Data, analytics and Leadership : My Grandmother, Customer Service and Big Data
Great customer service – in the 1920’s
My grandmother (Nellie) was born in Shoreditch in London, in 1887. After she married, in 1917, she eventually tried Sainsbury’s. They used to deliver her groceries to her door. Nellie was not a rich woman. Sainsbury’s leadership was built on convenience, value, cleanliness and great service – and home delivery was central to this for all of their customers.
Nellie moved to Burton on Trent after my mother was born, at the end of the 1920s. She found a new grocery store, Wilkinson’s, near the Town Hall.
It was not a chain, and the staff knew her by name. She often bought the same produce. A side of streaky bacon, if I remember her stories, was a staple. And whilst there wasn’t a formal loyalty program, Nellie was occasionally given new things to try, or handed special treats for the kids.
She got personalised service from the best and most convenient place she could find.
My mother later shopped at Wilkinson’s, too. In the 1950’s and 60’s, she visited on a Tuesday, placed her order, and paid. I remember walking in with her, and was fascinated by the huge bacon slicer. The order was delivered to our home by van on Thursday.
Consider how we shop today
Buying a camera, perhaps? Well, we probably first read the reviews on the web, ask the advice of friends both real and on social media, and comparison price shop. We might also pop into a bricks and mortar store to hold the camera and ask more details. Yet the chances are we don’t buy the camera then. We might order it from Amazon before we walk out!
Customers since retail was invented have looked for perfect, personalised service – using all the channels, information and search tools open to them. Today, they are also using all the data and mobile technology that they can get their hands on – more than ever before.
And today’s customers don’t see channels – they see information and results. That’s why all retailers must understand how data and insights generated are critical to the customer experience.
Today’s Customers – and their Digital Day
First thing in the morning, we download newspapers and check email. Facebook, Twitter, eBay, voicemail, phone calls: which of these are in your morning digital routine?
At each step, data is created. What we do, where we “visit” - both in the bricks-and-mortar world and the digital one – and what we see or buy is noted somewhere. Most of the data is collected by the websites visited or the services used.
The government watches us too - they are recording what taxes are paid, what education we have, what cars are driven, what laws are adhered to, how passports are used, who is in the family circle, and what our medical history is.
At work, there’s more email and the web. Depending on the company, customer data is collected – sales, credit histories, names, addresses, likes and dislikes. Internally, the company collects data on suppliers, its manufacturing plants, process performance, and distribution systems. There is data on employee performance, salaries, and more.
Booking flights, hotel stays, or going to a restaurant creates more layers of data. In the evening, during leisure time, millions of people stream music over the web, or upload pictures so that friends and family can see what they are doing and where they have been. Not just pictures are recorded - but also which friends view the pictures, their comments, where the photo was taken, and the camera settings used.
“Big Data” is a buzz-concept
Everyone is talking Big Data. But too often it gets discussed in technological terms. What is its real meaning for business strategy?
Remember the old adage “data – knowledge – insight – wisdom”? Well, “Big Data” is only as useful as the insight and ideas that are derived from it. We accumulate lots of data but we can’t always make sense of it. We need to extract wisdom.
First, let’s define “Big Data”
In the 1990s, an early example of the retail use of Big Data was deriving useful insights from customer loyalty programs to better communicate to and serve customers.
Since then Big Data has exploded in the number of different sources and the ways in which it is connected. The need to generate useful insights and action is exactly the same, though, however big and complex the data set is.
Big Data spans many dimensions such as volume, velocity, variety, and veracity. (Gartner pioneered this definition in 2001)
- Volume: Enterprises are awash with ever-growing data of all types.
- Velocity: For time-sensitive processes such as fraud, Big Data must be used as it streams into the enterprise.
- Variety: Big Data is any type of data, structured and unstructured, such as text, sensor data, audio, video, click streams, and log files.
- Veracity: Many leaders don’t trust the information they use to make decisions. Establishing trust in Big Data presents a huge challenge as the variety and number of sources grows.
There is another important aspect to consider.
Tiny Data + Unstructured Data = Big Data
“Tiny” Data (my definition) means data from a single source in a structured format, which, whilst it may in a huge quantity, is actually limited in its complexity. Too often we confuse “big” with “complex”. A single source could be the vast data set of the national census, a record of all the searches ever done on Google or all the purchase records held by Visa. But, “big” as they may be, with today’s computing power these data sets are all crunchable and analysable. When you combine such “tiny data” sets, then you start to get really “big data”.
And then there is another type of data beyond these structured sets.
Unstructured Data means exactly that - no fixed database format or pre-defined structure. Think of messages sent on Twitter, Instagram images, YouTube videos, Facebook likes, phone calls, customer service calls and so on.
There is no formal structure to these, in the sense that we don’t pre-determine what is in the database as we do, for example, for the shopping items in a retail database. The photograph may have a “blue” cast, but we don’t say that when we upload it. The tweet may be ‘happy” or “angry”, and whilst the context may be clear to the reader, there is no pre-determined structure to the tweet (other than it’s only 140 characters long). Yet this unstructured data can be extremely rich in helping us understand individual preferences and activities.
“Big Data” combines multiple “Tiny” Structured Data sets and Unstructured Data
Technologies are becoming available to combine and make sense of these disparate sources - and most importantly turn the analytical results into useful insight and action.
