Intelligent personal assistants: an opportunity for retailers?

Alexa is arguably the tipping point for intelligent personal assistants; with Amazon’s open source approach to sharing its app (“skill”) development capabilities the sky’s the limit for this new, disruptive form of natural language driven customer experience. But what could retailers make of this opportunity? Here are some ideas…

It’s not the hardware but the cloud analytics that matters

Critical to any retailer using an intelligent personal assistant to innovate their brand is that these use cases should primarily focus on the business outcomes from using its cloud analytics capabilities, not the front-end device itself.

A retailer, for example, could use Alexa to provide instore guidance to shoppers to help them find items or make simple queries, physical customer browsing behaviour captured in the cloud that when combined with online experiences enables deeper, more contextual forms of personalisation across all this retailer’s channels.

An opportunity to simplify (and risk of complicating) customer journeys

A unique strength of an intelligent personal assistant is that it has the potential to smartly rationalise customer queries and transactions – an opportunity to turn chatbots into compelling conversational experiences a customer would have a preference for using over engaging a person or using a digital channel.

But there remains a significant user experience design challenge for its natural language driven interface – at what point does the buying journey become too complex for this channel and risks increasing friction for a customer? Any form of customer experience that requires a customer to look at detailed product information or make comparisons between products could be difficult and hard to follow through spoken voice generated content alone.

Alexa’s use of APIs could enable a retailer to combine this channel with its mobile e-commerce site (or in-store tablets) for example to create a seamless, holistic experience where complex information is shared visually driven by a customer’s voice commands and smartly informed by Alexa’s AI.

Bricks and mortar as a truly experiential destination

Perhaps the most exciting thing about Alexa (and intelligent personal assistants in general) is the potential for them to create unique, personalised experiences instore – a direct, deep relationship between a customer and a retailer’s brand. And because its cloud driven this enables interconnectivity (IoT) with other instore technologies such as targeted digital signage, interactive mirrors, social media engagement and mobile point of sale.

If you would like more information about how digital transformation can benefit your retail business, leave a reply below or contact me by email.

Shopping with Artificial Intelligence: The frictionless family customer experience?

With Amazon, Facebook and Google all adopting an open source approach to development of their artificial intelligence (AI) services, what could this innovation mean for a family shopping on the High Street? Here are some ideas…

An end to Saturday morning parking mayhem – having to spend half an hour queuing to get into a shopping centre car park only to find out the only spaces left are on the hundredth floor can be a miserable start (and end) to a Saturday shop for the whole family.

An AI personal assistant could reduce the friction of this inconvenience by reserving a suitable car parking space at the shopping centre in advance, based on the family’s store preferences, accessibility requirements and other factors, like forecast weather. It can then send the reserved space location to the family’s in-car GPS and automatically pay for its ticket. The more an AI can effectively integrate or communicate with other systems the greater the convenience for customers.

No more bored kids looking at their mobiles – the family have spent hours traipsing from store to store failing to be engaged by any of these retail experiences. The kids are just itching to get their phones out to start socialising with their friends, and mum and dad are getting the feeling they are better off buying online.

An AI could transform the friction of this irrelevant customer experience by giving in-store products ‘personality’ –  a product can introduce itself using spoken voice to these customers (via a store branded mobile app for example), talk about its unique selling points and answer potentially any question about its suitability – all personalised using buying and social insights the AI has about the family. The more an AI can effectively apply analytics to create experiential, contextual shopping experiences, the more compelling and delightful bricks and mortar stores become for customers.

Empowered shopping without added wrinkles – So the family have found things they need and discovered lots of things they want, but mum and dad aren’t comfortable with uncontrolled spending across their bulging wallet of bank cards.

An AI could help remove the friction of this uncertainty by acting as a single channel for these customers to manage their disparate bank services in one place, giving on the spot advice about saving and spending to enable the right purchasing decisions and provide a secure, easy to use payment system using customer voice recognition (biometric authentication). The more an AI can create a platform that combines and simplifies a range of complex services; the better mobility customers have on the High Street – experiences that rival anything offered by online retailers.

