personal recommendations to the customers, suggesting
merchandize mix to the merchandiser and automatically set the planogram.

Project Planning and
Execution

Project Initiation – Any change effort in a big organization
has to have the buy-in from top leadership. The top leadership has to promote
the big data analytics project in the organization.

Stakeholders’ Buy In – Apart from TFM there are other supply
chain partners that need to collaborate for the success of big data analytics
project. These stakeholders will include – store managers, suppliers,
manufacturers, farmers, and other internal employees.

Creating project plan – Based on the three steps
approach mentioned in the previous section. It will involve starting small and
experimenting to understand the business better and then installing hardware
and employing human resources to come up with advanced solutions such as
predicting consumer behavior and prescribing supplier’s purchase schedule.

Project Monitoring and Control – the results will be
measured based on the target agreed in advance. Data analytics project often
take time because they require large amount of data to build predictive and
prescriptive models. We can make self-correcting Bayesian models that can help
managers.

Data driven decision making is not a step panacea for
business problem but a culture of taking decisions based on data and correcting
decision when the data suggests so. The project doesn’t have completion time as
the approach can be tweaked but the process needs to continue to deliver better
models.

Risks to implement
Big Data Analytics Project

            The risks
can come from various directions such as –

·      
Data Security, Data Privacy and Data governance
– The organization needs to have a strong data security policy for regulation
purpose and a robust data governance mechanism so that only eligible people
have access to relevant data.

·      
Data Architecture and Metrics challenges – In
fast evolving big data environment, data architecture is one of the biggest
challenges of the management. It needs to put in place infrastructure not only
for today but also for tomorrow.

·      
Source data may be misunderstood or contain
errors and analytics processes may introduce new errors or be less exact than
intended.

·      
Is Big Data Analytics aligned to the strategy,
values and culture of the organization. This is one of the biggest challenges
as TFM still prides itself for providing personal attention to shoppers in its
stores.

Data Visualization

The chart below shows various products that are most likely
to be bought together. The following chart is developed using apiori algorithm
using the POS data of 10,000 customer purchase history. This type of charts can
help the company to do better category management and upselling and cross
selling marketing efforts

 

 

 

Conclusion and Call to Action

The following functions where big
data can significantly add value to TFM are – customer analytics, marketing
analytics, store operations analytics, merchandising planning and optimization,
and web analytics.

TFM can execute the big data
analytics implementation in three stages – starting from what it has at
present, adding capturing, storing, processing tools at the second stage to
predict customer behavior. At the third stage it can use analytics to prescribe
solutions such as personalized offers, most fuel efficient routes and better
supply chain planning.

 

 

 

 

 

 

 

 

 

 

References

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