Competition is easily one of the most determining components in every vertical market today. It inspires business strategies, influences customer relationship management, affects sales and marketing drills, and is even factored in while expanding to a new region or niche. While the competitors deserve all the attention they get, it is very easy to get carried away by what everyone else is doing and start ignoring the single most important variable in every business equation – the customer.
The growing competition demands that you amplify your efforts to understand your customers, capture their attention at the right time and through the right mediums. During this post, we will discover how you can know your customer better with the help of customer segmentation analysis and how it translates into better sales figures.
Customer segmentation at a glance
A modern person is exposed to 5000 ads every day on average. Remember, a day has only 86,400 seconds, just to put that number into perspective. Your best bet to make sure that the right ads reach the right potential customer is customer segmentation. It is the practice of grouping your customers into segments while factoring in a wide array of personal as well as social attributes.
When a marketer uses data – purchase history, address, weather condition, festive dates, economic slabs, and whatnot, instead of gut feelings, to put a customer into a certain group and strategize the sales and marketing tactics for the same, we call it customer segmentation analysis.
Key data points to mind for effective customer segmentation
Product recommendations, personalized ads, and tailored offers for specific customer groups are all evidently terrific moves but these can easily go wrong and cost your business a fortune. Here are some key variables that play vital roles in customer segmentation analysis.
Age, gender, location, and similar demographic data is quite easily collectible, and they have been playing a pivotal role in customer analysis for ages. They are still relevant.
Understanding what a customer does after reaching your website after clicking on an ad or through some other medium is key to improving customer experience. Analyzing these actions give you a way into the customers’ behavioral patterns. This allows you to identify the pages from which most customers drop off and improve those. It helps you track customers who visit pricing details but do not make a purchase and send them curated offers.
Oftentimes sales figures, especially in the retail sector, rise and fall during certain social and natural circumstances. For instance, an online grocery store might sell more articles during Christmas. It is important to identify those short-term buyers and try to convert them into loyal customers.
The automation factor
Target, the major American retail corporation, ran a marketing drive to identify expectant mothers in their third trimester. Their data had shown that new parents are usually loyal buyers and tend to buy everything from one place. Target adopted a system to score people in terms of their probability of becoming parents. Their analysts were pretty accurate in finding out and targeting the customer segment.
That was back in 2002, and Target, being the 8th largest American retail giant, could afford the human capital required to run such a campaign. It is 2021 and you have hardly any option but to trust machine learning.
Unsupervised learning is a kind of machine learning that can identify similarities in data and categorize them according to those patterns. Ideally you should not have to worry about the attributes that work as the base of the categorization if you can choose an effective customer segmentation tool.
Simple demographic data does not cut it anymore and even if it does, the amount of data to strife through is way too much for human analysts.
A deep customer segmentation tool allows you to divide your customers into smaller groups or micro-segments depending on various attributes. For instance, if one customer segment includes all female customers under the age of 35, a micro-segment would be all female customers under the age of 35 with a personal vehicle. This reduces the difficulties involved in targeted sales efforts and increases the morale of the salespeople.
A study by Infosys reveals that 78% of customers prefer buying from brands that send them personalized offers. Moreover, when a customer has shopped from a certain source for more than 30 months, they spend 67% more per purchase than their first experience. The importance of building trust among customers cannot be stressed enough and customer segmentation analysis opens the gates for that.