EBOOK

Five Big Data Use Cases for Retail

Datameer EBOOK

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DatameerTOP FIVE RE TAIL USE CASES FOR ENTERPRISE

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Sales are expected to grow by 3.5 percent in 2017 and e-commerce continues to make massive gains with an expected growth of 15 percent this year (Kiplinger, 2017). Tied to that, data volumes within the retail industry are growing and the pace of that growth is accelerating.

Sensor data, log files, social media, transaction data and other sources have emerged, bringing with them a volume, velocity and variety of data that far outstrip traditional data warehousing approaches. Proactive retail organizations harness these new sources in innovative ways to achieve unprecedented value and competitive advantage in an industry space.

Sales are expected to grow by 3.5 percent in 2017 and e-commerce continues to make massive gains with an expected growth of 15 percent this year.

Kiplinger, 2017

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How can retailers use big data as a strategic asset in their organization?

Harnessing Big Data as a Strategic Asset

From a business standpoint, retailers will need to

empower people across their organization to make

decisions swiftly, accurately and with confidence.

The only way to achieve this is to harness big data

to make the best plans and decisions, understand

customers more deeply, uncover hidden trends

that reveal new opportunities and more.

All of these priorities require data engineering

that drives action. These tools can rapidly bring

together and explore massive sets of structured

and unstructured data to uncover hidden patterns,

new correlations, trends, customer insights and

other useful business information.

The business impacts are real. According to a

recent study conducted by IBM’s Institute for

Business Value:

“Sixty-two percent of retailers report that the

use of information (including big data) and

analytics is creating a competitive advantage

for their organizations, compared with 63

percent of cross-industry respondents. We also

discovered that retailers are taking a business-

driven and pragmatic approach to big data.

The most effective big data strategies identify

business requirements first, and then tailor the

infrastructure, data sources and analytics to

support the business opportunity.”

An effective data engineering platform can help

retailers harness this vast amount of data to:

• Optimize the customer experience

• Increase sales across all channels

• Make merchandising a data-driven process

To better understand the value of big data

analytics in the retail industry, consider the

following five use cases, which are currently in

production in various leading retail companies:

1. Customer behavior analytics

2. Personalizing the in-store experience

3. Increasing conversion rates through

predictive analytics and targeted promotions

4. Customer journey analytics

5. Operational analytics and supply chain

analysis

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Because deeper, data-driven customer insights

are critical to tackling challenges like improving

customer conversion rates, personalizing

campaigns to increase revenue, predicting and

avoiding customer churn, and lowering customer

acquisition costs.

But consumers today interact with companies

through multiple interaction points — mobile,

social media, stores, e-commerce sites and

more. This dramatically increases the complexity

and variety of data types you have to aggregate

and analyze — web logs, transaction and mobile

data, advertising, social media and marketing

automation data for instance. Not to mention

product usage and contact center interactions, as

well as CRM and mainframe data.

Even publicly available demographic data can

come into play. When all of this data is aggregated

and analyzed together, it can yield insights you

never had before — for example, who are your

high-value customers, what motivates them to buy

more, how do they behave, and how and when

is it best reach them? Armed with these insights,

you can improve customer acquisition and drive

customer loyalty.

Use Case 1:

Customer Behavior Analytics

As a CMO, digital marketing or customer loyalty executive responsible for optimizing customer acquisition and loyalty campaigns, you need greater visibility into the customer-buying journey. Why?

Data Engineering at Work

Data engineering is the key to unlocking the

insights from your customer behavior data —

structured and unstructured — because you can

combine, integrate and analyze all of your data

at once to generate the insights needed to drive

customer acquisition and loyalty.

Data engineering platforms are designed to help

you understand your customers and their journey

more precisely. By adding more data to your

analysis and using increasingly sophisticated

techniques to analyze it, data engineering gives

you a more diagnostic and predictive examination

of customer attributes and behavior so you can

better align your actions to customer needs. In

turn, this allows your organization to dig deeper

into what makes your customers tick, with three

important benefits:

• Answer new questions — A data engineering

platform helps you integrate and use more

data, whether structured or unstructured, and

makes it easy to apply advanced analytics to

find undiscovered patterns and trends.

