The document discusses customer lifetime value (CLV), including:
1. CLV is the predicted net profit attributed to the entire future relationship with a customer.
2. CLV is an important metric because the best customers account for the majority of sales, and retaining existing customers is often cheaper than acquiring new ones.
3. Companies can use CLV to inform customer acquisition and relationship management strategies, such as spending more to acquire or retain the most valuable customers.
This presentation provides insight into how to forecast and calculate customer lifetime value (CLV). Here a startup applied a scientific approach to maximise customer retention and minimise churn. The outputs of the analytics were built into the system and business processes driving the success of the company and helping it to win the customer service of the year award, and to achieve a successful exit through acquisition.
Channel Information Systems
Purpose
Information - Advantages
Classification of Information
Information Process
Developing a Channel MIS
Use of Information
Sources of Data
Competition Tracking
Elements of a Channel Information System
Channel Performance Evaluation
IT System for Channels
Intensive Distribution
This presentation provides insight into how to forecast and calculate customer lifetime value (CLV). Here a startup applied a scientific approach to maximise customer retention and minimise churn. The outputs of the analytics were built into the system and business processes driving the success of the company and helping it to win the customer service of the year award, and to achieve a successful exit through acquisition.
Channel Information Systems
Purpose
Information - Advantages
Classification of Information
Information Process
Developing a Channel MIS
Use of Information
Sources of Data
Competition Tracking
Elements of a Channel Information System
Channel Performance Evaluation
IT System for Channels
Intensive Distribution
Chapter 4 Creating Customer Value, Satisfaction, and LoyaltyNishant Agrawal
Creating Customer Value, Satisfaction, and Loyalty
Organizational Charts
What is Customer Perceived Value?
Determinants of Customer Perceived Value
Steps in a Customer Value Analysis
Measuring Satisfaction
What is Quality?
customer loyalty is very important for a company or a brand, these slides contain the detail about loyalty, customer loyalty, types of customer loyalty and loyalty status...
created by:
Umair Ahmad
umair.100@hotmail.com
CRM is a competitive strategy and process of acquiring, reacting and partnering with selective customers to create superior value for the company and the customer.
How can companies attract and retain the right customers and cultivate strong...Sameer Mathur
Based on the chapters from A South Asian Perspective by Philip Kotler
Learn how companies attract and retain their customers. Learn how companies personalize the market, how they empower the customer and how the customer reviews affect the companies growth, how companies reduce defection rates, the retention dynamics
Click on the link below to watch full video on youtube :-
https://youtu.be/gbsJ9D9BL8A
Customer Relationship Management Model defines framework to manage customer relationship through stages from acquisition till retention.
CRM Model lays down strategy to develop customer relationship by focusing on :-
Customer Satisfaction
Building Customer Loyalty
Enhancing Customer experience through customized product/ service
Providing competitive advantage
Establishing strong multi-channel communication network
CRM MODELS- IDIC Model, QCI Model, Value Chain Model, 5 Forces Model.
Thank You For Watching
Subscribe To DevTech Finance
Basic information on Customer Lifetime Value models.
- Demything frequent doubts with CLV.
- You can not calculate CLV in Google Analytics.
- First steps and outputs that you have to prepare when thinking about CLV.
- Presentation of possible outputs a CLV model can give you.
- Discussion on early estimation of CLV using cohort analysis and simple models to understand what interactions lead to a success.
The presentation was prepared in the pub White Swan for MeasureCamp London, March, 13, 2015.
Chapter 4 Creating Customer Value, Satisfaction, and LoyaltyNishant Agrawal
Creating Customer Value, Satisfaction, and Loyalty
Organizational Charts
What is Customer Perceived Value?
Determinants of Customer Perceived Value
Steps in a Customer Value Analysis
Measuring Satisfaction
What is Quality?
customer loyalty is very important for a company or a brand, these slides contain the detail about loyalty, customer loyalty, types of customer loyalty and loyalty status...
created by:
Umair Ahmad
umair.100@hotmail.com
CRM is a competitive strategy and process of acquiring, reacting and partnering with selective customers to create superior value for the company and the customer.
