What Most Companies Miss About Customer Lifetime Value #clv #customermanagement

For managers and marketers alike, the power to calculate what customers might be worth is alluring.

That’s what makes customer lifetime value (CLV) so popular in so many industries. CLV brings both quantitative rigor and long-term perspective to customer acquisition and relationships. “Rather than thinking about how you can acquire a lot of customers and how cheaply you can do so,” one marketing guide observes , “CLV helps you think about how to optimize your acquisition spending for maximum value rather than minimum cost.”

By imposing economic discipline , ruthlessly prioritizing segmentation, retention, and monetization, the metric assures future customer profitability is top of mind.

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My point of view: In this age of digital marketing, disruption and new business models and platforms understanding how to drive customer life time value is central to the success of any organization. Imperative for  any marketing manager and, for that matter, any manager concerned with growing the value of the customer base.

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Customer Loyalty Is Overrated #customerservice

The answer, we believe, is rooted in some serious misperceptions about the nature of competitive advantage. Much new thinking in strategy argues that the fast pace of change in modern business (perhaps nowhere more obvious than in the app world) means no competitive advantage is sustainable, so companies must continually update their business models, strategies, and communications to respond in real time to the explosion of choice that ever more sophisticated consumers now face. To keep your customers—and to attract new ones—you need to remain relevant and superior. Hence Instagram was doing exactly what it was supposed to do: changing proactively.

Read all: Customer Loyalty Is Overrated

Mindbreaker MITSloan’s How Should You Calculate Customer Lifetime Value? #marketing

First posted at http://sloanreview.mit.edu

Authors: Neil T. Bendle and Charan K. Bagga

Should marketers subtract the cost of acquiring a customer before assessing that customer’s lifetime value?

Customer lifetime value (CLV), which is the present value of cash flows from a customer relationship, can help managers make decisions regarding investments in customer relationships.

For example, a marketer might use CLV to decide whether to spend marketing dollars to acquire new customers or to increase the retention rate of existing customers. CLV can be difficult to calculate because it often relies on the ability to predict future customer retention rates.

However, we think one major source of confusion among marketers — whether to include customer acquisition cost in the CLV calculation — can be easily avoided. CLV is easier to understand, and in our view more useful, if marketers don’t subtract the acquisition cost from their calculation of CLV before reporting it.

To be sure, customer acquisition costs are a major item in marketing budgets. Such costs should affect decisions as to whether to pursue prospective customers.

But this does not mean that acquisition costs need to be subtracted from CLV before the value of the customer is reported.CLV is often used to measure the value of customers who have already been acquired. The acquisition costs have therefore already been incurred.

Even if the company made a mistake in acquiring a customer and the acquisition costs exceeded the customer’s value, knowledge of this cannot change the earlier acquisition decision. Acquisition costs are “sunk” and should be ignored when making forward-looking decisions.

How Should You Calculate Cus

Read all: How Should You Calculate Customer Lifetime Value?

My point of view: in industries transformed it’s a bet to assume your customers lifetime value. And even in stable industries to model a future-proof robust clv is a hell of a jog=b.

This is the one that got it iversity Online Course- Predictive Analytics in Commerce (but not for free)

 

Predictive Analytics in Commerce

Learn how to use predictive modelling and its applications in commerce.

This course will enable you to maximise marketing effectiveness and drive revenue in your professional life.

Today’s customers are overwhelmed with information and choices, and often struggle to find the product or service that best fit their needs. Advances in technology, data and analytics make it possible to help these customers by offering the right product, while providing the service they expect.

Companies are increasingly looking for ways to use data in order to deliver the right kind of products, offers and services to the most valuable customers.

This course will provide you with insights into predictive modelling and its applications in commerce. Applying predictive analytics can help your company grow and increase their marketing performance. By the end of this course, you will feel confident to apply predictive analytics resulting in an increase in customer satisfaction, company performance and even team performance.

What will I learn during this course?

This course will teach you how to identify situations in which predictive analytics can add value by better meeting customer needs, smarter allocation of marketing budgets and improving the financial performance of the company.

You will know when to use the following modelling techniques and understand their pros and cons:

• Logistic regression

• Decision tree• Random forest

In the end, you will be able to apply all learned theory into practice and start building a more data-driven culture within your organisation.

Who should take this course?

This course is suited for a variety of different experience levels. We invite anyone who is eager to learn more about data analytics and predictive modelling to join us. More specific groups include:• Marketers who want to learn more about predictive modelling and especially what data can do for you.• Data scientists who want to bring their modelling as well as data presentation skills one step further.• Business managers who want to have a better grasp and understanding of data models and how to build a strategy based on this data.

Read and register: iversity – Predictive Analytics in Commerce – Online Course

My point of view. Iversity and VODW are fine companies (yes, companies). If your boss will not pay for the course, this book is a fine alternative.