Changing Landscape of Customer Analytics – 2021 edition
Smruthi Shanbog Ramamurthy • September 23, 2021
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Customer analytics represents the process companies use to capture, identify, and analyze their customer’s various behavioral data patterns to make better business decisions. Customer decisions are the inputs for businesses, which they utilize to optimize their decisions.
A 2016 survey by Mckinsey showed that extensive use of customer analytics has a considerable impact on corporate performance. Companies that make extensive use of customer analytics are more likely to outperform their competitors on key performance metrics, such as profit, sales, sales growth, or return on investment.
Moreover, according to a report from ‘MarketsandMarkets’, the customer analytics market size is projected to grow from USD 10.5 billion in 2020 to USD 24.2 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 18.2% during the forecast period.
It is no surprise that since the dawn of Big Data – the oil of this century, and the supportive tech such as AI on the cloud, the scope of Analytics has amplified exponentially. A 360 degree – thorough understanding of the customers is the greatest opportunity for businesses.
There are three major areas of Customer Analytics, in which typically a company would invest to gain a competitive edge in the market – descriptive analytics, predictive analytics, and prescriptive analytics. Each branch tries to provide insights about different aspects related to the customers & their engagement journey with the business. Customer analytics offers insights into nearly every phase of the customer’s lifecycle. How?
• Identifying trends in the customer lifecycle, segmenting the customers with similar behavior, and designing the relevant actions for each of the customer segments (Descriptive analytics)
• Predicting the next action that customers would take prior and be prepared with either to prevent the worst action or guiding them to take the right action. (Predictive analytics)
Customer lifecycle encompasses all the stages a customer goes through before, during, and after they purchase a product/service from a business. We can divide the different stages of customer lifecycle into Reach, Acquire, Nurture, Retention, and Engage. Within each stage, customer analytics can be applied to yield tremendous growth in ROI and creating a positive impact on customer’s experience. We can also categorize the customer base respectively undergoing this cycle into B2C and B2B, based on whether the customer is an end consumer or just an intermediary.
A high-level view into the end-to-end supply chain process of a typical CPG/FMCG Business will give us a complete picture of how these to the customer are different.
One of the key areas for B2B businesses with web/app as one of the channels is focusing the energies into leads that are generated in the process of converting the visitors to customers through various phases of the sales and marketing funnel. As the web traffic has increased substantially, it’s crucial to understand which probable spam leads have no intention of purchasing or subscribing to the product/service and filtering them out to improve the efficiency of the conversion cycle.
Post-Pandemic Impact & Management
It is no doubt that the global pandemic has left its imprint on customer lifestyle, preferences, and sentiments. This poses a large set of challenges in front of the businesses – they must not only adapt to the changing market conditions but also keep their businesses afloat in the wake of shifting business priorities, budget cuts, supply chain disruptions, price hikes, etc. The pandemic is a disruptive change that is going to lead businesses into a new world order as what was known to the businesses pre-pandemic no longer holds true and customer experience has a whole new definition.
This necessitates, now more than ever, a solid and structured approach toward customer analytics. While this sounds daunting, it is also a monetizable opportunity for companies to rethink their customer analytics strategy.
So, what can companies do in these uncertain times?
They can start with a COVID impact assessment on its existing processes. This would entail classifying the effects into short-term/ temporary and long-term/ permanent. After having a clear picture of these effects, the business can steer its course towards customer analytics and let the data guide them. Some basic nudges in that direction could be generated potentially by:
• Collecting new data elements: Companies can enrich their information vaults by considering picking up customers’ digital footprints that would ultimately fuel the analytics and data science effort.
• Re-establishing the customer understanding from scratch: If the business assessment of COVID impact indeed shows that there are lasting changes in the customer behavior, it is worthwhile to delve into creating customer profiles based on behavioral data. A kitty of analyses come in handy for this, like Customer’s lifetime value, Customer segmentation, Customer purchase & spending pattern analysis, Customer Churn, and so on. This activity should manifest the changing customer needs.
• Rethinking Customer Acquisition and outreach: According to the Adobe Digital Economy Index 2021, the pandemic has added a boost of 183 billion to eCommerce alone. More and more customers have a digital presence, which is likely to grow, and businesses shouldn’t hesitate to tap that. Market expansion via computing the customer acquisition cost, designing new outreach campaigns, and planning omnichannel strategies based on insights from customer profiles should be the way to go.
• Refresh pre-pandemic Models: For many companies data drift & concept drift could be the natural outcomes post-pandemic, rendering most of the pre-pandemic data science models unstable. This could be disastrous from the point of view of taking decisions for existing customers. A decision to retrain or refresh such models would produce relevant and robust insights that greatly align business decisions with the new reality of existing customers.
• Winning customer trust: Paying attention to the customer experience and needs by personalized interactions, relevant communication via contact center, reduced spamming and noise, relationship marketing, quick complaint redressals via chatbots, etc. would prolong the customer journey, build loyalty & improve company bottom-line.
• Re-scale operations based on market demand patterns: The market is as unstable as it gets, businesses need the power of agility in scaling their customer analytics efforts. What comes to the rescue is the power of cloud computing.
In summary, these unprecedented times have opened a plethora of opportunities for companies to understand this drastic change in the customer’s behavior, which is likely to be a new normal for the coming times and build robust models to serve the customers better and stay ahead of their competition.
This article is co-authored by Tarshita Kanoujia, David Sam Elavarasan and Sai Surya Prakky.
Seasoned Data Scientist passionate and curious about applying Machine learning and Deep Learning techniques to solve a various business use cases. Well equipped with understanding domain specific problems, advanced concepts of data science, cloud technology, and modern analytical tools. Believes in converting the technical results to business relatable metrics in best possible way. Over 8+ years of experience in applying data science techniques across different domains like Telecom, Mass media, Retail, Pharma and Real estate.