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.

 

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.

Privacy: Is there a missing third party in our emerging Big Data society? (new white paper)

Personal data can be used for great harm as well as for great good. The more that data is shared between organisations then the more value it can create.

But personal privacy is becoming more and more of an issue, although the way firms handle, share and reuse data is much too complicated for most individuals to be fully aware of or able to deal with if data is mishandled.

For the last year I’ve been running roundtables, interviewing experts and going to workshops to try to look for some answers to these problems. This white paper explains some findings so far.

Personal Big Data: Is there a missing third party in our emerging Big Data society?

 

Executive summary

New Big Data technologies are rapidly changing marketing, healthcare, government, financial services, retailers and whole supply chains.

We are rushing towards a ‘Big Data society’ that is using data analytics to more efficiently target resources and to deliver incredibly personalised user experiences. But the precise use of resources and the personalised delivery of services require access to deeply personal consumer data.

Personal data can be used for great harm as well as for great good. The more that data is shared between organisations then the more value it can create – and the more difficult it is to control who uses it and what they use it for.

The change in how organisations use our personal data is happening whether we like it or not and we risk destroying trust if consumers are harmed or even surprised, by how their personal data is used. We need consumers to trust how their data is used or they will be slower to engage by sharing their data. This will delay the benefits of a Big Data society and leave the UK to be potentially overtaken by other countries with a different view of the importance of consumer trust.

But current systems of legislation and regulation are based on older technologies and ways of working that did not include cheap access to mass data sharing capabilities and personalised data analysis in real-time.

Our investigation incorporates the views of experts from regulators, government, commercial data firms and consumer privacy organisations. It concludes that there are several missing roles in our emerging Big Data society – a missing ‘Third Party’.

This ‘Third Party’ would support individual consumers to deal with networks of large and small firms; help firms to share and use data in new ways in return for doing so appropriately; aid regulators to bridge the gap between the market and individual consumers, staff and firms; and give privacy and consumer organisations a platform to help more consumers and to engage with more firms.

We propose a solution, a design for a ‘Third Party’ that engages the attention and resources of the different stakeholders to watch and help each other. Firms would have a strong interest in behaving appropriately; and in turn they would encourage their staff to behave appropriately and become more successful in the process.

Here is the full white paper: Personal Big Data white paper 3.0.

My new Econsultancy post – Why it’s always good to share in our Big Data society

I did a new post on Econsultancy, the digital marketing blog. It’s about the opportunities and dangers of sharing customer data.

Sharing lets us use our resources much more precisely and produce completely new services. But misusing customer data risks destroying customer trust.

Still, we all need that missing piece of the Big Data puzzle, so we all need to share more. You can read it here.

My new post on Econsultancy – Google Now: it’s about the data not the service

I recently did a post about Google Now on Econsultancy, the digital marketing blog. It’s about how Google Now is a great new service with its near psychic ability to make inferred suggestions.

But the real story is in how it gives Google a much much wider window onto users’ data than Search ever did. You can read it here.

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?

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)

They are looking at you: Google’s telescope versus Facebook’s telescope.

I love the way that Google+ is an even bigger reason to login and give Google a token to link together all my other interactions with Google products. Each of which tells Google a little bit about what I’m interested in.

Google products are not there to get customers to use the web, they’re there to watch customers use it. I work with loyalty card and customer data firms and Google’s array of products give a much better cumulative view of each customer’s interests than any single big retailer’s loyalty scheme, even those of Tesco Clubcard or Boots Advantage Card in the UK.

Think  about those lines of small radio telescopes that you see in the desert. Astronomers combine the data from each of the perspectives of the individual dishes into one big view – and the wider the telescopes are spaced out then the broader the perspectives have access to and the more insight they can gain.

In digital marketing and ecommerce insights are about ‘who’ is interested in ‘what’ products and services, ‘when’ and ‘where’ – even if they do not know it themselves.

Loyalty programmes do a great job of helping to figure this out but their insights are limited by the actual transactions and the relationship that generate the data. For example, an insurance company knows a lot about a customer’s ‘insurance life’ but that’s just like looking through a key hole at the rest of the customer’s life.

Supermarket chains get much broader perspectives than insurance companies because they sell customers things that help them in more diverse parts of their lives. But even that is a small part of their whole lives. Truly indispensible, personal and timely suggestions need to be in the context of large parts of who each customer is and what they do (and what they want to do). Especially if they do not know themselves.