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As a result, your team can answer the deeper

behavioral questions that lead to highly

actionable customer insights, such as:

• Which campaign combinations accelerate

close?

• Which features do users struggle with?

• Which product features drive adoption?

• How can we acquire more customers at

less cost?

• How can we proactively address churn

before customers actually leave?

• Do keywords influence deal size?

• Deliver more results — Customer analytics

is a deep discipline, covering many different

departments and areas of the business.

Data engineering dramatically increases the

productivity of your business analysts so they

can deliver results for the many customer

analytics use cases.

• Put your insights to work — Insights aren’t

valuable to the business unless they reach

the business teams that need them on a

regular basis. A data engineering platform

makes it easy to operationalize your analytics,

execute them regularly, deliver results to the

business teams and continuously improve

the processes. Data engineering takes your

customer data to an entirely different level.

The analytical results can reveal totally new

patterns and insights you never knew existed

— and aren’t even conceivable with traditional

analytics.

Deeper, data-driven customer insights are critical to tackling challenges like improving customer conversion rates, personalizing campaigns to increase revenue, predicting and avoiding customer churn, and lowering customer acquisition costs.

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In the past, merchandising was considered an art

form, with no true way to measure the specific

impact of merchandising decisions. And as online

sales grew, a new trend emerged where shoppers

would perform their physical research on products

in-store and then purchase online at a later time.

The advent of people tracking technology allows

omni-channel retailers to better measure the

impact of their merchandising efforts.

Data Engineering at Work

New people-tracking data allows retailers to

analyze the entire in-store customer experience.

With this new data retailers can:

• Track the path of shoppers in their stores to

determine the optimal merchandising and

displays for various products

• Monitor which products and displays the

customer sees to measure the impact on sales

for that customer and overall, both in-store and

online

• Link this information to loyalty programs and

applications to ensure they stay top of mind

with customers so the in-store experience isn’t

wasted

Use Case 2:

Personalizing the In-Store Experience

The advent of people-tracking technology offers new ways to analyze store behavior and measure the impact of merchandising efforts. A data engineering platform can help retailers make sense of their data to optimize merchandising tactics, personalize the in-store experience with loyalty apps and drive timely offers to incent consumers to complete purchases with the end goal being to increase sales across all channels.

Data engineering can turn this new data source

into a major competitive advantage for retailers.

The key is using data-driven insights to optimize

in-store experiences that provide unique value

and pleasure for consumers. Insights based on

data from websites, point-of-sale systems, mobile

apps, supply chain systems, in-store sensors and

cameras, and other sources can be used to:

• Improve in-store experiences

• Optimize online experiences

• Increase customer loyalty

• Drive up cross-selling

• Increase promotion effectiveness

• And more

Using data engineering platforms, omni-channel

retailers can:

• Test and quantify the impact of different

marketing and merchandizing tactics on

customer behavior and sales

• Use a customer’s purchase and browsing

history to identify needs and interests and then

personalize in-store service for customers

• Monitor in-store customer behavior and drive

timely offers to customers to incent in-store

purchases or later, online purchases, thereby

keeping the purchase within the fold of the retailer

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Historically, customer information has been

limited to demographic data collected during

sales transactions. But today, customers interact

more than they transact – and those interactions

occur on social media and through multiple

channels. Because of these trends, it’s in the best

interest of retailers to turn the data customers

generate via interactions into a wealth of deeper

customer information and insight (for example, to

understand their preferences).

Data Engineering at Work

Data engineering is capable of correlating

customer purchase histories and profile

information, as well as behavior on social media

sites. Correlations can often reveal unexpected

insights — for example, let’s say several of a

retailer’s high-value customers “liked” watching

the Food Channel on television and shopped

frequently at Whole Foods.

Use Case 3:

Increasing Conversion Rates through Predictive Analytics and Targeted Promotions

To increase customer acquisition and lower costs, retail companies need to target customer promotions effectively. This requires having a 360-degree view of customers and prospects that’s as accurate as possible.

These insights can help the retailer better

understand the interests of their high-value

customers. The retailer can then use these insights

to target their advertisements by placing ads and

special promotions on cooking-related TV shows,

Facebook pages and in organic grocery stores.

