Inside you will learn:
How do we assess the value of our email addresses?
How developed is our data management strategy?
How do we audit our current quality of customer insight?
How do we set goals for quality of customer insight?
What are our options for integrating different data sources?
Customer data
Introduction
Do you have a strategy for data management to support your email marketing? Do you
measure the quality of your email lists and more importantly, the business value you
are able to generate from them? Email marketing is viewed within some enlightened
companies as a strategic communications asset where data quality is managed
accordingly. But in many other companies, email marketing is seen instead as a tactical
activity involving running different email campaigns to meet short-term targets of leads,
sales or customer engagement.
While best practice in creative and email content tends to get all the attention, how you manage and integrate different sources of data to target your email marketing is arguably the most important route to success for email marketers. Why? In a word “relevance”. Your capability to deliver engaging emails tailored to your audience which gain a response is directly related to how well you know your subscribers.
There are many examples of leading email marketers who have prospered through developing a customer data management strategy: Amazon, easyJet, ASOS, TUI in travel and many newspapers such as those in the Associated Media Group such as the Daily Mail spring to mind.
In most organisations, the capability to deliver relevance is fundamentally limited for two main reasons:
1
Technology integration. Information about
customers is stored in separate places, i.e.
information about customer characteristics,
needs and behaviour is inevitably stored in
different databases accessed by different
systems
2
Marketing team integration. A common
organisational design and governance issue
occurs where email marketing activities are not
integrated with direct mail, sales or website
management activities.This is perhaps a bigger
issue in larger organisations where different
teams operate in silos with siloed data!
These are not small issues, they are major issues which require commitment to improve. There is a very real threat that when silos occur, a list member can unsubscribe from one email list while emails are still sent from another silo. Of course this will break privacy laws in many countries and present the brand in a very negative light.
This report will help you to review how well you manage your customer data through answering five key questions to shape your data strategy:
1
How do we assess the value of our email
addresses?
2
How developed is our data management
strategy?
3
How do we audit our current quality of
customer insight?
4
How do we set goals for quality of customer
insight?
5
What are our options for integrating different
data sources?
How do we assess the value of our
email addresses?
If you’re responsible for email marketing and you want to increase awareness of the importance of data, calculating and communicating the average value of email addresses or the overall value generated by a list can help this aim.
Calculating the average value per email address is helpful since it:
Provides a baseline, a benchmark against which
to compare as you strive to increase list quality
and the relevance of your communications
Highlights the importance of managing
customer data quality
Can encourage focus on collecting new
addresses since they then have a known
acquisition value and staff can potentially be
evaluated against this goal
Places a value on the opportunity cost of
unsubscribed or inactive customers
Enables review of the variation in average value
between different segments and different lists
within a company.
Best-practice tip: How to measure the value of an
email address
Value generated per email address has to be measured over a defined timescale to be meaningful. Alternatives are:
For a single campaign
Monthly
Longer periods of quarter, 6 month or annually
Based on lifetime value – profitability based
on number of purchases in first year and then
customer retention rate over a 3 or 5 year
period
For strategic assessment, a review over a longer period such as 6 months or a year is best to average out the fluctuations from individual campaigns. Sometimes companies will evaluate value per 1000 email addresses to best show the value.
Of course, the average value per address reflects a wide variation in value within a list and since many list members belong to the non-responsive “emotionally unsubscribed” category it may be worthwhile calculating the value for this segment (for example, only subscribers who have clicked on an email within the last 6 month period). Response and value will also vary a lot according to how long subscribers have been on a list, so some email marketers will evaluate customer value in a six month blocks of how long they have been on the list.
How developed is our customer
data management strategy?
It’s likely that your colleagues won’t understand or be excited by “data management strategy”, so it will help if this is part of a broader initiative around “customer communications” or “customer targeting” strategy.
Best-practice tip: Develop a data management and
customer communications strategy.
Create focus on the need to improve data quality through producing a data management strategy, which is ideally part of a customer communications or targeting strategy.
A customer data management strategy is needed for the same reason as any strategy, i.e. to get buy-in from colleagues, set goals, review options and create a plan for implementation. A data management strategy should include:
A vision for relevant communications. Outline
your vision of how data management will
provide a better experience for subscribers and
improved leads and sales for the organisation.
An audit of current communications quality
and relevance. A review of current data
management practices and the relevance of
emails compared to competitors. A key area
to review is comparison of competitor use of
event-triggered emails and dynamic targeting.
