“Perhaps the biggest threat and opportunity organisations face is Natural Language Processing (NLP); where ever increasingly smart robots simplify transactions for customers.”
Yet the user experience of such intelligent personal assistants can at times feel underwhelming because they lack a sufficiently broad range of services versus other digital channels. Facebook M for example relies upon human trainers to complete more complex customer service tasks requested by users and Alexa utilises ‘skills’ – tailored apps such as Spotify. None of them appear to offer the same level of complete user freedom as using traditional web browsers to access any available content.
“Any organisation regardless of its size able to master NLP can potentially compete in previously unreachable or unscalable markets.”
One way these robots could overcome these limitations is to “learn” how to use NLP to access any digital service through its front-end without the need for any technical integration or human touchpoints. All transactions could then be consumed or simplified into one customer experience accessed by a single AI.
The implication for competitive advantage is that potentially any organisation regardless of its size that can effectively master these “platform on platforms” cloud capabilities will be able to compete in previously unreachable or unscalable markets
“In this “open season” competitive environment, NLP can enable an organisation to transform its relationship with an existing customer and steal new ones from competitors.”
One such service could be an AI that searches and buys the best priced goods from competitors from their own customer-facing channels (without their co-operation or collaboration) so empowering a customer to create their own “perfect basket” free from the constraints of only shopping with one brand. These competitors would still get revenue from these purchases but critically won’t have direct access to this customer relationship or loyalty – NLP is disrupting their competitive advantage by reducing their market power.
In this “open season” competitive environment, where switching costs are practically nil for customers, NLP can enable an organisation to radically transform its relationship with an existing customer and steal new ones from competitors – David becomes Goliath.
In a previous blog post, I unpacked the latest buzz word in the tech world, digital transformation, and what it means for businesses in the UK. But while many UK businesses are investigating and adopting digital technology, others are experiencing challenges. So what are these challenges and how are the early adopters tackling these issues? Sopra Steria surveyed 120 FTSE 500 companies to find out.
Our research showed that 84% of businesses currently think they could be doing more with digital. This shows there is a real appetite in the UK to try and get the most out of digital. However, it also highlights the complex challenges that lie behind succeeding with digital. The most common barriers to digital success were integration with existing systems and infrastructure (27%), management culture (26%) and skills gaps – particularly in the design phases of a programme (16%).
To overcome such barriers, nearly two thirds of businesses were using external partners or third parties to help them deploy their digital projects. Our research showed that whilst the use of third parties was primarily to help fill the skills gap, it also provided additional benefits such as stronger governance. It also, on the whole, led to more successful transformation programmes.
Interestingly – and encouragingly – once organisations had overcome the initial barriers and had taken the decision to initiate a digital programme, the implementation ran in line with expectations – something very rarely heard of in the IT industry. Delivery times were quick, with 83% of businesses saying they were pleased with the pace at which digital projects were being delivered.
Right now, 52% of businesses have at least one digital project underway. These are the innovators and experimenters who will lead the way in Digital Transformation and win early competitive advantage. They have shown the way to overcoming the barriers to digital transformation success. Those who fail to follow will fall quickly behind. Doing nothing is no longer an option.
Incumbents and new players alike are exploiting digital ways of working and technology to create new forms of competitive advantage. Here are some examples impacting retailers today…
CX shifters positively change customer behaviour to drive wider engagement and sales – often through encouraging new forms of brand advocacy and self service that increase wider demand while lowering lower costs. Domino’s Australia applied this form of social commerce innovation to successfully create its Pizza Mogul campaign; where customers designed their own pizzas, self promoted their creations on social media and then shared in a percentage of any revenues generated from Domino’s selling them. An estimated 15% plus revenue growth for Domino’s Australia was attributed to this initiative by the end of last year.One approach for retailers with limited experience of such disruptive customer experience design is to engage tech start-ups to jointly create new forms of market engagement unique to its brand (with John Lewis’ JLAB initiative being one such example). Such an approach can accelerate the “ideation process” although scaling these opportunities to the level or volume required for market use can be challenging given start-ups may lack the experience or capabilities for such rapid industrialisation.
Agile SCM operators combine big data and analytics with traditional just-in-time production and distribution methods to optimise their supply chains. Zara’s “rapid fashion, minimal inventory” approach levers such capabilities to control and drive synergies across its own supply chain that often results in lead times as little as two weeks from catwalk design to production to distribution of a garment to its physical and online retail stores. This disruptive approach has helped Zara become one of the world’s leading fashion retailers with a presence in over 80 countries.One competitive response to challenge Zara is to adopt “fully linked collaboration” supply chain management; where retailers and suppliers use cloud platform technology to openly share data and other capabilities to deliver goods and services together. However to be successful this may require radical new ways of working such as co-opetition between competitors for their shared mutual benefit.
