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.

Big Data – the need for integration.

Sandy Pentland of MIT points out some of the key issues about Reinventing Society in the Wake of Big Data here. Data ownership is important because it concerns the main driver, as well as one of the main barriers, to developing Big Data services.

The main driver for developing Big Data services is value creation – not just commercial value for firms and the value that customers get from great services – but also the possibility of sharing some of the value that personal data generates with the person that generated it. It’s the main driver because people commonly do things because they get something in return that they value.

One of the main barriers to developing Big Data services is fragmentation and the fragmentation of ownership is critical. Big Data is the personal diary of everyone and everything. This personal diary is sometimes called the ‘data exhaust’ . It is co-created by customers consuming a service and the service provider as it produces the service. I.e. as all the service provider’s staff and machines produce the service.

But who owns this flow of bi-products, this data exhaust? Is it the service provider? Is it the service provider’s partners in its supply chain, the telecommunication company, the content providers, the payment services partner, the security and authentication partner or others? Or is it the customer?

What makes matters more complicated is that data is always copied rather than consumed. So potentially all stakeholders could use the information – for different purposes. And maybe here lies the solution.

If the value of data depends on its use and it is infinitely copyable then we should be asking who owns the right to use it for specific purposes rather than just asking who owns it? Maybe the problem of ownership fragmentation is a licensing problem. Framing data ownership as a licensing problem may partly help to solve the problem of controlling the use of customers’ personal data as well all the problem of sharing out the value that it can create.

Another Big Data fragmentation problem that Sandy Pentland mentions is that Big Data rarely has one source. It’s nearly always generated on different parts of the firm and partly bought-in from partners – and even in a single firm there are database and business process silos.

Database fragmentation makes it hard to integrate the vast volumes of data even when you are aware of what data is available. It’s another main barrier to developing Big Data services because data services are emergent – they only exist as a whole and they can be combined in many different ways.  So its difficult to judge which services will be a success.

It is much easier to start with a good description of customers’ service-needs and then work backwards. But that’s difficult for the firms in the supply chain with tonnes of data and little direct customer knowledge.

That’s why the supermarkets are taking over the world – direct customer contact gives them deep customer knowledge, which helps them to build-up current and new business areas. Then the new business areas give them new insights into different aspects of their customers’ lives – which enable them to build up further new business areas. Look at how Tesco and Sainsbury’s went from groceries to clothes, consumer electronics, then financial services and more services.

If your firm has tonnes of data that you are sure can be leveraged to create new value, but it’s fragmented and you do not know how to use it or when more data  needs to be bought in, then I’d suggest using a customer journey analysis approach, like PIGs. Fragmented product and service components always come together at the point of consumption. And anyway, if you want to assess service quality, value is ‘in the eye of the beholder’ – the consumer.

Rick Smolan – The Human Face of Big Data

Here’s a video that publicises Rick’s latest project on exploring and explaining the potential of Big Data. Big Data is like the web before we called it Web 2.0 – we’ve got some of the basic technologies but we’re still figuring out what we can do with them.

Rick makes a great point: “Just imagine if your whole life you’ve been looking though one eye, and all of a sudden a scientist created a way for you to look out of both eyes…What if you could open a third eye … or thousands of eyes?”

Big Data gives us access to many new perspectives because Big Data is like the minute-to-minute personal diary of everyone and everything.

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.

About this blog


I’ve been interested in what people are interested in for years. I was the customer satisfaction manager, in Europe, for the part of Motorola that Google bought. Then I researched customer loyalty and how value is created for a decade as an academic.

Now I still teach managers, research and consult in this area. And through my research and work with different brands I’ve uncovered some startling new insights on how firms can create huge commercial value by getting a deeper understanding of their customers’ interests  – using some up and coming technologies.

I talk to a lot of managers and firms in my work at Nottingham University Business School but I wanted to share these ideas with a wider audience. Also new technologies like AI, Big Data, the cloud and mobile phones are presenting some game changing opportunities for firms and private individuals.

So I started this blog on how to take advantage of these cutting edge ideas.