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

 

How deep learning is advancing AI in leaps and bounds

by Michel Sebag, Digital Practice, Sopra Steria France

Nature has given human beings an amazing ability to learn. We learn complex tasks, like language and image recognition from birth and continue throughout our lives to modify and build upon these first learning experiences. It seems natural then, to use the concept of learning, building up knowledge and being able to model and predict outcomes and apply that to computer related processes and tasks. The terminology used to describe the technologies involved in this paradigm in computing are Artificial Intelligence (AI).

It’s just a game

In the late 1990s, a defining moment in the world of artificial intelligence happened. In 1996 chess master Garry Kasparov played IBM’s Deep Blue, originally built to play chess using a parallel computer system, and won 4-2. A year later, Kasparov and Deep Blue played another match – this time, Deep Blue won. This win created a sea-change in the attitude towards the idea of AI. Chess masters minds have to perform highly complex calculations, evaluating multiple moves and strategies, on-the-fly. They can also take their own learning and apply novel moves. Being able to mimic this process, even if applied to a specific task like chess, opens up real potential for the technology.

Out of this success, new developments in AI have brought us to the point of maturity and sophistication. DeepMind, now owned by Google, uses deep learning algorithms. These algorithms are based on the same idea that allows human beings to learn, i.e. neural pathways or networks. Again, AI has been applied to gaming to prove a point. DeepMind has taken the idea of ‘human vs. machine’ and this time used it in the highly complex game of ‘Go’. DeepMind, the company, describe the game of Go as having “more possible positions in Go than there are atoms in the universe”. So then, this is the perfect challenge for an AI technology. DeepMind uses deep learning algorithms to train itself against known plays by expert players. The resultant system is known as AlphaGo and has a 99.8% win rate when pitted against other Go programs, and has recently won 4 out of 5 games against the Go pro player, Lee Sedol.

It may seem that it’s just a game being played, but in fact, this is proving the technology, showing it can learn how to model and predict outcomes in much the same way that a human being does. In almost 20 years AI is already 10 years ahead of what was anticipated of the technology. The games have proven the capability and now the technology is entering a stage of maturity where it is being applied to more real-world problem solving. Following the AlphaGo success, Google has understood the benefits of these technologies and has promptly integrated AlphaGo technology in its cloud based Google Machine Learning Platfom.

Some definitions in the world of Artificial Intelligence

At this juncture, it is worth looking at some of the terminology and definitions of AI technology.

It can be viewed as this: Deep Learning is a sub-set of Machine Learning; Machine learning is a sub-set of Artificial Intelligence.

Artificial Intelligence: This is a general term to describe a technology that has been built to demonstrate a similar intelligence level to a human being when solving a problem. It may, or may not use biological constructs as the underlying basis for its intelligent operations. Artificial Intelligence systems typically are trained and learn from this training.

Machine Learning: In the case of the games we used earlier as examples, machine learning is trained using player moves. In learning the moves and strategies of players, the system builds up knowledge in the same way a human being would. Machine learning based systems can use very large datasets as training input, which they then use to predict outcomes. Machine learning based systems can use both classical and non-classical algorithms. One of the most valuable aspects of machine learning is the ability to adapt. Adaptive learning gives better accuracy of predictions. This, in turn, facilitates the handling of all possibilities and combinations to provide the optimal outcome from the incoming data. In the case of game playing, this results in more wins for the machine.

Deep Learning: This is a sub-set of machine learning, a type of implementation of machine learning. The typology of the system is vital; when learning, it’s not so much about ‘big’ but it’s more about the surface area or depth. More complex problems are solved by larger numbers of neurons and layers. The network is used to train a system, using known question and answers to any given problem and this creates a feedback loop. Training results in weighted outcomes, this weight being passed to the next neuron along to determine the output of that neuron – in this way, it builds up a more accurate outcome based on probabilities.

Real world applications of AI

We’ve seen the use of AI in gaming, but what about real-world commercial applications? Whenever it comes to predict, forecast, recognize, clustering, AI is being used in a multitude of processes and systems.

