prepaid customer lifecycle management
TRANSCRIPT
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PRE-PAID CUSTOMER LIFECYCLE MARKETINGAn overview of Best Practices in the Telecommunications industry
Customer Value Delivery Guide
This document collects common practices and is intended to be a reference guide when implementing Lifecycle Marketing in Telecommunications companies having a Pre-pay business line. It contains the key conceptual building blocks, best practices and examples to get you started.
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1. INTRODUCTION
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Need to show great promotion results? The easiest short term win is to send your message to the entire customer base. If only things were so simple...
The first practitioners of direct marketing quickly realized that a single campaign sent to the whole customer base is not going to deliver lasting results, as customers grow dissatisfied with irrelevant messages. They install anti-spam rules, request to be unsubscribed from direct mailing lists, ignore the communication or become vocal in complaining.
The first step beyond the „send to all“ scenario and reducing customer dissatisfaction is easy – just segment the customer base. One of the best known methods is Recency, Frequency and Monetary (RFM) segmentation invented by database marketers in the 90s. Of course, there are many more ways to segment customers (for a complete overview of common segmentation practices see another Exacaster whitepaper „PREPAID CUSTOMER SEGMENTATION IN TELECOMMUNICATIONS“). With segmentation in place, what‘s next?
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1.1 Getting Started with Direct Marketing
Segmentation is a great starting point that has one important drawback . The biggest drawback is the inability to time messages appropriately, as customers within the same segment are in different life moments and states of mind. Relevance of message varies dramatically with time of day, week and personal circumstance. At the time of communication one customer may be considering and upgrade, while another has just made a complaint, while yet another has just received an error message and is currently deciphering it – yet they all are part of the same „Top customers“ segment.
Trigger marketing is the answer to the issue of timing. The concept of „one-to-one“ marketing, as defined in the seminal book by Don Peppers and Martha Rogers „One to one marketing“, published in 1994 came to dominate this discussion. The classical four „P“s of marketing was extended with five „I“s: Identification, Individualisation, Interaction, Integration and Integrity. See the book for full discussion of these concepts.
1.2 One-to-one Marketing
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While the overall one-to-one concept is easy to grasp the necessary technology and know-how has been spreading slowly and inconsistently. In fact, the biggest biggest advances and best show-cases have come from not from the classical enterprise world, but from online marketing. With fully personalized re-targeting and trigger-based marketing ecommerce sites manage consistent and measurable campaign improvements.
Many companies have created elaborate trigger-marketing schemes and discovered that creating and maintaining these schemes in the face of constantly changing business realities is neither simple nor cheap. The biggest drawback to trigger marketing is lack of longer-term context, and the ability to define only the simplest interaction orchestration logic. Prioritizing among several competing offers to be proposed at any moment or taking into account previous interactions with other triggers is a common challenge with trigger marketing. Nobody wants to end up with „stuck“ triggers resulting in the same message being repeated daily, weekly or monthly to the same customer, but the reality of many cluttered email inboxes speaks otherwise. Trigger campaigns are hard to manage well. Clearly, there must be a way to handle such situations – and there is.
Today the most advanced customer-oriented marketing implementations recognize the need to combine a number of approaches for best results:
Segmentation – to set the large scale context;
Trigger/Event-driven context – used to initiate communication, identify timing and set micro-context.
At the same time, machine learning and robotics technologies have made significant advances and have been successfully applied in marketing, blurring the line between direct marketing and service itself:
Unsupervised pattern discovery – used to create segments that autonomously finds similar customer groups in a very efficient manner. This can make segmented marketing relevant far beyond what was available to early practitioners by including the timing dimension as well;
1.3 Customer Oriented Marketing
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Online personalisation – micro-adjustments made to digital channels as the customer is using them in order to change the choice architecture has proven to be a great approach. The best examples are: predictive online search; online recommendations for e-commerce shopping. While it may seem exotic, these are nothing else but an ongoing closed-loop direct marketing campaign embedded directly into the channel;
Online optimisation and feedback control – coming from industrial engineering and robotics, this is a group of techniques that automates the control of computer-managed processes such as robotic arms, autonomous drone navigation or computerised manufacturing lines. The control algorithms become highly relevant as direct marketing becomes automated and human supervision is no longer feasible.
