The future of transport is digital: transport services production

by Philippe Clapin and Didier Le Guirriec, Sopra Steria France

In the second of our series looking at the challenges and benefits of the digitisation of transport we are going to delve into the area of digitised transport services production and all that entails.

The challenges and the needs

Digitisation is cutting across all layers of society. We have an expectation that virtually every action we take now has a digital approach, and transport and logistics have not escaped this. Digitising transport services, if done well, can improve the efficiency, create better experiences for customers and ultimately increase profitability of an integrated transport infrastructure.

The transportation industry, like many others, is under pressure to improve cost efficiency.  A report by transport and logistics analysts, Oliver Wyman, found that in a ten-year study, the companies involved showed increased revenue, yet reduced profits. Oliver Wyman suggests that to improve the situation, logistics and transport companies should focus on “standardizing and streamlining structures and processes, developing industry oriented and innovative solutions, thinking and acting in terms of networks.”

And digitisation is also being driven by consumer needs. Consumers are pushing the boundaries using ‘collaborative consumption’ to envisage new models of transport, including app-initiated car sharing and personal car rental.

An example of the power of digitisation: traffic management

In a report by Deloitte Research, Digital-Age Transportation: The Future of Urban Mobility’, they speak of American commuters spending 34 hours per year delayed in traffic. Europe can be even worse, with Paris having the worst traffic jams in Europe – unfortunate drivers are losing up to 70 hours a year stuck in traffic. A German Automobile Club study found that the impact of traffic jams on a country’s economy, the related fuel consumed and lost time could be up to 200 billion Euros.

This situation is not good for anyone – for  drivers, the road system or the councils. The issue arises when transport planners try to rectify these issues by adding new infrastructure – without intelligent application, this can prove slow and costly.

One of the emerging ways of managing traffic is through the use of drones. The U.S. Government is currently piloting a drone-based traffic monitoring system. In Europe there has been a number of research projects looking into the use of drones for traffic management. Some examples being the Czech Republic, Spain and France.

Drones offer real time data of traffic issues and allow planners to build patterns of traffic use and spot areas and times prone to traffic problems. They give a more accurate way of measuring and predicting traffic patterns. Big data obtained in real-time, from real events, can help to build a smarter approach to traffic planning and can inform smart infrastructure improvements. This can mean changes such as encouraging flexible working and creating ‘park and ride’ areas for busy town locations. The Netherlands has used this type of approach to manage their increasing traffic and cut traffic jams by 20%.

The importance of trains

The use of trains as a way of managing traffic should not be overlooked. Digitisation does not stop at roads. The automation of train management is crucial to the optimisation of the use of trains, which ultimately impacts on the optimisation of other modes of transport. Examples of how to improve train traffic have been identified by planning and prediction initiatives such as ‘Project Darwin’, which looks at how to link real-time train running information, to web sites and social media platforms. This information can then be used to predict journey times and allow passengers to plan journeys.

An example in action: La Poste Courrier

La Poste delivered around 15 billion parcels and letters in 2012 and is France’s foremost postal service. To say they have complex logistics is an understatement. To improve productivity and increase profits, La Poste Courrier has digitised their processes across 50 applications. By digitising their services and logistics, La Poste Courrier has been able to expand its product offering and improve their overall responsiveness by simplifying operations. One of the key areas in which a business like la Poste has to engage is customer engagement and commitment. Being able to optimize logistics and transport has ensured that delivery schedules are maintained and customers see the best service – giving La Poste the competitive edge in an increasingly competitive market space.

One of the challenges of digitising La Poste and other similar transport and logistic organisations is supporting existing infrastructures. Drawing on the use of modern Internet programming languages like PHP and .net as well as supporting enterprise architecture languages like Java, are essential to the success of digitisation of transport. In addition, understanding the needs of the various integrated departments within any given industry can only help to optimise the digitisation processes.

