# 3.4: Understanding data

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## Consumers, technology and data

To understand data and its role in a business, you need to understand consumers and their relationship to technology. Many people believe that technology changes and consumers adapt in response. Really, consumers are leading the change themselves through the technological choices they make. They decide which technology to embrace, usually favouring whatever facilitates speed and ownership of their own experience. This is particularly true on mobile.

Brands need to meet consumers in the technological spaces they have chosen. The consumer relationship with technology is about accessibility, theirs to brands and products, and brands’ to theirs. This has shifted a large deal of power to the consumer.

This connection to technology offers many opportunities for marketers. Every new technology embraced by a consumer offers brands new ways to collect information about them. This leads to more granular segmentation and more targeted marketing messages.

### The Internet of me

Consider the Internet of Things, which is the idea that more and more everyday objects are technologically enabled to send and receive data via the Internet. The information these objects transmit is, for the most part, related to the consumer using the objects rather than about the objects themselves. Consumers use this connected technology to communicate, create content such as social media posts, and consume and share products.

More than an Internet of Things, you can think of this as an Internet of ‘Me’. ‘Me’ is the consumer, and the technology-enabled connection between objects and the consumer allows brands to access reams of data about consumers that they could never have considered a few years ago.

## What is data?

Remember the difference between owned, earned, and paid coverage in the digital sphere? Your owned properties cover your websites, social media profiles, and anything else your brand controls. Read more about this in the Social media and strategy chapter.

The intention behind the collection and careful use of data is to create more value for your customers. Value can be defined as any means through which the brand delivers on its purpose. Whatever that value is, it needs to be something that customers actually want and that is relevant to them. Data can help you identify what is relevant and useful and what really works.

### Forms of data

There are four main forms of data relevant to brands:

1. Algorithmic intelligence – the algorithmic methods used by companies such as Google and Netflix to help drive revenue. In the case of Google, to assess what people want to read, and in the case of Netflix, to assess what they want to watch.
2. BI: Business intelligence – the technology-driven process for analysing an organisation’s raw data, about profits and performance, and presenting that information to help brands make better informed business decisions.
3. CI: Customer intelligence – information derived from customer data, that comes from internal and external sources, to build better customer relationships and make stronger strategic decisions.
4. SI: Software intelligence – software tools and techniques used to mine data for useful and meaningful information, the result of which is similar to BI.

By combining all four forms of data, you could say that you are using data intelligence (DI), and this can easily make you the most powerful brand in your field.

### Sources of data

Data can come from any number of sources, particularly thanks to the Internet of Things. You don’t need to restrict yourself to website-based analytics. To get a full picture of audience insights, try to gather as wide a variety of information as you can. Some places to look:

##### Note

Take a look at this video on the Internet of Things , how it works, and what we can do with the data: https:// www.youtube.com/ watch?v=QSIPNhOiMoE.

• Online data – everywhere your audience interacts with you online, such as social media, email, forums and more. Most of these will have their own datagathering tools. For example, look at Facebook Insights or your email service provider’s send logs.
• Databases – look at any databases that store relevant customer information, like your contact database, CRM information or loyalty programs. These can often supplement anonymous data with some tangible demographic insights.
• Software data – data might also be gathered by certain kinds of software, for example, some web browsers gather information on user habits, crashes and problems. If you produce software, consider adding a data-gathering feature (with the user’s permission, of course) that captures usage information that you can use for future updates.
• App store data – app store analytics allows companies to monitor and analyse the way customers download, pay for and use their apps. Marketplaces like the Google Play and Apple App stores should provide some useful data here.
• Offline data – in-store experience data, customer service logs, in-person surveys, in-store foot traffic, and much more.

You should consider looking for data in unusual places or consuming data in an unconventional way.

##### Example $$\PageIndex{1}$$

Amazon Dash is an excellent example. Amazon Dash is a Wi-Fi-connected service that reorders products with the press of a button. It consists of three components.

1. A scanning device used to inventory consumer goods in a house.
2. The Amazon Dash Button, which can be placed anywhere in a house and programmed to order products of the consumer’s choosing.
3. The Amazon Dash Replenishment Service, which allows manufacturers to add a button or auto-detection capability to their devices.

Consumers see this as a brilliant innovation that gets them the product they want, when they want it. They see it as being about convenience, and it is! As an example of incremental innovation, it stands out, and convenience will drive the use of the product. It is also an excellent data collection tool that helps to gather data for granular segmentation. This is good for both the customer and the brand.

### Lagging, current, and leading indicators

Your data-driven, customer-first strategy should be built around three data indicators.

1. Lagging indicators are past data such as financial results, sales history and past campaign results. Profits can be seen as a result of your marketing efforts and how you responded to the competition. These indicators are important because they show your past performance, but they are only one part of the whole.
2. Current indicators are pieces of information from right now. For example, you can use website analytics to see what customers are doing on your site and which pages they visit. You can use this data to segment around that. The immediate environment is also a current indicator, for example, the #deleteuber hashtag was a huge current indicator for the Uber group about how their customers were reacting to their political actions. Current indicators can encourage you to think about what you can do to be agile in response to them.