Sure, you could buy-in data but bought-in data is generally more indirect and aggregated than the data that comes from your own relationship with that customer. The more removed from the particular relationship that you want to influence then the less relevant and understandable it is – bespoke always fits better than off the shelf.

Helen Taylor’s post on Econsultancy got me thinking about how Google has developed a very broad array of perspectives on each customer’s life and how it is using Google+ to glue them together and to dig deeper. The +1 button is the simplest way to tell Google what is interesting. But all of Google+’s features help to generate deeper insights and each one gives a subtly different perspective on customers’ interests:

Streams – tells Google different things that the member might be interested in. On timeline to enable insights about trends at the person and group levels.

Circles – tells Google which members might be interested in these different things. Members can segment by some preset categories (Friends,Family,Acquaintances, Following) and define more categories themselves. Analysis of these user-defined categories will give valuable insights into how members thing about their different interests in terms of interest-to-interest associations and higher level groupings of interests (like analysing the category structures of folksonomies and socially generated tag clouds).

Brands can segment by global versus local because its useful for them. So brand partners can also signal to Google what type of members interest them.

Hangouts – helps Google to get the sort of deep insights that only come from closely monitoring small groups of people talking openly. As Helen said, these are panel sessions. The Hangouts On-Air feature enables panel session content to be broadcast, stored and edited. The members and brand partners who choose to view this content are telling Google about their own interests.

The other features of Google+ (and other Google products) are designed to cumulatively generate live and updating ‘process Interest Graphs’, i.e. very wide arrays of perspectives on each member’s life.

Each perspective is a key hole on a person’s life and together they give much more diverse, and deeper, insights for Google’s brand partners than a loyalty programme can – maybe more than Hunch or Gravity can as well. So Google can partner with more brands and do so in more actionable ways.

Facebook, LinkedIn and Twitter have very different ‘arrays of telescopes’ to Google and these give them very different arrays of perspectives to look at their members’ interests from. First, each of these social networks focuses its arrays on different general aspects of their member’s lives. Although they do overlap – and Google+ seems to overlap the most:

Facebook – entertainment and social life, short-term issues, life curation

LinkedIn – work life, short-term issues and long-term projects, network curation

Twitter  – all your life, immediate issues, bare bones content

Facebook and LinkedIn have much tighter feedback loops between members – in terms of more levels of connection (ways to directly exchange content) and some features that enable actual two-way conversations.

Twitter is a bare bones way to connect with people who you think might have interesting things to say. Mostly its about broadcasting with some ability for loose two-way communications.

Second, each of these social networks uses different features to get the data that gives them their arrays of perspectives. Google use product-based features outside of their social network, as well as inside like the others.

The bottom line is that they all try to be really useful in their chosen aspect of their member’s lives because they know that being really useful help requires clarification and clarification leads to much deeper customer knowledge than bare transaction data.

Update: Facebook is looking at combining information across its other services here.

Big data + smart phone app = global as well as local, centralised as well as decentralised.

The huge data center which runs in the cloud can now connect to you via an app on your smart phone. This set up combines scale and power with precision and personalisation.

Firms are starting to make apps that are really useful helpers for shoppers. Like this one for shoppers at the DIY chain Lowe’s on econsultancy.

These apps are really useful because they can work at ‘global’ as well as ‘local’ scales. They have access to a firm’s worth of data and insights – including those of its partners and customers. But can they also serve up exactly what a specific shopper needs at a specific moment. They combine all-time with real-time.

Of course firms need to get past the data fragmentation barriers of big data, one one hand, and specific customisation requirements, on the other – because each user is a segment-of-one. But phone apps that are powered by big data technology link large scale resources to individual service moments.

The biggest potential benefits of big data come from its ability to act globally as well as locally – it is centralised as well as decentralised.

The implications of this are huge. Firstly, customers need to receive real-time services, i.e. services when they need them not just where they need them. But a real-time experience also means that they can play around with ideas.

Real-time means feedback, iteration and experimentation. A properly designed mobile app can give them suggestions that they would never have thought of. Or they can test out their ideas and compare what works best.

But most of all the local provision of global resources means that apps can be guides – life ‘sat navs’ – that help customers through all sections of the sales funnel. But guides need to accompany the customer, so they need to be immediate as well as continuous, i.e. that’s why they need to be real-time services.

PIGs (Process Interest Graphs)

Social Graphs connect people that know each other, like social networks. Interest Graphs connect people with what they are interested in. John Battelle’s been writing about them for years. If you can find out what people – retail customers, business customers or colleagues – are interested in then that is the first step to actually giving them what they want.