The combination of machine learning, control algorithms and predictive analytics is the next wave in Customer-Oriented Marketing because they enable automated decision making that truly learns over time. Such process controls, orchestrates and learns which segmentation, trigger and personalisation techniques work best in a given context and resulting in the overall process being both more productive and easier to maintain. This combination is currently the current state of the art in providing better personalisation of customer experience.
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We use the concept of „Lifecycle States“ to simplify management of various communication programs that we will be automating and orchestrating.
The Lifecycle States are implemented by following the „Finite State Machine“ overall conceptual automation framework. The FSM framework is widely used in Automation and Robotics. The main characteristics of FSM carried over into our implementation are:
The Customer is at any given moment in one and only one State;
The Customer can transit from one State to another State due to a triggering event or a timer;
Entry and Exit actions can be peformed when entering or exiting a State, as well as within a State as determined by subprogram;
Each State has a timer that calculates how many days the customer is within that State, as well as an entry counter that calculates how many times the customer has re-entered the State;
Each State will be executing a different marketing program, to be refined later.
2. ESTABLISHING THE FOUNDATION FOR CONTROL: LIFECYCLE STATES, SEGMENTS AND TRIGGERS
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The customer-oriented marketing program designer should be aware of the main customer Lifecycle States and the major transitions between them.
The very first State where the customer enters our program is called the „Interested“ or „Lead“ state, indicated by green ovals and the dotted line in the diagram. Here, the person is not a customer yet, but we will already reach him with our Lead Nurturing programs.
A customer may be passing through same States multiple times with only a few rules to follow, thus the marketer should be aware of the different nuances that can emerge with repeated entry and exit between different States.
Our first task is to decide which Lifecycle States will be used in your progam, and then define their entry and exit criteria. The initial set of Lifecycle States to be considered in every deployment is:
Interested Lead – Person is not a customer yet, but has provided a method to reach him.
Passive Lead – Person has not converted to a customer over a period of time despite our attempts to do so. Passive leads also contain customers whose contact details are no longer correct (i.e. email bounces).
New customer – Person has bought a SIM card. Lifetime is =< 30 days.
Regular customer – Customer‘s lifetime is above 30 days and number of days without activity =< 30
2.1 Overall Schema of Lifecycle States
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INTERESTEDLEAD
NEWCUSTOMER
INACTIVECUSTOMER
PASSIVELEAD
REGULARCUSTOMER
IRREGULARCUSTOMER
FORMERCUSTOMER
The exact parameters of lifetime that control transition between states is to be refined in every project.
Irregular customer – Customer‘s lifetime is above 30 days and number of days without activity > 30 and =< 90
Inactive customer – Customer‘s lifetime is above 30 days and number of days without activity > 90
Former customer – Customer‘s account has been terminated, the contact details remain.
Dissatisfied customer – not in the diagram. This is a special state that is reached when a customer expresses a complaint, and is used to interrupt all other campaigns while the issue is not resolved.
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Within each State customers may be segmented as appropriate for that state.
For example, the customers in a New Customer State are often split into „Returning“ and „New“ segments to offer different treatment to these customer groups.
For regular customers, more complex segmentation schemes are applied.
As a minimum, two schemes should be implemented: segmentation by customer value and segmentation by behavior.
Additional ways to segment are added as needed.
