Data-Driven Decision Making

What is Data-Driven Decision-Making?

Data-driven decision-making refers to strategic plans and initiatives based on quantifiable metrics and data. 

Within every industry and across every brand, the intelligent analysis of data in pursuit of key business goals is a common ambition. And certainly, lots has been written about it in terms of product development, pricing and so on.

In this guide, we’re going to focus on data-driven decision-making as it relates to B2C marketers and CRM professionals. In particular, we’re interested in the collection, unification and activation of data to inform and enhance your customer engagement and retention campaigns.

Until recently, marketers were typically reliant on in-house data analysts and the IT department to run the numbers and present data on which to base your decisions or alter your strategy.

Indeed, one of the biggest trends in B2C marketing right now is an increased focus on gathering customer data (behavioural, transactional, psychographic and demographic) and making it readily available for analysis and activation.

This is why software solutions like Customer Data Platforms, and Mobile Marketing Platforms or Multichannel Marketing Hubs with robust analytics tools are becoming increasingly commonplace.

These products are empowering c-level leaders and senior management to make better decisions, based on factual evidence of how their customers are behaving and what they are interested in. 

And crucially, they are able to make these decisions (and any necessary adjustments) faster because they, and their teams, are self-sufficient in their understanding of the data.

The old approach to managing customer data no longer works

In addition to the volume of data generated, one of the most prevalent challenges facing modern marketers is the complicated and often frustrating combination of tools required to unlock value from it.

The table below paints a picture of a bloated, inefficient martech that still fails to deliver on core business goals. 

Brands are using too many tools within their martech stack

And then below, we can see that even within these tools there is very often an overlap in capabilities, with resources being unnecessarily wasted.

Inefficient use of martech tools and software

Right now, the most significant blocker to a data-driven approach to customer engagement is a tech stack that is not built for purpose.

Customer Data Platforms: Data-driven decision-making

A CDP takes data in, organises it, and prepares it for analysis and actioning. 

A Customer Data Platform ingests data from multiple sources and creates comprehensive customer profiles for analysis, decisioning and activation.

It’s a marketer-friendly solution for understanding your customers and delivering relevant, win-win communications at scale.

The result is a comprehensive, accessible and actionable record of your customers’ online and offline interactions at an individual level, that can then be used to enhance your engagement campaigns. 

The key features of a customer data platform

If you want to learn more about CDPs, you can read our comprehensive guide.

Data-Types: First-Party Data

This is customer data collected directly, and openly, from your own properties (website, mobile app, PoS). 

It includes transactions, behaviour (views, clicks), biographical information and anything that would typically be part of a detailed customer profile. It’s the essential raw material for personalisation and one to one marketing.

First party behavioural data is commonly obtained via web analytics tools, where you measure how many people come to your site, what they engage with, and whether they perform a desired goal or conversion.

Within the Xtremepush platform it’s possible to collect this kind of detailed behavioural data via our SDK, discretely deployed as a line of code on a brand’s website and/or mobile app.

For brand’s using CRM systems, eCommerce solutions, loyalty platforms or any other software within their ecosystem where first-party data is being collected and stored, it’s possible, and preferable, to consolidate that data in one location.

First-party data is incredibly valuable. Whether it’s related to past purchases, activity levels or specific interests, it helps you to create relevant, personalised campaigns that drive results throughout the life cycle.

Quote from Gartner on the importance of first-party data

Data-Types: Zero Party Data

This may well be a new concept to some, but soon everyone will be talking about zero party data.

Essentially, this is data proactively shared by the customer which relates to their preferences and future intentions. 

With so many marketers and CRM professionals attempting to predict the desires and goals of their customers, this type of data can be transformative for an engagement strategy.

How do you collect zero party data? 

You need to ask for it! We talk all the time about the “value-exchange”, usually in the context of increasing your marketing subscriptions. If you want a customer to share something of such personal importance and obvious value, then you must offer them something of value in return.

In the pre-digital age, surveys were a fantastic, though laborious, method of collecting data. In return for conceding personal insights, they might be entered into a competition or given some sort of discount.

Learn more about collecting and using Zero Party Data.

Data-Types: Third-Party Data

Third-party data is increasingly seen as a relic of a bygone digital age. This is the kind of anonymous data used to feed acquisition at the top of your funnel through targeted campaigns.

It’s anonymous, behavioural information, usually purchased through one of the massive aggregators out there or collected via tracking cookies. It’s typically used to find “look-alike” prospects whose broad demographic profile matches your current customers.

Apple’s Safari browser no longer supports the collection of third party data, eliminating the use of the once ever-present cookies in 2020, following the lead of Mozilla Firefox. 

With Google set to do the same by 2023, third party data will eventually exist only within the walled gardens of social media.

Recently, Apple sent shockwaves amongst app developers when it revealed that iPhone users will need to be canvassed for permission to track their unique Identification for Advertising (IDFA) number.