How can companies attract and retain the right customers and cultivate strong...Sameer Mathur
Based on the chapters from A South Asian Perspective by Philip Kotler
Learn how companies attract and retain their customers. Learn how companies personalize the market, how they empower the customer and how the customer reviews affect the companies growth, how companies reduce defection rates, the retention dynamics
Click on the link below to watch full video on youtube :-
https://youtu.be/gbsJ9D9BL8A
Customer Relationship Management Model defines framework to manage customer relationship through stages from acquisition till retention.
CRM Model lays down strategy to develop customer relationship by focusing on :-
Customer Satisfaction
Building Customer Loyalty
Enhancing Customer experience through customized product/ service
Providing competitive advantage
Establishing strong multi-channel communication network
CRM MODELS- IDIC Model, QCI Model, Value Chain Model, 5 Forces Model.
Thank You For Watching
Subscribe To DevTech Finance
Basic information on Customer Lifetime Value models.
- Demything frequent doubts with CLV.
- You can not calculate CLV in Google Analytics.
- First steps and outputs that you have to prepare when thinking about CLV.
- Presentation of possible outputs a CLV model can give you.
- Discussion on early estimation of CLV using cohort analysis and simple models to understand what interactions lead to a success.
The presentation was prepared in the pub White Swan for MeasureCamp London, March, 13, 2015.
What Is a Customer Worth? Understanding Customer Lifetime ValueAdam Toporek
On November 29, 2011 we posted a “back of the napkin” guide for calculating the economic value a customer brings over their “lifetime” with a business. We designed Understanding Customer Lifetime Value: A Non-Geek’s Guide as a thorough, yet non-academic, approach to determining the lifetime value of customers
The step-by-step process of determining customer lifetime value seemed like a natural fit for SlideShare, so we decided to re-release the post in a presentation format.
Check out What Is a Customer Worth to learn more about Customer Lifetime Value and to make better decisions about marketing and retention.
An outline of the differing role of KPIs at startups vs mature businesses, drawing out the implications for the approach and methodology to their development.
nfinite Insight is a strategic market research and insight agency operating across the African continent.
We are a team of experienced market research professionals, who have worked with blue chip clients across Sub-Saharan Africa, Europe and America.
Our collective personal experience of working in these diverse markets informs our sensitivity to the peculiarities and cultural differences of individual markets.
We adapt and fine-tune research tools and techniques to give you infinite insight...
Jerry Chen, partner at Greylock and former VP of Cloud and Application Services at VMware, shares his Unit of Value framework for startups building a go-to-market strategy. He developed this strategy while managing product and marketing teams at VMware that shipped many “1.0” releases, including VMware VDI, Cloud Foundry, and vFabric, and continues to use the framework to evaluate companies as an investor.
Next Generation Measurement with Google Analytics - Dara Fitzgerald, Fresh EggFresh Egg UK
At the 2012 summit, GA announced some of the biggest feature releases in its history. These features will help to shift the analysis view from session-centric to user-centric, allowing the ever growing number of companies using GA to optimise for people, rather than sessions.
At BrightonSEO 2013, Dara presents an outline and provides use cases for new features such as custom dimensions and metrics, customer lifetime value and universal analytics.
Customer Segmentation for Retention StrategyMelody Ucros
IE Business School
Marketing Intelligence Project by Group F:
Melody Ucros
Jina Kim
Andrea Blasioli
Adedeji Rodemade
Fergus Buckey
Alex Kyalo
Louis Rampignon
Data Source: http://archive.ics.uci.edu/ml/datasets/online+retail
Consumers are smarter today than ever before. In fact, today's consumers own your brand. Their shopping expectations are higher, they make decisions faster, and they research thoroughly and independently. Consumers also know they have a lot of choice regarding when and where to purchase. Added to the mix are consumers who are increasingly technology savvy, more demanding, and are making tradeoffs by focusing on value, transparency and accountability. If retailers are unable to provide the convenience or service consumers expect, loyalty will evaporate, competitive advantage will erode, and retailers’ value proposition will crumble.
In this webcast you will discover how to satisfy the smarter consumer by providing a seamless customer experience that reaches across all touch points, spanning human, digital, social and mobile modes of access that are optimized according to customer preferences...a customer experience that delivers products and services flawlessly to keep customers coming back for more.