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2.2 Adding Segmentation
RETURNING NEW
TEXTERS CALLERSDATAUSERS
SUPERUSERS
TOP10%
CORE30%
GROW30%
POTENTIAL30%
INTERESTEDLEAD
NEWCUSTOMER
INACTIVECUSTOMER
PASSIVELEAD
REGULARCUSTOMER
IRREGULARCUSTOMER
FORMERCUSTOMER
Segmentation by value splits customers into 4 standard groups:
Segmentation by behavior splits customers into cluster. Based on similar behavior patterns, customers may be grouped into 4-10 groups, for example:
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TOP10%
CORE30%
GROW30%
POTENTIAL30%
INTERNATIONAL CALLERS
HEAVY VOICE USERS
HEAVY TEXT AND DATA USERS
TOP CUSTOMERS
10
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2.3 Adding Propensity Scoring (Predictive Analytics)
Predictive Analytics is an important perspective that adds another layer to the picture by spliting the customers within any State into two main groups: Normal behavior vs At Risk to gain advance warning of what will happen next.
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RETURNING NEW
TEXTERS CALLERSDATAUSERS
SUPERUSERS
TOP10%
CORE30%
GROW30%
NORMAL AT RISK
POTENTIAL30%
INTERESTEDLEAD
NEWCUSTOMER
INACTIVECUSTOMER
PASSIVELEAD
REGULARCUSTOMER
IRREGULARCUSTOMER
FORMERCUSTOMER
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The Normal behavior group is classified as following a favourable path:
Among Interested Customers – Likely to become New
Among New Customers – Likely to become Regular
Among Regular Customers – Likely to stay Regular
Among Irregular Customers – Likely to return to Regular
Among Inactive Customers – Likely to return to Regular
The At Risk group is exhibiting negative signs of change as defined by the predictive algorithm configuration- i.e. movement into Irregular or Inactive State.
Among Interested Customers – Likely to become Passive
Among New Customers – Likely to become Irregular/Inactive
Among Regular Customers – Likely to become Irregular/Inact.
Among Irregular Customers – Likely to become Inactive
Among Inactive Customers – Likely to become Former
The split into „Normal“ and „At Risk“ groups creates a ready-made rule that can be checked later to enrich triggers during a real-time campaign. Use of Propensity Scoring is optional, but highly recommended in situations where uniform treatment of all customers is too expensive.
With Lifecycle States setting the context we can start building our marketing program that runs when a Lifecycle State Change occurs. However, that is not enough: we want to add a variety of other, business specific situations as potential triggers that can initiate campaigns. There are three main sources of triggers to consider: those happening within customer‘s life, within the general external environment and those visible in behavioral data of telecommunications providers.
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2.2 Adding Triggers
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2.4.1 Triggers in Customer‘s Life
The aim here is to identify events in customers‘ life that have the potential to alter the relationship with the mobile service provider. The typical examples are reaching adult age and starting to pay for mobile services indepentently; getting a job; losing a job; finding a spouse; moving home to another location; moving to another country. Every occasion for an incumbent is a risk to lose a customer, and an occasion to acquire a customer for a challenger. Some situations, such as change of home location or finding/losing a job can be detected from internal telecommunications data, albeit imperfectly. In reality, few campaigns utilize these triggers because they are very difficult to capture reliably.
2.4.2 Triggers in External Enviroment
A number of events in the external environment affect mobile usage substantially. For example, seasonal holidays typically have abnormal volume (either very high or very low); vacations have a different service usage pattern (i.e. more usage of international roaming services when travelling). Other influences, such as a change in economical environment can have more localized effects. Certain sectors of economoy shrinking or seasonal works beginning will lead to a dramatic change in communications usage, acquisition or churn. With experience comes the know-how necessary to anticipate such events, resulting in successful campaigns.
2.4.3 Triggers in External Enviroment
Majority of all triggers in telecommunications environment come from internal systems that capture customer behavior. The key step here is to identify the relevant touchpoints that can reveal to us what is happening with the customer and deploy trigger collection agents there. The typical pre-pay environment should consider the listed 11 touch points as trigger-collection points.
The events collected at various trigger collection points can be grouped into three classes: high value, low value and exceptional events.
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The high volume, low value events are calls, text, data or network usage. They are common and have low information value as candidates that directly trigger a specific marketing communication program. They are most often aggregated and can trigger a Customer State change the next day.
