”Customer analytics is defined as the use of analytics to study customer behavior for effective business decisions through market segmentation and predictive analytics.”

Customer analytics equips marketers with relevant information about customer behavior, helping them take effective predictive business decisions. It helps businesses hone their direct marketing or CRM efforts, improving customer experience through personalized interactions between the brand and the customer on the basis of customer segmentation as per insights from the data. In this article we define customer analytics and share key processes, trends and some examples of how it contributes to business growth.

Digitization has exposed businesses to abundant data, which when utilized well, helps them perform better. Hence, customer analytics is gaining more importance with each passing day. A 2013 McKinsey survey titled, ‘DataMatics’ had stated that companies that make extensive use of customer analytics are more likely to outperform their competitors on key performance metrics, whether profit, sales, sales growth, or return on investment.

Let’s find out what it means and how to get it right!

Table of Contents

• What Is Customer Analytics?
• Customer Analytics Framework: 3 Key Processes
• Turning Data Into Insights: Customer Analytics Best Practices
• Key Trends In Customer Analytics
• Examples

What Is Customer Analytics?

Customer analytics is the process of studying customer data accumulated across departments in a company, for assessing, understanding and interpreting customer behavior across the various stages in their buying journey.

Why analytics? Quite simply, it leads to better decisions. Informed decisions. Data-driven decisions. It connects various data points and sets to identify trends, patterns, anomalies and gaps. Luckily, the technology available today, does not do just that, but also presents the findings in a way that a regular (right-brained) marketer like you and me can make sense of and apply the insights and decisions to marketing action plans and execution strategies.

The basic concept is simple enough. You have data –> you use technology to run some analytics on it –> you get some actionable insight from it which will help you make better decisions –> that can drive better marketing results & ROI.

So, what’s the catch? Well, there are a lot of different sources of data, there are lots of different kinds of analytics (SEE BOX: Types of Analytics) and there are several use cases for which you can deploy decision science solutions

Additionally, customer analytics:

• Helps you increase customer response to promotions, strengthens customer loyalty and consequently boosts sales revenues
• Tightens your overall campaign cost by letting you focus on buyers who are most likely to make a purchase / perform a desired action
• Helps you identify unsatisfied customers and prevent brand detraction

With customer data analytics, you can create a more effective and efficient set of campaigns based on stage of buying journey, browsing behavior, purchase history, demographics, geographical details and so on. It is key to note that an organization’s technology stack and audience management capabilities play a key role in both data collection and analytics.

Learn more: What are Audience Management Maturity Levels?

Now, let’s find out how to put a strong customer analytics system in place.

Customer Analytics Framework: 3 Key Processes

Setting up a robust customer analytics framework certainly requires a strong technology stack, but there’s more to it. Here are 3 key processes for planning customer analytics.

1. Know the customers you wish to analyze

At the very onset of establishing a customer analytics plan, keep the end goal and the preceding customer journeys in mind. Customer journey mapping is the process of drawing a comprehensive, diagrammatic map of all possible stages and points of interaction between a customer and the company, starting from brand discovery to post-sales servicing and repeat purchases.

Here are some questions that may help you map customer journeys:

• Who are our customers – their age, demographic, general location, purchasing capability etc.
• What do customers prefer to buy from us vs competitors
• What is their preferred mode of purchase?
• What is their preferred mode of communication?
• What are their preferred touch points at different stages of the buyers’ journey

You could ask more such questions, depending on the objectives you want to achieve from your customer analytics framework. The journey mapping exercise gives you insights about the best touchpoints to collect the right data from, opportunities in the journey to collect relevant data, gaps in the journey where you may be missing opportunities to collect crucial input data, and it also helps you connect the dots between various touch points across the journey to draw better insights from the data.

2. Capture, organize, then analyze data

Once you have identified the data you want to collect and the sources from where to collect them, the next step is to actually collate as much data as is relevant to your goals. Gather data from various sources or customer touchpoints such as your website, in-store visits and purchases, email clicks, website browsing, activities on your app, blog communities and social media interactions, CRM system and other internal and external systems (see graphic: Analytics Big Picture). You may also run surveys, conduct user research or purchase third-party data to feed into your customer analytics framework.

3. Define outcomes

To establish a strong customer analytics practice, it is critical to define the outcomes you seek from the data. Based on the outcomes you are seeking, you will define the analytics that needs to be performed. For example, as a result of the analytics, do you hope to get clarity on what has happened (descriptive), why it happened (diagnostic), answers to specific questions and possible responses (prescriptive), or what may happen in the future (predictive)?

Turning Data Into Insights: Customer Analytics Best Practices

Customer analytics allows businesses to make the best data-backed decisions, boosting conversions, delivering a better customer experience, and bringing in operational efficiency. Hence, most successful businesses have already embraced it as a way of working across their customer programs. Here are some customer analytics best practices for you to adopt.

1. Organize your data

Your customer analytics is only as good as the data it relies on. It is important to connect the dots between data points, and have clarity on the data you actually want to analyze from the deluge of possible internal and external data you could use. The data you choose to analyze can then be funneled into a data warehouse or lake, a customer data platform, or a specialty customer analytics platform that can help organize the data in a way that it can be effectively analyzed.