The Future

In a Franhofer Institute study into the future of road and train transportation and logistics, they determined that three main changes needed to be put in place to effect positive and efficient improvements:

  • Digitisation
  • Flexible management
  • Use of technology

They state that “…transportation sector, too, increasing interconnectedness and digitisation offers new opportunities and solutions to tackle growing traffic flows.“

I believe we can safely say the future of transport and logistics is digital.

Discover more about our experience delivering intelligent transport solutions.

Products with personality – the Liquid Big Data customer experience

Digital technology is driving new forms of customer engagement that are rapidly eliminating the functional silos between online and offline retail channels. As a result many high street retailers are already experiencing falling footfall in their physical stores as customers increasingly switch to online competitors for better convenience, choice and prices.

However, these bricks and mortar (B&M) businesses could use Liquid Big Data (cloud-based analytics shared between partners, suppliers and, potentially, competitors for their mutual benefit) to integrate the physical and digital customer experience into a unique, responsive personal customer journey online competitors can’t imitate.

So what might the Liquid Big Data customer experience be like for a global retailer selling ready-to-assemble home furniture, appliances and accessories for example?  Here are some ideas…

An eidetic world

Traditionally high street retailers focus on their brand as a source of differentiation to attract customers to their physical stores. Yet conversely, digital empowers customers to focus on their specific wants or needs regardless of provide. That’s why their online competitors invest so heavily in user experience design to continually optimise how customers use their channels to browse and buy the products they sell – choice and accessibility as a form of differentiation. To combat this challenge, B&M businesses are increasingly using digital technology (such as touch screens, beacons and virtual reality) to differentiate the in-store experience as something equally empowering or seamless as being online.

However, by choosing to replicate the online experience, in-store risks ignoring a source of competitive advantage unique to B&M: a customer’s physical experience with a product and the wider environment.

Using Liquid Big Data, the retail customer experience does not have a beginning or end nor is it location specific – it’s contextual.  Powered by a smartphone app provided by collaborating retailers and suppliers, wearable technology (such as a watch or glasses) could capture the people, places and objects an individual customer likes, loathes or loves throughout their entire lives. Even if such encounters are fleeting, these moments are captured with photographic, eidetic clarity in the individual’s private cloud. The customer can then choose which of these experiences to share with the retailer via the app to create a unique, personalised shopping experience in-store every time they visit.

This could be the raised heartbeat of seeing Rome architecture for the first time – could our example global retailer offer this customer discounts on its in-store Baroque furniture offerings? Another customer loves the feel of velvet – could an in-store sales team member suggest some appropriate soft furnishings? One customer really liked his girlfriend’s coffee table she had at university three years ago – could today’s store visit be an opportunity to find something similar for their new home?

Liquid Big Data enables a high street retailer to use the eidetic physical world as a way to effectively personalise its in-store customer experience using digital technology – enhancing its existing brand as a form of differentiation that can’t be imitated by its online competitors.

Products with personality

Harnessing the power of the eidetic world may not be sufficient long term to differentiate the in-store customer experience versus online. Although it offers a targeted customer experience it doesn’t necessarily make a customer’s relationship any closer or more intimate with the specific products a B&M business sells – a key driver at the heart of the competing online experience.

Yet the customer experience of an online retailer is ultimately a passive, limited engagement typically contingent upon the specific browsing or buying history the customer has with their channel or brand or other self-selecting activity such as social media engagement.

In response, a high street retailer with its partners, suppliers or competitors could use Liquid Big Data to take personalisation to a deeper level – use cloud based artificial intelligence (AI) to create direct relationships between individual customers and the products they sell.

The idea is to personify a product using AI with a user experience similar to smartphone personal assistants or virtual customer service agents. A customer can have a text or voice conversation with the product to explore its suitability for purchase (including reviews or endorsements) and select any desired tailoring or customisation. A customer may also enable the product AI to access his or her eidetic memories or social media profile to help shape and personalise their relationship. The AI can either be used on request or continually available to provide product updates or after sales support. In addition, products may also talk to each other in the cloud as a form of machine learning to identify potential new product designs or opportunities for complements that better meet their individual customers’ needs.