The lower volume, high value events, such as top up, balance empty or rate plan change are relatively rare and tell us clearly what is happening with the customer. They are quite good direct candidates to trigger a campaign, but care should be taken to aggregate multiple events within a short time window (i.e. several top ups or balance zero events within same hour) to avoid „stupid“ implementations. Action should be taken within several minutes of such events happening.
Finally, there are exceptional events which are very rare and signify situations that are far out of normal. They are caused by system errors, operator errors, fraud cases and other situations that require action, such as a customer complaint, account blocking or a 10.000 USD top-up. They are great occasions for a trigger campaign that handles these exceptional situations in a well-defined way.
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2.4.4 Business Process Triggers
The Customer Oriented Marketing implementation in a Pre-pay telecommunications environment should consider the following business processes as good candidates for trigger collection:
PRE-PURCHASE
NEW CUSTOMER ONBOARDING
REGULAR USAGE
TOP-UP
AMOUNTCHANGE
NEW PAYMENTMETHOD USED
RATE-PLANORDER/CHANGE
FIRST TIME USE ROAMING
BOLT-ON FIRSTTIME PURCHASE
BOLT-ON REPEATEDPURCHASE
PLAN CHANGE
REPEATED PLANPURCHASE
SERVICE EXPERIENCE
FIRST TIME USEOF DEVICE
DEVICE AGEREACHED
MOBILE PHONE / DEVICE USAGE
CANCELLATION / TERMINATION
CANCELLATION
REQUEST
NUMBER PORT OUT
LOST OR STOLEN CARD
NEW CUSTOMERPREVIOUSLY SEEN
CUSTOMERCARD REPLACEMENT
(AFTER LOSS)NUMBER PORT-IN
CUSTOMER IS ROAMING
DATAUSAGE AND
LIMITSFAIR USE LIMITS
REACHED
BUCKETSUSAGE AND
LIMITSBUCKET EXPIRED
NETWORK CONNECTION
TROUBLEDROPPED
CALL
CUSTOMER SUPPORT
SERVICEREQUEST COMPLAINT
CUSTOMER NEAR BORDER
To illustrate how everything is put together, let‘s imagine a „Exacaster PrePay“ telecommunications company which has detected the following event: A Customer has just made a 10 USD top-up at our Exacaster Prepay Online Self-care site at 10pm.
3. ENRICHING TRIGGERS: THE KEY SUCCESS FACTOR FOR CONTROL AND GOOD CUSTOMER EXPERIENCE
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Take by itself, this event is largely meaningless. After enrichment with segment, lifecycle state and other information the trigger starts to cary a fuller picture:
By enriching the trigger with information about the lifecycle state, we realize that this is an inactive customer that has essentially already churned. Adding segmentation tells us that this was once a high value customer (Top 10% by value), who previously used text messaging most intensively (Behavioral segment „Texter“). He‘s been with us for 560 days and has spent in total 5000 USD with us at a good margin. Propensity model tells us that the „Risk to deactivate“ is „high“. Last communication tells us that it‘s been a while since this customer was contacted with an offer, and he‘s used an offer long time ago.
This looks like a great situation to re-acquire a customer that we almost lost – if we can give him an appropriate incentive.
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TRIGGER
LIFECYCLE STATE
CUSTOMER MADE A 10 USD TOP-UP AT OUR EXACASTER PREPAY ONLINE SELF-CARE SITE AT 10PM.
INACTIVE CUSTOMER
DURATION IN STATE 180 DAYS
AT RISK TO DEACTIVATE? YES
LIFETIME SPEND 5000 USD
LIFETIME MARGIN 1500 USD
LAST KNOWN REGULAR SEGMENT VALUE: TOP 10%BEHAVIORAL: TEXTER
LIFETIME SINCE NEW CUSTOMER SIGNUP 560 DAYS
LAST OFFER COMMUNICATION TO CUSTOMER, DAYS AGO 100 DAYS
LAST OFFER TAKEN 450 DAYS AGO
NUMBER OF OFFERS PROPOSEDIN LIFETIME 25
WELCOME BACK – PLEASE ACCEPT OUR GIFT OF 500 FREE TEXT MESSAGES TO ALL NETWORKS. IT‘S PRE ACTIVATED FOR YOU ALREADY!
