A study by Forbes shows that 66% of user data is used to focus on targeting & positioning offers, messages, and content. This data is used for market segmentation, allowing us to find well-defined and profitable segments in this diverse market. By observing and bookmarking these segments, companies can better understand their customers, their purchase behavior and customize their marketing plans to be more contextual.
Understanding & Analysing Data
My first experience with market segmentation and analytics came while I was working with a bank in Bahrain. I was asked to analyze the exposure the Bank had in our credit card portfolio and I was asked to come out with segments where we can reduce it. I had to look at the data, understand the different parameters and come up with a solution.
The data was observed through the behavior of our customers which included their pattern and transactional details such as how many customers were actually using their cards, how many rotating their balances, what type of customers were a risk lot, or what % paid their late fees. Back then, Microsoft Excel was the handy tool, so I sliced the data, analyzed it and had a realization. A huge % of customers were not using their credit cards, unnecessarily utilizing our total credit exposure. Upon making this observation, I proposed reducing the exposure of the unused cards and increasing the exposure for customers who were utilizing their cards and rotating their balance.
Beating the Competition
Having had my first taste of analytics and segmentation, I was in for a real eye-opener when I moved to India’s leading DTH entertainment platforms. Their use of data analytics was core to many of their business decisions. Their analytics and business intelligence were miles ahead of the competition all thanks to the advanced data collection, warehousing, and mining techniques. This was a massive advantage, one that can make organizations six times more likely to be profitable year-over-year, as per Forbes.
An area they really shone at was in collecting customer data right from the start. Right from the onboarding, we would track both stated as well as observed customer behavior & that would give us insight on user viewing preferences, allowing us to profile them immediately. Having ready data like this made my goal of increasing the ARPU (average revenue per user) easier.
Having this stated data ready meant we already knew their preferred viewing medium(TV or Tab), language and content preferences and made my job much easier. It also benefits the consumer as well, with a Salesforce report stating that 52% of consumers willingly share their personal data in exchange for product recommendations that meet their needs. For us, it was essential to find customers with the paying propensity and inclination to buy the product or content.
Tools of the Trade
The market of potential customers might number in the millions, and so, to find the most appropriate information requires specialized data mining tools. This is of special importance to marketers, with 33% of the elite believing that the right technology for data collection and analysis is very useful in understanding consumers as per a report by Econsultancy & IBM.
While segmenting this data, it is preferable to look at both stated and observed data. Gaining a complete picture of the customer requires integrating data from several sources. Some might be in-house and others acquired through third-parties. According to a survey by Adobe, leaders mostly use CRM & real-time app/web data for predictive and prescriptive analytics.
Speed and Context
I realized analytics was the future while in its infancy. It was obvious that we were going from carpet bombing our customers to contextual marketing the right product to the right people. Not only that, but speed also plays a vital role in segmentation, especially in customer-driven domains. CMO Council did a survey and found that 67% of marketers believed speed to be one of the primary benefits of data-driven marketing, resulting in the ability to execute their campaigns quickly.
There are now more advanced analytics models that machine learning to more precisely target audiences. This makes sense as customers today require you to anticipate their needs, with 50% likely to switch brands if you don’t (Salesforce). The need for today is to stay Contextual, catch them young and catch them now.