Smart contracts using blockchains could help IoT devices to share data appropriately and securely.

The success of the Internet of Things (IoT) depends on sharing data between different devices but it needs to be done appropriately. But there are some brand new technologies that might support appropriate data sharing and they can do it on a massive scale. 

Schengen Agreement

Sharing (data) is good for us. The more we share then the more perspectives we have on any given consumer or any given problem. In retail this means getting a better understanding of what customers need even before they do. For the IoT this means combining the sensors and abilities of many devices to get a fuller picture of what’s needed and having more options on how to do it.

Firms that manufacture IoT products only get direct data from the customers that use their products. One device only ‘experiences’ a part of a customer’s life. To get a fuller understanding of a single customer’s life different devices need to share data, preferably devices that help the same customers in different ways.

The classic example is a shopping app on your phone that uses data from your refrigerator to suggest recipes based on what you actually have in stock. Or a firm that uses data from all the devices that it has sold in the past to give advice to specific customers, like Waze uses crowd sourced data on traffic jams to warn its users.

But really useful data can come from external sources.  

Just like good feedback and useful suggestions, external information is more likely to give you a new perspective.  So devices that share data with many other devices will tend to get more useful information and make better decisions.  Decisions that are either more personalised, because of complementary information about the same user, or decisions that are chosen from more options, because of information on more user situations.

However, the problem is that firms don’t like to share with other firms for commercial reasons. More importantly, firms can’t just share customer data with any firm that they want to because of data protection legislation. How would you feel if your data was shared with anyone and everyone?

Of course you need some physical way of sharing the data, like Temboo’s stack or a platform like EVRYTHNG. But you also need some rules too.

The problem is that sharing data makes for better user services but sharing data must be done appropriately.

I’ve blogged about this before here, here and here. But now I’ve figured out how to do it using new technologies like smart contracts and blockchains.

Here’s the solution: if you want to share your data, meaning sell it or even just release the value in it for free, then first you need to stop talking about ‘data’. Be more specific. Being very specific is the key to reducing data sharing sensitivities.

If you ask me whether you can share all my data with everyone, everywhere and let them use it as they choose and pass it on to whoever they want to, forever. Then I’m going to get a bit tense.

But if you tell me exactly how you want to use it, why you need to use it, who else might get a look at it and what they are entitled to do with – either the data or the products of the data – and how long this is for. Then I will be much more open to your suggestions.

I still won’t give you my medical data so that your algorithm can charge me a higher insurance premium. And I hate it when it feels like my data is being used to sense when I will pay more for dodgy science fiction books on my Kindle or more expensive airline tickets.

But generally speaking a highly specific definition use of will remove a lot of my reservations. And real anonymisation will remove all of them. Q: who owns this anonymous data? A: dunno.

So how can we apply this to the IoT one devices at a time?

It is relatively easy to run a loyalty card programme because it is centralised and largely automated. But the IoT needs to be decentralised. If every device asks you or its manufacturer for permissions and instructions every time it needs data – or when is asked to supply data – then the IoT will not get very sophisticated.

So how do we ensure highly specific data sharing? Well, we need to design smartness into the IoT. So let’s use smart contracts.

Smart contracts are a mixture of contract law and software. The rules that govern a relationship between two parties are embedded in the software that facilitates the same relationship. To put it another way, the contractual terms are embedded in the thing that they govern. So they control it to help ensure that agreements are kept.

Software is made of rules and instructions for use but contracts also need proofs of things like identity, ownership, endorsements, permissions and rights to do things. And they need to be cryptographically secured, decentralised and machine readable so that every device can automatically ‘negotiate’ with other devices as required.

Fortunately all this can be written into blockchains, the distributed and cryptographically encoded databases that underlie digital currencies. In principle, the data sharing rules for each device can be broadly pre-specified; for consuming third-party data as well for supplying data.

Smart contracts could automate a multitude of interactions between IoT devices and pre-specify what constitutes appropriate behaviour in different circumstances.  Automation is important because the more devices the better and the more granular the interaction then the smoother the service. Pre-specification is important because you do not know which devices your devices will meet.

Each IoT device has it’s own ‘umwelt’ – a self-world – and combining umwelts is the key to successful IoT services.

The self-world of an IoT device is made up of all that it senses and all that it can do. All its sensor data and all its capabilities to change the physical world. Each device has different sensors and effectors, so many diverse devices working together can help each other.

The self-world of a single IoT device is very limited, like the self-world of a tick

Jakob von Uexküll’s idea of an ‘umwelt’ is really useful for understanding the Internet of Things (IoT), either for building a single IoT device, or an IoT app, or for building a whole IoT ecosystem.

If you want to design a single IoT device or a single IoT app then you need to know how it fits into one or more ecosystems. It cannot exist on it’s own. An IoT device on its own is just a device. The potential of the IoT lies in the combined capabilities of many devices working together.

If you want to design an IoT ecosystem – maybe because you want to build a platform, a network or something to help lots of people – then you need to understand how the devices in the ecosystem can help each other.

Whether you are wondering how an individual device can work with other devices or how can many devices all work together, then the umwelt idea helps to answer both these questions.

What exactly is an umwelt?

