19th May, 2020
, , Designation
These are the fragile times for businesses, globally. Lockdown due to the pandemic has pushed disruption outside the positive quotient and it threatens to gobble up the economy. Nevertheless, it is never too late to put your best foot forward. This is possible with the leverage received by gaining deep insights over the course of crisis – thanks to Data Analytics. Data Analytics provides business intelligence and the scope of making data-backed decisions making it a critical consideration while navigating the crisis. This is an opportunity to collect as much data as possible, break down that data to analyse and yield insights and lastly to act basis that insight – to make a better tomorrow.
Let’s start by zeroing down on our customers
Actionable data provides a pivot to the marketing strategy for making the most opportune call. Going by customers, an understanding on their historical purchase behaviour, the recency quotient in this behaviour, the monetary value of it all and at the same time insight into the frequency of the purchase can help understand the sale team to differentiate between the high value and least value customer. Delving into customer lifetime value (CLV) is an important element to design customer retention strategy by identifying customers with least lifetime value and gunning for a targeted list of customers with high lifetime value. At the same time, it makes possible to identify the product or service which can be put on the radar for fresh sell / cross-sell / up-sell and design a holistic personalized marketing campaign for improving RoI.
Recency – a recent purchase makes the buyer a more mauled customer. The elements of this parameter may vary between industries, however, let us mention a standard here. If a purchase has been made in the last 60 days, or if it is a 1st-time buyer then the recency is weighed on the higher side. A churned customer score low on the recency parameter. Recency also takes into account the popular products or trends in recent purchases.
Frequency – we all understand a loyal customer is one who is a frequent buyer and must be valued. He scores the highest on the frequency scale than an occasional/intermittent buyer. Besides this parameter also helps in understanding the average wait time of the buyer before making a return for the purchase.
Revenue – This takes into account the continuity of the purchase and hence pertinent here is to collect data that helps in identifying customer average lifetime value, his purchase growth trend at standard intervals, identifying customer segments that may have different lifetime value or low lifetime value.
Breaking the Data for Analysis
All the above data once collected can be grouped on a continuum starting from low value to mid-value to high value. An average calculation of each of the three categories can provide customer segmentation in the terms of count of customers, an average of recency, an average of frequency and an average of revenue.
This data can be then compartmentalized for a targeted marketing strategy. If you notice that this segmentation is almost akin to an upside-down sales funnel! The high-value bracket that lies at the lowest bit of the sale funnel would call for high customer retention marketing strategy. A mid-value segment lying in the mid area of sales funnel desires that marketing strategy focuses both on improving retention and increasing the frequency of the purchase. While the low-value segment that lies on the top of the sales funnel wherein the marketing strategy ideally should be oriented towards increasing the frequency of the purchase. Cumulatively, the marketing intent should be to navigate the low-value customers to the mid-segment and from the mid-segment to the high-value segment by altering the strategies basis the results it wants to achieve.
The data segment as mentioned earlier is important for designing the collage of targeted marketing activities. One can start with A/B testing post grouping set of customers on the grounds of commonalities. Some of the commonalities that can be deep-dived for making customer groups can be like below.
How they were acquired – due to seasonal trends, promotional campaigns, discounts, or just field activities.
Is there a common product they purchased?
Is there a common event that brought them closer to purchase – like referrals, surveys, etc.
An A/B testing makes sense only when similar behaviours are taken into considerations. The results can deliver insights into the best way to engage a set of customers like whether the group can be swayed by discount only or a seasonal trend suffices in making customer acquisition. Also, this bares out clearly the target customer group for fresh sell, cross-sell & up-sell. A customer with an affinity to the product is the target for a cross-selling marketing campaign, customer segment with similar monetary value is an ideal group for up-sell marketing campaign while the churned customer segment should be targeted for an engagement marketing strategy.
The segmentation also goes a long way in predicting customer churn and next purchase day, analyzing customer basket, cohort purchase behaviour, supply-chain and on-field survey, and data analysis.
To sum up
Business goals do not change in its essence – it is always about maximizing revenues, driving efficiencies in the processes and performance and the ability to accurately predict the evolving market landscape to be able to make the first move. Understanding your customer, segmenting them, and designing a targeted marketing strategy always bear results. And what better time than now to dial-in the data analytics strategy for better results tomorrow.