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We could reach out with a campaign that uses the following rules to match the trigger: Trigger:
Top up > 10 USD; Lifecycle state: Inactive customer; At risk: Yes; Last offer taken:>100 days.
Our message could be:
That‘s fine – but how should this communication be delivered? We‘ve reached the topic of communication channels.
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The number of communication channels that can be used for campaign delivery is potentially quite large. Any customer touchpoint is a communication channel if it can convey messages to the customer. It is important to realize, however, that every channel has it‘s specfic audience, demographics, conversion rate and cost which will impact decisively the effectiveness of the campaign. To overcome limitations of a channel it is often necessary to use a combination of channels. When deciding which channel combination is best it is further important to evaluate additional characteristics, such as:
How many customers are using the channel – what is the maximum possible audience reach within our chosen lifecycle state and segment? In each lifecycle state, the channel with the widest reach can be different.
How effectively can the channel be used to communicate to the customer – can the channel convey rich messages, or is it limited to short, text-only messages? This determines the communication power, understandability and consequently – the maximum possible conversion rate.
Can the channel be used by the customer to effectively respond to the campaign? Channels that support easy ways for customers to respond to the campaign directly are preferable as the customer can react spontaneously to give a great conversion rate.
4. COMMUNICATION CHANNELS
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Can the channel be completely personalized down to the individual customer? Most digital channels can be fully personalized so that each customer gets an individualized message.
Is the customer actively engaged with the chosen channel? If the customer is using an online self-care to interact with our business right now, a relevant message displayed there will have a much higher conversion rate compared to sending a text message during weekend.
Cost per thousand contacts (CPM – Cost per Mille) – we recommend to use CPM, a standard web advertising metric, to evaluate all digital channels. Some digital channels will carry a cost for display, others – not. In any case, this will provide a great framework to compare channel cost effectiveness.
Cost per acquisition/conversion (CPA – Cost per Acquisition or Click or Conversion) – we recommend to use CPA, a standard web advertising metric, to evaluate all digital channels. Some digital channels will carry a cost for conversion, others – not. In any case, this will provide a great framework to compare channel conversion cost effectiveness.
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CHANNELCUSTOMER‘S ENGAGEMENT WITH CHANNEL
1 CLICK TO BUYPOSSIBLE?
FULLY PERSONALIZABLE?
MAX REACH AND CONVERSION RATE1
Public Web Site Active No2 No* R: 50%C: 10%
Within Online self-care
Active Yes Yes R: 50%C: 80%
Within Mobile app Active Yes Yes R: 50%C: 80%
Inbound call to interactive voice response menu (IVR)
Active Yes if orderable via IVR menu
Yes R: 10%C: 50%
Inbound Call Center Call Active Yes if orderable
by CC agent YesR: 10%C: 50%
Email PassiveYes if ordering can be done via click on link
Yes R: 50%C: 30%
Re-targeted advertising in third party sites
PassiveYes if directed to online selfcare
YesR: 50%C: 50%
Outbound IVR call Passive Yes if orderable via IVR menu
Yes R: 100%C: 30%
Outbound Sales Call
Passive Yes if orderable by human agent Yes R: 100%
C: 50%
Display advertising in third party online site
Passive, can be active if online site well chosen
No No*R: 20%C: 2%
Direct mail to home address
Passive No Yes R: 98%C: 10%
Facebook campaign with direct targeting by mobile number or email
Passive Yes Yes R: 30%C: 50%
Push Message to Mobile App
PassiveYes if ordering can be done via click on message
YesR: installed app base, typically < 50% of baseC: 50%
Using the criteria listed above we can assess a number of communication channels that are typically available for Telecommunications companies:
1 Reach and conversion numbers are provided as a template2 Online purchase process typically takes at least 4-5 screens to complete with large drop-off rates.