Consider looking at someone’s Facebook timeline, and noting that they tend to like wearing blue but never wear orange. If you are a clothing manufacturer, and knew that fact about your customer, wouldn’t that help you make more appropriate offers to them? And if you could match this insight against the customer’s purchase records of clothes or other items over time, wouldn’t that give a richer insight into their behaviour?
This raises privacy issues, of course, yet there’s another revolution going on – the move from business and government ownership of the data to individual access, and thus ownership.
Personal data Ownership
Governments, companies, and universities collect all kinds of data, yet often an individual has to fight to access their own data; individuals create 70% of the data but enterprises are responsible for storing and managing 80% of it.
As individuals find more and more ways to use the web, and take advantage of data, they are also gaining unique, personal access to all of their own data. Companies or government won’t have the same broad access across all of the many data sources we generate.
Obvious privacy issues make it hard for even governments to aggregate disparate personal data sources (at least legally). Yet individuals will be able to analyse all of their own data, accessible via mobile devices and connected through the cloud, and create their own action plans from the insights. No longer will business or government have the monopoly on data and what to do with it.
Data Control, or at least the right to control, is shifting. And I think my grandmother would approve!
Industry has another challenge – trust. Would you let Facebook have your bank account details? Would you let your bank or Amazon have your emails to friends and family? That seems unlikely.
On the other hand, even if individuals are not directly using some form of “aggregation app” all their data turns up on their locally controlled device, whether PC, Mac, tablet, or phone. Accounts are already connected in the social media sphere (login with Facebook or Twitter, anyone?), but the technology now allows us to go much further.
Many companies are racing to create a secure “wallet” on our devices (Google, Apple, Square, credit card companies, and many others). In the first instance, these wallets will be for secure bank and shopping transactions. But when travel or restaurant details are added, movies and music included, you “chat” profile and what your friends like and do, the wallet will be a hugely powerful data source for analysis and predictions of all kinds.
At its crudest implementation, knowing a little more about individual preferences and likes can lead to more targeted and therefore more effective advertising. That’s what Google does. But it also gives us, the individual, a better understanding of the value of our own data.
We are entering a data marketplace. Individuals create and increasingly access all of their own data, and government and business want it. An exchange will take place. Individuals will want something of value to them in return for access to their data.
Pre-Big Data, businesses offered loyalty programs in exchange for customer purchase data. The currency of exchange was rewards, gifts, miles, and cash back. Today, when the individual has the data, the exchange will go the other way.
With ever increasing demands from customers and dramatic advances in technology, business has no choice but to learn how to understand and then effectively work with this change in control.
So, in today’s omni-channel world, what are the key business strategies to consider?
Customer Centricity and Innovation Networks.
1. Customer and client interactions are all moving from “push” strategies to “pull” (Hagel, Siegel). Instead of businesses “pushing” and marketing services and products at customers, the individual can now discriminate and “pull” services to them - to suit their exact needs, preferences and timing. We all do it all of the time – searching for product reviews on the ‘net, and asking advice from friends, both real and on social media. This “pull” approach to buying things fundamentally changes the business dynamic.
“Big Data” makes this possible. Individuals can view recommendations from other customers, access products, services, resources and media that they need, and optimize how and when it is all delivered and how it is subsequently used. As individuals we are adept at combining structured and unstructured data to get the results we want.
As customers, we “pull” and personalise everything.
Customer centricity is then about businesses meeting customer needs, and using data-driven insights to build effective customer programs and offers. This means a move in the mind set of the organization, a coherent enterprise strategy built solely on customers and rigorous execution to meet their needs. The business must embrace data driven decisions and use a common customer language to connect things together.
2. Innovation Networks are the second strategy. Many businesses already use networks with their suppliers and others to create innovation and build competitive advantage. Innocentive is an excellent example of a business that matches “problem seekers” with “problem solvers” to create new ideas. These innovation networks increasingly rely on “Big Data” to speed up the flow of new ideas, products and services.
In the world of “Big Data” Leaders must choose operational processes that open up their Enterprises via Innovation Networks. Ideas will come from both internal and external resources, and business needs dynamic structures that dramatically accelerate innovation.
“Big Data” changes everything - strategies, decision processes and innovation processes.
Retailing has always been about customers – like my grandmother and my mother. But today’s customers’ use more complex, data based methods to make their decisions.
Funnily enough, Big Data is a misnomer. It’s not about “big”, and you don’t have to be a big business. It’s about deciding to delight your customers, and then use the data to help.
Here’s an idea showing how even small retailers could better use data
Imagine you run a coffee shop, serving high quality latte, and you have a world class Australian barista. But you don’t know your customers well enough.
Give them free Wi-Fi and collect an email address, and a phone number if the customers are willing. You will notice that Fred comes in often and sits for a while, connected to your Wi-Fi, He seems to be working. On the other hand, Jane is a quick customer. She collects her morning shot and checks the news while she waits.
E-mail them both. Offer Fred a private space to sit and work, and a free bagel to go with his coffee. Tell Jane to text you 5 minutes before she needs her coffee, and it will be ready.
That’s personalised service – isn’t it easy? It’s not “Big” data, but it is data, and is actionable insight.
My grandmother liked tea rather than coffee – maybe we should offer her biscuits?
The moral of all of this:
Great service has always been prized, and customers search for personalised service.
It’s just that today retailers must embrace Big Data to keep up with their own customers!
Note: Burton Town Hall Image courtesy of http://www.burton-on-trent.org.uk/category/surviving/townhall/townhall3. The corner shop is not Wilkinson’s – that was on the other side of the road!