Interest Graphs and PIGs (Process Interest Graphs) help firms to figure out what each customer is interested in by using their past behaviour, e.g. past purchases, answers to current prompts, the things that people like them are interested in – their social network – and other bought-in data.

Its like when Amazon suggests a book that you really would like to read but you would never have thought of. PIGs are most powerful when they help firms to tell customers what they truly but they would never ever have thought of.

Process Interest Graphs (PIGs) model customers’ lives

But Interest Graphs are not that great at modelling what people need on a timeline, i.e. how their needs have changed and will change in the future. They are more like a stack of photos of one person which have been stapled together in a book. You can quickly flip through the stack of photos to make appear that the person is moving but any one photo is not naturally linked to any other. This makes it difficult to analyse customers’ behaviours based on ‘stream’ or ‘timeline’ data like Facebook are staring to collect with Facebook Timelines. Also, Interest Graphs are fundamentally static so they are not very good at modelling multiple stages of a customer journey – at different timescales of the customer conversion process or different aspects of the relationship with a brand.

The way to solve this problem is to use Process Interest Graphs (PIGs). PIGs model customer journeys by using stream or timeline data at any scale – from second-by-second to year-by-year or higher scales. PIGs can model what people are interested in at any and all timescales because they naturally link to what people are interested in moment by moment. They can also do this for machines, teams and whole organisations, i.e. they scale up for any level of organisational structure as well as for for any timescale.

This means that PIGs can also be used as an overall data architecture for integrating customer loyalty data, CRM data, social network data – any sort of internal or bought-in data. So PIGs are the ‘glue’ for linking fragmented databases, business processes or services.

From the firm’s perspective, they are a basis for multi-channel strategies. From the customer’s perspective they are a basis for a smart phone app that supports all aspects of each customer relationship – both during and between purchases. Here’s a couple of examples.

How do PIGs work?

The thing that makes PIGs different to normal Interest Graphs is that they make it much easier for data to be modelled in terms of ‘customer journeys’. They work on streams, processes and timelines as well as on networks, like social networks and organisations, that link people into static structures. Interest Graphs only work on static structures.

So you can use PIGs to:

  • improve customer journeys – in terms of speed, experience, usefulness and ease
  • create new services – by brainstorming new unmet needs
  • create social networks and communities – by joining customers and orchestrating how they interact
  • do life-stage segmentation – by personalised targeting based on peoples’ life stages or just the current stage of their day

PIGs make it much easier to manage customer experiences, and organisations, in terms of customer journeys – i.e. the daily, weekly and longer term lives of your customers, staff, projects, products or services. PIGs can be used to build personal guides for shoppers or they can be a ‘sat nav’ for the work lives of each of your staff.

Loyalty cards are changing into loyalty apps.

Loyalty cards are great at linking together the different visits that a shopper makes to a store so that the retailer can build up a better picture of what the shopper is interested in buying. But its like studying the shopper’s life through a key hole – its only about what they do in the store. And they are only in the store for a short time every few days.

Retailers do lots of clever analytics to link up loyalty data with different products, other customers and bought-in data but at the end of the day loyalty cards are a very limited window on a customer’s life. Loyalty data is great for segmenting what is communicated (topic of interest, offers, suggestions, calls to action) and how its communicated (communications media, tone, format, even the colour scheme).

But it is still segmentation – not true personalisation, i.e. it seldom analyses down to a segment-of-one. Loyalty analytics does not give an individualised service. It segments top-down, from the market level down to smaller and smaller segments, so it is inherently standardised rather than bespoke.

This fits the nneds of a centralised marketing function and a centralised marketing function does certain things very well – like planning strategy, whole-business decisions, economies of scale, branding, imagining the future and dealing with the slower business rhythms, like the seasons.

Okay, segmented communications and offers enable a much better fit with different customer types than ‘one size fits all’. But any top-down approach is bad at things like local knowledge, individual customer care, minute-by-minute decisions, customer emergencies, personalised advice, on-the-spot help and solving special problems. And top-down approaches are never very good at what a customer might perceive as sensitivity, agility, emotional support or empathy.

Smart phones + the cloud + apps = personalised + real-time + local

However, more and more of us are buying smart phones and using them to shop. These powerful mobile computers, with the support of a massive cloud-based infrastructure, now allow us to take the web with us wherever we go – 24 hours a day. And a common way to use this technology is an app, which has three game changing characteristics for the Customer Loyalty programmes.