We’re not going to get into the debate about the value of third-party cookies and their role in personalised online experiences. That ship has sailed.

If you really want to thrive going forward, focus on gathering more of this earned, consent-lead first and zero party data.

How did the DAA consolidate their data and ensure GDPR compliance?

A new approach to data-driven decision-making

With customer data being generated and collected across so many different platforms and properties, centralising it all in a single location is paramount.

Here is a high-level breakdown of how our platform has been built to help solve the challenge of modern data management for our clients.

Data collection, unification and activation.

Our customer engagement, data and personalisation platform has been purpose-built to ingest, and collect, data from multiple sources, unify and assign it to individual customer profiles, and prepare it for activation either via a 3rd party system or through the range of engagement channels which we provide.

In each of the following sections we’ll focus specifically on collection, unification and activation in more detail.

Data Collection: Data-driven decision-making

The first step is, of course, the collection of data. Naturally, we’re talking about the source of the data and how it’s acquired, but let’s also think about what you are actually collecting and why. What purpose does each data-set serve and how are you planning on using it?

There’s almost no limit to what can be collected in theory, but predominantly it will fall into one of the following categories; transactional (online and in-store), behavioural (e.g. pages viewed and session length on-site and in-app), preference (content and channels), subscription (marketing permissions) 

Good customer data management is about being selective and cherry-picking only what you need to execute on business goals.

We strongly recommend that you have a clear intention behind each piece of information you collect and store. Ask yourself what potential business objective will this help you achieve. It’s often beneficial to start with an objective and then work backwards to identify what sort of data is required to reach it.

The four quadrants of Customer Data from Gartner

The above graphic represents the different ways in which First and Third Party Data are used at various stages of the customer life cycle.

While both types are valuable on their own, the changing parameters around the collection and usage of Third-Party Data mean that marketers must begin reducing their reliance on it for filling the top of their funnel.

Data Unification: Data-driven decision-making

A fundamental part of the data management process is unification, bringing together data from all of the disparate sources in your ecosystem. However, this must extend beyond the simple “warehousing” of data.

In order to prepare your data for activation, it’s necessary to create unique customer profiles before assigning all relevant attributes and events to each of them.

For this reason, Customer Data Platforms (CDPs) have become a must-have technology. The Xtremepush platform has incorporated CDP capabilities, allowing brands to better understand their users across various devices and channels.

In this section, we’ll look at three core aspects of how this is achieved; customer profiles, identity resolution and the Single Customer View.

Customer Profiles

Customer profiles are not fixed in stone, they are living records of their interactions with your brand, peppered with key information regarding their behaviour, preferences and interests.

Rich first-party data forms the bedrock of a customer profile. Essentially, there are two component types of first-party data; attributes and events.

What are attributes?

‘Attribute’ is a very broad term that can include any number of data points known about a customer or user.

Examples of attributes include; 

Biographical information like email address, mobile phone number, address, and so on.

Customer preferences like subscription statuses, content interests (menswear, current affairs), preferred engagement channel (email, SMS, push etc), mobile or desktop user, and so on. It also includes lifecycle information like whether a customer is a VIP or a new user, what loyalty tier they are at, and so on.

And lastly, an attribute may be a monetary value, like the customer’s average order value, total spend, and so on.

What are events?

Event data relates to particular decisions a customer has taken, whether that’s a purchase, a page view, mobile app download.

Essentially, it can be any choice or action that takes place within your ecosystem.

Examples of events include behaviours like a purchase, an abandoned cart, an account created, a page viewed, and so on.

What does an ideal customer profile look like?

Creating a customer profile

  • The customer data can come from anywhere within your ecosystem, whether that’s a CRM tool, PoS/eCommerce solution, web or mobile app analytics, or a loyalty platform.
  • There are potentially hundreds of possible events and attributes. You don’t need all of them to create a useful profile. Select only the ones that lend themselves to furthering business goals.
  • Having selected the data points you want to collect, each relevant attribute and event is linked to the customer individual profiles. This is what gives you a detailed and actionable Single Customer View.

Tips for better building better customer profile

  1. Prioritize the collection of first party data, and reduce dependence on third party data.
  2. Don’t hoard! Be selective about which data points you collect and store.
  3. Identify the necessary data points (particularly customer attributes) to create your ideal customer profile.

Identity Resolution

In an increasingly complex landscape, with customers interacting across multiple channels, and through multiple devices, identity resolution is an essential component of data management.

Identity resolution sits at the core of modern digital engagement, allowing organisations to create accurate, unified customer profiles that span behaviour and transactions across multiple channels and devices. It’s also commonly referred to as cross-device identification and underpins the Single Customer View.

It is the bedrock of important data-related use cases such as collection and storage, audience and segment building, and personalised, multichannel customer engagement.