Such insights are then gathered by the retailer and participating stakeholders to inform the customer experience in-store (and beyond), support product development and address any supply chain issues.

For example, our global retailer has found that people across the world keep asking the same question about the performance of a specific brand fridge freezer it sells. Could there be a quality issue with this particular product that needs investigating? Customers in a specific region like the way the product is sold in-store by staff based on their after sales conversation with the AI – how can this be replicated in other regions where demand is falling? The collective cloud AI has also designed a new cooling, energy-efficient feature for the next model – a potential hot seller that could be delivered in collaboration with the supplier?

The potential headline benefits of a high street retailer using the Liquid Big Data customer experience include:

  • Enables new forms of personalisation and innovation deeper than anything previously available in the market by integrating real life and digital customer experience
  • Challenges the seemingly unbreakable competitive advantage of  online retailer competitors and other digital disruptors (such as platforms and social media channels)
  • Links in-store digital technology directly, explicitly with specific customer needs daily – materially lowers the risk of this investment and increase its ROI

If you would like more information about how cloud-enabled big data and analytics can benefit your organisation please contact the Sopra Steria Digital Practice.

Power your competitive advantage using digital thinking

Digital Thinking – the ability to disrupt markets using cloud-enabled big data and analytics capabilities – can positively transform how an organisation differentiates itself from competitors and optimise costs.

So how can an organisation maximise the benefits of Digital Thinking? Here are some ideas…

The Mirror Technique

A digital disruptor turns a sector on its head by making it a lot harder for incumbent players to strategically identify, assess and respond to the threats they pose. For example, Uber, Airbnb and TaskRabbit don’t buy or supply the services in the sectors they compete in – they are not direct competitors in a traditional sense. Rather they are convenient intermediaries that offer both customers and suppliers faster, smarter ways to transact with each other using their own monetised cloud services.

Arguably for many organisations (large and small) Digital Thinking like these outlier disruptors can be challenging, particularly where barriers of entry to their sector are considered to be high. To help validate any threats or opportunities from such market innovation, an organisation can apply “The Mirror Technique” – no matter how impossible or unfeasible it looks, what would be the exact reverse of its current approach to market or operating model? What would be the impact on the organisation and its customers if a direct competitor or new entrant implemented this approach first? And critically, how fast could available cloud capabilities realise these potential disruptive forms of competitive advantage?

This application of scenario analysis can help an organisation identify and assess the risks and opportunities it faces from previously unforeseen cloud-powered Digital Thinking – could it be standing on a burning platform or an untapped goldmine?

Big Data Learning

Gathering large volumes of unique data about customers and operating model performance is emerging as a key source of competitive advantage for many organisations. For example, UK high street retailers are capturing data about customer buying behaviours across their physical/in-store and online channels to better personalise their offerings against the experience delivered by Digital Disruptors like Amazon.

A key challenge is learning how to yield these disjointed, complex data sets into something coherent that delivers effective moments of delight for an individual customer using an efficient value chain. Furthermore, by an organisation treating its big data capability as a barrier of entry for competitors, there is a risk of delaying the time to market of any resulting initiatives because it’s having to learn by itself, in isolation, how best to use this form of Digital Thinking.

One way to potentially accelerate and de-risk this learning process is to for an organisation to collaborate with its partners or suppliers in the gathering and application of big data using a cloud platform. In addition, it may even want to consider coopetition with its competitors where participants share their experiences and capabilities together for mutual benefit – “Big Data Learning” enables greater competitive advantage for all the more its shared.

The face to face test

Digital disruptors exploit analytics to inform the design of new forms of personalised engagement including customer centricity, social media and media content. Combined with their ability to offer competing services with lower (or even zero) switching costs for customers; such Digital Thinking risks rendering an organisation’s established market offerings obsolete.