An umwelt is the ‘self-world’ of a machine, a person or an animal. It is a combination of all that it senses by plus all that it can do to change it self-world.

For example, a female wood tick hangs in a bush waiting for a deer or other prey. When it senses the butyric acid produced by all mammals, it lets go of its perch. If it lands on some fur this impact is the input trigger that makes the tick scurry around. If it then senses a warm membrane, like skin, then that input triggers piecing and sucking actions.

The tick’s self-world has three input signals and three output action. Its sensors are so limited that any warm membrane will trigger piecing and sucking. A rubber sheet holding warm glycerine will give the same input signal and generate the same action as skin.

Humans can see that ticks usually pierce and suck mammals’ blood through skin but the ticks’ actual sensors and are more limited that what humans see and understand. Ticks have a very different umwelt to humans, just as every IoT device has its own special sensors and effectors.

Von Uexkull also helps us to understand how every IoT device has its own perspective. He wrote about the ‘magic journey’ of animals. For example, ticks can survive for many years just hanging in wait for the scent of butyric acid; some birds migrate each year from pole to pole; and some insects just move from one end of a cereal gran to another.

The umwelts of animals are different because of their different sensors and effectors. And what they sense and do is strung together into the very different journeys of their lives. So the magic journeys of animals are hugely different in terms of timescale and distance. This gives each species a very different perspective on the same events.

Devices and applications have different sensors and effectors – different umwelts.

What devices and applications sense is subjective and specific to each one. For example, a phone might have special information because it monitors a particular person’s physical activity levels. That phone has the right sensors – accelerometers and a clock – and only that one is in the right place at the right time to record that user.

Also, the things different devices can do are subjective and specific to them because they all have different capabilities. Phones use screens, audio speakers, vibrators and other effectors to influence you. Cars do this as well but they can also move you. Websites can inform and guide you. And rowing machines can simulate different water conditions or just say when your exercise time has finished.

Devices and applications have different sensors and effectors. So if devices work with other devices they can get access to different information and different ways to help a user.

The secret to successful IoT services

No single device has enough information to help you with anything but the simplest of problems. For example, apps are usually highly specific in what they are for and they usually need you to supply most of the information. And no single device has enough capabilities to guide you using multiple ‘touch points’ and to deal with most of the problems itself.

For example, Sat Navs and GPS apps are best when they integrate lots of data sources and they are indispensable when then they can actually do something about the different problems that come up. A sensible shopper commonly looks at several different retailers’ websites using several different devices, plus some in-store checking, in their shopping journey to buy a high value item.

Bigger problems are solved by solving smaller problems one after the other in a sort of ‘journey’. And each smaller problem requires different information and different capabilities. The more complicated the problem is then the more complicated is the service that it solves. But a single device is too limited on its own. It can only know about its own self-world and it can only change it’s own self-world in a small number of ways.

Real-word problems are complicated. The more that devices can combine their information-sensing capabilities and their abilities the change the real world, then the more sophisticated are the IoT services that they can jointly produce.

The IoT offers the potential for ‘personal Sat Navs’ that use information from a network of sources and that employ a variety of ways to smooth and guide each user’s journey. Journeys in shopping, travelling, education, recreation and work – IoT services can solve the collections of serial problems that we call life.

If you want to design a single IoT device or a single IoT app then think about what extra data you need and what extra capabilities would complement whatever your device or app can do on its own. How do these change along the course of each user’s journey?

If you want to design an IoT ecosystem then think about the mix of data and capabilities that you have access to. Do the devices that produce them work smoothly together and how do they combine to fit the needs of the different users? Including the needs of the devices themselves.

What is an IoT ecosystem and how does it work?

Internet of Things devices work better together, the more IoT devices that link up the better. So which devices and which apps should yours connect to?

Natural ecosystems can tell us ideas for building Internet of Things ecosystems

In the Internet of Things (IoT), the more devices that connect with each other then the more perspectives and capabilities there are to be shared. More data from different sensors and data suppliers; and more ways to change the real world. Like operating cars, home appliances and other machines or getting really useful options from screens or bots.

If you are a device manufacture or an app developer the problem then is: which devices and apps should yours connect to? If you can potentially link to any device and any app then which are most appropriate?

How do you avoid confusing your users when they use your product? How do you avoid confusing yourself? What to connect to is not a problem for the user. The device manufacture or the app developer needs to figure this one out. Just give users a simple list of high quality options that are personalised to their current situation.

Your product cannot link to every other device on the Internet. So which devices have the most useful perspectives and capabilities? You need a strategy that helps your product to be better at its purpose.

And don’t forget security.

Next, having chosen which other potential devices or apps should work with your product you then need to persuade their makers to partner with you. There might be an API to help you connect but close data sharing and brand associations needs discussions and agreements. And that means you need to get noticed, get taken seriously and get a mutually beneficial deal.

The prize is that the first products to build up their IoT ecosystem of partners will get more data and features to build into better services. As my son knows very well, a bigger and more varied pile of Lego bricks means he can build a more interesting spaceship or a more secretive secret base.

There is a lot of talk about business ecosystems and an ecosystem of IoT devices is a lovely thought in principle, but what actually is it and how do you build one? Looking at natural ecosystems might help us.

