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.
At this webinar, Stephan Sorger, Vice-President of On Demand Advisors and Author of the book, "Marketing Analytics: Strategic Models and Metrics" discussed:
• Trends driving Marketing Analytics adoption
• Important advantages and facets of Marketing Analytics
• Marketing Analytics models vs metrics
• Essential tips on how best to allocate your marketing budget and provide a high ROI
• Promotional metrics for traditional and Social Media
This presentation discusses customer lifetime value (CLV) modeling. CLV is defined as the net profit attributed to the entire future relationship with a customer. It is typically used in business-to-consumer contexts but can also apply to business-to-business. CLV must be greater than customer acquisition costs. The presentation outlines different levels of sophistication in CLV prediction models and lists key inputs like contribution margin, churn rate, retention cost, and period. It provides a simple e-commerce example and discusses how churn rate strongly impacts CLV for subscription businesses. Standard and more accurate CLV calculation formulas are also shown.
What is customer segmentation and what are some best practices around segmenting your mobile app users? This Slideshare presents 4 best practices for effectively grouping your customers into actionable segments.
To know more, visit: https://clevertap.com/blog/customer-segmentation-examples-for-better-mobile-marketing/
The document discusses distribution channel management. It defines key terms like distribution channel, intermediaries, direct and indirect selling. It describes different types of distribution channels like intensive, selective and exclusive distribution. It also discusses various channel partners like wholesalers, distributors and retailers; and their roles and functions. Factors affecting the choice of distribution strategy are also highlighted.
This document discusses customer lifetime value (CLV) and how to optimize marketing strategies around it. It covers:
1. Choosing the right key performance indicators (KPIs) to focus on like CPA, ROAS, profit, or CLV.
2. An overview of customer centricity and how CLV is the "Holy Grail" metric for understanding customer strategies and analytics.
3. How to take action and put the chosen KPI into practice through campaign reporting, optimization, and ensuring the right data integration and technology platforms.
4. A short introduction to marketing experiments for proving the effectiveness and value of marketing efforts through controlled experimentation.
The marketing mix for services is extended beyond the traditional 4Ps of product, price, place and promotion. It also includes people, process and physical evidence. The service marketing mix considers how the organization designs the service, delivers it through processes, and provides tangible evidence to customers through physical surroundings and materials. It also focuses on the important role of employees and customers in service delivery.
This document provides an overview of a digital marketing course. It includes a schedule with various session topics like digital marketing strategy development, social media marketing, paid search marketing, and website design. It also lists required and reference reading materials. The document discusses frameworks for conducting external and internal analysis as part of a digital marketing assessment phase. This involves analyzing the macro and micro environment, market situation, and a company's offering mix, marketing mix, resources, and competencies. It also provides examples of segmentation, targeting, and positioning strategies.
1. The document discusses the service marketing mix, also known as the 7Ps, which extends the traditional 4Ps marketing mix to account for the unique characteristics of services.
2. The 7Ps include Product, Price, Place, Promotion, Process, Physical Evidence, and People. Each P represents an important consideration for marketing services, such as the role of customers in service delivery and the importance of employee training.
3. The service marketing mix framework was developed by Booms and Bitner in 1981 to better capture all the elements that impact a customer's experience with a service beyond just product, price, place, and promotion.
This document provides an overview of customer relationship management (CRM) implementation. It discusses:
1. The 8 steps to developing a CRM roadmap: gaining sponsorship, gathering information, assessing the current and future states, identifying value opportunities, linking opportunities to capabilities, defining projects/requirements, developing business cases, and creating a rollout strategy.
2. Emerging trends in CRM like near-field communication, location-based services, augmented reality, social media on mobile as a customer service channel, and next-generation mobile apps.
