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Data and database applications Digital (Interactive) Marketing (Lecture 3.1) Dr Elvira Bolat C113, Christchurch House, Talbot campus @dimbsbu

Learning Outcomes • To explore types of data valuable to DIM practice • To identify sources of data – research • To understand data management process – data strategy (linking with CRM)

STP (deterministic & probabilistic approaches)

Customer lifecycle management

Source: Chaffey and Ellis-Chadwick 2016, p. 312

Example: A sense and Respond Approach

Tip for assignment: see how and if you can use this

Types of data how valuable is knowing customers pet’s name?


Types of data 1.Identity data – Name Information – Title, First Name (Forename), Last Name (Surname), Designatory letters, etc. – Person Information – Date of Birth, Gender, etc. – Postal Address Information – Building Number, Building Name, Address Lines, Town, County, Postal/Zip Code, Country, etc. – Telephone Information – Home Telephone No., Work Telephone No., Mobile No., etc. – Email Address Information – Personal Email Address, Work Email Address, etc. – Social Network Information – Facebook Identifier, Twitter Address, Linkedin identifier, etc. – Account Information – Details of your customer’s account ids or user ids. – Job Information – Company Name, Department Name, Job Title, etc. – Permission and Suppression Data – Not distinctly an identity element of data, but equally important is the information concerning permission to communicate and reason for not communicating (suppressions).

2. Quantitative data – Transactional Information (Online and Offline) – Number of products purchased, actual products purchased, Order/Subscription Value, Order/Renewal dates, product abandonments (abandoned baskets), Product Returns, etc. – Communication Information (inbound and outbound) – Communication date, communication channel, Opens, Click throughs, etc) – Online Activity – Website visits, product views, online registrations, etc. – Social Network Activity – Facebook likes, Twitter interactions, etc. – Customer Services Information – Complaint details, customer query details, etc

3. Descriptive data – Family Details – Marital status, number of children, age of children, etc. – Lifestyle Details – Property type, car type, number of car doors, pet ownership, etc. – Career Details – Profession, Education level, etc.

4. Qualitative data – Attitudinal information – How do you rate our customer service, how do you rate the value of the product, how likely are you to purchase our product again, etc? – Opinion – What is your favourite colour, where is your favourite holiday destination, etc. – Motivational – Why was the product purchased (personal use, gift for someone, etc), what was the key reason for purchasing our product (locality, price, quality), etc.

Other classifications of Data • Personal and profile data • Transaction data • Communications data • 1st party data vs. 2nd party vs. 3rd party data

Customer data • • • •

Customer media consumption Customer search behavior Website audience Communities!!!!!

used for Persona development 1. General profile (demographic and psychographic information)

used for Persona development (cont) 2. Digital profile i.e. digital usage habits content consumption preferences content creation profile 3. Individual profiles (i.e. existing customer)

Online communities

Source: Hagel and Armstrong, 1997. Community types, p. 118

Online communities (cont)

Source: Brunold, 2000. Community types, p. 30

Online communities (cont) Gemeinschaft - community • A membership is based on a common purpose or shared values • Bonds around culture, beliefs, language, values, history • Organic bonds that transcend space and time • The archetype - family

Gesellschaft - society • Weaker ties between individuals – subject to erosion • The archetype – the organisation

Online communities (cont)

In digital marketplace is vital to create strong bonds/relationships between a brand/organisation and individuals (or communities which is more effective)

Online communities (cont)

Tip for assignment: use Chaffey and Ellis-Chadwick’s (2016, p. 311, p. 316), Matrix of customer touch points for collecting and updating customer profile information!

The contact strategy • Frequency (i.e. minimum once per quarter and maximum once per month) • Interval (i.e. a gap of at least one week or one month between communications) • Content and offers (limit on prize draws and offers) • Links (online and offline) • A control strategy

What is DATA Strategy?

Data Strategy • Collection (including digital research) • Quality • Compliance &

Measuring (Analytics)

Data collection imply an opt-in approach 1. 2. 3. 4.

Incentives (information, entertainment, monetary) Educating the consumer Reinforcing the consumer Additional incentive ((information, entertainment, monetary + privileges) 5. Conversion (lead and sales generation offers – e.g. Facebook gated pages) (Godin 1999)

Digital Research – LISTEN! • Research questions! • Then find best suited methodology – to collect, analyse and present data • Consistency

Digital Research – LISTEN! (cont) Step 1. Exploratory research

Public data

Digital Research – LISTEN! (cont) Step 1. Exploratory research

Digital Research – LISTEN! (cont) Step 1. Exploratory research


Digital Research – LISTEN! (cont) Step 1. Exploratory research

Search & filter

Digital Research – LISTEN! (cont) Step 1. Exploratory research

Archive / history

Digital Research – LISTEN! (cont) Step 1. Exploratory research

Archive / history

Digital Research – LISTEN! (cont) Step 2. Data Collection

Screen capture

Digital Research – LISTEN! (cont) Step 2. Data Collection

Screen capture

Digital Research – LISTEN! (cont) Step 2. Data Collection


Digital Research – LISTEN! (cont) Step 2. Data Collection

Tools to create, collect and integrate data

Digital Research – LISTEN! (cont) Step 3. Data Analysis and Visualisation

Social / semantic networks

Digital Research – LISTEN! (cont) Step 3. Data Analysis and Visualisation

Maps / infographics

Data Quality, focus on on collecting: • Behavioural data – social and web interactions of the prospect, their likes and dislikes. • Historical data - past purchases, support issues and known requests. • 360 view of customer – buyer interactions across all your channels – web, store, direct sales, etc.

Compliance • Assessing the quality of existing data and its degree of reliability and consistency • Converting these rules into processes that transform and correct the data into a common format • Finally, the same process created for step two can also be embedded into your marketing systems to automate the validation and correction of data at the point of capture and to continually audit the data to check quality levels continue to meet defined requirements

Crucial stage: Mapping data against KPIs

Last Word: Big Data & AI Future!

Additional Reading! rstand-economic-principles-

DIM 2017_Lecture 3.1 Data and Database applications  
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