Learn how Best Buy responds to customer control and demands and how the electronics and home appliances retailer is tackling issues with mobile, digital, social media, inventory availability, fulfillment flexibility and convenience.
Once consumers get a taste of the seamless shopping experience, they expect it. It doesn’t matter what your product or service is. Join us and learn how to put your business back in control of the shopping experience.
These slides are an extract from a workshop on Saas Analytics I gave in collaboration with the Dutch National Association for Private Equity and Venture Capital. In there, I explain how to create a frame of analysis for Saas Businesses starting from understanding the customer dynamics and then identifying the right metrics for the case.
Omnichannel Customer Experience. Companies such as Amazon, Facebook, Google, Apple already know that the future of user experience is automated interface creation depending on customer needs.
Snowplow: evolve your analytics stack with your businessyalisassoon
Deep dive into how digital analytics stacks need to evolve with businesses, and how self-describing data and event data modeling are the key elements that enable Snowplow data pipeliens to elegantly evolve over time
2016 09 measurecamp - event data modelingyalisassoon
Presentation by Christophe Bogaert to Measurecamp London September 2016. Christophe discussed what makes consuming and analysing event-streams difficult, and outlined a number of techniques for overcoming those obstacles.
On the importance of evolving your data pipeline with your business, and how Snowplow enables that through self-describing data and the ability to recompute your data models on the entire event data set.
Yali presentation for snowplow amsterdam meetup number 2yalisassoon
Digital analytics is a very exciting place to work because digital event data is becoming more interesting as more of our digital lives are intermediated by digital platforms.
In this presentation I explain how at Snowplow we're working to make it easier to build insight and act on digital event.
Presentation given by Christophe Bogaert at the inaugural Snowplow Meetup New York in March 2016. Christophe described the event data modeling process at a high level before diving into specific tools and techniques for developing performant models.
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Presentation authored by Simon Rumble covering the journey that Bauer Media Australia have gone through implementing Snowplow, and the central role Snowplow now plays in their data strategy / products.
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Talk on the role Snowplow plays as part of the larger project to make data accessible to product marketing and other data-driven teams at StumbleUpon. Touches on technical and organizational challenges
A talk through the journey we've been through at Snowplow thinking about event data, starting with our focus on web and then mobile analytics, and exploring our current and future technical and analytic approaches
Big data meetup budapest adding data schemas to snowplow
Customer lifetime value
1. Customer lifetime value
What it is
Why it matters
Using it in practice
SnowPlow Analytics Ltd
2. What is customer lifetime value?
• Prediction of the net profit attributed to the entire future relationship with a
customer (wikipedia)
£50 £10 £1000 £100
• The most important metric in business analytics (incl. digital)?
• Not widely used… (Because it is hard to calculate, esp. in digital)
• Example: using CLV to acquire customers for a mobile game
SnowPlow Analytics Ltd
3. Why is customer lifetime value important?
20% of our customers Customer acquisition
account for 80% of costs keep rising
our sales
The best customers might be It is often more cost effective to
– Brand loyal spend money retaining existing
customers than acquiring new
– Don’t “shop around”
customers
– Rich
– Different from the average
SnowPlow Analytics Ltd
4. Where is customer lifetime value used?
Customer acquisition Customer relationship management
1. Use average CLV to inform • Maximize customer lifetime value
acquisition cost – Instead of maximizing other metrics
– E.g. pay more for a customer than e.g. utilisation
recoup on first purchase, based on – E.g. email marketing to encourage
Increasing sophistication
likelihood that he / she will make a repurchase
second / third / forth purchase)
• Differentiated approach for different
2. Calculate CLV per channel customer segments
– pay more more to acquire customers – Spend more cultivating loyalty in the
on channels with higher CLV most valuable customers
– E.g. search engine marketing vs price (personalisation) e.g. loyalty
comparison sites schemes
Acquire valuable customers Retain valuable customers
SnowPlow Analytics Ltd
5. Calculating customer lifetime value: 2 challenges
• We need to be able to attribute profit to a customer over his / her entire lifetime
– Profit across sales channels (on and offline)
– Single customer view?