3. The "4Ps" of CRM roadblocks: process issues around modifying business processes, perception challenges of viewing CRM as enabling rather than mandatory, privacy concerns
Sales quotas are performance targets set for marketing and sales units like regions, branches, territories, and individual salespeople. Quotas are used to motivate performance, control results, and identify strengths and weaknesses. The main types of quotas are sales volume, profit, expense, and activity quotas. Sales volume quotas set targets for revenue, units sold, or points. Profit quotas factor in costs. Expense quotas limit spending as a percentage of sales. Activity quotas focus on non-sales tasks that support sales. Combining quotas can influence both selling and non-selling activities. Setting quotas involves considering territory potential, forecasts, past performance, management judgment, and salesperson input to make the objectives specific, measurable, attainable, realistic
Chapter 2 developing marketing strategies and plansAamir Khan
The document discusses key concepts in marketing strategy and planning. It covers customer perceived value, the value delivery process in three stages, value chain analysis, core competencies, corporate and division strategic planning, and Ansoff's product/market matrix. The value delivery process focuses on choosing value for customers, providing that value through the marketing mix, and communicating the value. The document also discusses intensive and integrative growth strategies including market penetration, development, product development, and diversification.
This document discusses segmentation strategies for creating profitable customers. It begins by renewing the understanding of why segmentation is important for driving higher profitability through understanding customer needs. It then discusses profiting through segmentation by understanding customers and developing targeting strategies. It emphasizes investing in tools, skills, and systems to operationalize segmentation. Finally, it stresses the importance of demonstrating segmentation strategies through testing, measurement, and gaining organizational buy-in to make segmentation efforts successful.
This document discusses sales quotas and quota setting procedures. It defines what sales quotas are and their purposes. There are different types of sales quotas, including sales volume quotas, profit quotas, and activity quotas. The document outlines the quota setting procedure which involves setting parameters, adding expected growth, and allocating individual quotas. Sales territories are also discussed, including what they are, different types, and elements of territory management.
Novoally Software provides innovations for changing business dynamics. It recommends that companies implement Customer Lifecycle Management (CLM) to unify sales and after-sales business management with a holistic view. CLM integrates initial product sales with customer service to maximize revenue over the product's lifetime. Companies must invest in building customer knowledge foundations and offering value-added services throughout the customer relationship.
Sales force management involves analyzing, planning, implementing, and controlling activities related to a company's sales force. It includes designing sales force strategies and structures as well as recruiting, selecting, training, compensating, supervising, and evaluating salespeople. Recent trends in sales force management include a global perspective, technological advances, customer relationship management, sales force diversity, team selling approaches, and managing multiple sales channels. Sales force automation systems allow salespeople to work more effectively using technologies like laptops, smartphones, and customer relationship management software.
The document discusses how marketing is adapting to the new economy. The major forces driving the new economy are digitization, connectivity, disintermediation, reintermediation, and customization. As a result, businesses are shifting from organizing by product units to organizing by customer segments, focusing on customer lifetime value over individual transactions, and considering additional stakeholders beyond just shareholders. Marketers are using tools like the internet, customer databases, and customer relationship management to better understand and serve customers in this new environment.
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.
Creating customer value is the driving force behind a company's goals, and identifying the appropriate customer value measure is not an easy task. Adding services, relationships, and experiences differentiates company offerings in the market. No real customer value exists without a close relationship with customers to understand their needs and provide sophisticated customer interactions.
Chapter 1 Creating And Capturing Customer ValueKathyBright
This document defines marketing and discusses the marketing process. It explains that marketing involves understanding customer needs, designing a customer-driven strategy to deliver value, building relationships, and capturing value from customers. The document also discusses trends like digitalization, globalization, and the importance of ethics. It emphasizes satisfying customer needs over selling and defines key concepts like the marketing mix, segmentation, value propositions, and customer relationship management.
This document provides an overview of key concepts in developing marketing strategies and plans, including:
1) It discusses different levels of strategic planning within an organization and what a basic marketing plan includes.
2) Core frameworks for developing value for customers like segmentation, targeting, positioning and Porter's value chain are summarized.
3) The importance of strategic planning processes, conducting SWOT and opportunity analyses, and setting goals and objectives at the business unit level are highlighted.
