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How to Think as a Business Data Scientist


Our guest today is Ashish Patel, Chief Data Scientist at IBM. He’ll share his story, how to get into the field and best practices to follow as a business data scientist. He’ll also tell us why the agile methodology is not the best to use for ML/AI projects, what are the important things to follow […]

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Author: George Firican

How to Become a Data-Driven Organization


A data-driven organization recognizes the importance of collecting raw data and understands that it shouldn’t make business decisions using raw data alone. Instead, they collect, analyze and derive insights from data to address business problems, identify new growth opportunities, and drive profitability. In essence, data-driven organizations use data for several use cases, such as analyzing […]

The post How to Become a Data-Driven Organization appeared first on LightsOnData.


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Author: George Firican

Ensuring ROI on Predictive Analytics Projects


With the fast-paced growth in data professionals: Data scientists, analysts, engineers,  and the lines between data roles being blurred, measuring and communicating the ROI of data teams is no easy feat. But given the large investment in this area, understanding this value presents an existential question for the data industry. Holistically, to understand the ROI […]

The post Ensuring ROI on Predictive Analytics Projects appeared first on LightsOnData.


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Author: George Firican

What you should know about data democratization and what it can do for your business data operations


In this digital age, the growth and success of businesses depend on the utilization of data. According to a 2020 report by Experian, 98% of organizations say that having high quality data is extremely important in achieving their company objectives. While data is a valuable asset, historically it has been controlled by in-house IT teams. Data […]

The post What you should know about data democratization and what it can do for your business data operations appeared first on LightsOnData.


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Author: Ben Herzberg

Meeting customer expectations on the road

Japan’s third-largest car manufacturer is Nissan and they are currently improving their operation practices as they aim for better sales output and of course brand awareness.

Nissan is considered an example of one a European car brand that is notable and successful. Founded in 1933 by Engineer Kenjiro Den, Nissan initially produced just motorcycle engines before branching out into automobile production in 1934.

For more than 80 years Nissan has built cars in Japan and in developing markets around Europe (and beyond) selling more than 4M units annually under the Nissan brand – including well-known models like the Nissan Leaf and Nissan X-Trail SUVs.

Nissan is also the owner of the premium Infiniti brand and the heritage brand Datsun which it discontinued on April 22. In addition to cars and trucks, it has in-house performance tuning products and cars labelled Nismo

It is a tough job, focusing on the environment, vehicle and product quality and customer loyalty. The approach has to be multi-pronged and is driven by data.

In the late 1990s, Nissan was Europe’s best-selling Asian car brand thanks to the Micra, Almera and Primera, which were the epitome of Japanese cars in that period “not very exciting, but extremely reliable”.

By the early noughties, Nissan tried to bring more frivolous design into their cars with the 2002 Micra K12 and 2002 Primera P12, and as a result, the once faithful customers looking for anonymous transportation stayed away.

Nissan recently referenced a project whose plan is anchored on providing improved consumer service. The new program aimed to make Nissan dealerships a strong competition to outside repair facilities. This is important because once the manufacturer warranty expires, car owners more often than not approach independent repair facilities.

A data Project

Some years earlier, Nissan’s ValĂ©rie Clert and CGI’s Christophe Jeandidier presented some important insights on customer master data to an Informatica World conference audience.

The results that Nissan harvested from taking a good hard look at their data suggested that for marketing organizations at least, they could see improved campaign conversion rates using a “preferred channels” approach. This would in turn lead to 1.5 to 2.5-factor increases in campaign effectiveness. How did they do it? Implementing more rigour around customer master data management.

According to technofunc, the top players in the automotive industry globally, are Toyota, General Motors, Volkswagen, Hyundai, Ford, and Honda. Nissan isn’t in that top 6, yet today in Europe Nissan places as No5 according to WheelsInquirer.

The automotive industry is quite competitive at the brand level.

Although dealerships possess some of the localized market power, the retail market in general is still quite competitive. Demand is relatively elastic because consumers have different dealerships and cars to choose from but more importantly, the brands that they chase have different appeals according to brand recognition and perceived status, understood utility, styling and other factors.

Customer loyalty and rewards

As can be seen, the perception vs. the reality also varied depending on regional market differences. Although Nissan has dealerships all over the world the desire is to make the Nissan experience even more fulfilling for customers, hence the launch of a special loyalty program – Nissan: One to One Rewards â€“ Customers earn points for spending money on vehicles, parts and services. Buying a vehicle provides a $250 discount for the next vehicle purchase Enrolled members in the Nissan loyalty program are also eligible for a physical loyalty card.

Every industry has its own challenges and the automotive industry is no different.

In an interview with HervĂ© Moulin: Alliance Project Leader – Telematics in Finished Vehicle Logistics and Transport Means Specialist in Alliance Logistics Europe – Vehicle Operations, Moulin suggested there would be major challenges in future years.

“Customers are now changing their behaviour – they are more reliant on the internet and prefer car sharing than going somewhere themselves. Customers now prefer staying in large cities, where they must, understandably, share their transport capabilities. Also, local authorities don’t want traditional freight deliveries anymore. Cities are getting increasingly congested and trucks are not welcome in the cities. ”

Nissan Europe had been facing a declining customer retention rate and a customer renewal rate lagging behind that of competitors. Shortfalls were driven by poor customer data management leading to poor customer communications and interaction. Personalized communication is viewed as key to sustaining customer loyalty.

