Winning Through Customer Analytics
With the power of data analytics, businesses are now capable of fine-tuning their revenue-generating decisions while making course corrections in real-time.
Recognising that data is capital, many organisations today operate with a vast amount of data collected through various sources with the ultimate objective to create competitive advantage.
However, many enterprises are struggling to make sense of their data in a more meaningful manner. Is there a business need to transform the whole company into a data-driven organisation, ensuring every organisational node is well-equipped with the acquisition and handling of customer data? Or do we leave strategic decisioning to the hands of a few data analytics experts? Is there a need for management to be trained in data analytics so that everyone in the boardroom can have a more meaningful conversation?
Banks, healthcare and Fast Moving Consumer Goods (FMCG) organisations live and die by data. But does that mean Small and Medium Enterprises (SMEs) and organisations without “a lot of data” are unable to leverage on data analytics? How do we achieve economic benefits from our data strategy?
This article aims to give an overview of Customer Analytics and its applications for businesses seeking to monetise existing data, with a particular focus in applying data analytics techniques on the Customer Life Cycle framework.
The Difference Between Data and Information
Simply put, data are basically bits of facts (eg numbers, text, video, etc) and may appear random to most people. By itself, it has no context on its own and can be highly unorganised. Once data sets are processed or arranged, it now becomes more structured and may be interpreted in a more meaningful manner.
To give a clear example, let’s look at “111111”. Now, this may appear to hold no meaning by itself but once we give it context (ie structure), say, DDMMYY, then this bit of data becomes clear – “111111” now means “11th November 2011”. To take it further, arrange a number of rows of such data together with the value of the last 2 characters being more than “81” and it becomes a database segment of Millennials and beyond.
Now that we understand the differences between data and information, let’s move on to discuss an outline of Customer Analytics.
What is Customer Analytics?
Customer analytics is more than just decisioning science. The ability to make clear decisions, backed by data, is a battle half won even before the marketing campaign is launched. To begin, we define Customer Analytics as the process by which data is used to make or refine business decisions via Customer Profiling, Customer Segmentation and through the use of Predictive Analytics. It is also a systematic examination of a company’s customer information to identify, attract, retain and manage the most profitable customers. Testing of promotional campaigns, A/B Testing (or Spilt Testing) can also be deployed to help businesses get a better sense of what elements are working and what aren’t, with the overarching mission to improve marketing Return of Investment (ROI).
The application of Customer Analytics helps companies to understand why (insights-based) and when (using predictive analytics) their customers make that purchase, and ultimately, deepening the customer relationship by entrenching their customers deeper into the product ecosystem. It also transforms customer (internal data) and market data (external data) into insights that can provide facts-based directions for strategic decisioning and in building sound business strategies. Typical uses of such information resides in direct marketing, sales channel optimisation and Customer Relationship Management (CRM).
Offensive vs Defensive Data Strategy
Every organisation requires both forms of data strategies to succeed, and getting the balance right with limited resources or well-trained data scientists is often particularly challenging. Let’s take a look at the key differences between these two forms of data strategies.
A defensive data strategy, often concerned with minimising downside risks, seeks to be compliant with legal regulations, is in the business of fraud detection and prevention and it also ensures data integrity through identifying, controlling and governing data sources via a “Single Source of Truth” (SSOT) approach where it works at the raw data level. An example of a SSOT data set would be sales data – it must be error-free, authoritative and absolutely clear.
Data offense, revolving around flexibility, is about driving revenue, profitability and customer satisfaction. Its main activities focus on generating customer insights and supporting business decision making through integrating market data and sales analysis where “Multiple Versions of Truth” (MVOT) dominates this approach as there is a need to transform data to aid specific business needs – data modelling, interactive dashboards etc takes place here. Customer Analytics forms part of the data offense strategy.
The key point is, the more flexible the data, the more readily it can be used to support front line business activities. On the other hand, the more uniform data is, the easier it is to execute and comply with regulatory requirements. In short, SSOT works at the data level, MVOT works at information management.
“Do we think this is true, or do we know this is true?”
HIPPO or Facts?
Most people are resistant to change. Data analytics, in its very nature, seeks to challenge the status quo. Every manager’s decisioning process, and to some extent, an organisation’s cultural makeup are directly confronted by cold, hard facts. Questions such as “It has always been done like this as it worked well in the past” or quite commonly, especially for smaller organisations, the HIPPO approach – the Highest Paid Person’s Opinion rules them all. In reality, people think in terms of their mental models, past experiences and…you’ve guessed it – human biases. This form of decision making, in itself, possess an inherent risk to business. This is exactly where data analytics can play the role of devil’s advocate in key business meetings exceptionally well.
Concluding Thoughts:
The data analytics team is best-equipped with the right tools and training to explore, uncover and develop insights to aid enterprises in their business objectives. However, they are mostly very logical thinkers. This important trait by itself, may be self-limiting.
While discovering statistically meaningful relationships between variables is critical, an equally important component is the business domain knowledge, an intimate understanding of how its target audience process marketing information and their interaction with products (including competitors) in the market place. It gets even more complex when one adds another layer of cultural dimension to it.
For example, while data analytics can predict when a customer may click on the purchase button on an e-commerce site, it may not be able to fully give you the reasons why he did it. Lest we forget, consumers are humans after all – we can be irrational at times and we may do things on a whim, influenced by a cocktail of totally unrelated factors. This is exactly where social science enters the picture.
Therefore, it may be worthwhile to add in cognitive and behavioural experts into the picture to gain a deeper understanding of how people think and behave. Some organisations such as Lego employ anthropologists to deep dive into how children play to develop new products – the researchers get down to the eye level of a child to see what they see, noting their body language and interaction behaviour under varying circumstances, and that also includes accompanying the families in their shopping trips. The ability to understand the mindsets of the target audience, their thinking process and motivations might prove extremely valuable.
After all, discovery is a meld between hard and soft sciences.
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