Creating value for customers involves understanding what benefits customers perceive from a purchase relative to the price. Customer value is maximized by choosing the right value proposition for different customer segments and effectively communicating, delivering, and maintaining the promised value over time through relationships and customized delivery systems. Key steps are to analyze customer value needs, develop products and services to provide that value, distribute and service customers, and communicate the value proposition.
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.
Digitale verlage by Günther Haslbeck / Ovenga MediaGünther Haslbeck
Digital publishers can implement individualized product strategies like customization, versioning, and modularization for digital goods. They must also consider pricing models like freemium, tiered, and consumption-based approaches. Key metrics for subscription businesses include monthly recurring revenue, churn, customer lifetime value, and customer acquisition cost. Publishers should analyze customer value, awareness, segmentation, demand variability, and important value metrics to determine the optimal pricing strategy.
The document summarizes a webinar on overcoming challenges in using analytics to turn data into profits. It discusses identifying unique customers, differentiating them by needs, interacting with customers efficiently through multiple channels, and customizing experiences. It also covers measuring return on customer and using data to understand lifetime value. Challenges include access to clean, accurate data, leveraging data for high-value activities, and gaining support by proving ROI.
KPIs play a different role in startups than mature businesses. In startups, KPIs should focus on measuring progress towards achieving product-market fit rather than traditional metrics like customer acquisition and retention. To develop startup KPIs, companies first identify key success factors that drive product-market fit, then establish one or a few KPIs to measure each success factor. Good startup KPIs are relevant, responsive, easy to understand, and part of a broader analytics effort to inform ongoing product 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.
Portrait Software is a customer experience management company that provides software to help companies become more customer-centric. The document discusses how customer centricity is important for business success and increased revenue. It then summarizes Portrait's software suite which includes analytics, outbound marketing, and inbound recommendation tools to optimize customer interactions across channels. The software aims to provide a single customer view, personalized recommendations, and consistent service to improve customer retention and cross-selling.
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 relationship management the emperor has no clothesARC Advisory Group
This document discusses customer relationship management (CRM) and the concept of profitable-to-promise (PTP). It argues that traditional CRM focuses on gaining and retaining customers, rather than determining customer profitability. True PTP requires understanding customer behaviors, product costs using activity-based costing, and dynamic pricing models. Implementing PTP brings cultural challenges but often achieves quick payback by focusing on the most profitable customers and orders.
MA8 Digitaalinen markkinointi (luento 5)Joni Salminen
The document discusses key metrics for tracking website and customer success, including unique visitors (V), conversion rate (C), and loyalty/retention rate (L). It explains that the overall success of a website is derived from multiplying these three factors. Subsequent sections discuss challenges with analyzing customer journeys over time across devices, attribution of sales to different marketing channels, and calculating customer lifetime value. The document emphasizes the importance of analyzing metrics at a cohort level rather than only looking at aggregate numbers.
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.
Smarter Customer Analytics - Customer DNAJerry J. Stam
This document summarizes a case study of how a major US retailer used customer analytics to optimize their marketing budget. It involved (1) building a data-driven customer profile, (2) applying customer insights to optimize spending across channels, regions, and customers. Advanced customer clustering analyzed over 30 variables to segment customers into 12 highly differentiated groups with tailored marketing strategies, such as a "Brand Fanatics" group that represented 9% of customers but 30% of revenue.
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.
This document summarizes Amazon's item-to-item collaborative filtering recommendation algorithm. Unlike traditional collaborative filtering which represents customers as vectors based on items purchased, Amazon's algorithm focuses on finding similar items to a user's purchased items, rather than similar users. It scales independently of customer and item numbers and produces recommendations in real-time for massive datasets.
Customer Analytics in Retail - Know Thy CustomersDhiren Gala
A solution to decode the mysterious ways in which customers move is closer than you think.
Customers are at the heart of any business. One unshakable rule of any business is to “know your customer.” In today’s business climate, this means using Business Intelligence (BI) to analyse complex customer data. With BI, companies can answer a wide range of critical questions about their customer base.