– Web analytics packages visit rather than customer-centric
• We need to be able to forecast lifetime value based on past behaviour to date
– Need a model that matches the data (reasonably well)
– Needs to be done fast if used to acquire customers
– Limited data set
– Prediction is an art, not a science
SnowPlow Analytics Ltd
6. Meeting those challenges:
1. Measuring actual customer lifetime value
1. Identify the moments in a customer journey where value is generated
2. Tie records for a specific customer together into a complete journey
– E.g. using sales records, loyalty programmes, cookie IDs
– If it is not possible to do at a customer level, then do at a segment level (and infer
average CLV from segment lifetime value / number of customers)
3. Measure the profit made at each point
– Normally use gross profit for simplicity Doing this is getting easier all
the time:
1. Improvements in
4. Sum them over the customer’s “lifetime” analytics solutions e.g.
Universal Analytics
2. Companies are getting
better at getting user’s to
identify themselves e.g.
via logins
SnowPlow Analytics Ltd
7. Meeting those challenges:
2. Forecasting value based on past behaviour to date
1. Identify the moments in a customer journey where value is generated
2. Examine the value created at each moment: what is it a function of?
– Does it vary much by customer / segment/ time / anything else? (I.e. wide variance in
values)
– If that variation is significant, what is it a function of?
3. Examine the likelihood of moving from one moment to-the-next: what is it a
function of?
– Does it vary much by customer / segment / time / anything else?
– If that variation is significant, what is it a function of?
Developing a model is likely
much easier for a telecoms
operator (reliable subscription
revenue) rather than an online
clothing retailer
SnowPlow Analytics Ltd
8. An example: using CLV to drive customer acquisition
• Mobile game
• Free to download, monetise by in-app purchases or virtual goods
• Virtual goods can be bought at any stage of playing the game (i.e. very frequently or
never at all)
• Wide variety across customer base in terms of customer lifetime value
– Zero value from majority of users. (Who play without ever buying an item.)
– Small fraction account for disproportionate amount of value
• Crucial to acquire users from channels where a high proportion of acquisitions
have high CLV
SnowPlow Analytics Ltd
9. Calculating CLV: the steps
• Measuring the lifetime value of existing customers was easy:
– All the data in a single system
– Easy to track customer consistently (through single account)
• Forecasting value based on behaviour to date was hard:
– Massive variation number of purchases by customer (from 0 to a very high number)
– Massive variations in the length of time consumers play game (download and never play
vs download and play for months / years)
– However, limited variation in each purchase value (all virtual goods cost roughly the
same)
SnowPlow Analytics Ltd
10. One key insight led to a simple model for CLV
• Customer lifetime value varied widely between channels
• The best predictor of whether a customer would purchase a virtual good in future was
whether they had purchased a virtual good in the past
• Within each channel, the likelihood that a customer would make another purchase was
constant (i.e. independent of the number of purchases they had made to date)
– This means lifetime value can be modelled as a geometric series where each term in the series
represents a purchase event
– The ratio between terms represents the probability that a user makes an nth purchase having made
an (n-1)th purchase. That ratio, r, is what needs to be measured for each different channel
– Once you have r for a channel, then the lifetime value of the customers acquired can be estimated:
(p = average price of virtual good)
Value of 1st purchases Value of nth purchases
SnowPlow Analytics Ltd
11. So what?
• Easily prediction lifetime value by channel:
– Measuring r is easy: it is calculated simply from the ratio of 1st purchases, 2nd purchases etc.
Keep the model as simple as possible. Use intuition about
customer behaviour to derive key modelling insights
• Fast results:
– Purchase events were, as a whole, frequent enough that a value could be calculated for r based on
only a few days worth of data
• Accurate results:
– Estimations of lifetime value were found to be accurate to 12%
• Powerful results:
– Marketing budget was optimized to those channels driving the most valuable users
If you have large variation in customer lifetime value
between segments, your CLV prediction might not be very
precise but canAnalytics incredibly useful
SnowPlow still be Ltd
12. Questions
• Where do you use CLV? Where do you want to be using it?
• What type of models have you built?
– What worked?
– What didn’t?
– Why?
• Any other questions or insights?
SnowPlow Analytics Ltd