What are the benefits of Big Data in the retail industry?

What are the benefits of Big Data in the retail industry?

The recent COVID-19 crisis has forced retail players to reinvent themselves and accelerate their digital transformation. To gain competitive advantage, the retail industry must rely on Big Data. Personalized experiences, optimized pricing, supply-chain connectivity; discover how data has transformed the retail & e-commerce sector.

Figures from the FEVAD (Federation of e-commerce and distance selling) indicate that the e-commerce sector has exceeded 129 billion euros in 2021, up 15.1% since 2020. The increasing success of online vendors has attracted more and more historical retailers to launch their e-commerce adventure. The consequence? The line between e-retail and retail is becoming increasingly tenuous. According to the LSA/HiPay 2021 study, 63% of French shoppers say they have used click & collect at least once, 44% have had a product delivered to their home, and 37% have returned a product purchased online.

In this very competitive context, the use of Big Data, driven by Artificial Intelligence and Machine Learning, has many advantages for this industry.

Benefit #1: A 360° view of customers

Faced with an increasing amount of digital and omnichannel consumers, retail players are able to have a 360° view on their customers through data. Indeed, data plays a huge role in building a relationship between customers and the retailer by providing point-of-sale experts with in-depth knowledge about a customer or a product. With better understanding of the customer, their habits and expectations, as well as the products that were sold, salespeople can have richer and more satisfying jobs. An advantage that can be seen as a response to the talent shortage affecting the industry.

But that’s not all! By using purchase records from a large portfolio of customers, retailers can use predictive analysis to adapt their offers in real time. For example, companies can define specific personas or offer personalized discounts. 

Benefit #2: Optimize pricing

The analysis of supply and demand is a must for any business. In a hyper-competitive context, with tense consumer purchasing power, selling at the right price is an absolute necessity. One of the main guarantees of the use of data to optimize pricing is to preserve the attractiveness of the brand while protecting its margins. 

This is even more critical for multi-site brands, spread over a vast territory. They must not only adapt their pricing based on customer expectations and behavior, but also to the competition in a given area – two major strategic levers for the retail industry.

Benefit #3: Innovate to improve products & services

Under the effect of digitalization, consumer habits are evolving at a very fast pace. Brands must therefore constantly innovate. But innovating can be a risky and expensive process. 

With data, retail players can rely on the knowledge they have on their customers’ preferences and expectations to define the roadmap for innovating their products and services. The challenge? Winning the race to constantly conquer new markets, while keeping the R&D budgets under control. 

Benefit #4: Offer personalized shopping experiences

Since the beginning of the COVID-19 crisis, the explosion of online shopping has attracted a population that used to shop in physical stores. To differentiate themselves, retail players must do everything they can to offer personalized shopping experiences. 

Data is the basis of all personalization, especially for retailers who have embarked on the path of e-commerce. The ambition? To exploit the knowledge of on and off-line customers to harmonize experiences. Optimized and controlled data allows retailers to take advantage of the benefits of digital platforms while reinforcing the quality of in-store contact. 

Benefit #5: Fluidify the supply-chain

One of the reasons why customers will visit a physical store rather than purchase a product online is to make physical contact with the said-product. In fashion for example, trying on a piece of clothing almost always makes the difference. In addition, having the possibility of leaving with one’s purchase without shipping delays is one of the most important factors for purchasing in a physical store. 

Optimized inventory management, fluidity of supplies, control of logistics costs… It is crucial to ensure the excellence of data in order to guarantee the availability of products at the point of sale.

Retail 4.0: How Monoprix migrated to the Cloud

Retail 4.0: How Monoprix migrated to the Cloud

Omni-channel leader with a presence in more than 250 cities in France, Monoprix, french retail chain, offers varied innovative products and services every day with a single objective in mind: “making the good and the beautiful accessible to all”. 

The company’s stores combine food retailing with hardware, clothing, household items and gifts. To give some stats on the firm, Monoprix in 2020 is : 

  • Nearly 590 stores in France,
  • 22,000 employees,
  • Approximately 100 stores internationally,
  • 800,000 customers per day,
  • 466 local partner producers.