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
SigFig is a financial technology company founded in 2007 whose mission is to make investing more accessible and affordable for everyone. SigFig uses Snowplow, an open-source data collection platform, to track user behavior and metrics on their website similarly to Google Analytics. Snowplow provides critical data for SigFig's reporting and data science needs by replicating their production environment. The document provides examples of how SigFig uses Snowplow data to analyze metrics like user interactions on pages, traffic sources, landing page and funnel performance, and A/B testing results.
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.
Snowplow at DA Hub emerging technology showcaseyalisassoon
This document discusses Snowplow Analytics and its approach to reinventing digital analytics. Snowplow allows users to define their own event types, track events across all channels, and answer any questions by storing all digital event data in their own data warehouse. This enables users to join their data to other data sets, pick their own processing logic, and plug in any analytics tools. Snowplow is also open source, free, allows users to own their data and intelligence, and is scalable for tracking large volumes of events.
Using Snowplow for A/B testing and user journey analysis at CustomMadeyalisassoon
This document discusses user journeys, analyzing how users interact with a website over time to understand conversion rates. It describes building a unified data model to visualize customer journeys and see that most visitors to listing pages leave the site. The document also discusses how more page views are linked to more conversions and how A/B testing can be done using event tracking to test different page variants and their impact on user behavior and conversions.
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.
The analytics journey at Viewbix - how they came to use Snowplow and the setu...yalisassoon
This document summarizes the evolution of video measurement and analytics solutions used by a company. It describes several solutions the company implemented, including sending tracking events to a server hosted on Rackspace [1], distributing event collection to Akamai and processing with Hadoop/Hive/SQL in Azure [2], and ultimately implementing a solution using Snowplow that addressed all of their requirements [3]. Key benefits of Snowplow included no limits on data, flexible data modeling, fast reporting and owning their own data. The document ends by discussing lessons learned around data quality, infrastructure costs, modeling needs and focusing on small, actionable insights from big data.
Snowplow is at the core of everything we doyalisassoon
This document discusses Bauer Media's use of Snowplow for data collection and analytics across their 116 websites in Australia and New Zealand. Some key points:
- Bauer Media started collecting Snowplow data in 2014 without a specific use case in mind.
- They now use Snowplow data for cross-site reporting, ad hoc analysis, checking audience reports, and stalking individual users.
- Snowplow allows them to track things like page views, user behavior, content metadata, and ads that can't be tracked as well with Google Analytics.
Implementing improved and consistent arbitrary event tracking company-wide us...yalisassoon
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
Chefsfeed presentation to Snowplow Meetup San Francisco, Oct 2015yalisassoon
The document discusses ChefsFeed, a marketing platform for chefs and restaurants. It outlines some of the key challenges with traditional marketing data systems, which focus only on direct conversions like purchases, installations, or registrations. However, this ignores a lot of important user behavior data that occurs between marketing exposure and conversions. The document then introduces Snowplow as a solution that can track all user behavior events consistently across systems, providing a complete picture of the user journey from first exposure through conversions. This unified event data is valuable for understanding user experiences and improving marketing strategies.
This document discusses event data and the Snowplow data pipeline. It notes that 3 years ago, analyzing user behavior and engagement using tools like Google Analytics was difficult. The Snowplow data pipeline was created to collect and analyze event-level data at scale using open source big data technologies. The pipeline has expanded to encompass different types of digital event data by developing a schema for structured JSON events and a versioning system. A real-time version of the pipeline is also being built to feed event data into applications in addition to batch processing. Developing a semantic model and standard framework for describing events is discussed as being important for enabling downstream applications to consume structured event data.
Big data meetup budapest adding data schemas to snowplowyalisassoon
The document discusses adding data schemas to Snowplow, an open-source web and event analytics platform. It describes how Snowplow is evolving from a web analytics platform to a general event analytics platform to handle an infinite number of possible event types from any connected device. To address this, the document proposes adding JSON schemas to define the structure of each event type. These schemas would be versioned and stored in a central schema repository/registry to define the structure of raw and enriched events processed by Snowplow.
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