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.

 

What do recent AWS announcements tell us about the cloud market?

As always, Amazon Web Services (AWS) made a bunch of announcements at their recent Chicago Summit.  The new features have been reported to death elsewhere so I won’t repeat that, but there were a few observations that struck me about them…

Firstly, the two new EBS storage volume types aimed at high throughout rather than IOPS – are 50% and 25% of the normal SSD EBS price, so are effectively a price cut for big data users.  As I’ve commented before, the age of big headline grabbing “across the board” cloud price reductions is largely over – and now the price reductions tend to come in the form of better price/performance characteristics.  In fact, this seems to be one of Google’s main competitive attacks on AWS.

Of course, I welcome the extra flexibility – it’s always comforting to have more tools in the toolbox.  And to be fair, there is a nice table in the AWS blog post that gives good guidance on when to use each option.  Other cloud vendors are introducing design complexity for well-meaning reasons also, e.g. see Google’s custom machine types.

What strikes me about this is that the job of architecting a public cloud solution is getting more and more complex and requires deeper knowledge and skills, i.e. the opposite of the promise of PaaS.  You need a deeper and deeper understanding of the IOPS and throughout needs of your workload, and its memory and CPU requirements.  In a magic PaaS world you’d just leave all this infrastructure design nonsense to the “platform” to make an optimised decision on.  Maybe a logical extension of AWS’s direction of travel here is to potentially offer an auto-tiered EBS storage model, where the throughput and IOPS characteristics of the EBS volume type is dynamically modified based upon workload behaviour patterns (similar to something that on-premise storage systems have been doing for a long time).  And auto-tiered CPU/memory allocation would also be possible (with the right governance).  This would take away some more of theundifferentiated heavy lifting that AWS try and avoid for their customers.

So…related to that point about PaaS – another recent announcement was that Elastic Beanstalk now supports automatic weekly updates for minor patches/updates to the stack that it auto-deploys for you, e.g. for patches on the web server etc.  It then runs confidence tests that you define before swapping over traffic from the old to the new deployment.  This is probably good enough for most new apps, and moves the patching burden to AWS, away from the operations team.  This is potentially very significant I think –  and it’s in that fuzzy area where IaaS stops and PaaS starts.  I must confess to having not used Elastic Beanstalk much in the past, sticking to the mantra that I “need more control” etc and so going straight to CloudFormation.  I see customers doing the same thing.  As more and more apps are designed with cloud deployment in mind and use cloud-friendly software stacks, I can’t see any good reason why this dull but important patching work cannot be delegated to the cloud service provider, and for a significant operations cost saving.  Going forward, where SaaS is not an appropriate option, this should be a key design and procurement criteria in enterprise software deployments.

Finally, the last announcement that caught my eye was the AWS Application Discovery service – another small tack in the coffin of SI business models based on making some of their money from large scale application estate assessments.  It’s not live yet and I’m not clear on the pricing (maybe only available via AWS and their partners), and probably it’ll not be mature enough to use when it is first released.  It will also have some barriers to use, not least that it requires an on-premise install and so will need to be approved by a customer’s operations and security teams – but it’s a sign of the times and the way it’s going.  Obviously AWS want customers to go “all in” and migrate everything including the kitchen sink and then shut down the data centre, but the reality from our work with large global enterprise customers is that the business case for application migrations rarely stacks up unless there is some other compelling event (e.g. such as a data centre contract expiring).  However, along with the database migration service etc, they are steadily removing the hurdles to migrations, making those business cases that are marginal just that little bit more appealing…

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

Lead by listening

We’re taught to listen from a very early age so why, sometimes, does it feel that c-suite executives have unlearnt this? Executives in an organisation need to be seen to be leaders, to be the people that drive forward initiatives to achieve an organisation’s desired outcomes. Traditional board structures produce a hierarchical delineation of responsibility and with that often a culture of

This is what we’re doing. I’m in control. Do it.’

With every market being disrupted in some way by technical or customer culture evolution then a c-suite leader who simply dictates runs the risk of becoming detached from those they are there to ultimately serve – their customers. Yes, we could say ‘shareholders’, but happy customers usually result in better profits – which keeps the shareholders happy.