4.1 Communication Channel Checklist
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Instead of using the one or two channels for all campaigns we can achieve better results by combining campaigns with the best fitting channel and context, for example:
Top up stimulation campaign in online top-up area of self-care;
Top up stimulation campaign delivered via USSD immediately after top up done via USSD;
New service trial campaign communicated during call center conversation;
New rate plan campaign communicated in mobile app that features rate plan ordering functionality;
New add-on offer delivered within search results or within navigation menu;
Different prioritisation of menu choices made available to customer during interaction with IVR, mobile app or self-care.
Choosing and connecting all desirable communication channels for your campaigns is not a trivial implementation but fortunately campaign channels are integrated once and used many times.
In our example „Exacaster PrePay“ case, we choose to use:
A) Display online after top-up is complete in the self-care site and
B) Follow up with text message to ensure that the offer was noticed.
C) Follow up with expiration text message to ensure that the offer deadline did not pass by noticed.
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4.2
Customer to Act?
Freebies versus Opt-In: Shall we Ask The
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Consumer behavior research from retail loyalty programs shows that opt-in
programs are enjoyed and influence behavior more when customers take some
action to opt-in.
In fact, there is always a group of customers who do not care and do not opt-in
to any programs, and thus we can save a fair amount of loyalty program
investment by not investing it into customers who are not interested in our
rewards. The opt-in action is also a great signal back to the marketer if his
campaign is working as intended, and an essential control variable for
automated control.
When designing opt-in programs is important to understand the power of
„default option“ – the option that does not require any action by the customer
is the one that is chosen most often. Therefore, campaign designer must find
the right tradeoff between ease of use and obtaining opt-in. Best practice
recommends to ask for opt-in in almost all cases, with the caveat that the opt-in
is done with the simplest possible way - a one-click action.
Thus, our example „Exacaster PrePay“ campaign should be modified as follows:
WELCOME BACK – PLEASE ACCEPT OUR GIFT OF 500 FREE TEXT MESSAGES TO ALL NETWORKS.
CLAIM YOUR GIFT THIS WEEK BY TEXTING Y TO 1500.
Notice the addition of deadline („Claim your gift this week“). This creates an
opportunity to followup before the deadline expires.
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4.3 Automated Campaign Control
Managing one or two automated campaigns is relatively simple. Once the
number of automated live campaigns grows beyond 10 with new ones going
live every week tracking all of them becomes a complex undertaking. The
biggest challenges are two: one - making sure that all of these campaigns
deliver a good overall customer experience and two – that marketers do not
waste resources by shutting down campaigns that do not work.
To control customer experience, a common practice is to create specific rules
ensuring that the same campaign is not repeated too often to the same
customer. To control campaing effectiveness overall it is necessary to define
whole campaign suspend rules that terminate campaigns with low effectiveness
by looking at the aggregate campaign results. Here's one example of such a
campaign configuration below:
CAMPAIGN METADATA PARAMETER
Message:
Rules to activate:
Online channel CPC
Online channel CPM
SMS channel CPC:
SMS channel CPM:
Days live:
Unique customers processed:
Number of customers processed in
lifetime:
Lifetime conversion rate:
Number of customers processed in last
30 days:
Last 30 days conversion rate:
Followup schedule
Customer experience control rule:
Campaign effectiveness control rule:
Welcome back – please accept our gift of 500 free text messages to all networks. Claim your gift this week by texting Y to 1500.
Trigger: Top up > 10 USD; Lifecycle state: Inactive customer; At risk: Yes; Last offer taken:>100 days;
0 (free)
0 (free)
0 (free)
1000 x 0.01 USD = 10 USD per 1000 impressions
120 days
1000 (8.3 per day)
1200
5% (60 converted)
130
3% (4 converted)
Immediately: Display online after top-up is complete in the self-care siteIf no conversion in 24 hours: Follow up with text message to ensure that the offer was noticed. If no conversion in 6 days: Follow up with expiration text message to ensure that the offer deadline did not pass by noticed.
