Customer data management strategy

Transcription

1 Customer data management strategy By Dave Chaffey Inside you will learn: How do we assess the value of our 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? BlueVenn. All Rights Reserved 2013

2 Introduction Do you have a strategy for data management to support your marketing? Do you measure the quality of your lists and more importantly, the business value you are able to generate from them? marketing is viewed within some enlightened companies as a strategic communications asset where data quality is managed accordingly. But in many other companies, marketing is seen instead as a tactical activity involving running different campaigns to meet short-term targets of leads, sales or customer engagement. While best practice in creative and content tends to get all the attention, how you manage and integrate different sources of data to target your marketing is arguably the most important route to success for marketers. Why? In a word relevance. Your capability to deliver engaging s tailored to your audience which gain a response is directly related to how well you know your subscribers. There are many examples of leading 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 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 list while s 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 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? Within each section of the report we will give you best practice tips and checklists to help you review and shape your customer data management approaches to improve the relevance and response of your marketing.

3 How do we assess the value of our addresses? If you re responsible for marketing and you want to increase awareness of the importance of data, calculating and communicating the average value of addresses or the overall value generated by a list can help this aim. Calculating the average value per 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 address Value generated per 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 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 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 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 s compared to competitors. A key area to review is comparison of competitor use of event-triggered s and dynamic targeting. Are competitors providing a better, more relevant experience in the context of customer interactions with the website or ? Audit of legal compliance. This will identify the appropriate compliance needs and should then

4 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 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 s before building in dynamic content insertion into your enewsletters. Finally you may want to integrate 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 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 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 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 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 activities which span a broad segment such an 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 If you can drill down and see reports of all list members who opened, clicked on a campaign or bounced then you have this capability. Most service providers have this capability although again, the ease of finding the information can vary. [Editors note: bluemarketer echannel provides a range of standard reports to identify individual behaviour, this is often useful in a business-to-business environment since account managers can be provided with lists of companies where an individual has left (indicated by a hard bounce) or have shown interest by clicking through to the site but haven t responded.]

5 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 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 marketers such as Amazon are now seeing the benefit of integrating customer behavioural information from the website with . In this example, Amazon explicitly recognizes past customer web behaviour to provide an in context (this approach of explicit recognition of behaviour in messaging may not fit all brands). A note about using Google Analytics for 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 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 identify an individual. That said, Google Analytics can be used to report on marketing at an aggregate level, although it requires specific tagging which many marketers are unaware of. Google Analytics tracking for 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 templates. Level 5 Multichannel integration Level 5 is most relevant to multichannel businesses where the customer may engage with an but ultimately purchase in store or via the phone. Such offline sales can be attributed to a previous campaign within a window of 7 days, for example. This requires integration between the response database and the offline sales transaction system a common unique user identifier such as an address or customer id is required for this. Multichannel integration also applies to integration with customer services following a phone contact or booking arranged on site as with this example: Fewer ESPs will provide this capability since it will require fairly sophisticated tracking of individual interactions with a site. Most 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 interactions through a common identifier like an address or customer ID.

6 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 s is detailed in the companion BlueVenn report Creating an 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 ). 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 In a business-to-business context, this often defines the level of interest in a prospect in a product are they in the market to buy in the near future? For a consumer, lifecycle may describe their engagement in terms of number of purchases or level of interaction with a service. This type of information (and demographic data) may often be integrated from third-party data providers.

7 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 or category browsed on a website. Best-practice tip: Defining your category preferences The distinction between category preference selections disclosed by customer, observed via purchase or inferred through 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 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 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 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. 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 response event Behavioural information on the types of links clicked on within an 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. Once subscribers are registered with Econsultancy, they can then amend their communications preferences as follows:

8 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 s or dynamic content insertion, i.e. how do different forms of targeting help determine the propensity of respond. 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 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) 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. Targeting variables most predictive of response Behaviour Lifecycle and value Preferences and attitudes Demographic profile data and psychographics Unknown Category purchased Category clicked or browsed Product abandoned What are our options for integrating different data sources? The 360 degree view of the customer has been a vision for CRM nirvana for many years, but it is still not a reality in many companies. The companies that have achieved this are often smaller, relatively new companies who have selected an integrated multichannel CRM system or larger companies that have integrated legacy and systems. What they have in common is a strong vision for the benefits of data integration and the commitment to put the resources behind this vision.

9 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 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 systems. However, solving these challenges doesn t have to be costly if your 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 realtime. 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. Offline Customer Profile Data Data exchange 1: Profile data Data exchange 2: Campaign response data Customer Profile Data Best-practice tip: Define data integration capabilities of new systems carefully Offline Response Data Response Data To assess the capability of different supplier systems such as those for marketing and CRM systems to integrate, it is best to identify your long-term data integration requirements carefully before you select a supplier. Data exchange 3: Campaign history data To review your data integration requirements of marketing with other systems, there are 5 main different types of data exchange to think about. These 5 exchanges or interfaces occur between an 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: Offline Campaign History Data exchange 4: Purchase history data Campaign History 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. Offline Purchase History Data exchange 5: Web interaction events Purchase History 2 Campaign response data 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. Web Interactions and purchases

10 3 Campaign history data All systems will store details of past 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 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 , then this data is wasted. So, those are the key integrations between 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 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 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 Marketing. He is also author of the Econsultancy best practice guides to Search Engine Optimisation, Paid Search Marketing, Web site design, Marketing and Managing Digital Channels. To support digital marketers, Dave has compiled a wiki of digital marketing tools and statistics at and a blog of latest developments in digital marketing at to help you improve your results from online channels. For monthly tips and advice about Digital Marketing subscribe to our newsletter now We ll respect your privacy and won t flood you with s or share your address.