They say knowledge is power, so if we gain knowledge from listening is it such a leap to suggest that we gain power from listening?

Knowledge = Power, Listening = Knowledge, ergo Listening = Power

Accepting my transitive relation argument as correct, then if you’re a c-suite executive who should you listen to?

Fellow board members

By understanding the needs and opinions of your peers you can better understand how your decisions affect other parts of the business. This is crucial to ensuring cross-organisational alignment and reducing the risk of introducing counter-productive initiatives; tightening one guy rope on a tent can often loosen another. It’s also important not to be too protective of your domain. If a decision elsewhere could greatly affect your area of the business, but is better for the positive growth of the organisation, then perhaps embracing the change is the better option?

Direct reports

Gaining a clear understanding of the pressures that your direct reports are under (especially where those pressures have been introduced by you) will help you understand the impact of your decisions. Better still, collaborate on decisions before setting out changes – ask your reports, ‘If we do this, what’s the direct impact?’ Greater involvement in the decision will lead to increased support in implementation.

Those at the coal face

Multi-tiered management structures often hide the main causes of business inefficiency through messages of discontent or frustration, with business processes or systems never reaching those who make the investment decisions. By getting out there and talking directly to those that deliver your day-to-day business functions you’ll quickly get to the root cause of business ‘churn’ and with open encouragement be told of the little things that could make a big difference. A simple way of doing this is asking the question ‘What’s getting in your way of delivering value here?’ Do the staff need more time with customers? More training? Less policies?

Jeanne Bliss, in her book “Chief Customer Officer”, suggests introducing a “Kill a stupid rule movement” which encourages staff to identify rules or processes that used to make sense but now, as the business has moved forward, are just getting in the way. There may not be a simple fix but this type of open communication channel can highlight issues early, leading to more cost-effective solutions.

Customers

In most businesses, this is the most important set of people to listen to as to continue to be a successful organisation you need happy customers. Some organisations fail badly here by focussing on the good interactions when there is much greater value in following up with customers who’ve had a bad experience. Acknowledgement and empathy when something has gone wrong is important to get the customer to share crucial feedback – the goal is not to win them back but to listen and adapt accordingly.

Leadership today is not just about dictating from on high. It’s about creating a culture where everyone in the organisation feels that they contribute and have a voice. Time is up for the autocrat, we’re now in the time of the listening leader.

Let me know what you think. Leave a comment below, or contact me by email.

How to avert a storm in your cloud

The closer IT expenditure is to the front line of genuine business need, the better the return on investment should be.  So the positives arising from the growth in shadow IT – spend on digital applications and services by business teams rather than the IT function – are huge.   Estimates suggest that shadow IT expenditure now accounts for over 30% of total spend and 55% of digital spend.  And a key driver of this growth is the increasing prevalence of cloud solutions which can be deployed by a business team with minimal support from IT.

But the full scale of benefits will only be realised if risks created by business owners’ unfamiliarity with technology solution governance and inefficiencies generated by distributed decision-making are identified and managed.  The traditional IT-led approach to solution governance, based on large ERP or CRM implementations, will not work for Shadow IT solutions – it is over-engineered for the rapid evolution demanded by business teams.  A new model is required – one that is business-led and balances the need of business functions for speed and flexibility with the assurance that IT teams can provide.

So what risks does business ownership of IT solutions create?  Operational risk increases in direct proportion to any gap between the knowledge managers need for effective supervision and the knowledge they actually have.  The increasing digital divide between senior managers and their younger, junior tech-savvy colleagues is one such example.  And as cloud offerings enable solutions to be deployed by functional teams without IT oversight, the need for digital understanding among senior managers is increasing.  Research by the Harvard Business Review Analytics Services concluded “Digital acumen is essential for business leaders in today’s hyper-competitive, technology enabled world. But most companies lack the knowledge and skills needed to succeed in the digital aspects of their business.”

