Using Customer Data to Understand Why they buy

“Customers, through their devices, are leaving vast footprints of customer data for marketers to leverage,” – Blake Darcy

Modern marketing campaigns are powered by a variety of techniques that are premised on various types of information. Brands, merchants, and businesses gather customer information by studying commercial transactions, online browsing behaviour, consumer surveys, buying habits, customer testimonials, social media interactions, Internet discussions, etc. The motivation behind such actions is simple: by using customer data, businesses can help to create a picture of evolving customer behaviour and gain deep insights into consumer preferences, motivations, needs, choices, and tastes. We must note that the use of such information may vary depending on the industry, but the primary objective of analysing such data is to read the customer’s mind.

An e-commerce operator can choose to examine its customer’s shopping behaviour by studying browsing patterns on commercial websites and on mobile apps. The marketing strategies of the said business may hinge on using customer data to fashion the various aspects of said strategy. Modern technology enables businesses to record and analyse such data through a granular approach. For instance, impulse shoppers can create a particular signature trail when they browse e-commerce websites and apps before they arrive at a purchase decision. This pattern can emerge clearly, when businesses examine the relevant data and can help enterprises to frame a strategy that caters to such shoppers. Information accumulated from multiple shopping sessions can point to a strategy wherein, the business makes a decision to reward impulse shoppers with random discounts and free shipping offers. We could say that the e-commerce operator is using customer data to boost business outcomes, while rewarding frequent shoppers at the same time.

Different aspects of customer shopping behaviour can be used to frame a marketing strategy. A brick-and-mortar business may create a pre-campaign activity wherein the business actively monitors the time of day during a regular week that registers the maximum sales. Business managers may realise that a certain span of hours in the evening represents the busiest shopping sessions at the store and may tweak their selling strategies to suit said timeframe. New stock can be rolled on to the aisles in anticipation of said timeframe owing to the high likelihood of sales during said hours. We could state that the business would be using customer data, to frame an active marketing policy. Similarly, an online business may register the maximum sales during weekends. This information can be got by studying the shopping habits of customers; it could prompt the creation of a marketing strategy that hinges on boosting product availability for online shoppers during weekends. The strategy could be a roaring success because the business managed to decipher the fact that customers have substantial amounts of free time during the weekends, which, they devote to online shopping.

Online and offline businesses typically witness the highest number of footfalls during festivals and the weeks prior to the holiday season at the end of the calendar year. This applies to commercial enterprises all over the world and presents an exciting time for marketers to study and analyse consumer behaviour and shopping patterns. By using customer data, marketing departments can create specific offers and discounts to boost the uptake of merchandise in both the online and offline domains. We must note that an observant marketing department or an astute business management group can infer that customers have been saving their dollars to buy gifts for family and friends during the festivals and the holiday season. Customer motivation premised on such intent remains common all over the world and therefore, a business must prime itself to cater to the upsurge in market demand during said timeframes. We could also infer that discretionary shopping tends to meld with mass shopping patterns during festivals. These nuggets of information can prove priceless for brands and enterprises that seek to gain a deep understanding of consumer buying behaviour.

Loyalty programs operated by merchants can yield significant amounts of information pertaining to customer behaviour. For instance, a retail merchant that operates from physical business premises should be using customer data to analyse customer requirements and preferences. We are aware that loyalty programs typically reward customers that spend consistently at a shopping establishment. Daily shoppers or weekly shoppers are best positioned to gain from loyalty programs and should be classified as such. However, the discretionary shopper may choose to direct his or her custom at the said retail establishment only at certain times. This information should be recorded and the motivations behind such actions can be analysed in an effort to upsell to discretionary shoppers. Clearly, the motivations behind the different types of shopping behaviour remain distinct from each other. However, the analysis of such motivations can offer a trove of valuable insights to the merchant and marketing strategies may be tailored accordingly.

An apparel business that primarily deals in school uniforms can expect an upsurge in business at the beginning of the busy school season. This pattern of consumer behaviour is obvious and need not be analysed from any angle. However, by using customer data, the said business can identify customers that order school uniforms at different times of the school year. This could lead to insights into the shopping patterns of individual customers and may trigger a business decision to offer discounts to said customers. The reasons behind such shopping behaviour may include the purchase of reserve sets of school uniforms, the physical loss of such clothing leading to a fresh order for uniforms, a transfer student that has joined a school in mid-session, etc. These insights can be leveraged by said enterprise to fashion customer-facing policies that will likely improve the customer’s in-store experience and boost the business bottom line.

Modern commerce generates huge amounts of data that emanate from commercial transactions with customers. Big data and analytics software-driven techniques can be utilised by businesses to understand the prime reasons of customer behaviour. For instance, an online travel ticketing business can be found using customer data to analyse the various aspects of customer behaviour. The typical destinations and time of year chosen by customers can help to create buckets business travel, leisure travel, holiday travel, etc. This analysis can help the business to create specific promotional offers targeted at different types of travellers. We must note that in most instances, the customers of the said business will likely avail these offers because they can discern a distinct value proposition in said offers. The outcome of such actions will likely include happy customers and improved business profitability for the online travel business. In addition, by using customer data the business is demonstrating serious intent to create customer goodwill, which, can be classified as a priceless intangible and a critical tailwind for said business enterprise.

In the preceding paragraphs, we have examined some of the customer motivations that can be got by using customer data. Every business enterprise must realise that modern commerce has been changed irreversibly by the advent of the information age. The use of customer data to drive business outcomes is now mandatory on the part of every serious enterprise. The use of such information can be leveraged to drive business profitability and in the creation of an impeccable brand reputation.

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