With close to one million customers in store and more than 1.5 million users on their website each day, it’s no secret that Monoprix has hundreds of thousands of data to manage! Whether it’s from loyalty cards, customer receipts or online delivery orders, the company has to manage a huge amount of data in a variety of formats. 

At Big Data Paris 2020, Damien Pichot, Director of Operations and Merchandise Flows at Monoprix, shared with us the company’s journey in implementing a data-driven culture thanks to the Cloud.  

big-data-paris-monoprix-1

Big Data at Monoprix

In response to the amount of data that was coming into Monoprix’s data systems every day, the company had implemented various technologies: an on-premise data warehouse for structured data and a data lake in the cloud, which was used to manage the semi-structured data coming from their websites. In addition, a lot of data also comes from partners or service providers, in the context of information exchanges and acquisitions.

Despite the fact that the architecture had been working well and fulfilling its role for many years, it was beginning to show its limitations and weaknesses: 

“To illustrate, every Monday, our teams gather and analyze the profits made and everything that happened the previous week. As time went by, we realized that each week the number of users logging in to our information systems was increasing and we were reaching saturation. In fact, some of our employees would have to get up at 5am to launch their queries, only to retrieve it that day in the late morning or early afternoon,” explains Damien Pichot. 

Another negative aspect of the company’s IT structure was regarding their business users, and more specifically the marketing users. They were beginning to develop analytical environments outside the control of the IT department, thus creating what is known as “shadow IT”.  The Monoprix data teams were obviously dissatisfied because they had no supervision over the business projects. 

“The IT department represented within Monoprix was therefore not at the service of the business and did not meet its expectations”. 

After consulting the IT committee, Monoprix decided to break off their contract with their large on-premise structure. The new solution had to answer four questions:

  • Does the solution allow business users to be autonomous
  • Is the service efficient / resilient?
  • Will the solution lower operating costs?
  • Will users have access to a single platform that will enable them to extract all the data from the data warehouse and the data lake in order to meet business, decision-making, machine learning and data science challenges? 

After careful consideration, Monoprix finally decided to migrate everything to the Cloud! “Even if we had opted for another big on-premise solution, we would have faced the same problems at some point. We might have gained two years, but that’s not viable in the long term.” 

Monoprix’s journey to the Cloud

Monoprix started this new adventure in the Cloud with Snowflake! Only a few months after its implementation, Monoprix quickly saw improvements  compared to their previous architecture. Snowflake was also able to meet their needs in terms of data sharing, which is something they were struggling to do before, as well as robustness and data availability.

The first steps

During the conference, Damien Pichot explained that it was not easy to convince Monoprix teams that a migration to the Cloud was secure. They were reassured with the implementation of Snowflake, which carries out a level of security as high as that of the pharmaceutical and banking industries in the United States. 

To give themselves all the means possible to make this project a success, Monoprix decided to create a dedicated team, made up of numerous people such as project managers, integrators, managers of specific applications, etc. The official launch of the project took place in March 2019. 

Damien Pichot had organized a kickoff, inviting all the company’s business lines: “I didn’t want it to be an IT project but a company project, I am convinced that this project should be driven by the business lines and for the business lines”. 

Damien tells us that the day before the project was launched, he had trouble sleeping! Indeed, Monoprix is the first French company to embark on the total migration of an on-premise data warehouse to the Cloud! 

big-data-paris-monoprix-2

The challenges of the project 

The migration was done in an iterative way, due to a strong technical legacy, because everything needed to be reintegrated in a technology as modern as Snowflake. Indeed, Monoprix had big problems with its connectors: “We thought at the time that the hardest part of the project would be to automate the data processing. But the most complicated part was to replatform our ETLs in a new environment. So we went from a 12-month project to a 15-month project.