Natural ecosystems are glued together by ‘nutrient pathways’

The glue that binds together natural ecosystems, like rain forests, deserts and even a single puddle of water is their nutrient pathways.

What we think of as natural ecosystems are actually the ‘pathways’ that recycle scarce resources. The essence of natural ecosystems are nutrient flows along pathways which are based on the natural activities of many different organisms.

Whatever the ecosystem, the quality that makes a natural ecosystem stand out; the thing that makes people say ‘that collection of organisms and stuff is an ecosystem’ is how it moves resources around itself. Microbes, insects, larger animals and plants and other living things move resources around just by living their lives.

The animals, plants and other organisms can come and go, die off or just move to another ecosystem. The pathways need not be dependent any particular organism or even a single species. But the thing that makes an ecosystem appear to us as an ecosystem is the way it recycles scarce resources.

For example, rain forests actually have relatively few nutrients, the soils are very poor. When leaves fall to the ground they are broken up by tiny organisms. Then the nutrients are absorbed by fungi and quickly recycled back into the trees by their roots.

Recycling and reusing nutrients along specific pathways is what makes one natural ecosystem different to another. Different organisms have different ‘roles’ in the pathways and each role might be performed by several different species.

Business ecosystem pathways glue together the IoT ecosystem

If pathways that recycle scarce resources are the essence of ecosystems then what are the scarce resources that business ecosystems can recycle?

The scarcest resource for most businesses is customer knowledge. Customer knowledge about the situation any individual customer is in at the exact moment when they use your product; and knowledge about how all customers have used the product in different ways and in different situations.

Knowing the situation which an individual customer is in as they are using the product enables the product to be more responsive to the customer. And it enables the customer to get better advice and suggestions for using the product.

Learning about how all customers have used the product in different ways and in different situations helps a firm to improve the design of the product with software upgrades or with hardware redesigns. Or it helps to suggest solutions to common problems that customers find as they use the product. These solutions can even be suggested to customers by the product itself.

For example, Sat Navs make travel route suggestions and cooking apps make recipe suggestions. Knowing more about the bigger picture of the users life – the reason for the journey or the reason for the meal – would suggest more personalised options. Knowing what other users have chosen in similar situations would help generate more options as well as a more accurate link between a suggested option and a given situation.

This sort of information was scarce before devices connected to the Internet because the direct relationship with users was mainly with retailers rather than product manufacturers. Also, an Internet connection enables products to record how they are used and then to send this information back to their manufacturer.

Product usage information can be combined with information from different products and other information about users’ lives. A deep understanding of the wider situation that a product is used in helps it to be used more successfully.

The IoT technology stack is a good way of explaining how smart products can connect up and share data. But how do you build ecosystem’s pathways?

Building an IoT ecosystem by choosing devices to partner with

To start building your ecosystem, first ask ‘What customer knowledge do you need to make using your product more successful as it is used and also as you design and (re)design your Minimum Viable Product?’ Do this for every stage of your users’ journeys.

Next you need to choose the data suppliers who can share the data you need to manufacture this customer knowledge. The data suppliers who you partner with (the devices, apps and other sources) will be the components of your ecosystem pathways. The order in which they work together is the flow plan of the pathways.

And how do you persuade them to do it? Just explain to them how it all works using the logic behind your flow plan of ecosystem pathways. Your flow plan describes how each device or app plays its small part in the wider scheme of your ecosystem’s work just by doing its job.

Each device or app has a job to do, its role.  So your flow plan of ecosystem pathways is also the business model of why your new ecosystem will work.


What is the Internet of Things?

IoT devices can be anything with computing power and an Internet connection. Phones, tablets, PCs and games consoles can all be the ‘things’ in the Internet of Things. Even refrigerators, cars, washing machines and stand-alone sensors like web cams – if they have a web connection. And all the apps on your phone certainly have computing power and an Internet connection.

The Internet of Things is a network of any device with computing power and an Internet connection.

The ‘Internet of Things’ and the ‘Internet of Everything’ just mean collections of different devices and apps that work together with some common theme, which is usually called an ‘ecosystem’. The healthcare IoT ecosystem is the collection of all the devices that medics use on their patients. The Quantified-Self ecosystem is like the healthcare ecosystem but it is more about the devices and apps that we use ourselves, to monitor our own activity levels and our bodies.

For example, Fitbit and Jawbone gather physical activity data, Scanadu is a urine testing system that can be measured by a camera phone, Quealth assesses your risks for five major diseases, and there are many IoT sensor products.

Some early stage IoT ecosystems are themed around smart cities, which aim to use digital technologies to manage key services like food, energy, communications and transport as well as citizen participation. Smart cities need smart buildings, which are a whole ecosystem in themselves. And smart buildings are full of smaller devices that are owned by different people who do not necessarily own or live in the smart building itself.

IoT devices need to connect with each other

The key to the IoT is that the ‘things’ can connect to the Internet to help users to use them and to get better at doing so. An IoT toothbrush can use your phone as a keyboard, a touch screen and a dashboard to display how you clean your teeth every day. And it can make suggestions based on how other people do it or on the latest dental research.