Another outlier disruptor vividly illustrates the competitive advantage of such a move – FaceBook’s $16 billion acquisition of cross-platform mobile messaging service WhatsApp. Not only has WhatsApp decimated Telcos’ revenues from text messaging services, these incumbent players will have no direct access to this huge Social Media channel (estimated 900 million global user base) and the customer insights it generates – Facebook is using Digital Thinking to create an unbeatable barrier of entry as well using this unique analytical capability as a platform for future growth.

However, unlike many traditional organisations, disruptors arguably have no experience of physically serving customers directly to inform these capabilities. This weakness can be exploited by using “The Face To Face Test”, where an organisation applies its own tacit, historic experience of customer engagement to develop a new disruptive market approach using analytics. This test asks questions about how one of their own real life customers would react if a digital service was delivered to them physically by an employee. Example questions include: what information should your employee intuitively, instinctively know about this individual customer before they start serving them? What things shouldn’t they know or ask about? How can your employee genuinely surprise and delight this long standing customer every time?

By applying analytics as a representation of a physical employee drawing from real world experience, an organisation can arguably personalise their customer engagement approach in ways beyond the reach of digital disruptors.

If you would like more information about how cloud-enabled big data and analytics can benefit your organisation please contact the Sopra Steria Digital Practice.

Liquid Big Data: the next digital disruption?

Liquid Big Data is when competitors use Cloud technology and ways of working to openly share and analyse large volumes of data together for their mutual benefit. Yet an organisation engaging in this form of co-opetition risks losing competitive advantage over its peers and increases the threat of new entrants stealing market share. But could the strategic value of such a move outweigh these risks? Here are some ideas…

The customer comes first

Using Liquid Big Data to join up the customer experience across not just an individual organisation’s sales channels but complementary and even competitor offerings would demonstrate its commitment to personalisation. Customers themselves are already using digital services (such as price comparison websites) to disrupt the silo experience of individual brands to personalise their customer experience – how can organisations gain shared competitive advantage by working together to supercharge this form of empowerment? This approach could help address falling demand in physical stores being experienced by the UK Retail sector for example.

Agile supply chain performance management

Liquid Big Data can drive greater collaboration between organisations and their large (and small) suppliers to help reduce the risk of producing unwanted stock or inventory and deliver better resolutions to other supply chain issues. For example, by sharing real-time sales and operating performance data enables them potentially to work closer together to deliver more accurate, timely forecasting of demand that improves their management of Lean or Agile-like approaches such as just-in-time manufacturing. In addition, it creates opportunities for both partners to adopt new ways of working to further strengthen the agility of their own supply chains.

Data as currency

Given the rise of digital currencies for business to customer transactions using the Cryptocurrency approach, could there be an opportunity to extend this model to enable the trading of Liquid Big Data between organisations instead of cash payments? These business to business transactions could be occurring at different speeds – for example, the instantaneous sharing of insights between organisations wanting to sell tailored complementary services to the same customer or one-off trading of large volume, complex service performance data between suppliers to help them build collaborative services for the same client.

Increased resilience against cybercrime

Every day, many organisations face the risk of hackers trying to disrupt their digital services or steal their large volumes of customer personal data. To mitigate such risks, organisations could collaborate to build and jointly manage secure Cloud services to protect these critical Big Data assets together. Although this approach does not mean these competitors are sharing data with each other, potentially it could enable the creation of a secure Liquid Big Data platform that could be sold as a service to other organisations for their mutual benefit.

Component full life view

Some organisations are trialling the use of IoT sensors in their goods or products to track their performance through the supply chain and customer experience. This approach could also be used to gather data on “long life components” used in consumer electronics, cars or aircraft. Such Liquid Big Data could then be shared with competitors to help validate sector-wide benchmarks for component longevity or be combined with other information (such as environmental factors) to identify other issues that affect their performance.

If you would like more information about how Digital Transformation can benefit your organisation please contact the Sopra Steria Digital Practice.

“Liquid Big Data”: a new source of competitive advantage for high street retailers?

Many high street retailers in the UK are experiencing falling footfall in their physical stores as customers increasingly switch to online competitors for better convenience, choice and prices.

