(14 minute read)

I have explained Customer Relationship Management Systems and their role in Relationship Marketing and introduced ideas in Database Marketing in previous blog posts. In this post I will cover some essentials of Database Marketing Processes.

Database Marketing as a form of Relationship Marketing is primarily meant to build strong, long-term relationships with customers. A simple but fundamental truism in marketing is that loyal customers buy more. It is equally true that it is easier and more affordable to sell to existing customers than it is to acquire a new customer. Customer acquisition is costly and time consuming. It makes good business sense for organisations to leverage their existing relationships with customers for maximum mutual benefit.

Customer Lifetime Value (CLV or LTV)

This thinking translates into Customer Lifetime Value, the notion of treating each customer as a prospective lifelong customer who will continue to provide ‘value’ to a firm.

Lifetime value is calculated from:

  • Number of years customer buys from firm
  • Percentage of customers remaining loyal
  • Amount spent per annum

It is calculated historically or as a prediction and can be a powerful segmenting method better than demographics.

Data and Its Uses

Data in the hands of an organisation can provide many uses. It allows for:

  • Personalisation:
    • Customisation: allowing people to set up their own preferences
    • Individualisation: drawing from user data to deliver specific content online and offline
    • Group Characterisation: tailoring suggestions and recommendations to customers based on preferences of people ‘like them’ – collaborative filtering
  • Email Marketing:
    • Outbound
    • Inbound

Database Marketing Processes

Given the importance of data, much of marketing efforts can go into capturing, organising, storing, mining, analysing, and managing data. Some of these processes will include:

  • Preparing to capture data
    • Gathering together potential sources of customer data from multiple customer touchpoints
    • Understanding the type, nature, and characteristics of data
    • Understanding where the data is located
    • Examining how the data can be accessed
  • Organising captured data:
    • Cleaning and monitoring accuracy of data
    • DATA ‘HYGIENE’ AND ENHANCEMENT
    • Understanding where to capture and store the data and in what formats
    • Examining access to data
    • Knowing how to use data once it is structured
  • Understanding Types of Data
    • Individual Customer Data
      • Infographics captured from External sources such as Demographics, Lifestyle and Psychographic Characteristic (hobbies, interests), Geo-demographics (postcode analysis, credit history)
      • Internal Data captured from online and offline contact touchpoints transaction histories (RFM analysis), solicitation histories, self-reported information, online behaviour, click patterns, digital tracking, social profile, interactions and engagements
  • Managing Data Collection Principles
    • Split into ‘Essential Now’ and ‘Possible Future Use’
    • Should allow ease of sourcing and updating
    • Cost of raw data must be offset by benefits
    • If you won’t use it, don’t collect it
  • Understanding Exceptions to Data Collection Rules
    • Ignore once in a lifetime purchases
    • Don’t waste time over those with little brand loyalty
    • Overlook where unit sale is very small
    • Avoid where gathering information is cost-prohibitive
  • Mining Data
    • Detecting anomalies – unusual, interesting data
    • Association rule learning – inter-variable relationships
    • Clustering – groups and similarities
    • Classification – generalising new data using existing knowledge
    • Regression – seeking the function modelling the data with the least error
    • Summarisation – compacting, reporting, representing
  • Transaction and Storage using various tools
    • Tools for Data extraction, transformation, data scrubbing, cleansing, de-duping
    • Tools for data movement – extract, transform, load (ETL) warehouse
    • Tools for data repository – maintaining meta-data
    • Tools for access – retrieve, view, manipulate, analyse, present
    • Tools for delivery – communication, storage, retrieval, access
  • Profiling – Single Customer View (SCV)
    • Collected data on each customer allows a business to obtain a singular view of the customer and helps them
      • Define the changing customer
      • Identify growth opportunities by better targeting and segmentation and product offers
      • Deliver better day to day ongoing customer service
  • Creating and Delivering Campaigns
    • Creating customised, targeted marketing communication campaigns for customers
    • Delivering the campaigns at the right time through the right channels at the right frequency with the right message and right product offers
  • Campaign Management:
    • Measuring, monitoring, and evaluating campaign effectiveness
      • Responses to marketing communications, who responded, how responded, ROI
      • Benchmarking against KPIs and further RFM
      • Better segmentation
  • Analytics
    • ‘SMART PROFILING’: Creating a ‘descriptive’ view of the customer base by comparing a sample of your client base to the regional or national average using common infographic variables such as demographics, lifestyle, psychographics etc.
    • Using smart profiling to develop PREDICTIVE ANALYTICS – using past performance to predict future behaviour
    • Predictive Analytics can be used to create models
  • Modelling based on Predictive Analytics:
    • Customer Management – Retention, Reactivation, Cross-selling, Up-selling, Lifetime Value
    • Customer Acquisition – Response Rates, ROI
    • Benchmark it against Ideal Customer
  • Attribution and Reporting:
    • The success of a marketing communication campaign drawing from a database should always be accurately measured
    • Report generation can include:
      • Data Hygiene and Accuracy
        • Address Accuracy
        • Mail Merge / Purge
      • Campaign Elements and Attribution
        • Segments and Measurements
        • ROI / Segment
        • Historical and Expected
        • Response Rates
        • Average Sales
      • Campaign Performance
        • Response Rates
        • Sales and ROI / Segment
        • Compare to Projections and Historical Data
      • Database Performance
        • Year to Year
        • Product Category
        • Customer Segments
      • Measuring online campaigns:
        • Metrics for website performance, emails, opens, clicks, bounces, unsubscribe, complaints, creatives, subject line, day of deployment, time of deployment, QR code uptakes, responses
      • Reporting results can be analysed by:
        • Channel, offer, packaging, price points, customer segment, RFM, FRAC, list source, demographics etc. (RFM – Recency Frequency Monetary Value / FRAC – Frequency Recency Average Amount Category)
  • Integrated marketing communications
    • The power of databases and customer intelligence provide businesses with many options for choosing the right mix of marketing communication tools in order to contact customers.
    • Tools can be a combination of targeted mailing, newsletters, email, direct mailers, social media approaches, phone calls, face to face contact, inbound and outbound communication etc.
    • One of the most cost-effective methods is email marketing