Apps are personalised – the phone identifies the user, it tends to be one phone, or at least one app download per user (because apps commonly store histories of use that are useful for the user, like past searches, play lists or wish lists). Also, the user decides to download an app to a phone, i.e. its pull, not push. So apps are personal in an emotional sense which makes them a powerful tool for understanding each customer in a deeper way.

Apps are real-time – people take their phones with them all the time. People usually do not turn them off when they go to sleep. So two-way interactions with customer via an app can be when the customer actually needs them. Instead of slow responses to monthly direct mail shots they can be fast cycles of answers to prompts that quickly get to the heart of what’s needed right now. They can even use marketing automation like a sort of iterative version of FAQs.

Apps are local – apps know where they are all the time. This is critical for suggesting services that depends on location – which is a lot of services.

So apps are a medium for finding out ‘who’, ‘when’ and ‘where’ a customer is on a potentially continuous basis. Which is a much more powerful way of linking up successive touch points between your firm and its customers than a plastic card – and also a platform for delivering a rich collection of value add services.

Loyalty cards are changing into loyalty apps because their tighter co-creation loop produces greater benefits to both the firms and their customers: more accurate and timely understanding of customer’s needs, for firms, and services that fit the customers better.

Apps enable bottom-up loyalty programmes.

At the moment most apps are just catalogues, they are not the personal guides or ‘life sat -navs’ that they could be. They do not act as personal real-time guides that help customers through repeat cycles of the customer buying process.

But they could do – if they were designed around PIGs – then customers would be loyal because of a brand’s indispensability rather than because a retailer bribes them with money-off loyalty points. PIGs help to model what each different customer is interested in right now.

This would be a bottom-up approach to customer loyalty and CRM. Retailers, and B2B firms, could use apps as their main platform for both CRM and capturing loyalty data. These apps could help each and every customer’s journey; generate loyalty from deep experience and complete indispensability; access infinitely more data; create new modes of product offer and customer service innovation – and appeal to customers that are attracted to mobile technology, like males and young people.

Best of all a bottom-up approach starts with a single customer so it can act as the glue that integrates multi-channel and multi-device strategies.

The secret is knowing what they want.

“Any colour you like as long as it’s any colour you like”

Martin Hayward, dunnhumby white paper, 2009.

If you know what customers want then you can it sell to them or give them great service. Of course there’s the small matter of producing and supplying. But however sophisticated your capabilities are you first need to know what they want, need or are interested in – even if they don’t know it themselves.

The problem is that each individual customer has different needs. You could give them some off-the-shelf product or standardised service – a sort of ‘shotgun’ approach – and it might work for a lot of them. But it would not be a perfect fit for many, you would never satisfy some segments and you will always  run the risk of a niche competitor poaching some of your customers.

This is because the greater the fit between your product and the individual needs of a customer then the more they will value it. This applies to everything people use or consume from groceries and clothes to delivery and information services. Fit can be in terms of all product and service attributes: size, colour, look and feel, materials, form, price and all the wraparound services like advice, support, delivery and brand.

Get the fit right and you will get a perfect customer satisfaction score because the customer experience will be great and your product or service will do exactly what that customer requires. But it’s hard because each customer requires subtly different things.

Worse still, what people want changes from second to second. When I’ve had a coffee I do not immediately want another – although my wife will happily chain-drink mugs of tea.

So your understanding of what your customer wants has got to be different for each customer and it must be timely.

So how do I know what each customer wants and when they want it – even if they do not.

Most firms use a mixture of CRM and Customer Loyalty data to get a better idea of what different customers want, whether it’s new products, general product improvements or just things that only apply to the current transaction.

Loyalty programmes, like Tesco Clubcard and Boots Advantage Card, combine loyalty card data with CRM data and bought-in data to segment customers by type and – this is the clever bit – by life stage. For example, if they guess that you are expecting a baby then they invite you into giving much more detailed information (like when the baby is due), in return for much more specific advice (like product suggestions and advice that fits the exact stage of pregnancy that you are at). Sainsbury’s uses Nectar Card data to do a similar thing and IKEA uses Family card data to help people with specific home projects, like a new kitchen.

Key point: loyalty programmes only indirectly tell you what a customer is interested in because they only tell you what customers buy. They tell you nothing about the rest of the customers’ lives – its like trying to study customers lives through a key hole.

But you can use loyalty data to ‘open the door’ and get customers to tell you a lot more about themselves – if you give them something in return. Which does not have to be money-off bribes.

But loyalty cards are changing into loyalty apps. Smart phones provide a massive opportunity to leverage much more personalised data because they are natural platforms for individuality, real-time relationships and location-based interactions. I’ve talked about the opportunities here.