The result? The retailer is likely to encounter much

higher conversion rates and a notable reduction in

customer acquisition costs.

Using data engineering platforms, omni-channel

retailers can:

• Test and quantify the impact of different

promotional tactics on customer behavior and

conversion

• Use a customer’s purchase and browsing

history to identify needs and interests and then

personalize promotions for customers

• Monitor customer purchasing behavior and

social media activity to drive timely offers to

customers to incent online purchases with a

specific retailer

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This informs what they should buy, from where and

at what price. (For millennials, information includes

the opinions of strangers, which they value more

than product information provided by companies.)

Based on the information available to them,

customers make buying decisions and purchases

whenever and wherever it’s convenient for them.

At the same time, customers expect more. For

example, they expect companies to provide

consistent information and seamless experiences

across channels that reflect their history,

preferences and interests.

More than ever, the quality of the customer

experience drives sales and customer retention.

Given these trends, marketers need to

continuously adapt how they understand and

connect with customers. This requires having

data-driven insights that can help you understand

each customer’s journey across channels.

Data Engineering at Work

To precisely understand your customers and their

customer journey, you need a way to integrate

data from every channel — structured and

unstructured — and analyze it all at once for an

integrated customer view and holistic insights.

Use Case 4:

Customer Journey Analysis

Today’s customers are more empowered and connected than ever before. Using channels like mobile, social media and e-commerce, customers can access just about any kind of information in seconds.

Most importantly, big data enables you to perform

iterative data discovery that leads to insights you

never had before and questions you never knew

to ask.

Data engineering can help you achieve this. With

these technologies, you can bring together all

of your structured and unstructured data into

Hadoop and analyze all of it as a single data set,

regardless of data type. The analytical results can

reveal totally new patterns and insights you never

knew existed — and aren’t even conceivable with

traditional analytics. You’ll be able to get answers

to complex questions such as:

• What’s really happening across every step in

the customer journey?

• Who are your high-value customers?

• Which campaigns motivate customers to buy

more?

• How do they behave?

• How and when is it best to reach them?

• Which offers drive customer retention and

referrals?

• What are the top campaigns across all

channels?

• What combinations of campaigns convert

leads to customers most effectively?

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Faster product life cycles and ever-complex

operations cause retailers to use big data

analytics to understand supply chains and product

distribution to reduce costs. Many retailers know

all too well the intense pressure to optimize asset

utilization, budgets, performance and service

quality. It’s essential to gaining a competitive edge

and driving better business performance.

Armed with the right solutions, retailers can

analyze product availability and predict product

failures before they occur, optimize existing

infrastructure to increase up-time, and reduce

operational and capital expenditures. In addition,

they can better meet service level agreements by

proactively identifying and fixing potential issues.

Data Analytics at Work

The key to utilizing data engineering platforms to

increase operational efficiency is to use them to

unlock insights buried in log, sensor and machine

data. These insights include information about

trends, patterns and outliers that can improve

decisions, drive better operations performance

and save millions of dollars.

Use Case 5:

Operational Analytics and Supply Chain Analysis

To increase customer acquisition and lower costs, retail companies need to target customer promotions effectively. This requires having a 360-degree view of customers and prospects that’s as accurate as possible.

Servers, plant machinery, customer-owned

appliances, cell towers, energy grid infrastructure

and even product logs — these are all examples

of assets that generate valuable data. Collecting,

preparing and analyzing this fragmented (and

often unstructured) data is no small task. The

data volumes can double every few months, and

the data itself is complex — often in hundreds of

different semi-structured and unstructured formats.

Data engineering allows you to quickly combine

structured data such as CRM, ERP, mainframe,

geo location and public data and combine them

with unstructured data. And then, using the right

analytical tools, you can use this data to detect

outliers; run time series and root cause analyses;

and parse, transform and visualize data.

For example, you can use customer and device

usage across networks to identify high-value

usage. Or you can integrate and analyze historic

machine data and failure patterns to predict and

improve mean time-to-failure — or ERP purchase

data and supplier data to optimize supply chain

operations. And you can use sensor and machine

data to identify and resolve network bottlenecks.