At Sopra Steria, for example, we use AI components in industry solutions, including banking and energy. We are integrating Natural Language Processing (NLP) and voice recognition capabilities from our partners’ solutions such as IBM Watson or Microsoft Cortana. NLP, voice recognition – and image recognition in a near future – are now widely used and integrated in a multitude of applications. For example, for banking industry, text and voice recognition are used in qualification assistants for helpdesk and customer care services. More generally, some of the best-known modern applications include everyday use in our smart phones. Voice and personal assistance technologies like Siri and Google Now brought AI into the mainstream and out of the lab, using AI and predictive analytics to answer our questions and plan our days. Siri now has a more sophisticated successor named VIV. VIV is based on self-learning algorithms and its topology is much deeper that SIRI’s more linear pathways. VIV is opening up major opportunities for developers by creating an AI platform that can be called upon for a multitude of tasks. Google recently announced a similar path to its widely acclaimed assistant Google Now becoming Google Assistant.

Machine Learning is also used in many back-end processes, such as the scoring required to allow things like bank loans and mortgages. Machine learning is used in banking to specifically offer personalization of products giving banks using this method a competitive edge.

Deep learning is being used in more complex tasks, ones where rules are fuzzier and more complex. The era of big data is providing the tools that are driving the use cases for deep learning. We can see applications of deep learning in anything related to pattern recognition, such as facial recognition systems, voice assistance and behavioral analysis for fraud prevention.

Artificial Intelligence is entering a new era with the help of more sophisticated and improved algorithms. AI is the next disruptive technology – many of Gartner’s predictions for technology into 2016 and beyond, was based on AI and machine learning. Artificial Intelligence holds the keys to those unsolvable issues, the ones we thought only human beings could do. Ultimately, even the writing of this article may one day, be done by a machine.

What are your thoughts? Leave a reply below, or contact us by email.

The Brave Little Toaster

We are currently sitting on the precipice of the fourth industrial revolution which is set to re-think the way we live and work on a global scale.  As with the first industrial revolution, what we know roughly is that change is being driven by technology, but we lack any concrete knowledge of how great the change will be or just how dramatically it will disrupt the world we live in.

The technologies driving the upcoming revolution are artificial intelligence and robotics, technologies which have been the territory of sci-fi for generations which think and act as humans would.  Just as steam power, electricity and ultimately computers have replaced  human labour for mechanical and often mathematical tasks, AI looks set to supplant human thinking and creativity in a way which many see as unsettling.  If the first industrial revolution was too much for the ‘luddites’ doing their best to stamp out mechanical progress, the reaction to AI and robotics is going to be even more unsettling.  There are several clear reasons I can perceive that may drive people away from AI which are:

  • Fear of redundancy: the first reason we can see replicates that of the first industrial revolution. People don’t want technology to do what they do, because if a machine is able to do it faster, better and stronger than they can then what will they do?
  • Fear of the singularity: this one is like our fear of nuclear bombs and fusion. There’s an intrinsic fear people hold, entrenched in stories of Pandora’s Box where we believe certain things should not be investigated.  The singularity of AI is when a computer achieves sentience, and though we’re some way off that (without an idea of how we’d get there) the perceived intelligence of a machine can still be very unnerving.
  • The uncanny valley: the valley is the point where machines start to become more human-like, appearing very close, but not exactly like a human in the way they look or interact. If you’re still wondering what it is, I’d recommend watching these Singing Androids.

Just like we’ve seen throughout history, there is resistance to this revolution.  But if history is anything to go by, while it’s likely to be a bumpy road, the rewards will be huge.  Although it’s the back office, nuts and bolts which are driving change behind the scenes, it’s the front end where we interact with it that’s being re-thought to maximize potential and minimize resistance.  What we’re seeing are interfaces designed to appear dumb, or mask their computational brains to make us feel more comfortable, and that’s where the eponymous title of this blog comes in.

“The Brave Little Toaster” is a book from 1980, or – if you’re lazy like me – it’s a film from about 8 years later, ‘set in a world where household appliances and other electronics come to life, pretending to be lifeless in the presence of humans’.  Whilst the film focused on the adventure of these appliances to find their way back to their owner, what I’d like to focus on is how they hide intelligence when they come into sight – and this is what we’re beginning to see being followed by industry.