Instant branders use their high recognition, flexible brands to drive successful market penetration of their diversified products or services as sources of rapid growth. One such example is Amazon Web Services that not only powers Amazon’s retail business; it has also fast become the dominant provider for B2C cloud infrastructure services with a 25% worldwide market share reportedly gained over the last five years.Responding to these challengers can mean an organisation completely redefining itself as a “tech company” where its operating model is primarily focused on using technology driven innovation to differentiate and cost optimise its products or services. Such an approach is core to the success of “Digital Disruptors” such as Uber and Airbnb where they established their own global enterprise platform brands to penetrate B2C markets in different geographies simultaneously. However, these new entrants did not need to transform their existing technology, processes or culture first to be successful unlike existing market players.
Price racers lever their bulk buying power and other supply chain economies of scale to lower prices for targeted quality products to challenge established competitors. The discount supermarkets Aldi and Lidl have applied this model to grow faster than the “big four” UK supermarkets including opening more new stores than these established players combined during the last year.One way to challenge these discounters is to offer competitive prices with greater convenience – same day grocer delivery at home (a model currently being piloted in the UK market by AmazonFresh). However, effectively delivering this complex supply chain and distribution of potentially hundreds of product lines from multiple suppliers is challenging; it took Ocado over a decade to turn a profit using a similar model. In addition, successfully replicating an individual customer’s preferences for picking fresh goods in-store arguably requires more sophisticated personalised experiences using emerging digital technologies such as big data analytics or even virtual reality.
As an advocate for the use of Scrum and in need of some Scrum Juice, I went with Sopra Steria colleagues Steve Forbes and John McNeill to the Scrum Day London 2016 – an event held by Scrum.Org where Ken Schwaber, co-creator of Scrum, was giving the keynote speech, and the day’s theme was “Business Agility through Professional Scrum.”
The story of the day for me was that while Scrum is popular, seen as necessary and is adopted by many, Scrum success as represented by teams delivering working software into Production every Sprint or Iteration (i.e. every one to four weeks) continues to be a challenge. Very few teams are able to report success against this measure – in fact, I was the only person in the room with a raised hand when Ken asked the question “How many of you release software every Sprint?”
The fact is, technology exists for teams to be able to release into Live every 5 minutes (or even less).
The issue appears to be that Scrum and Agile require a change in organisational thinking and support that is hard for many to implement, and in a way that allows the innovations a Scrum Team offers to be realised.
We heard first from Gunther Verheyen, co-developer of the Scaled Professional Scrum Framework, who laid out the map of the journey from a ‘waterfall’ type structure (and mind set) to one that supports Scrum. Gunther has a vision:
‘Management’ is not a collection of people exerting hierarchical powers. It is an emergent, networked structure of co-managers. Removing Impediments. Optimising a product’s value. Updating the organisation’s OS.
Karen Bowes, Head of HR & Sustainability at Capital One gave an impressive and honest insight into how Capital One was adopting Scrum not just in software delivery, but throughout its management structure. We were reminded by Ken Schwaber that Scrum requires courage, and courage was used by Capital One to great effect: they realised that there is always ‘noise’ and conflict when new practices and change are introduced and accepted this as a fundamental part of deep transformation. The focus of their management and strategising was consciously shifted from detailed micro-planning and control to providing support for Scrum teams and the removal of impediments to Scrum team success. Not an easy journey, but one that has already reaped rich rewards for Capital One.
Ken Schwaber’s new initiative is to propose a ‘Scrum Studio’ approach, which effectively places a Scrum team (or group of teams) in a special location within an organisation, with all the support structures it needs, and allow it to get on with its job. In this way, the hope is that the impediments to successful Scrum uptake are removed and organisations can then further adopt Scrum practice at a pace they can manage if and when they see a benefit in doing so.
Whatever the future for Scrum and Agile, it is going to take motivated, influential and courageous individuals to lead and support the kind of transformations that business is being challenged to undergo.
It was a privilege to meet some of them at Scrum Day London. Do let me know your thoughts – leave a reply below, or contact me by email.
Digital can pose a range of risks for a bricks and mortar (B&M) retailer including:
Declining market share as customer loyalty to its established, traditional brand is eroded away by disruptive new on-line entrants and more innovative high street competitors
Poor ROI from implementing new in-store digital technologies because they fail to create a superior personalised customer experience across its physical and online channels
The inability to deliver better inventory management using big data and analytics due to immature organisational capabilities in these areas across its supply chain
So how could scalable retail artificial intelligence in the cloud – Brand AI – potentially turn these challenges into unique opportunities for competitive advantage during the next five years? Here are some disruptive ideas…
Brand AI as a personal human relationship
A retailer could personify its brand as a virtual customer assistant accessible anywhere, anytime using voice and text commands from a mobile device. But unlike today’s arguably bland, soulless smartphone versions that focus on delivering simple functionality, Brand AI would have a unique, human character that reflects the retailer’s values to inform its interactions and maturing relationship with an individual customer. Intended to be more than another ‘digital novelty’, this disruptive form of customer engagement builds on and enhances a B&M’s traditional brand as a trusted long-term friend throughout the entire customer journey by offering compelling, timely presale insights, instant payment processing and effective after sales support and care.