The new architecture 

Monoprix therefore handles two types of data: structured and semi-structured data. The structured data comes from their classic data warehouse, which contains data from the Supply Chain, Marketing, customer transactions, etc. And the semi-structured data that comes from website-related events. All of this is now converged via ETLs into a single platform running on Azure with Snowflake. “Thanks to this new architecture in the Cloud we can attack the data we want via different applications,” says Damien.

big-data-paris-monoprix-3

Conclusion: Monoprix is better in the Cloud

Since May 2020, Monoprix has been managing its data in the Cloud, and it’s been “life changing”. On the business side, there is less latency, queries that used to take hours now take minutes, (and employees are finally sleeping in the morning!). Business analyses are also much deeper, with the possibility of making analyses over five years, which was not possible with the old IT structure. But the most important point is the ability to easily share data with the firm’s partners and service providers.

Damien proudly explains.  “With the old structure, our marketing teams took 15 days to prepare the data and had to send thousands of files to our providers, today they connect in a few minutes and they fetch the data alone, without us having to intervene. That alone is a direct ROI. 

How does data visualization bring value to an organization?

How does data visualization bring value to an organization?

Data visualization definition

Data visualization is defined as a graphical representation of data. It is used to help people understand the context and significance of their information by showing patterns, trends and correlations that may be difficult to interpret in plain text form.

These visual representations can be in the form of graphs, pie charts, heat maps, sparklines, and much more.

What are the advantages of data visualization?

In BI, or Business Intelligence, data visualization is already a must have feature. With the emergence of Big Data, data visualization is becoming even more critical to help data citizens make sense of the millions of data being generated everyday. Not only does it help data citizens curate their data into an easy-to-understand visual representation, it also allows for employees to save time and work more efficiently.

In a way, data visualization also allows organizations to democratize data for everyone within an organization. With this, Data Leaders like Chief Data Officers see in this discipline a way to replace intuition decision-making with data analysis. Thus, be able to evangelize a data driven culture within their enterprises.

How can you get more value from modern data visualization platforms?

Most organizations that adopt data visualization tools struggle to visually represent their data in a way that maximizes data value. However, modern data visualization tools are expanding to include new use cases. These tools enable enterprises to find and communicate opportunities on important data analysis. Their strengths are:

Better communication and understanding of data

Data visualization allows employees, even those agnostic to data, to understand, analyse and communicate on data with new more interactive formats. This corporate will to become data-driven leads them to better inform and train their organizations to understand how to use data visualization tools and their relevant formats. These formats can be heat maps, bubble charts, tree maps, waterfall charts, etc.

More interactions on data analysis

Reporting data is becoming more collaborative in organizations and presenting data a daily activity. Thus, data visualization is becoming more “responsive” allowing it to adapt to any device and any place the data is being shared. These tools open up to web and mobile techniques to share data stories and explore data collaboratively. Moreover, it’s large-format screens that create a more general understanding of the data in management meetings, for instance.

Supporting data storytelling

Data storytelling is about communicating findings rather than monitor or analyze their progress. Companies such as Data Telling and Nugit specialize in this. With the use of infographics, data visualization platforms can support data storytelling techniques in communicating the meaning of the data to the management teams. These kinds of representations grab people’s attention and better help them recall the information later.

An automatic data visualization

Data users are increasingly expecting their analytic software to do more for them. Augmented data visualization is very useful, where people are not sure which visual format is best-suited for the dataset they want to explore or analyze. These automatic features are best made for citizen data scientists, whose time will be spent on analyzing data and finding new use-cases rather than visualizing them.

 

Gartner’s top Analytics & BI platforms

According to Gartner, the analytics and business intelligence platform leaders are:

microsoft bi

  • Microsoft: Power BI by Microsoft is a customizable data visualization toolset that gives you a complete view of your business. It allows employees to collaborate and share reports inside and outside their organization and spot trends as they happen. Click for more information.

tableau data visualization

  • Tableau: Tableau helps people transform data into actionable insights. They allow users to explore with limitless visual analytics, build dashboards, perform ad hoc analyses, and more. Find out more about Tableau.

qlik data visualisation

 

  • Qlik: With Qlik, users are able to create smart visualizations, and drag and drop to build rich analytics apps acceleratedby suggestions and automation from AI. Read more about Qlik.

thoughtspot data visualization

  • ThoughtSpot: ThoughtSpot allows user to get granular insights from billions of rows of data.With AI technology, uncover insights from questions you might not have thought to ask. Click for more information on ThoughtSpot

In conclusion: why should enterprises use data visualization?