With high risk activities – such as proprietary trading in investment banks – these knowledge gaps can be catastrophic.  But most cloud solution deployments will not come into this category.  A more relevant analogy can be found in the recent history of data and reporting solutions.  These are often owned and deployed by business functions – marketing, finance, risk, compliance, operations and HR – in which case multiple reporting solutions are typically being licensed when one would do, generating inefficiency and excess maintenance costs.

Alternatively the deployment may be centrally owned (by IT) with space in the enterprise data warehouse made available to different functions to do with as they would wish.  This typically results in multiple ungoverned cottage industries with no documentation of which marts are being used for what purpose and what would happen if they were removed (and probably multiple versions of the truth as well).

This is the type of trap that business-owned, cloud based applications will fall into if there is a lack of management understanding of how such solutions should be governed.  Governance has always created tension between business functions and IT teams, with the former seeing the controls IT teams introduce as being over-engineered and a brake on rapid progression.  In the absence of IT involvement, the risk – as we have seen with reporting and analytics solutions – is that such disciplines are ignored.

Obviously a balance is required.  With digital implementations, there need to be good enough levels of governance.  Our experience with delivering data management and reporting solutions over the past fifteen years has given us relevant insights into what this looks like.  As one client put it, ‘you provide enough governance to keep IT happy and not so much as to delay delivery’.

So with that in mind, herewith our primer for business leaders on good enough governance.

  1. Ownership

Every cloud solution should have an owner who maintains a business case for the solution’s continued use as part of their accountability to whoever the budget holder is.  Unlike traditional implementations where most of the investment is sunk up front, the rental model for cloud solutions requires a living business case with quantifiable improvements in KPIs the solution is delivering tracked against ongoing and forecast costs (including potential spikes).  Such an approach facilitates the solution being swapped out should a new one that will generate greater value become available.

  1. Monitoring

The business case requires the determination or inference of linkages between the operational metrics that the solution can impact and the strategic goals and financial objectives of the organisation.  These metrics and the hypothesised linkages need to be tracked so both the operational efficacy of the solution and its strategic relevance can be tracked.  Hence the second component is the creation of a dashboard to support the living business case.  The dashboard also needs to track compliance related metrics and cover change request progress.

  1. Responsibilities

Effective governance requires a sequence in solution deployment of requirement documentation, solution design, delivery, test, release and support, with the same process applying for subsequent changes requests.  In the traditional model, these activities are performed by different teams.  Cloud solutions typically follow a DevOps model whereby these activities are carried out in rapid sequence by a single business team.  Either way, all stages need to be completed so both processes for how changes will be managed and who will be responsible need to be defined.

  1. Oversight

The governance committee needs to have both business and IT representation – IT teams’ experience of solution design and demand management being particularly important to success.  The governance committee needs to meet on a regularly scheduled basis – monthly or quarterly – and focus on organisational (e.g. responsibilities), security and the commercial model (to avoid the risk of unbudgeted spikes in costs).

  1. Documentation

There are two facets to the knowledge that needs to be captured in documentation – explicit and tacit.  The former includes the business requirements the solution is meeting, process maps for the processes that the solution enables, and the underlying policies and procedures.  It should provide all the information required for someone new to operate the solution from scratch under normal conditions.  Tacit knowledge covers what to do in abnormal conditions, when problems arise and the process isn’t running smoothly – e.g. who to contact if an important feed is not available, fixes for when the solution doesn’t run as it should, answers to common questions about the outputs generated.  Tacit knowledge is typically captured as FAQs and answers.  The basic principle should be that a solution SME can’t progress to a new role unless all the necessary knowledge that their replacement will need has been codified and documented.

  1. Integration

Cloud solutions don’t stand in isolation.  Typically they require data inputs of some form and generate data outputs.  Where does this data come from, how is static data in the solution maintained, what happens with the outputs?   All integration points need to be included in the documentation.