Some Tips for Leveraging Customer Intelligence

  1. Tracking customer purchases and grouping them according to purchase history – frequency, per capita spend – and reward customer loyalty accordingly
  2. Building a database that can capture relevant information and build in processes for data mining and analysis
  3. Adhering to communication channel preferences of customers – e.g. email, phone, mail, and other touch points
  4. Capturing customer feedback and ensuring customers are able to communicate using their own preferred communication channels
  5. Empowering staff to take action and make decisions based on available data
  6. Monitoring and tracking staff actions based on results and making improvements
  7. Identifying and experimenting with new ways of leveraging customer intelligence – new types of offers, communications, services, cross-selling and upselling opportunities
  8. Always adding and updating data – capturing new data through primary research – e.g. focus groups, surveys, feedback, reviews, secondary research – reports, sales histories, market intelligence, and in-house systems – inventories, POS
  9. Analysing customer intelligence on past customers, tracking their footprints to competitors, and using recovery and retention methods
  10. Act!

A Useful E-CRM Checklist

 Using the web and social media for customer development, generating leads, and conversation to sales

  1. Quantity and quality of emailing list
  • Automated email marketing
  1. Data mining for targeting and segmentation
  2. Personalisation and mass customisation
  3. Online customer service and feedback
  • Online service quality
  • Multi-channel customer experience

 Some Ideas for Keeping the Relationship Alive

Use the pneumonic DRAMA

  • Drama : keeping a conversation going, sending messages responding, and demonstrating evidence of listening
  • Relevance: making mass communication relevant and personalised so it is valuable to the customer
  • Accuracy: ensuring that the database and customer information held in it is always up to date and accurate
  • Magic: constant efforts at ‘delighting’ and ‘surprising’ the customer through promotions, special offers, reward schemes, personalised information, interaction etc.
  • Access: encouraging repeat visits to the website and continuing loyalty

This is a somewhat extensive list of ideas and processes that any organisation can adopt to integrate data and databases into their marketing approach. One of the most effective database marketing techniques is Email marketing. Read more here…