Marketers tend to focus on a customer’s last click stream or touch point. However, most conversions occur only after multiple contacts, spread over days and different touch points. As high as 50% of qualified leads are not ready to buy at the first instance and have to be nurtured. Customer analytics can help companies drive customer lifetime value to realize the following benefits.
The application of customer analytics helps marketers tailor marketing initiatives differently to customers in different stages of the marketing life cycle, and thereby optimize their efforts targeted at early, mid, and late stage customers. The application of analytics at each stage shed light on:
There are no one-size-fits all solution in marketing. Marketers have to approach customers at each stage of the life cycle separately. Techniques that work best for early-stage customers need not be effective for late-stage customers. For instance, a customer at the early stage of the lifecycle process may be more interested in knowing how the product can satisfy their basic requirement, whereas at a later stage, the customer would be looking at added value that the product delivers, and the price.
According to Gartner, lead management campaigns that integrate four or more digital channels outperform single or dual-channel campaigns by 300%. Marketers who are flexible and operate on multiple channels stand to score.
Today’s customers are highly aware and have a thorough knowledge about the product even before they initiate contact with a brand. And when contacted, they like to be pampered, given personalized attention, and processed quickly. Applying deep analytics allows marketers to understand what customers exactly want, what they like and prefer, and how they like to be approached. Through such insights, marketers can chalk out strategies and personalized interactions across multiple channels.
Deep analytics offers a 360-degree view of customers, and based on such insights, companies can develop transformation strategies that enable frontline customer facing staff change their behaviors, and align touch points, to suit what customers want.
Marketers ideally want to prioritize engagement with customers who are most likely to yield the maximum during the course of their “lifetime” with the company.
Understanding customer life cycle value makes it possible to evaluate the present value of customers’ total financial contributions to the cash flow, and thereby identify customers who actually deliver profit to customers.
The time-tested Pareto’s Law estimates that about 20% of customers generate 80% of all profits. Truly smart companies have a deep understanding of the characteristics and behaviors of their most, and least, valuable customers, and position their strategies accordingly.
Marketers can use analytics to reduce cost-to-serve, improve productivity, and increase satisfaction.
The application of deep customer analytics to analyze past behavior helps to project trends. This helps marketers and sales personnel gain insight into how customers would likely react or behave to an intervention, and allow them to chalk out strategies and plans with greater accuracy, keeping the long term in mind.
Tracking customer lifetime value using deep analytics involves tracking and understanding how customers progress through the customer journey, or milestones in the progress from first-time visitors to advanced customers who engage with the brand through repeat purchases, and other engagement. Through such analysis, marketers gain a deep, broad perspective into the customer lifecycle over time, enabling them to evaluate the campaigns and tactics most effective in progressing customers from milestone to milestone.
However, just because deep analytics has the ability to offer deep intelligence throughout the customer lifecycle, it will not realize automatically. The marketer still has her task cut out to connect data across all touch points, integrate it, identify the right metrics, and correct in real time. Above all, the marketer needs the right tool to implement customer lifecycle management and integrate deep customer analytics.
Do you have anything to add, or a contrarian viewpoint regarding how to drive customer lifetime value using deep analytics? We love to hear from you.