Customer analytics have turned consumer visits to the online world into a one long Baader-Meinhof phenomenon experience. A good example of this in practice would be; If you had searched for a new car on a particular website you had visited before, then all of a sudden versions of cars similar to your original search / previous searches start popping up all over the website (including customised offers based of your customer record).
While this happens naturally in real life, online, it is engineered by data-based technologies that ensure digital marketers can feed you what the analytics of your behavioural or other forms of data held shows you are most interested in.
Making sense of a customer analytics record
Customer records are retained data a business holds on any existing or prospective customers, this data is used to build a profile for each customer (this is critical to marketing and customer service functions). These records enable businesses to build models that enable them to personalise a web-based marketing or sales experience to individual customers (with the aim of creating the best chance of conversion and revenue maximisation per customer).
These models are most commonly referred to as customer analytics record and are usually constructed to help build a full analytics model (also known as CAR).
What makes up a typical customer analytics record?
These models are typically built using records with information ranging from behavioristic, geographic, psychographic to demographics.
- Demographic data and geographic records for existing customers are easy to obtain, and they range from sex, income, and age to climate, country or the city you reside in.
- Psychographic data is not as easy to come by. It is data about your hobbies, opinions, personality or interests. This kind of information is usually sourced from social media platforms.
- Behaviouristic data, on the other hand, is what feeds digital marketers clues about your purchase, surfing and communication data, as well as your payment history.
Overall customer analytics models enable businesses to make sense of raw data, join up the dots and better understand who their customers are, what they want and deploy digital marketing strategies that will maximise the sales potential of any particular customer (hence the frequent cases of the Baader-Meinhof phenomenon that customers encounter online).
Such records are life-saving for companies with millions of customers whom all require a listening ear and a personalised customer experience to maximise conversion, upselling and overall revenue generation. Customer analytics is employed in such a case to analyse a customer analytics record and to unearth critical insights that drive patterns and trends.
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What are the benefits of an analytics record?
- Costs reductions from well-targeted marketing campaign executions that have gone a step further and anticipated the sales outcomes. No more shots in the dark; hello certainty!
- Most often businesses assume that they understand what their customers want. An analytics record will provide you with accurate insights into what drives your customers. This will help you innovate more around your customers as you solve the problems they encounter with your product or service and as you get feedback on emerging trends from your records. This will lead to the creation and design of new and improved products that promote your customer’s satisfaction.
- Due to advancements in technology and readily available data, most customers now have taken charge of their conversion journey. Through a quantitative and qualitative analysis of your customer’s records, you can help adjust your digital marketing strategy to enhance your customer’s journey and acquire more conversions.
A final word
Analytics models are only as good as the data their built off, thus trusted data sources are critical to building an accurate and detailed customer analytics record. Without reliable data and accurate models you might as well just go back to mass marketing and forget personalisation.