One way retailers combat these disruptors is by analysing their customers’ buying behaviours to identify new ways to further differentiate their in-store and digital offerings – for example, using insights from loyalty card data to target offers at individual customers.

Yet such an approach gathers data from isolated, self-contained engagements that only form one part of a customer’s time spent on the high street shopping – the customer experience as a set of disjointed silos versus the seamless, end-to-end personalised experience of buying online. Arguably this fragmented engagement is also one of the root causes driving customers away from physical retail stores.

One way to potentially address this challenge is by competing retailers sharing real-time, dynamic in-store customer browsing and buying behaviour data. This free flowing – “liquid” – Big Data would enable collaborating retailers to make on-the-spot offers and other personalised engagements to a customer directly in their stores based on an individual’s wants or needs that day; creating moments of customer delight not even possible when buying online (with the added bonus that purchased products are available to take home immediately). This co-opetition model would be mutually beneficial to all with any revenues from sales (direct, cross or up-selling, etc.) attributable to this process being split equitably across participating retailers.

An example of this model could be a “Virtual High Street Assistant” – a mobile app that a customer opts in to that gathers data about their behaviour across different high street stores (such as recording any purchases made using Mobile Payments or by detecting IoT sensors in labels of products being browsed). Cloud Analytics is continuously, rapidly analysing this data for insights like identifying cross or up-selling opportunities to complement goods already brought, the price a specific customer is likely to pay for an item they have been browsing across different stores and potential Social Media activities that could promote further engagement. Based on their smartphone’s GPS location, suitable insights (like nearby relevant special offers) are then shared with a customer via the app. In addition, analytics aggregates different customers’ data to identify any buying trends on the high street that day and makes recommendations to participating retailers about how to best exploit these sales opportunities.

This notion of Big Data as “liquid” that can be shared (rather than solid, hidden in one organisation’s silo) is not without considerable challenges and barriers.  In addition to technology, there is a range of legal and security issues affecting the use of personal data in such a model. However, UK Health and Social agencies have been pioneering new ways to share highly sensitive patient data across different organisational boundaries to improve services. Learning from this experience could help build a compelling business case to fund a pilot to test the risks and benefits of “Liquid Big Data”.

If you would like more information about how Digital Transformation can benefit your organisation please contact the Sopra Steria Digital Practice.

Personal best

Our propensity for feeding internet services with personal data is exploding

In 2014 we made 2.4 million posts on Facebook, sent 204 million emails, sent 277,000 tweets and made 4 million searches on Google – every minute.

For users of the Ashley Madison extra-marital dating site, personal data was meant to be just that: personal. The recent hacking and exposure of data is an unfortunate example of how much private information we are willingly, and sometimes unknowingly, giving away about ourselves.

So what is personal data, and why is it so important?

To get an idea of scale we have to understand that not only are we talking about your search history, social media and emails which you knowingly generate, but also a vast amount of other data from your smartphone tracking your location and your medical history to your buying habits. The scale of this data is so huge that it’s recorded in terms of Exabytes – a unit of storage 1 billion times the size of a gigabyte (and, if written, contains 18 zeros).

For years businesses have been decidedly opaque in the value they extract from personal data.  What we are now seeing is that customers are becoming more aware of this data and its value. And this is leading to them being more protective and selective when giving it away and more concerned over the security and privacy surrounding their personal data.

In Europe the midata initiative is exploring this growing change by putting tools and processes in place for people to access their own data and understand the value that is holds. One early project has been brought together by midata and GoCompare for the financial services industry – who has used personal data to enhance the value of their products for a long time.

By understanding their customers’ spending and living habits they have been able to carefully select specific products and market them to the right customers with the right risk appetite. The GoCompare tool however lets consumers conduct the same kind of analysis on their spending data as banks, running this data through a catalogue of financial products to tell customers clearly and visually what the best product should be for them and exactly how much they could save, demonstrating the financial benefit and personalisation they can receive through access and control of their personal data.