And naturally, it plays a vital role in any kind of data-driven decision-making, as it enables a holistic understanding of each customer’s relationship with your business.

Different types of data used to create a customer profile

In action, identity resolution combines multiple identifiers to a) pinpoint an individual customer as they interact with you in multiple ways and across various channels and b) attribute this behaviour to a single profile.

As the volume of devices, channels and data points increases, the need for accurate customer profiles becomes more urgent. Spend on identity resolution is predicted to reach $2.6 billion in the US alone by 2022.

Learn more about identity resolution.

The Single Customer View

Single Customer View (SCV) is the consolidation of all the data a brand has on an individual customer, across all of the channels and touchpoints they interact with you on, presented in a way that is easy to access and action. 

You’ll also see the Single Customer View referred to as a 360-degree view, or a “unified” view.

An example of a Single Customer View

This is an example of how the Single Customer View is presented within the Xtremepush platform. As you can see it draws together multiple types of data, from across a brand’s ecosystem. 

It details the customer’s known devices, their engagement history, associated attributes and events hit, as well as monetary data.

3 benefits of a Single Customer View

The impact of achieving a Single Customer View is significant, offering value in several core areas; data management, personalised marketing and customer engagement and GDPR compliance.

Data Management

By collecting all of your data together in one location, the accuracy and integrity of this data becomes easier to maintain. It dramatically reduces the risk of duplicate or incorrect information, as well as evening the playing field between different departments. 

Within organisations that use a variety of disconnected data management tools, you will find that different departments (sales, marketing, customer success/support) have hugely different amounts of data to work from.

Personalised marketing and customer engagement

On a day to day level, the existence of a SCV across all of your customers provides the big data required to facilitate data-driven decision-making

If you aspire to genuine personalised, one-to-one marketing then a SCV is essential. It will allow you to create highly targeted micro-segments of customers based on multiple data types (behavioural and transactional).

Not only that but it will enable you to orchestrate multichannel campaigns, with a clear understanding of which channels each customer prefers to be engaged on.

GDPR compliance

And finally, the Single Customer View can play a role in ensuring your organisation remains GDPR compliant. With all of a customer’s interactions, opt-ins and subscriptions clearly presented you can access, modify, delete or export a profile quickly. 

In instances where a customer requests a thorough list of all of the data a brand has stored on them (a request which must be granted under GDPR), then the SCV does give a brand the ability to quickly identify and export this information. 

These Subject Access Requests (as outlined in article 15 of GDPR) are becoming more and more commonplace, and the ability to efficiently fulfill them is now seen as a non-negotiable.

Data Activation: Data-driven decision-making

The final element in the data-driven decision-making process is Activation; using the Single Customer View to identify target audiences throughout the life cycle and then actually connecting with them across as many engagement channels as needed.

Creating Customer Segments

Conditions

A “condition” is the qualifying reason why a customer is included in a particular segment. The range of conditions available for selection within the Xtremepush platform is huge, and includes everything from the type of device the customer uses, to the source of attribution (for app installs) to their general on-site and in-app behaviour.

More often than not you will be segmenting based on an event and/or an attribute; so that’s something the customer has done/not done and something to do with their personality. 

Attributes

One of the most important elements of segmentation are “attributes” we mentioned earlier. An attribute is a sort of tag that is assigned to a customer’s profile. It can be based on any number of factors, as decided by you, and a profile can have as many different attributes assigned to it as you want. These attributes, in turn, are then used to segment your users for micro-targeting.

Examples of attributes in a customer profile

Events

An event can be a starting point for a campaign or a condition for including/excluding customers from a segment. An event can be any clear action taken by a customer on your website or app. 

You may very well be using something like Google Tag Manager (GTM) at the moment to tag particular events in order to measure performance and calculate goal completions. These events can be easily imported into the Xtremepush platform.

What makes a successful Customer Segment?

ROI and Goal-Driven 

This has got to be your starting point when creating a customer segment. Don’t waste time creating segments that are not going to help you drive your core business goals. 

The goal should be quantifiable in some way, whether that’s directly attributable revenue from a campaign, driving traffic to your website or re-engaging a lapsed customer.

From here you can start to apply rules to identify the ideal group, before creating the actual campaign itself.

Having a clear goal in mind is also a great way of identifying where there are gaps in your data. If you don’t have the right data in order to create the segment you need, then that’s something you need to address. 

Multiple conditions

The most successful marketers create segments based on a number of conditions, using classic Boolean and/or rules. For example, you create a segment of users who haven’t opened the app in the past 2 months and are interested in soccer and are using an Android device. 

This level of depth allows you to create hyper-personalised campaigns that resonate with the recipient and drive key business goals. 

Want to learn more about segmentation? Read our guide to creating audiences that drive results.

Xtrempush is the leading customer engagement, personalisation and data platform. It’s purpose-built to help you execute data-driven decision-making across all of your communication channels.

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