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.

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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)

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.