The key to the future of personal data lies with a clear appreciation of its value. In the future people like you and I should have access to our own data and a full understanding of how sharing our data can benefit us. We should be able to personalise the amount of data that we are sharing, decide how it’s used and understand the level of security and risks that it brings. Whether you are engaging in a relationship you shouldn’t be or building your nest egg, you should know what you’re giving away and to what end.

So is the future of personal data ownership a bright one?

Personally, I think the data points that way.  The age of Big Data has already arrived, but the era of Small Data is yet to begin.

If you’re interested in this subject and want to join the conversation, leave a message below or contact me in the the Aurora team.

Mind the GaaP – shared technology platforms and data analytics

The outcome of the government’s digital strategy has been higher adoption of on-line services and the introduction of new technologies – including social media, mobility, analytics and cloud computing. But as government delivers services that are simpler, clearer and faster to use it also creates increased expectations.

First, citizens demand services that are often universal but also reflect the levels of personalisation they get as private consumers. But government operates as a series of silos. Services, processes and technology reflect inward-looking departmental needs.

Second, the public finances demand that government boost productivity using innovative digital technologies. The government saved £18.6 billion in 2014-15 through various reform projects. But the savings attributable to digital transformation are significant but relatively small (£391m).

In an environment of increasing citizen demands and top-down cost reductions, how can technology help government be more responsive but at least cost?

Government as a Platform might reduce unnecessary bureaucracy and costs

Two years ago the Government Digital Service (GDS) set out to transform twenty-five major public services. Twenty digital ‘exemplars’ are now publicly accessible. GDS continues to work with departments to build these and other services in agile and iterative ways.

The next phase of the government’s strategy is ‘Government as a Platform’ (GaaP). This is the sharing of the core infrastructure of systems, technology and processes across departments. GOV.UK Verify is a good example. Rather than having to prove who you are to every government department, Verify uses certified companies (and public and private sector data) to confirm a person’s identify once and for all. Other potential platforms are payment processing, case management and appointment bookings – common services used all around government.

GaaP offers a number of potential benefits. First, enhanced user satisfaction by eliminating the need for a citizen to input unnecessary data and information. Second, cost savings by eliminating administrative procedures and processes (and associated transactions) that are not needed. Third, wider economic benefits by making the data open, as others who are unrelated to government can create new businesses that complement public services. Forth, citizens or community groups might also use this data to hold government to account.

Tailored and automated services offer even greatest benefits

In the private sector an ability to share systems and data through technology is leading to a more personalised service. A user is in full control of navigating, choosing and terminating a set of offers. Back-office integration enables the private sector to offer proactive, enhanced and efficient services.

How might this approach be applied in the public sector? At its most simple, the government might pre-fill data in an application form that it already possesses, based on taxation or benefit entitlements, and notify the citizen via email or text of any changes. But more significant improvements to the quality and cost of public services are available through the analysis of this data (a data platform), leading to earlier and more focused interventions.

For example, approximately 40% of hospital admissions in England are unplanned admissions. They are a problem for hospitals because they are costly and disruptive and increase waiting times. Vulnerable patients with complex physical or mental health needs tend to be the biggest problem.

The detailed analysis of historic patient level data, identification of patterns and predictive risk modelling can predict and identify ‘at risk’ individuals. Unplanned admissions can then be avoided through changes to the hospital discharge process and better co-ordination of care.

Taking it to the next level, ICT-enabled simulation and decision-support tools are also able to analyse large and complex socio-economic data sets on deprivation, crime, health, education, etc. This deeper analysis can inform early intervention and screening programmes, with resources focused on communities and individuals who most need them.

Costs can be avoided by highlighting incidences of unnecessary care or delays in treatment. And by making evidence-based information about options, outcomes and uncertainties available, patients are also in a better position to make informed choices about the treatments available to them.

This proactive approach may not be appropriate for all types of service. It will, for example, depend on access to necessary data and protection and legal access. But, when applied to high-risk and often disproportionately high cost individuals, the savings potentially far outweigh the up-front costs of investment.

What do you think? Leave a reply below or contact me by email.