Each device’s Internet connection allows it to compare how you clean your teeth with anyone else that uses a connected toothbrush. Learning from other users is a great way to make any product easier and more successful to use. The same applies to the Waze app as it crowdsources warnings of delays and snippets of journey advice. Rolls-Royce also learns from huge numbers of its engines by using sensors to track their health in real-time as they fly around the world.

Without an Internet connection each IoT device is just a device. But when lots of devices link up they potentially get access to two things – all the other devices’ perspectives and all the other devices’ capabilities.

The other devices’ perspectives are like when Wayz users share information about delays or traffic jams, as they experience them. The other devices’ capabilities are like when the purpose of one device complements the purpose of another device.

For example, your home weighing scales, the treadmill at your gym, your refrigerator and your supermarket shopping app could all share information with a cooking app on your phone. The cooking app could then suggest meal recipes based on your weight, your exercise levels, what you have in your refrigerator and the ingredients will be delivered that evening.

The scales, the treadmill, the refrigerator and the two app have different perspectives on your life, their sensors ‘see’ different things. These devices and apps also have different capabilities to do different things for you. Like ordering ingredients or making recipe suggestions.

Even a passive device like your home weighing scales can make useful suggestions, if it knows more about your life than just your weight.

The success of an IoT ecosystem is based on the ‘network effect’.

The ‘network effect’ is the idea that the more members in a network there are then the more valuable it is to be a member of that network. The opposite of this idea is like when a new social network has very few members.

In the IoT, the more devices that connect with each other than the more perspectives and capabilities there are to potentially be shared.

But the challenge for ecosystem builders is to figure out which devices to link together into an ecosystem. And not just devices. Apps, firms, government departments, public services and consumers are all potential members of the Internet of Everything. You cannot link and share with every single one, so how do you choose?

One way to choose is to focus on a theme – health, travel, a sport or a particular job role. But there are still many firms to partner with and many sources of data to potentially access.

In the next post I’ll explain how to choose partners and devices to build an ecosystem from and how to persuade them. I’ll take some ideas from natural ecosystems and use them to show you how to build your own IoT ecosystem around your device, app or business.

My new Econsultancy post – How purchase intent data can help you understand the customer journey.

I’ve a new post on Econsultancy, the digital marketing blog. It’s from some research I’ve just done with Maybe*: How do millennial shoppers decide what to buy?

We’ve shone a light into the dark recesses of the customer journey. The earlier on along the shopper journey you go then the less you know. But earlier on is when you want to influence shoppers. You can read it here.

Big Data Analytics is changing the decision-making structure of organisations

lego tower

[Source Wikimedia Commons]

Big Data is changing how organisations make decisions, who makes the decisions and even the types of decisions to be made. If we think of organisations in terms of decision-making structures and processes of decision-making then what will these look like in an organisation that fully uses Big Data analytics?

Some people are well past the ‘bigness’ and IT focus of the Big Data conservation and onto the Analytical Strategy (e.g. McKinsey or Booze & Co). But I haven’t seen much about how decision-making in organisations will change and how this will change organisational structures and activities.

But the issue of how Big data Analytics changes decision-making has cropped up recently in my dealing with several organisations and it could completely change in the next few years. Here I’ll explore some of the options and possibilities for organisations and their decision-makers.

Centralised versus decentralised decision-making

Lots of firms with huge numbers of staff in different locations have a highly centralised way of making decisions. Take the major retailers. Most of them have moved to a highly centralised ordering policy – so centralised that store managers are not in charge of ordering stock for their own store. Store managers handle staff and service but they can only indirectly influence the orders that are delivered to their store.

I once asked senior managers from one major UK retailer if it would be a good idea to give some decision-making powers back to store managers. The reaction was shock, a waving of hands and shouts of ‘it will be chaos!’.

But some decisions are handled locally, like staff-related issues. I’m just wondering which other decisions would be better handled locally, in a way that reflects local conditions. Decisions that help a store to better fit the needs of its local area or the events that happen that day or that hour.

For example, weather can be very local. Different local weather conditions can lead to very different local needs for different stores in the same retail chain. Events happen differently in different towns and urban areas – not all fairs, celebrations, processions and markets happen on the same day. Different local churches, schools, Scouting and other organisations hold events when it suits them. Different places have very diverse histories and customs and we all know the local customers vary – just look at Experian’s Mosaic.

Personalising a store for it’s village

The best way to satisfy a customer’s service-needs is by personalising your service. What I’m talking about here is personalising a store for it’s village, town or urban area.

So how do you let local staff make more decisions to without causing chaos? Answer: keep the decision-making framework centralised – you own it, you control it – and let the staff make their decisions within that structure.

Levels of self-service

If you want to design a decision-making framework where the framework itself is centralised and global but where the decisions themselves are made locally then a good place to start is with the decisions that customers make.

One way to model the customer journey is as a decision-making process. If you can help customers to understand the decisions that they need to make, help them get the information that they need to make those decisions and help them progress along their personal decision-making process then you will make it easier for them to buy from you or buy from you again. This is the objective of lots of content marketing .

Automated services are increasingly being used to ease customers along their personal decision-making processes by helping them make the decisions that they need to make, e.g. Goole Now.

In different industries mobile apps or web sites provide automated analytical services to customers in the form of self-service functions help them as they decide to buy and then as they use firms’ core products and services.

