Database marketing ethics (MR, unit 3)

Not acceptable to incorporate personal information from market research into customer record.
Market research is NOR basis of direct-marketing list creation.

Only acceptable to integrate is data is anonymous and partly aggregated.

Data warehousing & data mining (MR, unit 3)

Data warehousing contains a wide variety of data (centrally stored) that presents a coherent picture of business conditions at a single point in time

Data used for: queries/reports, online analytical processing (OLAP for multidimensional analysis) and data mining.

Data Marts are smaller than warehouses. Hold selection of data for specific purpose. Cheaper and quicker to build although one big warehouse better than lots of marts.

Advantages:

• easy access
• wide range data results in wider organisational perspective
• proven good at
  
• quantifying effect of marketing initiatives
• improving knowledge about customers
• identifying & understanding most profitable revenue streams

Data mining is a class of database applications that look for hidden patterns in a group of data. Used for decision-making & predicting future behaviour.

Examples of patterns (mainly predictive or description)
Clusters - patterns between ranges of data items e.g. 20+, unmarried, £50k+ more likely to buy sports car
Association - one event correlates with another e.g. men buy nappies & beer on way home
Forecasting - identify trends that can be extrapolated into the future

4 key statistical techniques (CCND):

1. Clustering - group with similar characteristics
2. Classification techniques - assign people to predetermined classes based on their profile data
3. Neural networks - non-linear predictive models, adjust weighting through 'training'
4. Decision-trees - decision points governed by rules (follow paths to solution)

Datamining software from NeoVista could be used to refine inventory stock levels, predict those with health problems, optimise store layours

Profiling customers & prospects (MR, unit 3)

Customer intelligence
Used to be language of warfare (campaign, targeting), now about

• retaining existing customers
• encouraging existing customers to spend more

Retention Marketing
See in terms of potential for future purchases
About value of added services & quality of service
Require good database profiling past, present & prospective customers and systemically-acquired customer feedback
Can overlay specialist data from other databases (e.g. ACORN) to create in-depth customer profile

Customer segmentation
Exploit the difference in customer's expectations:

• Loyal customers but not profitable - do they have potential to spend more, long-term friends
• Profitable customers but not loyal - 'butterflies' milk for all while you can
• No profitability or loyalty - 'strangers' to be avoided

Database applications (MR, unit 3)

• Operational support - e.g. bank check password correct in telephone banking
• Analytical uses - e.g. bank segments customer base for marketing purposes

Potential objectives:
1) Identify most profitable customers
Pareto's 80/20 principle. Use purchase frequency & value per customer determine where promotional budget can be most profitable

2) Identify buying trends
Tracking purchases per customer (or group) to identify

• Loyal customers - retention cheaper than acquisition
• Backsliding - lost customers or decreased usage
• Seasonal/local purchase patterns
• Demographic purchase patterns
• Promotional campaigns - how purchase patterns respond to

3) Identifying marketing opportunities
Information on likes, dislikes, complaints, feedback, lifestyle may be useful for:

• product improvement
• customer care programmes
• NPD
• decision-making across the mix

or... cross-selling, targeting niche markets, converting occasional users to regular


Using database information

1. Direct mail - maintain contact, generate leads, direct-selling,
   
2. Transaction processing - link to programmes that generate invoices, receipts
   
3. Marketing research & planning - surveys
   
4. Contacts planning - who to contact or give incentives, track event invitations
   
5. Product development & improvement - track purchases, queries, complaints, warranty claims
   

Setting up a database (MR, unit 3)

2 types of database:

1. Flat file system - single worksheet, all data in one file
2. Relational database - linked tables, more efficient as user can interrogate multiple tables and generate an integrated report

Storage structure: fields (labels for types of data), records (collections of fields relevant to one entry), tables (collections of records describing similar data), databases (collections of tables relating to particular set of information)

Data cleansing

ensuring information is correct, properly formatted and not duplicated

Cleansing new data (FVV)

Form elements: introduce pre-defined responses

• radio buttons
• check boxes
• list-boxes

Validation: apply pre-programmed tests to ensure types data input is reasonable

• format checks - numeric, alphabetical or combi / min & max characters
• range checks - test data within appropriate range (e.g. Feb 31 doesn't exist)
• existence checks - compares input data with other data in system to stop duplication or deliberate
• completeness checks - ensure all required fields completed

Verification: compares input data with source document (postcodes & automatic street names can trigger checking process)

Cleansing imported data
Format & consistency: sensitivity to format differences, essential for high-speed analysis. XML can alleviate.
Deduplication: duplicating will annoy customers. Happens through acquisition from different databases, people inconsistent. Easier in B2C by comparing common fields. Harder in B2B where different businesses can have same location or the same business, multiple locations

OVERKILL - delete all but most recent
UNDERKILL - rely on customers to tell them of changes


Database maintenance
Should be regular and systemic. Add new fields, updates, remove names with no response, check returned mail. delete any request to insubscribe

CRM databases (MR, unit 3)

Permits mass customisation (resurgence of traditional values of the corner shop)

What is CRM?

• describes methodologies, software & net capabilities that help build customer relationships
• identifies & targets best customers
• form relationships aiming to improve customer satisfaction & maximise profits
• provides employees with information & processes required to know their customers
• assists improved sales by optimising information sharing & streamlining

Example: Clubcard is great example of business driven by CRM, enables experimentation and constant improvement, all invisible to competitors. Its success is unique to supermarket structure (frequent purchases, multiple categories etc.). Marketing Week argues that web 2.0 provides opportunity to access before-the-event, rather than after-the-event, information. Suggests if app. safeguards in place, individual volunteered information (rather than corporate generation) we could move from predictive models to respond rapidly to actual signals of demand.

Sources of information & benefits of databases (MR, unit 3)

Benefits of customer databases: possible to exploit to drive future marketing programmes

• understand customers & their preferences
• communicate with customers
• easier to collect & store info
• customised reports
• increase retention through better targeting
• increase sales/market share due to better lead follow-up & cross selling

Customer databases (MR, unit 3)

a manual of computerised source of data relevant to marketing decision making about an organisations customers (Wilson '02)

What is database marketing? interactive, uses individual addressable media to extend audience, stimulate demand and stay close to them

Every purchase a customer makes has 2 functions:

1. sales revenue
2. provides information for future market opportunities

Types of details in a database:

• customer/company details - acc #, name, address, contact info, relationship to other customers, parent company, subsidiaries, no. of employees
  
• professional details - company, job titles, responsibilities, industry type
  
• personal details - sex, age, spouse's name, children, interests, other relevant data (newspapers read)
  
• transaction history - what ordered, when, how often, how much, payment method
  
• call/contact history - sales/after-sales calls, complaints/queries, meetings, mailings sent
  
• credit/payment history -credit rating, amounts outstanding
• credit transaction details - items currently on order, dates, prices, delivery info.
• special account details - membership #, loyalty point earned, discounts awarded