Are competitors providing a better, more
relevant experience in the context of customer
interactions with the website or email?
define how data will be collected and managed
to ensure compliance. This issue becomes more
complex for international companies subject to
different laws or for companies with distinct
business units.
Strategic options. This should specify options
for the level of email targeting required and
how different information sources will have to
be integrated to achieve this.
Goals for improving relevance. Improved data
management and systems should relate directly
to customer and business benefits. You should
set goals for improving response (open and
click rates and sales on individual campaigns).
Longer-term engagement goals should also be
set such as the activity levels or hurdle rates
over a longer period time such as open, click
or sales rates over a 6-12 month period. These
goals, along with costs of new systems and
processes and improvements in lifetime value
can form the business case.
Business case. How can the investment in the
project provide a return through increased
sales? The case should not only include uplift
in open, clickthrough and website conversion
rates for campaigns, but should also model
longer-term improvements in customer
engagement, subscription and lifetime value.
Plan. A plan for introducing different levels
of improved targeting. These are effectively
your strategic options. For example, you can
start with a simple welcome strategy then add
targeting of solus promotional emails before
building in dynamic content insertion into your
enewsletters. Finally you may want to integrate
email marketing activities with activities that
occur on your website.
Let’s now look at some of the issues involved with managing data strategy. We start with an audit of your current capabilities, which can then be refined into strategic options which the data management project will deliver.
How do we audit our current
quality of customer insight?
The customer knowledge ladder for email marketing is a simple framework I have developed to assess your capability to deliver relevance depending on data quality and availability of integration through different systems. The stages, from bottom to top, with the questions to ask to determine your level are:
Level 1 Aggregate response data
If your reports of email open and click rates are aggregated across the whole list and you can’t drill-down to see the response of an individual or segment, then you are at this basic stage. You can expect all email service providers (ESPs) to offer this level of detail.
Level 2 Segmented response data
At the next level of sophistication you will be able to compare response or activity levels of different segments, for example male vs female or active against inactive for a consumer list.
The capability and ease of comparing responses for different segments varies considerably for different email management systems, so it is worth testing when reviewing reports in a proposed system.
Best-practice tip: Review campaign or newsletter
response by segment
Comparing response by segment can be really valuable for email activities which span a broad segment such an Email Newsletter. For example, a consumer surfing brand found that their newsletter appealed more to an older age-group segment than its core younger audience, so it changed the tone, style and offers to appeal to both groups.
Level 3 Individual
Level 4 Website integration
At level 4 you will be able to link on-site activities such as searching for a specific product or browsing a category to an individual email address. This is very powerful since you can then follow-up on an individual’s site preferences. For example you can follow-up with relevant offer for situations such as:
Browsed category and didn’t buy
Searched for a product and didn’t buy
Abandoned cart
More advanced email marketers such as Amazon are now seeing the benefit of integrating customer behavioural information from the website with email. In this example, Amazon explicitly recognizes past customer web behaviour to provide an email in context (this approach of explicit recognition of behaviour in email messaging may not fit all brands).
Fewer ESPs will provide this capability since it will require fairly sophisticated tracking of individual interactions with a site. Most email systems will be able to identify a sale occurring, but identification of pre-sale customer interactions such as searching and browsing is less common. Likely it will need integration with a capable “enterprise-level” web analytics system such as Coremetrics, Omniture or Webtrends which is able to track an individual’s interactions on a site and then relate them back to email interactions through a common identifier like an email address or customer ID.
A note about using Google Analytics for
email tracking
Note that although Google Analytics is now widely used and is capable for analysing aggregate patterns of site visitor behaviour it is not appropriate for integrating with email systems for tracking response at an individual level. This is because of Google’s stance on privacy. Its terms of service clearly state that it should not be used in this way, although it is possible if links from an email identify an individual.
That said, Google Analytics can be used to report on email marketing at an aggregate level, although it requires specific tagging which many marketers are unaware of.
Google Analytics tracking for Email is possible using campaign tagging using the conventions defined in the Google Analytics URL Builder. blueMarketer eChannel makes it easier for you to track after the click by automatically integrating Google Analytics campaign tracking into the hyperlink in your email templates.
Level 5 Multichannel integration
Level 5 is most relevant to multichannel businesses where the customer may engage with an email but ultimately purchase in store or via the phone. Such offline sales can be attributed to a previous email campaign within a window of 7 days, for example. This requires integration between the email response database and the offline sales transaction system a common unique user identifier such as an email address or customer id is required for this.
How do we set goals for quality of
customer insight?