For example, recent regulatory changes to how financial products are sold in the UK have meant that the cost of financial advice is now not included in the cost of the product. This summer we are running a research project to investigate the advice and decision-support requirements that lower income customers have when considering certain financial products.

We hope to understand how to provide low cost, standardised or automated support to fill the advice gap that is felt by customers that are too poor to easily shoulder the burden of bespoke financial advice but with personal financial requirements that are too complex for basic products.

Higher levels of self-service

But self-service is increasingly being used within and between organisations. It is now common for people to be able to update or check their own HR and financial details just by logging in to the corporate intranet. Jobs that would have been done in the past by HR or finance staff.

Self-service is also becoming a way of putting decisions in the hands of the person who is best able to make them. The person at the right place, at the right time or just with a personal understanding of the context of the decisions that need to be made.

The integration of multiple organisational databases, and APIs from outside the organisation, has led to much more data being available and now Big Data analytics is increasingly being used to produce self-services.

Big Data analytics can be used to produce self-services for staff as well as customers. These are based on centralised decision-making frameworks that reflect the global needs of the organisation whilst preserving local flexibility.

If you want to design such a decision-making framework, i.e. a strategy for how your organisation makes decisions that fit global as well as local needs, then think about how you would design self-service decision-making tools for customers and the rest of your staff at different levels.

How can you help customers progress along the decision-making journey of their lives? How can you help your staff progress along the decision-making journey of their job roles?

Decision-making journeys and Big Data visualisation

Here’s a clue: it’s part of the on-going conversation about Big Data visualisation. Some analytical outputs cannot be represented in a pie chart. Some need a 3D graph, some can only be conveyed in a video, some can only be usefully explained in an interactive simulation.

If you want to support (or constrain) people in the decisions that they are making then you need to present then with enough information to make that decision. Too much information and you get information overload. Too little and they will make biased or less useful choices.

Practically speaking, there is no ‘optimal amount’ of information but the human brain can only fit so much in at once. So the objective becomes getting the most out of a brain’s capacity and this depends very much on the way that the information is conveyed, presented or visualised.

The reason why visualisation is developing into a major subset of the Big Data bandwagon is that it (a) the decision-maker is usually not an analyst or a data scientist and (b) visualisation itself is a product of many previous decisions about what information to pass on and how to portray it – filtering decisions and combining decisions.

In other words visualisation is also decision-making. Deciding how to visualise analytical outputs is just as much a part of the decision-making process as the ‘final’ decision that whoever looks at the visualisation makes.

So if you are designing a decision-making framework think then you should about it as an organisational process that starts centrally and globally, in a standard and stable manner, and then moves towards the edges of the organisation where it can be finalised in a varied and agile manner in many different instances.

Think about your decision-making framework as a self-service environment that connects all the people in the business process in question, including the customer and the suppliers.

The benefits of global + local decision-making

Here’s a few:

  • localisation – decisions fit the local context better, they fit the local needs of a situation better
  • personalisation – decisions fit the local customers better
  • agility – decisions are made in a more timely fashion with locally empowerment
  • motivation – staff are more empowered to do their job, the job becomes more satisfying
  • sophistication – a greater amount of all the above points helps you reach further down the Long Tail of your market’s more difficult to reach customers
  • orchestration – making decisions as a network of people working in the same business process can organise and focuse more brains and more senses together

New research project – get involved

Not many firms are thinking about how Big Data will affect their organisational decision-making. So it would be interesting to talk to senior people in HR, business transformation about this or a more Big Data analytics oriented department. We are starting a new research project to look at the organisational design implications of decision-making that is supported by Big Data analytics. Possible research questions include:

1 Which types of staff roles will start to use Big Data analytics technologies, e.g. simulation sandboxes to do low cost, low risk, quick checks of hunches?

2 Which types of decisions will move between roles, up/down, across functions, e.g. nearer to the person who understands the context of the decision or nearer to the enactment of the decision outcome?

3 Which types of decisions will become more data-driven, either real-time, not real-time or on what timescale?

If you want to talk about this subject then please contact me at

Mobile Big Data: how to link very large scale analytics with very small scale personal needs.

Finger of God

[Source: Wikimedia Commons]

Retail is being drastically changed by new digital technologies like Big Data analytics and the services that mobile smart phones can deliver. Big Data has been discussed a lot but there is little analysis on what it can specifically do for firms and their customers.

Also, apps that run on Mobile devices, like smart phones and tablets are revolutionizing multi-channel retailing and customer relationship management. But mobile apps are usually just catalogues and directories when they could be personal shopping ‘sat navs’like your best friend owned the store.

Most pressingly of all, there is very little useful thinking, or practical advice, available on the overlap between Big Data analytics and personalised services that are delivered by your phone, i.e. the links between the outputs of very large scale analytics and the very small scale personal needs of individuals.

Firms are still working out what they can do with these technologies. Customers are still deciding how they want to use them.

We need a roadmap

New digital technologies are producing a bewildering number of options: new shopping and customer relationship technologies, new Big Data information resources, new analytical possibilities and new business strategies.

Firms need an underlying roadmap for taking advantage of these new tools and resources as they unfold and develop. We need to connect back to the fundamental business objectives of commercial success and amazing customer service in the face of a chaotic digital landscape.