Skip campaign after >2 repetitions to the same customer;
Suspend campaign if running last 30 day conversion rate drops below < 1%
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5.
MEASURING THE EFFECT
OF CAMPAIGNS
TEST AND LEARN:
5.1 Control Groups
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The purpose of Control group within Lifecycle Marketing is to perform scientific
measurement of incremental impact that a specific lifecycle campaign or the
program overall has on customer behavior. In „comparative“ experiments,
members of the complementary group, the „control group“, receive either no
„treatment“ or a „standard“ treatment.
The control group shall be set up in the following manner, either:
Witholding group: From every new customer acquisition daily cohort, a 10%
random group is split off and added into a permanent control group. The
permanent control group is not receiving any lifecycle treatment, but can be
contacted for ad-hoc campaigns. This group or a stratified subset thereof is
compared vs. all campaigns.
Or:
Control group: From every campaign, a 10% stratified control group is split off
that does not receive coommunication.
Or:
Test and Learn: Setting up a dedicated „Testing and Learning“ procedure that
tests out the impact of campaign on a smaller target group and then
subsequently extrapolates the results to the real/production campaign.
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5.2 A/B Testing
5.3 Tracking Campaign KPIs
The use of A/B testing has been popularized by leading online players who
claim that „every pixel on our web site has been A/B tested“. The reality is
different: to be realiable, A/B testing requires very large traffic (e.g. over 1000
conversions per branch). Often conversion rate and traffic is small, therefore
big sample sizes become necessary. Campaigns with small traffic in campaign
portfolio cannot be really tested with A/B experiments as it will last too long to
bring any conclusions in time. To evaluate your sample size, use
http://www.evanmiller.org/ab-testing/sample-size.html calculator online. A
good rule of thumb is that if there is less than 1000 conversions per branch, the
results will not be reliably determined.
Every campaign should ensure that it has at least the following KPIs collected
for every channel:
• Campaign dates
• Target group size.
• Control group size.
• For every channel used: Message delivery rate, conversion rate, delivery cost
• Long-term revenue gain (if relevant)
• Long-term retention gain (if relevant)
Conversion rate calculation is typically set up for every campaign individually as
campaigns have different objectives and accordingly, their conversion KPIs are
different.
A FEW WORDS
ABOUT EXACASTER
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80% of Big Data cost is data
extraction, manipulation,
aggregation. We’ve made it
simple.
80% of predictive
modelling is known best
practice. We’ve built it in.
Even at of the cost,
analytics is worthless without
action. We bridge the gap
with campaig n automation
and analytics .
20%
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Exacaster is built on an open source Big Data stack of Cloudera Hadoop
It allows marketers to:
Reduce churn. Exacaster finds the best target groups for retention by
analyzign historical customer behaviour and identifying the most risky
customers then measures the impact of different retention offers and allows
selection of the best ones for each customer group.
Up-sell additional products/services. Exacaster creates a data-driven and
statistically rigorous basis for all up-sell decisions by predicting customers with
the best conversion rates for a particular product.
Identify the optimal additional products/services for a target group.
Exacaster selects which offers should be sent to the desired target group
optimizing for conversion rates, revenue increase or other goals.
Test the business case of new products/services by distributing an offer to a
random group of customers, measuring the impact and establishing the
potential in the remaining customer base.
Measure branding activities by comparing customers who are exposed to
branding activities vs. a control group, so a clear value of brand activities can be
established.
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CONTACTS
UAB Exacaster
Gedimino av 5, LT-01103, Vilnius, Lithuania
Sarunas Chomentauskas, CEO
Phone: +37068506502
Jolita Bernotienė, Sales Director
Phone: +37063606360