Going into a little more depth, your audit should also include a review of the level of knowledge you hold about an individual customer since this will affect your capability to target. This can then be used to set future goals for the types of customer data you will collect. Longer-term goals or a roadmap to collect additional data types are useful since they may require investment in new software or data integration projects that may be delivered across several years.
The targeting options available through collecting this data and combining it with dynamic content insertion into emails is detailed in the companion BlueVenn report “Creating an Email Marketing Engagement Strategy”.
This Odeon enewsletter example uses a dynamic template where the different offers in different content containers are based on an individual user’s profile preference and behaviours.
Here is a comprehensive checklist of 10 types of customer data attributes to capture either directly (disclosed by customer) or inferred (tracked through clicks on the web site or email). You should review which of these you have currently and which you should set as goals for future collection.
1 Demographic profile fields
These are the key demographic fields on which you target, for example male vs female consumers or companies in different industry sectors.
Best-practice tip: Create a common customer
profile
A common customer profile is a definition of all the database fields that are relevant to the marketer in order to understand and target the customer with a relevant offering. Many organisations will have created a common customer profile that they use for offline data capture, perhaps through a call-centre, but often, if e-marketing is not integrated, the data captured offline will be separate. Once defined, the common customer profile can then be used as a means of structuring e-permission marketing and refining understanding about the customer. A plan with targets can be created about how to learn more about the customer. For example, level 1 could be basic customer information such as contact details, but without sufficient information to target. Level 2 could include key profile fields for targeting, but without detailed insights. Level 3 will include a full level of customer insight.
The overall database can then be evaluated for the proportion of customers who are in profile level. If the majority are in level 1, then this needs to be improved. It is particularly relevant in situations where data collection is less well controlled because customer details are recorded offline, such as in a business-to-business situation.
2 Status or lifecycle fields
3 Category preferences
Interest in particular product categories or types of information. This may be disclosed, for example through a communications preference centre or inferred through types of links clicked on in an email or category browsed on a website.
Best-practice tip: Defining your email category
preferences
The distinction between category preference selections disclosed by customer, observed via purchase or inferred through email or web response is important. Ideally, you need separate database fields to show category preferences either disclosed through communications preferences selection, for example in a tick-box, observed through purchase or inferred through clicking on links in an email or browsing or searching behaviour on site.
4 Communications preference
A preference expressed for a particular type of content or frequency within a preference centre such as that shown for Econsultancy right.
5 Psychographics and lifestyle
information
Information which suggests the needs of the audience can be useful. This is often appended from research about where consumers live.
6 Value scoring
At a simple level this will be spend in a given period. More advanced scoring will look at current value against future value using lifetime value or propensity modelling.
7 Email response event
Behavioural information on the types of links clicked on within an email – for example different categories of products or types of promotions.
8 Web site interaction event
Again, behavioural information, this time collected through web analytics systems, for example, searching by product or browsing a category.
9 Online outcome event
A specific event type generating value for a company such as a lead or a sale.
10 Offline outcome event
Also a sale or lead which can be related back to email or web activity.
Best-practice tip: Best Practice Tip. Limit disclosed
profile fields
Although there is potential to capture many data attributes with the wish to understand the customer and target them better, care should be taken to not try to collect too much data. As you will know, this can reduce conversion rate to signup, lead or sale. A good rule of thumb is to limit disclosed data to two or three key profile fields which you will use for targeting.
In this example from Econsultancy.com, one of the key disclosed profile fields is whether the subscribers is client or agency side and the company is careful to scope what this means with additional text and help prompts.
To help achieve goals for targeting, having specific targets and responsibilities for list quality is a big help. Often marketers will only look at list volume using measures such as:
Subscriber number including unsubscribes and
% change through time
Coverage (% of database with e-mail addresses)
But additional list quality measures can help improve the capability to target. Such measures include the quality of:
Permission (opt-in % to different
communications types within the list
preferences)
Profile depth (from level 1 to 3 as described in
the section on E-permission marketing)
Audience composition (are the demographics
or roles of list members consistent with your
target audience, for example, what proportion
of gatekeepers)
Deliverability (% bounces and messages that
are delivered)
Response activity (% opens / clicks across
year) – may want to breakdown by segment to
see how well your communications are received
by different audiences. You can setup an
activity score that shows the number of opens,
clicks or outcomes over a longer-period such as
quarterly or for 6, 12 months.
Value delivered – Calculate revenue / cost and
profitability per list member, again it may help
to break this down by segment and compare to
other media.