This is the first of three linked posts that show the significance of these technology-driven changes; explain the underlying processes at work; point out the business challenges on the horizon; and map out the strategic options that are now possible for retailers, their customers and the brands that they partner with.

Part One covers how new Big Data and mobile technologies are changing marketing, retail and the rest of business from a business strategy perspective rather than from a technological perspective.

Part Two maps out and explains the confusing new options and approaches that these new digital technologies are now making possible. From the retailer’s perspective, from the customer’s perspective and from a business strategy perspective.

Part Three explains some ideas on how to deal with the emerging possibilities described in the first two parts – from a strategic and analytical perspective, then from an implementation perspective and then with a consideration of how these fascinating changes will continue to unfold.

Part One: A mobile app is like the finger of God

Mobile apps are location-here, segment-of-one, stage-of-now, and downloaded by YOU.

Every app can potentially give you incredibly personalised recommendations, suggestions and advice based on knowing where you are, who you are, being with you every minute of the day and being trusted by you (you downloaded it).

These dimensions are revolutionising how firms communicate with you, learn about you and produce services for you. Everyone you know and most people that you don’t know also has a phone so you could multiply the previous sentence’s possibilities by most of the human population.

But neither firms nor consumers have fully worked out how to use these dimensions. Using mobile phones to figure out a person’s location has grabbed a lot of headlines and initiated a few start-ups and service features like geofencing.

Services that run on smart phones naturally segment customer populations down to a single individual because we rarely share our phones. So phones are platforms for personalisation, i.e. precise learning about individual needs as well as giving customised advice and information.

Also, we carry our phones around everywhere and a lot of people never switch them off – so services can potentially be real-time and anytime, whenever customers need them. Most importantly of all, customers choose to download the app and then give the app the permissions it needs to work.

So services that are delivered by phone apps [or mobile sites] have the capability for complete personalisation in terms of ‘where’, ‘who’ and ‘when’ plus they are a potential bridge for two-way exchange of information.

What goes around comes around

Complete ‘who’, ‘when’ and ‘where’ personalisation is great but an app [and the huge supply chain behind it] needs to know which service options, product variants, SKU number, model, user experience configuration or other permutation of what it could potentially provide is best for each particular ‘who’, ‘when’ and ‘where’ customer at the moment that they tap the screen to say they want it.

This need to decide which service would best fit the immediate needs of a specific consumer will be even more pressing when apps suggests useful things without an actual request from the consumer. Like Google Now is starting to do.

The absolute best thing about mobile phone apps and services is not their in-build sense of ‘who’, ‘when’ and ‘where’ – it’s the information that the consumer gives to the app owner or service provider [and the huge supply chain behind it] plus the permissions to use it.

Mobile apps are not just a bridge to God-like services – they are a two-way bridge to God-like services.

‘God-like’ means not quite omniscient, i.e. you have to give the app some clues as to what you need from it.  Mobile apps know who you are [you registered], when you ask for something or contextually might need something [they are always on] and where you are [they are in your pocket]. So if the app does not abuse the data permissions that you give it [an unresolved issue] and keeps being indispensible then it can potentially act as the ultimate loyalty card.

Introducing the ultimate loyalty card – the Mobile + Big Data version

Big Data is the minute-to-minute personal diary of everyone and everything. Mobile apps have the potential to be the pen that writes your Big Data diary.

All our on-line transactions, communications and surfings are recorded and increasingly stored, analysed and used. But there are still vast gaps in our personal Big Data diaries. For example, a huge retailer like Tesco with a tremendously sophisticated loyalty programme, like Clubcard, only directly knows about the part of your life that is your shopping-life. It can buy-in shared data from its partners to get a better insight into your specific needs. But it can never know your full Big Data diary.

But a mobile app [or collection of apps and mobile services] – that you trust enough to give the right permissions to about collecting your personal data and that you interact with as you go through your life events – could potentially know your whole Big Data diary. The Big Data diary of your on-going minute-to-minute life.

Just think of how helpful [or dangerous] this could be. Just think of the services, new services and benefits to society that this could be the platform for. Nobody could accuse those people behind Siri and Google Now of aiming too low.

Linking smart phones and Big Data means everything in the cloud delivered to you, right here and now. The intimacy, immediacy and relevance of smart phone apps is combining with the vast scale and power of Big Data and the cloud.

This generates many confusing and unfolding possibilities. A massive aggregation of information and other resources is combining with a highly specific understanding of customised requirements. Very large scale Business Intelligence is colliding with very small scale human needs.

The scale and variety of Mobile Big Data is both an enabler and a barrier. For firms and government it changes the problem from ‘we would like to do’ to ‘which should we do?’ plus ‘are we sure that’s the best thing we could do?’ There are too many new possibilities in too many new areas.

Part Two maps out and explains the confusing new options and approaches that Mobile + Big Data is now making possible. It does this from the retailer’s perspective, from the customer’s perspective and from a business strategy perspective.

The flipside of the personal data coin: useful suggestions on one side and ownership, control and responsibility on the other

Think about how Tesco Clubcard and many other firms are finding out more and more of our needs, interests and other personal data – in fact every time we use a phone or a web browser to make a transaction or to communicate with friends we generate data that firms collect and can then analyse. These are the firms that we buy things from or that we ask for information to help us buy things. Or they are the firms whose services help us in other ways.