Journalism is a career typically viewed as creative and the product of human thought, but did you know that a fairly significant chunk of the news that you read isn’t written by a person at all?  For years now weather reports from the BBC have been written by machines using Natural Language Generation algorithms to take data and turn it into words, which can even be tailored to suit different audiences with simple configuration changes.  Earlier this month The Washington Post also announced that their writing on the Rio Olympics would be carried out by robots.  From a consumer standpoint it’s unlikely that we’ll notice that the stories have been written by machines, and if we don’t even notice it shouldn’t be creepy to us at all.  Internally, rather than seeing it as a way to replace reporters, it’s being seen as an opportunity to ‘free them up’, just like the industrial revolution before which saw people be freed up from repetitive manual tasks to more thought based ones.

Platforms like IBMs Watson begin to add a two-way flow to this, with both natural language generation and recognition, so that a person can ask a question just as they would to a person, with a machine understanding their phrasing and replying in turn without ever hinting that it’s an AI.  At the stage when things become too complicated, the AI asks for a person to take action and from there on the conversation is controlled by them, with no obvious transition.

A gradual approach to intelligence and automated systems is also being adopted by some businesses.  Tesla’s autopilot can be seen as an example of this, continuing a story which began with ABS (automatic breaking) over a decade ago, and developed in recent years to develop a car which, in some instances, can drive itself.  In its current state, autopilot is a combination of existing technologies like adaptive cruise control, automatic steering on a motorway and collision avoidance, but the combination of this with the huge amount of data it generates has allowed the system to learn routes and handling, carefully navigating tight turns and traffic (albeit with an alert driver ready to take over control at all times!).  Having seen this progression, it’s easy to imagine a time not too far from the present day where human drivers are no longer needed, with a system that learns, generates data and continually improves itself just as a human would as they learn to drive, only without the road rage, fatigue or human error.

The future as I see it is massively augmented and improved by artificial intelligence and advanced automation.  Only, it’ll be designed so that we don’t see it, where the boundary between human and machine input is perceivable only if you know exactly where to look.

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

Augmentation, AI and automation are just some of the topics researched by Aurora, Sopra Steria’s horizon scanning team.

Teachable Brand AI – a new form of personalised retail customer experience?

Within the next five years, scalable artificial intelligence in the cloud – Brand AI – could potentially transform how retailers use personalisation to make every store visit a memorable, exclusive customer experience distinct from anything a competing digital disruptor could offer.

Arguably the success of this engagement approach is contingent upon a retailer’s ability to combine a range of data sources (such as social media behaviour, loyalty card history, product feedback) with its analytics capabilities to create personalised moments of delight in-store dynamically for an individual customer that drives their decision to purchase.

But could the truly disruptive approach be one where a customer is continually teaching the Brand AI directly about their wants or needs as part of their long-term personal relationship with a retailer?

Could this deliver new forms of customer intimacy online competitors can’t imitate? Here are some ideas…

  • Pre visit: Using an existing instant messaging app the customer likes (such as WhatsApp or Skype), he or she tells the Brand AI about their communication preferences (time, date, etc) and what content about a specific retailer’s products or services (such as promotions or new releases) they are interested in. This ongoing relationship can be changed any time by the customer and be pro-active or reactive – the customer may set the preference that the Brand AI only engages them when they are located within a mile of a retailer’s store or one week before a family member’s birthday, for example. Teachable Brand AI empowers the customer to be in complete control of their own personalised journey with a retailer’s brand.
  • In store: The Brand AI can communicate directly with in-store sales staff about a customer’s wants or needs that specific day to maximise the value of this human interaction, provide on-the-spot guidance and critical feedback about physical products their customer is browsing to drive a purchasing decision, or dynamically tailor/customise in-store digital experiences such as virtual reality or media walls to create genuine moments of customer delight. Teachable Brand AI has learned directly from the customer about what excites them and uses this deep insight to deliver a highly differentiated, in-store experience online competitors can’t imitate.
  • Post purchase: The customer can ask the Brand AI to register any warranties, guarantees or other after sales support or offers for their purchased good automatically. In addition, the customer can ask the Brand AI to arrange to return the good if unsatisfied or found faulty – to help ensure revenue retention a replacement or alternative is immediately suggested that can be exchanged at the customer’s own home or other convenient location. The customer can also share any feedback they want about their purchase at any time – Teachable Brand AI is driving customer retention and also gathering further data and insights to enable greater personalisation of the pre visit and in-store experience.