Once the potential different types of customer insight or data fields that could and should be collected about a customer have been defined, the next step is to assess the capability of these fields to develop relevant, targeted messages through targeted emails or dynamic content insertion, i.e. how do different forms of targeting help determine the propensity of respond.
This chart, based on the strategy of an online travel company case study shows that that although they could target readily by demographics, it was the behavioural information such as time and type of last purchase which were most relevant and go gave the biggest uplift on response so were the key fields to use.
Lifecycle and value Preferences and
attitudes Demographic profile data and psychographics
Unknown Behaviour Tar geting v ariables most pr edictiv e o f r esponse • Category purchased • Category clicked or browsed • Product abandoned
One of the reasons why the “360 degree view” of the customer is not more common, is that in some cases, the integration features of email systems with related customer management systems are limited. Indeed many ESPs do not facilitate integration, making the problem worse as suggested by this diagram which shows potential data silos arising through the use of email systems.
However, solving these challenges doesn’t have to be costly if your email marketing vendor has the right data interfaces. Standard interfaces to transfer data between systems known as APIs (Application Programming Interfaces) are increasingly available to integrate different data stores used by different systems in real-time. Such interfaces are preferable to batch uploads or downloads which are time consuming, more error prone and give rise to inconsistencies in data between the different systems.
Best-practice tip: Define data integration
capabilities of new systems carefully
To assess the capability of different supplier systems such as those for email marketing and CRM systems to integrate, it is best to identify your long-term data integration requirements carefully before you select a supplier.
To review your data integration requirements of email marketing with other systems, there are 5 main different types of data exchange to think about. These 5 exchanges or interfaces occur between an email marketing broadcasting and measurement system and an existing customer relationship management (CRM) or campaign management system(s) which are used to manage direct mail, in-store or phone interactions with customers in non-digital channels. Links to data collected from the website, for example through a web analytics or Ecommerce tracking system are also important. They are shown in the figure right:
The five data exchanges summarised in the diagram are:
1 Customer profile and preference data
Information collected online through a web form via an ESP or other web data collection mechanism, which are often stored separately from customer details in a CRM system.
2 Campaign response data
Email response data such as when an individual opens or clicks on a specific link. This is often stored within the ESP system only, but it is useful to decide whether this needs to be integrated with the core system or data warehouse.
Data exchange 1: Profile data Offline Customer Profile Data Offline Response Data Offline Campaign History Offline Purchase History Email Customer Profile Data Email Response Data Email Campaign History Email Purchase History Web Interactions and purchases Data exchange 2: Campaign response data
Data exchange 3: Campaign history data
Data exchange 4: Purchase history data
3 Campaign history data
All email systems will store details of past email campaigns, but they are often not integrated with results from offline campaigns. Many systems struggle though to aggregate results from different waves of a larger campaign.
4 Purchase history data
Online purchases recorded through email tagging of a transactional ecommerce site may well not be reconciled with Ecommerce systems tracking.
5 Website interaction of event data
We have seen the power of identifying individuals who have searched for a product or browsed a product but not bought. However, if this web data can’t be readily integrated to trigger an event-based email, then this data is wasted.
So, those are the key integrations between email marketing and other systems. It has taken many companies who have full integration at all levels many years to reach this stage, so if you are at a relatively early stage, remember that you are not alone and it’s a long journey for most.
As a final example of the power of integrated multichannel email marketing, view this example from Tesco.com which was a finalist in the Econsultancy Innovation Awards in the category of Innovation in Mulichannel Marketing:
In this example, Tesco delivered a personalised ‘shopping list’ unique to each individual customer. This used the customer’s previous in-store and/or online grocery purchase history. To increase relevance, the shopping featured actual grocery items the customer had purchased across different departments, the same items that they would find in their ‘Favourites’. The email supported a wider, multi faceted campaign combining above and below the line media to convey a single-minded, consistent message about ‘Favourites’, with the aim of increasing awareness of this feature. As a result, the campaign encouraged new and re-activated customers to shop online.
About the author
Dave is the author of five best-selling business books including Total E-mail Marketing. He is also author of the Econsultancy best practice guides to Search Engine Optimisation, Paid Search Marketing, Web site design, Email Marketing and Managing Digital Channels. To support digital marketers, Dave has compiled a wiki of digital marketing tools and statistics at
www.marketing-online.co.uk/wiki and a blog of latest developments
in digital marketing at www.davechaffey.com/blog to help you improve your results from online channels.