Whatever you do online generates data – when you search, when you click through web sites, when you register for information or when you ask questions you are generating data for those firms that are able to see what you are doing. Your broadband supplier, your web browser supplier and the owners of the web sites that you visit all know what you do because it is their service that helps you do it.

Firms can also get very detailed location information and timing information – especially when you use your phone for browsing or on the web. And that’s great when they use it for suggesting really useful things that you would never have thought of – like Amazon’s ‘People who bought these books also bought these’.

But what happens when firms lose your data or when they get hacked? (And many will) So how can you be protected? What happens when you say ‘its my data, I want to control how it is used!’ And what about sharing the value that this data generates with the person it is about, i.e. me?

So, on one side of the coin there’s the increasingly detailed personal information (Interests, Location, Time) that firms can use to help people a lot and create immense value. But on the other side of the coin there are unresolved issues of control, share of value and fixing damage when its lost or stolen. This general lack of clarity about control, value and fixing damage also means that the role of the regulators is also unclear because these issues are what the regulators’ roles are based on.

I think I have a solution to most of the problem of fixing damage, and the control problem, but I’d be interested to hear of any ideas that help with the share of value problem. I know how to create it, read my blog posts, but sharing value is different.

For example, what if I buy tomatoes from Sainsbury’s and then Sainsbury’s sells data on my buying behaviour to its tomato supplier so as to help it produce ‘better’ tomatoes. Then what if the tomato supplier makes more profit from ‘better’ tomatoes, e.g. because they are more popular. Sainsbury’s might have a clever deal to get part of that value. Like a reduced price on he supplied tomatoes or a percentage of the tomato supplier’s increase in profit.

But how can I get part of that increased profit? Should I get part?

I’m looking for different ways to look at this issue and for some underlying logic to work out how to share the value that is created. Any suggestions?

Socialising your data: Social data needs intermediaries

Any single retailer gets a very limited view of its customers’ lives – just the part of their lives that it helps them with. But deep insights need to be based on very broad views of customers’ personal contexts, which are understood by looking at lots of different aspects of their lives.

The social data that one firm generates by its own interactions with customers can be massively enriched by using data intermediaries that are gateways to other firm’s social data.

Let me explain. If you want to make relevant suggestions to your customers then you need to personalise your suggestions. But personalisation requires a deep knowledge of the personal context your customers.

Unfortunately, what you know about your customers is limited by what your relationship with them is about. Each aspect of your relationship is like a telescope that observes from only one angle. You can buy-in data but it needs to be linked to your actual customers which is hard for third parties to do. Also data from customers’ relationships with other firms will rarely be focusing on the precise things that interest you. It might also be aggregated or anonymized. For example, consumer classification services, like Mosaic, are really useful for getting a general profile of the people. But they cannot tell you what a specific customer is like. They enable you to take a bet with good odds, but it is still a bet.

So how can a firm with a limited view of their customers lives really understand them enough to make on target and relevant suggestions that are more than just repeat purchases? I’m talking about cross-selling and selling for different uses of their products. For financial services firms the relationship data that they generate is like looking at their customer lives through a key hole. So how can they learn enough about a specific customer to suggest different products or to suggest different uses for the same products? For example, how can an insurance firm know when a specific car insurance customer also needs pet insurance or even when they want to buy a new pet?

The answer is not scale, i.e. huge customer populations. Learning about your customers’ lives through their grocery buying habits has the same fundamental limits for Tesco as it has for a medium sized grocer. Tesco gets to learn a bit more because of the range of its offer – you cannot understand how a customer feels about electronic goods and music unless you sell a wide enough range for them to tell you their interests by what they buy and how they do so.

Customer buying data is like the tests that you go to the doctor for – they only answer the questions that you ask. If you want to check for a specific disease then you need to do that particular test. If you want to know a customer’s taste in music then you need to stock a detailed enough range of DVDs so that they can tell you what they like by buying specific DVDs. The granularity of the question enables the granularity of the answer, not the other way around.

The diversity of the different services that you provide for customers, and what you talk about with them, drives what you can learn from them. The more of their lives that you take part in then the more of their lives you can help them with – in terms of information, services and products.

One great way to get highly specific and up-to-date insights is to use social data. Social data can help with assessing sentiment, targeting communications, product and service innovation, identifying influencers and detractors, dealing with complaints and many other ways to support your marketing strategies.

Social data can be used on an individual level to communicate with specific customers.  And even better, it can be used in a bottom-up way to build groups or segments to interact with at higher and higher levels. This bottom-up method is the complimentary but opposite way to segmenting top-down from you total customer population.

Best of all you can take individual level social interactions, analyse them on a segment level and then take action back down on an individual level with the personalised manifestations of a segment-level strategy.

But you only get into deep and meaningful conversations about specific subjects and specific products. Usually this means your own products, and usually it is either some way down the buying funnel or after purchase. So what you can figure out about a customer still depends on your own product range.

Which is why social data needs intermediaries – firms that help other firms to collect and most importantly of all to share social data. With the proper permissions and safeguards, of course.