A reinvention of the corporate mission

Nissan’s plan as part of the Renault-Nissan-Mitsubishi Motors groups was to move from being considered just a carmaker, to a mobility service provider serving urban markets. Part of this strategy considered introducing an Electric Car-sharing Service something it has actively partnered with auto brand – Renault on delivering. This is in line with urban residents increasingly looking to multi-modal transportation systems to meet their needs.

Renault Group also supported Karhoo, a taxi, ride-hailing and private vehicle hiring service provider and offers a very broad choice of services with a total of more than 1.8 million vehicles as part of a signed partnership agreement with Karhoo. The company is also supporting Yuso through, RCI Bank and Services, a subsidiary of the Renault-Nissan-Mitsubishi Motors group.

While electric vehicles are quiet, cheap to operate, and offer most of the same conveniences available through conventionally-fueled vehicles; they are currently more expensive, have a reduced range of travel and taking longer to refuel. While these negative characteristics present the cars as being less capable, they are still possess the range and charging capabilities to meet the needs of almost every urban commuter, for 95% of their daily usage patterns. Yet perceptions persist for electric vehicles of being unattainable and undesirable. Providing the public with a positive first impression and direct experience with electric vehicles remains a challenge for any automotive manufacturer who is looking to enter the EV market.

James Hallam, PhD – Design Researcher, Design Strategist

All auto manufacturers would suggest that an improved unified view of the customer is needed to support the personalization of customer interaction but the challenges often include over one hundred data sources with silos of customer data, heterogenous data models and a lack of visibility into the end-to-end customer lifecycle. Every country in which Nissan operates for example also has different needs.

In 2021, Nissan laid out a plan to reach 50% electrification by 2030, spending 2 trillion yen ($13.8 billion) over five years. Based on customer demands for a diverse range of exciting vehicles, launching 23 new electrified models, including 15 new EVs, aiming for a 50% electrification mix by the fiscal year 2030. Nissan’s refurbishing infrastructure aims to support a circular economy in energy management, and Nissan aims to fully commercialize its vehicle-to-everything and home battery systems in the mid-2020s. Additionally investing up to 20 billion yen by 2026 towards charging infrastructure. The greatest success in all of these spheres has to include developing an ever closer relationship with the customer.

The Nissan Europe-CGI-Informatica tie-up at one point, saw 16 countries of Nissan Europe with their variability in data, live with the Nissan Customer Database and experiencing the benefits of improved customer insights, of course, a single view of each customer allowing Nissan to communicate in a personalized way too.

Personalization at work

According to Sinch, Nissan used their CRM data with Adobe Campaign to customize personalized Rich SMS campaigns in France and Spain, building long-lasting customer relationships after purchase that resulted in 4.7 times higher engagement, an 80% conversion rate and all off of a base of 200,000 customers targeted in the first 6 months.

Reinforcing its commitment to electric mobility and creating a carbon-neutral society, Nissan also announced a partnership with leading EV charging suppliers, Allego and E.ON, to update and increase the rapid charger offering provided by Nissan’s extensive European dealership network.

Nissan Europe also announced the launch of the GigCX peer-to-peer support. This is for pre-sale queries surrounding the Nissan LEAF® a leader in mainstream electric vehicles.

The partnership between Nissan Europe and Nissan United, Limitless, Amplify.ai and Facebook Messenger – offers a solution that features a combination of Facebook Messenger, Conversational AI, and seamless hand-off to humans for the peer-to-peer support model, a first for the automotive industry at the time and accelerated with the pandemic-driven digital shift.

Many prospective buyers of electric vehicles are first-time car buyers for whom empathy has become paramount when it comes to answering questions, increasing the importance of personal connection more than ever before.

As you can tell, the data is coming from a lot of places, different systems, different partnerships and in different formats.

The single-customer view

The approach to formulating the best possible golden nominal and single customer view means you need to make some careful choices about the data that you need to retain and curate. In the case of the CGI, Informatica and Nissan projects, this was done by focusing on the accuracy and reliability of the data coming from the different source systems. The analysis allowed source priority scoring so that the team could determine the leading system-of-record for each of the entities in the customer database. Data latency was also a factor with the recent data receiving a higher score.

The combination of both source priority and latency score determines which source data is kept in the Nissan Customer Database.

With so many sources in different formats, converging on a single, unique version of what a customer is, becomes a necessary part of establishing the Nissan Customer Database, ideally with the best possible version of the digital customer.

Advanced clustering and matching techniques identify groups of data that most likely belonged to the same customer, then cleansed the data, and then applied matching algorithms to identify the likelihood of two entities being the same customer.

This all paints a picture of the typical multithreaded nature of a big business pulling on many strings to improve top and bottom-line growth. The most important thing is that we should remember that the customer and customer records are central to the story. Without knowledge and insight, without convergence on a single source of definition for the customer, it is incredibly difficult to know whether all these efforts are being as effective as they possibly can be. It is a journey that never really ends.

Just like the transition from motorcycle engines to urban charging stations and local manufacturer-branded dealerships to maintain the smooth operations of after-sales service and fuel, maintaining customer data has to be considered a continuous activity and one where we believe Pretectum CMDM can help. Contact us today to learn more on how.