Brand AI as an invisible omni-channel
A customer is empowered to select what personal data they choose to share (or keep private) with the Brand AI to enrich their relationship. Social, location, wearable or browsing and buying behaviour data from complementary or even competing retailers could, potentially, be shared via its cloud platform. The Brand AI can analyse this liquid big data using its machine-learning capabilities to create dynamic real-time personalised actionable insights seamlessly across a customer’s physical and digital experience – it is the heartbeat of the retailer’s invisible omni-channel offering.
Critically, Brand AI can transform every retail store visit into a memorable, exclusive customer experience distinct from anything a competing digital disruptor could offer. For example, the Brand AI can advise in-store sales staff in advance what specific products a customer wants or needs that particular day to help personalise this human interaction, provide on-the-spot guidance and critical feedback about products available immediately to drive a purchasing decision, or tailor in-store digital experiences such as virtual reality or media walls to create genuine moments of customer delight. In addition, the AI can capture the customer’s emotional and physical reactions via wearables to these experiences (such as a raised heartbeat when seeing a new product for the first time). Such insights can then be explored later by the customer (including socially with family and friends) using the AI on the retailer’s integrated digital channel to sustain their retention.
Brand AI as an operating model
A further opportunity for using Brand AI is its potential ability to streamline inventory management to improve the customer experience and reduce operating risk. Key processes such as store returns and transfers could benefit from such an approach – not only would the invisible omni-channel AI enable a customer to easily raise the need to return goods, it can also capture the specific reasons why this is happening (rather than this information having to be interpreted by different customer service staff using prescriptive reason codes, for example). Also because the Brand AI has an established personal relationship with the customer it can proactively order a replacement for home delivery or pick up (store or other convenient location) or suggest a suitable alternative product or other cross-sell opportunities to keep the customer satisfied and minimise revenue losses for the retailer.
Managers can also use the AI to help interrogate and identify trends from this complex dataset on returns and transfers. Inventory management reporting and insights are available on demand in a manager or team’s preferred format (such as data visualisation) to support stock purchasing decisions, resolution of supply chain performance issues or investigate region or store specific fraud and theft. And because these analytics are running in the cloud they can be aligned to existing organisational capabilities in this area.
The illustrative benefits for a bricks and mortar retailer using scalable artificial intelligence in the cloud (Brand AI) potentially during the next five years include:
Refreshes the competitive advantages of an established, traditional high street retail brand using new disruptive forms of marketing and customer advocacy
Materially de-risks strategic investment in new in-store digital technologies by explicitly linking these capabilities to an holistic, long-term customer experience
Can improve organisational agility using big data and analytic capabilities to improve existing business processes that directly benefit the retailer and its customers
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
A Liquid Big Data Platform uses cloud technology and agile ways of working to enable organisations to share and analyse large volumes of data together for their mutual benefit.
If this model is scaled to a global level where any organisation (both large and small) anywhere in the world could use it collaboratively, what new business models could potentially emerge?
Here are some ideas…
Accelerated design advantage
Many organisations are already exploiting Big Data driven Machine Learning to improve their services in real time (such as search engine optimisation, medical diagnosis and fraud detection).
In the so-called “arms race”, big name tech, automotive and pharmaceutical companies are reportedly spending billions of dollars annually to realise their own IP in this area of Artificial Intelligence. A potential strategic implication is that these first movers will create barriers of entry that prevent other competitors (including small or medium sized enterprises) using AI as a disruptive source of rapid, responsive service design and organisational agility.
A global Liquid Big Data Platform could enable a form of co-opetition between these competitors to realise shared Machine Learning capabilities as a source of competitive advantage that would be unfeasible using their own limited resources. Also, by sharing with each other data or insights about their customers or services could lead to forms of innovation first movers can’t deliver in their silo positions.
Public sector power house
In the UK, health and social care organisations are exploring ways to share Big Data collaboratively to deliver better outcomes for their service users and wider society. A key technical challenge they face is interoperability – the ability of different systems to talk to each other effectively – as their data is often on different legacy networks and applications arguably not originally designed for such a cross-boundary approach.
A cloud-based Liquid Big Data Platform could enable these organisations to overcome these technical barriers to focus on the real value of this business model – joined up preventative and reactive care delivery. Also, if this platform is scalable it could enable organisations with the right analytical capabilities to efficiently power such services in other countries – global collaboration as a source of public service improvement.
Global cost optimiser
Many organisations are migrating their IT assets to cloud to enable cost savings and increased market responsiveness. This includes applications, data and other digital assets that are the source of their competitive advantage. For example, many digital disruptors exploit cloud capabilities to create platforms for services across different countries or the emergence of government transactional services on one shared platform.
An agile Liquid Big Data Platform could continually optimise such benefits by seamlessly moving these assets to different geographies or markets that offer the lowest costs and best support. For example it could be continually transferring hosting services to different countries with the most favourable exchange rates or where there are higher skilled technical development resources.