Social data intermediaries can help firms to get many more observation angles on their customers’ lives. They are a way of sharing the perspectives that have been gleaned from more than one product range, which means from more than one customer relationship, i.e. from more than one angle of observing each customer.

Of course, for several brands to share data in this way there needs to be things like methods of introduction, frameworks for generating trust, ways of enforcing good behaviour, and platforms for orchestrating efficient collaboration. But intermediaries are good at these as well. And there are plenty of precedents in older areas of analytics – like the data sharing clubs that Experian and Equifax host to help manage risk for member firms.

What retailers want – and how to get it (2/3)

Indispensable Customer Experience

How many loyalty cards do you have in your pocket? How many loyalty schemes are you a member of? … That’s not very loyal is it? Most loyalty programmes are just bribery in return for customer data.

But if each customer’s experience of a brand, through several cycles of awareness to consumption, was that it was utterly indispensable and an absolute joy to use then they would probably keep coming back. And spend more. And tell their close and trusting friends.

Experience, usefulness and ease are much more valuable to customers than vouchers or loyalty points. Although they mean different things to each customer, so they need to be pre-specified and then configured at the same time as the service is consumed by each customer. Which involves knowing a lot about the customer and producing your service in a very agile manner.

Most importantly, experience, usefulness and ease must be supported across all customer touch points. So they must be a foundation of your omni-channel retail strategy.

Solution: design your omni-channel retail strategy in terms of the customer journey – not just in terms of your sales funnel but by using all that you know about that customer. Use their diverse and changing personal circumstances not some static, aggregated, averaged and frozen history. What they need now and how they need it. Not what some people a bit like them needed at some time in the past.

You also need to segment as low as you can go – it all comes down to segments-of-one in the end because even an off-the-shelf product is consumed according to personal convenience. Every time you segment lower you get a more specific understanding of some customers’ needs and they will value it more.

What retailers want – and how to get it (1/3)

What retailers want – and how to get it (3/3)

What retailers want – and how to get it (1/3).

Retail is being disrupted by lots of new technologies right now. The social media that networks shoppers together in lots of different ways. The mobile devices that people use anywhere and everywhere as well as the apps that run on these devices. And the Big Data that shoppers and supply chains continuously generate.

But the technology is not the problem. The really difficult thing to figure out is how people will use these new technologies. Remember Web 2.0? It only got a name when the different ways that people used it started to settle down a little bit into common patterns.

Now this technological tsunami has reached retail – online and off-line.

What retailers want – E-commerce strategists, CRM Directors, Heads of Marketing, CIOs, Analytics and Insight staff, User Experience teams and Social Media managers  – can be boiled down to solving three key business problems:

  • Dealing with Fragmentation – multiple data sources, retail channels, mobile devices, business process silos, brand partners and other variables make it very hard to even choose appropriate strategies, never mind action them.
  • Creating an Indispensable Customer Experience – bribes are not the true drivers of loyalty. But usefulness, ease and enjoyment are. How then can we be indispensible?
  • Designing New Analytic Strategies – retailers know the business outcomes that they want. But the vast range of new multi-channel shopping technologies, and their associated new analytical technologies, makes it difficult to say which analytical approach will generate the strategic plan to deliver them. What’s possible, not just what to do and how to do it?


Nobody – customers as well as supply chain partners and competitors – has settled on their ‘usual’ ways of using disruptive new technologies like mobile devices, social communications, multi-channel synergies and Big Data analytics.

New hand held devices and web services launch all the time, things keep changing. Everyone is still figuring out how to use them – there is no norm, no ‘usual’. People are still getting used to the features of the devices, apps, sites and new information resources. They are still figuring out what works best for them and what new features they can offer their customers.

This has led to a stage of growing fragmentation – multiple data sources, retail channels, mobile devices, business process silos and brand partners are all variables. They present many different strategic options and opportunities for shoppers, as well as for retailers, and they are so new that there are no obvious or accepted patterns of use yet.

So shoppers, retailers and brand partners are confronted with too many different strategies to comfortably choose between. They also get the slightly disconcerting feeling that they are missing out on some even better ones.

Shoppers have way more sources of advice and new ways to shop than they have needs to satisfy. Retailers have to deal with shoppers who keep changing their customer journey and with brand partners who now interact directly with shoppers at scale, using social media. Also, brand partners now have to deal directly with shoppers’ obscure queries and frank reviews – and be watching out for dissatisfied rants as well as random image-building opportunities.

So in reality, retail is not fragmenting. It’s just that the links that connect shoppers to retailers and the rest of the supply chains are fragmenting. Complexity comes from the number of ways things connect up not from the number of things.

The destination is the same but there are lots of new ways to get there.

Solution: use the vast expertise that comes from your position in the supply chain to give customers and brand partners the advice that’s right for them, i.e. personalised content marketing. Not just what products suit them the best but how these solutions fit into their lives, and the tools and information they need to make the buying decisions themselves. Your position in the supply chain potentially gives you the total knowledge of all your staff, brand partners and past customers.

And also, if its going to fit into your customers’ lives then they need to tell you about their lives…so you get to know them even better.

What retailers want – and how to get it (2/3)

What retailers want – and how to get it (3/3)