  1. Compliance

Cloud solutions need to comply with the organisation’s security policies for access control and data protection.  Equally the organisation’s security policies need to evolve to reflect the new cloud-based world – relying on firewalls to lock data in a chamber with one door in and one door out is no longer feasible.  Cloud enables and encourages collaborative working practices and the inter-connectivity of system to system processes – data is moving all over the place  – and security policies need to evolve to reflect this new reality while still effectively mitigating risk.  And the more integrated a cloud solution is, the greater the risk that it opens a gate to other parts of the IT estate, hence controlling access or levels of access is critical.  Any data that resides in the solution also needs to be secured (e.g. via encryption or tokenisation) and where that data is hosted needs to comply with data protection legislation and organisational policy.

The rise of cloud requires IT teams to operate differently to how they have historically.  Control is no longer an option, collaboration will become the norm.  In turn, business owners of cloud solutions need to make the IT function their friend.  That will require compromises on both sides – less governance than IT are used to applying, more than business solution owners would like.  We believe that addressing the seven factors above will provide the ‘good-enough’ governance required to mitigate operational risk without inhibiting agility and slowing progress to a halt.

 

With thanks to my colleagues Manoj Bhatt, Mark Howard, Andrea Pesoli and Venkatesh Ramawamy for their contributions to this piece.

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.

The Liquid Big Data Platform – a digital business model for all organisations?

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.

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

Digital vision in 7 simple steps

Recently, I read (and posted to LinkedIn) two articles that both highlighted a) the need for organisations to have a digital vision outlining how digital technologies will reshape the business environment they operate in and b) the absence of such visions in most organisations with an ad hoc approach typically being taken.

Both articles begged the question – how do you go about generating a digital vision for your organisation?

So here’s a seven step guide:

Step 1 – Agree the purpose and focus for your digital vision

There is no point developing a digital vision unless it is going to help shape the long term direction and investments that need to be made.  Hence our recommendation would be that the focus should be on how the markets currently served will differ in 10-20 years, how emerging digital forces will or could reshape them, and what successful participants in those new digital landscapes will look like.

Step 2 – Catalogue the problems you solve

The father of modern marketing, Theodore Levitt, used to teach: “People don’t want to buy a quarter-inch drill. They want a quarter-inch hole!”  Or as Clayton Christensen has put it, “Customers want to hire a product to do a job.”

So as the starting point, rather than use current offerings directly, use them indirectly – create a list of the customer problems you solve or the jobs you enable customers to perform to uncover the fundamental needs that you are meeting.  Generate a long list of problems solved encompassing all segments served then cluster to create a manageable number of higher level ones.

Step 3 – Brainstorm how digital technologies can better solve these problems

There are multiple emerging digital forces that will have a very significant impact over the next ten years.  These include new data sources, analytics and artificial intelligence, biometrics, mobile and wearables, social interactions, cloud and usage-based models, and augmented or virtual reality.  For the groupings defined in Step 2, look at how each of these technologies could enable customers to do those jobs cheaper, with better performance or quality, with greater customisation, more conveniently, more responsively, more securely or more pleasurably.

Step 4 – Broaden and deepen

Step 3 should deliver a series of ideas, but a bigger transformation risk or opportunity may be missed.  Hence there is a need to widen and lengthen your perspective.  Firstly, by considering the offerings supplied by other companies that are bought in conjunction with yours – what jobs do they enable customers to perform?  This enables a more holistic, higher level view of the problems customers want solved.  Secondly, by considering the upstream dependencies and downstream impacts of what you are doing to provide a more value-chain or societal definition of the problems you are part of solving.

Step 5 – Brainstorm digital solutions to these larger problems

Repeat Step 3, but using the more holistic customer and societal problems defined in Step 4.

Step 6 – Draft the vision

From Steps 3 and 5, a series of themes will emerge.  Take these themes and use them to describe how the future will be different.

Step 7 – Start again with a different group (without sharing findings)

No one group has a monopoly on imagination or insight into how events will unfold.  Hence future-gazing is an ideal task for crowd sourcing.  So the more groups that are put through the process the better, so long as each group is not influenced by previous sessions – the wisdom of crowds only works when people decide independently and are not biased by what others think.  The outputs from all the different sessions provide the inputs for the senior team to pull together the organisation’s vision, informed by the richest sources of insight that the organisation can muster.

If you would like help in facilitating this process in your organisation, please leave a reply below or get in contact by email.