The possibilities are endless.

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The company focuses on understanding their

customer’s needs as a means to maintain and

increase wallet share. They strive to have a

360-degree view of customer behavior and were

looking to quickly integrate new and disparate

sources of structured and semi-structured data,

including ad hoc correlation of private catalogue

data with public, industry standard product

category codes and other information. Their

existing data warehouse was inadequate in that

it didn’t include some readily available third party

transaction web interaction and public data.

Real World Retail Use Case Example — Customer Analytics

This leading U. S. department store chain offers a wide range of men’s, women’s and children’s apparel, fashion accessories, cosmetics and home furnishings as well as a store-branded credit card.

The project used data engineering to integrate

point-of-sale data from a mainframe, external credit

card transaction data, web logs, and public data

sets to determine how their highest value customer

segment used the store-branded credit card at

other retailers. The company chose Datameer

because the spreadsheet interface provided a low

barrier to entry for business uses to start exploring

and running ad hoc analyses on raw data to test

hypotheses and then operationalize the most

relevant analytics when needed.

Optimize asset utilization, budgets, performance and service quality. It’s essential to gaining a competitive edge and driving better business performance.

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The intuitive platform for fluid data discovery,

which runs native on Hadoop, allows retail

companies to analyze all of their data, regardless

of its size, complexity or structure. Datameer’s

approach drastically reduces time to insight,

empowering business analysts to generate

insights on demand without IT assistance though

a unified, simple analytic process.

With Datameer, carriers get everything they need

to integrate, prepare, analyze and visualize all

of their data quickly and cost effectively. The

software supports:

• Integration — The platform contains more

than 70 out-of-the-box connectors and has

easy wizard-led integration of any data type,

size and source, eliminating the need for ETL

• Preparation and analytics — Datameer provides

a familiar, Excel-like spreadsheet interface

that includes more than 270 pre-built analytic

functions – from joins to complex analytics —

for preparing the data set and discovering the

insights. Advanced functions like automated

clustering, decision trees, recommendation

functions and column dependencies are

available with Smart Analytics™.

• Visualization — Using a drag-and-drop design

interface with over 30 visual widgets – plus a

free-form infographic designer for stunning

custom visualizations – carriers can annotate

results and share them on any browser or device.

• Operationalization — Using a combination

of management, comprehensive governance

and advanced security functions, carriers can

ensure trusted data and analytical results are

readily available to the right people based on

their roles, and people across departments

can cooperate around data with ease. Data

and analytical insights can also be fed into

core business processes to drive enterprise-

wide business results.

Datameer has the full spectrum of data discovery

functionality under one self-service roof:

Integration, preparation and profiling, analytics,

machine learning, visualization and yes, also data

export. We even integrate with other tools already

in a customer’s arsenal.

Unlocking the Power of Big DataDatameer is the only proven data engineering platform that quickly transforms businesses into agile, insights-driven organizations.

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Getting StartedWhere do you see the biggest opportunity to harness big data to improve your retail business? Datameer can vastly accelerate your time to insights about customers, offerings, operations and investments. It empowers a line of business analysts to access, analyze and visualize all of your data using a single, simple analytic process, without IT assistance, so they can discover new insights on their own.

To learn more about how Datameer can help your business, visit https://www.datameer.com/.

To get started with data engineering, you can take

the traditional path of brainstorming the problems

you want to solve and the questions you want to

ask. Or you can look at the data available to you,

and then determine the business problems that

the data — which can be combined with other

third-party data — can help solve. This approach,

recommended by PwC, allows out-of-the-box

opportunities to emerge.

Datameer is uniquely designed to support both

approaches. We understand that building an analysis

is an agile, iterative process, not a linear one. So we

let people discover as they go, and let the data

lead the way. Or, they can start with a question in

mind and deliberately work toward the answer.

Datameer works both ways by providing intuitive,

point-and-click features and optional add-ons

like Smart Analytics — our self-service data

mining functionality that allows business users

to find patterns and relationships in data without

the help of a data scientist. With an instant,

interactive visual preview, people can simply

drag-and-drop different data attributes to explore

patterns, determine relationships even build

recommendations interactively.