The main reasons that data visualization is important to enterprises, among others, are:

  • Data is easier to understand and remember
  • Visualizing data trends and relationships is quicker
  • Users are able to discovery data that they couldn’t have seen before
  • Data leaders can make better, data-driven decisions
How Big Data & Machine Learning contributed to Zalando’s success

How Big Data & Machine Learning contributed to Zalando’s success

For the second year in a row, Zeenea participated at Big Data Paris as a sponsor this past 11th and 12th of March to present its’ data catalog.

During the event, we were able to attend to many different conferences presented by professionals in the data field : chief data officers, business analysts, data science managers, etc…

Among those conferences, we had the opportunity to attend the Zalando conference, presented by Kshitij Kumar, VP Data Infrastructure.

 

Zalando: the biggest eCommerce plateform in Europe

With more than 2,000 different brands and 300,000 items available, the German online fashion platform conquered 24 million active users in 17 European countries since its’ creation in 2008 [1].

In 2018, Zalando earned about € 5,4 billion : a 20% increase since the year 2017 [2]!

With these positive results, Zalando has a lot of hope for the future. Their objective is to become the fashion reference :

We want to become an essential element to the lives of our customers. Only a handful of apps make it to being part of a customer’s life such as Netflix for television or Spotify for music. We aim to be this one fashion destination where the customer can fulfil all of their fashion needs. [3]”

explains David Schneider, co-CEO of Zalando.

But how was Zalando able to become so successful in such little time? According to Kshitij Kumar, it is a question of data.

Zalando on the importance of being a data-driven enterprise

Everything is based on data.” states Kshitij Kumar during his conference Big Data Paris this past March. For 20 minutes, he explains that everything must revolve around data : business intelligence and machine learning are built based on the company’s data.

With more than 2,000 technical employees, Zalando claims a Big Data infrastructure in different categories :

 

Data Governance

In response to the GDPR, the VP Data Infrastructure explains the importance of establishing data governance with the help of a data catalog: “It is essential to an organization in order to have safe and secure data.

 

A machine learning platform

It’s by exploring, working, curating and observing your data that a machine learning platform can be efficient.

 

Business intelligence

It’s by putting into place visual KPIs and trusted datasets that BI can be proactive.

 

Zalando’s Machine Learning evolution

Kshitjif reminds us that with Machine Learning, it is possible to collect data in real time.

In the online fashion industry, there are many use-cases: size recommendation, search experience, discounts, delivery time, etc…

Interesting questions were then brought up: How can you know exactly what a customer’s taste is? How to know exactly what he could want?

Kumar answers by telling us that it’s by repeatedly testing your data:

Data needs to be first explored, then trained, deployed and monitored in order for it to be qualified. The most important step is the monitoring process. If it is not successful, then you must start the machine learning process again until it is.

Another benefit in Zalando’s data strategy is their return policy. Customers have 100 days to send their items back. Thanks to these returns, Zalando can gather data and therefore, better target their clients.

 

Zalando’s future

Kshitij Kumar tells us that by 2020, he hopes to have an evolved data structure. “

In 2020, I envision Zalando to have a software or program that allows any user to be able to search, identify and understand data. The first step in being able to centralize your data is by having a data catalog for example. With this, our data community can grow through internal and external (vendors) communication.

 

Sources

[1] “L’allemand Zalando veut habiller l’Europe – JDD.” 18 oct.. 2018, https://www.lejdd.fr/Economie/lallemand-zalando-veuthabiller-leurope-3779498.

[2] “Zalando veut devenir la référence dans le domaine de la mode ….” 1 mars. 2019, http://www.gondola.be/fr/news/non-food/zalando-veut-devenir-la-reference-dans-le-domaine-de-la-mode.

[3] “Zalando Back in Style as It Bids to Be Netflix of Fashion – The New ….” 28 févr.. 2019, https://www.nytimes.com/reuters/2019/02/28/business/28reuters-zalando-results.html.