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

 

Brand AI: The invisible omni-channel for retailers?

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

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

The competitive advantages of Digital Elasticity

The time it takes from making a strategic or operational decision to its full implementation will directly impact an organisation’s competitive advantage. By leveraging digital ways of working and technology an organisation can potentially affect this process dynamically. So what are the strategic benefits of such Digital Elasticity?  Here are some ideas…

First Mover Advantage

Getting an innovative product or service to market first potentially confers a monopolistic position for an organisation that generates sustainably higher revenues than competitors. B2C markets, where customer switching costs are low (such as telco) or where governments are actively intervening to reduce barriers of entry to increase competition (like utilities and personal banking), are key battlegrounds to exploit such an approach.

Adopting a User Centred Agile approach to delivery or to change an organisation can enable First Mover Advantage capabilities. It should be noted that Agile isn’t necessarily faster or cheaper than a staged, gated Waterfall approach. However, it is far more responsive to changing market conditions because it drives the design and release of a product or service through a series of customer-focused iterations – rather than the “all at once” functional requirement focus typical of Waterfall. Consequently, an organisation can use the Digital Elasticity of User Centred Agile to get to market first and then use this approach to further enhance its offering dynamically to sustain profitability (and also create other barriers of entry for competitors).

The Digital Transformation challenges an organisation faces in embedding a scalable User Centred Agile approach across its value chain should not be underestimated. A key challenge is sustaining effective collaboration and alignment of pace (or velocity) between different, complex business areas of its operating model in an environment where these stakeholders’ goals may be materially divergent.

Second Mover Advantage

Exploiting the lessons learned from a competitor going to market first through imitating (or bettering) their design, pricing and brand positioning can be a source of sustainable competitive advantage. Players within the consumer electronics industry often demonstrate such competitive behaviour – for example, the fierce competition between next gen gaming consoles during the last couple of years, where rival companies played off each other’s hardware design, pricing information and launch date announcements to try to exploit Second Mover Advantages.

For an organisation choosing to delay going to market (by possibly months or even years) a key risk is the negative impact on its strategic (or non-current) assets. These under-utilised assets (such as IT property or equipment that drive its supply chain processes) will not generate revenue or value during this period but continue to generate costs and depreciation of commercial value. However, an organisation could lever Cloud Computing Capabilities to transform its IT asset base. Such a move transfers the risk of obsolescence of these assets to a third party while materially reducing operating costs. They should also become highly Digital Elastic through on-demand availability to enable Second Mover Advantages, and they shouldn’t create sunk costs or other liabilities like their on-premises equivalents.

This strategic move to be effective requires an organisation to select the right Cloud Services provider as a partner who can deliver these mission-critical capabilities competently short and long-term – successful Second Mover Advantage becomes contingent on “moving parts” beyond the direct control of an organisation.

Automatic Advantage

If Artificial Intelligence (AI) can live up to its promise to make effective business decisions using the complex data it rapidly consumes from potentially any source (including social media and Internet of Things sensors), an organisation could lever the Digital Elasticity of Automatic Advantage to eliminate human error, touch points or process bottlenecks to gain sustainable competitive advantage. An example could draw from usability testing data where an AI could make a design decision about a website and implement this change instantly to drive up conversion rates. Alternatively it could implement a new pricing strategy across all customer channels based on forecasted patterns of demand and anticipated competitive behaviour (AI algorithms are currently being tested as a way of predicting the stock market to help pilot such an approach).

Other applications could include the use of AI to apply Six Sigma or Lean approaches to eliminate defects or reduce wasted effort in manufacturing processes.

However, the Digital Elasticity of Automatic Advantage would mean an organisation accepting new forms of risk not previously encountered – such as who is accountable or responsible if the AI makes a bad business decision? How can tacit knowledge or experience gained by the AI be effectively shared with other systems or its human counterparts to drive sustainable competitive advantage? What are the implications for human resource talent management and retention within an organisation?

Despite these challenges however, the potential of an AI to make and implement informed strategic or operational decisions without the risk of “human delay or error” could potentially render First or Second Mover Advantages obsolete.

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