Specifically for customer data, organizing it is also about building complete, unified profiles of individual customers or segments. In case of first party data for martech applications, this would involve processes such as probabilistic or deterministic identity resolution, building identity graphs, 360-profiles of customers and integrating consent into customer data, to ensure compliance. In case of adtech applications that needs to be used through a DMP, the data needs to be anonymized in order to build ‘look-alike’ segments that define characteristics and traits.

Learn More: What is Customer Data Management (CDM)? Definition, Best Practices and Technology Platforms

2. Data visualization to storytelling

Data is as much an art as a science, and leveraging data visualization to present data in a way that marketers feel comfortable using it is crucial to overall ROI of your customer data analytics program. Visualizing the data with graphs charts, infographics and even videos helps bring the data alive so marketers can more easily spot patterns, gaps and opportunities that could otherwise be hidden in mind-numbing rows of numbers.

3. Data and AI: go for advanced tools

Analytics has evolved from being descriptive to becoming increasingly prescriptive now with the power of AI and machine learning to spot patterns and predict future outcomes more efficiently. With advanced AI powered customer analytics tools, marketers can run predictive analysis to model potential campaign outcomes etc. Intelligent systems can also start recommending next best steps and customized interactions and experiences based on the data, for each customer.

Learn more: Predictive Analytics for Right-Brained Marketers

Key Trends In Customer Analytics

Let us look at some trends that are disrupting the customer analytics space:

1. Emerging sources of customer data

the increasing penetration of voice enabled smart devices, in-home automation and wearables is unveiling a lot more about customer lifestyles and choices. Customer data from all these devices combined with social media journeys, is any marketers’ newfound goldmine. While email clicks, in-store visits, online purchases or browsing and content streaming still exist as important sources of customer data, these new sources of unstructured data are able to reveal even more interesting customer journey insights to marketers.

2. AI to make analytics more human-centric

Even though there are frequent speculations that AI and machine learning will take away the jobs of analysts, more advanced systems present a rather positive picture. With newer updates, these systems and tools are designed to work more efficiently in collaboration with human judgement. In fact, over the coming years, AI enabled customer analytics systems are expected to possess virtues such as empathy, data protection ethics apart from compliance checks and will be designed to handle human-machine collaboration.

3. Cloud analytics goes bigger

With the growing amount of external data and expectations of speed and accuracy, the adoption of cloud for analytics is witnessing growth. This is because, cloud brings in more agility into analytics, and is more powerful yet cost-effective. Despite the benefits, complete transition to cloud is also not that easy. Though businesses are taking baby steps in transitioning from traditional on-premise analytics models to hybrid or on-cloud models, it is surely going to get bigger in the years to come.

4. End-to-end integration for better decision making

Businesses are now adopting more integrated, end-to-end analytics processes to convert insights into actions. This means customer data is combined with information from across departments such as marketing, sales, customer service, and so on to get the best out of analytics. End-to-end integration between customer data analytics and other business functions combined with external social collaborations, provides deeper customer insights, allowing the marketer to make better business decisions.

Customer analytics is set to see more exciting times as devices get smarter and churn out more relevant real-time data. Now, let us look at some interesting examples of customer analytics being used for business profitability and success in the real world.

Examples

Many companies now use customer analytics not just for improved customer interactions or customer acquisition and retention but also for improving their supply chain or for curbing business risks. Here are some of the leading examples of companies using customer analytics to their business advantage.

How Netflix uses analytics for customer retention

Netflix knows the pulse of its audience so well that each time someone logs in, they find plenty of material that matches their interest. With over 130 million subscribers across the globe, Netflix gathers data from people of all walks of life. It extensively uses big data analytics to draw patterns from people’s search and viewing history, and offers suggestions for their next watch basis that. It goes beyond.

Netflix gathers data across various aspects. One example is that it knows which series you are watching and for how long you continue watching it, or at what point you switch to something else. If say, 70 percent or more users who started watching, finished all seasons of a cancelled show, Netflix will decide to launch another season, as this data shows greater chances of people watching the new season as well.

Through offering data backed compelling, personalized choices of content, Netflix keeps its customers or subscribers glued to its platform.

How Amazon uses data for enhancing customer experience

Amazon gets access to a wide range of customer data, owing to its presence as the biggest online one-stop shop for anything and everything you want. Through a strong recommendation engine, that feeds on browsing data from users, Amazon helps its users make convenient buying decisions instead of getting lost in the huge variety it offers.

In addition to what you buy, it gathers data about what you explore on the site, time spent browsing each page and much more, to build a 360-degree view of the customer. It further uses this information to not just help you find what you need much more conveniently, but also for lookalike-modeling to reach out to other similar people with product recommendations.

How Citibank uses customer data for retention and new user acquisition

Citibank is a great example of using big data and customer analytics for customer retention and acquisition. One of the ways they do it is by processing and analyzing customer data combined with machine learning algorithms to pitch promotional spending.

Additionally, they keep track of all transactional records to identify anomalies such as incorrect or fraudulent charges, if any. Spotting such glitches early on or even before it occurs, using predictive modelling. This not only saves the bank a lot of cost but keeps the customer trust intact. Citibank manages all this through an in-house integrated big data customer analytics platform architecture.