Chapter 2 Online marketplace analysis: Micro-environment

Learning objectives Identify the elements of an organisation’s online marketplace that have implications for eveloping a digital marketing strategy Evaluate techniques for reviewing the importance of different actors in the microenvironment: customers, intermediaries, suppliers and competitors as part of the development of digital marketing strategy Review changes to business and revenue models enabled by digital markets.

Questions for marketers What are our capabilities for understanding our online marketplace? How relevant is the behaviour of the actors in the micro-environment to the future of our business? How do I complete a marketplace analysis and how does this inform our digital marketing planning? How are customers’ needs changing as digital platforms develop and what are the implications of such changes? How do I compare our online marketing with that of our competitors? How do we find suitable intermediaries at the planning stage of a digital marketing strategy?

Scenario for marketplace analysis You have been appointed by Blackcircles.com as digital marketing manager. Your task is to create a digital marketing plan for the next year and beyond What would you need to review about the online marketplace to help create your plan?

Figure 2.1 Blackcircles.com

Figure 2.2 The Internet marketing environment

Figure 2.3 An online marketplace map

Figure 2.4 Correlation between search volume in Google Trends and retail sales volume Source: Chamberlin (2010)

Example searches – implications? Table 2.1 Top 10 search terms for all retailing and for apparel

Tools for marketplace analysis Google Agency Tookit http://www.google.com/ads/agency/toolkit.html Key Tools: http://www.smartinsights.com/marketplace-analysis/google-tools-you-may-not-know-about/ Alexa: www.alexa.com or www.compete.com Hitwise: www.hitwise.com Nielsen: www.nielsen-netratings.com Comscore: www.comscore.com Press releases IMRG: www.imrg.org

Issues in analysing the online marketplace Table 2.3 The micro-environment: issues for digital marketers

Figure 2. 5 A model of the Internet marketing conversion process Figure 2.5 A model of the Internet marketing conversion process. It shows key traffic or audience measures (Q0 to Q4), first-time visitors (Q2) and repeat visitors (Q2R) and key conversion efficiency ratios

Figure 2.6 An example of a conversion model

Evaluating demand levels Level of Internet access Includes type of access – broadband and mobile Consumers influenced by using online channel Includes understanding type of sites which have influence Transact online Includes different types of transactions, not just sales, e.g. support, forum comments, etc.

Figure 2.7 Model showing conversion between the digital channel and traditional channels during the buying process

Figure 2.8 Research Online Purchase Online example Source: Google, 2010

Figure 2.9 Variation of broadband penetration in different countries Source: OECD (http://www.oecd.org/sti/ict/broadband)

Figure 2.10 Consumer Internet Activities Source: http://internet2go.net/ (accessed 31 May 2011)

Figure 2.11 Development of experience in Internet use

Figure 2.12 Reasons for and for not purchasing over the Internet in the UK Source: Chamberlin 2010

Consumer characteristics Demographics: Age, gender, social group Webographics Access methods and times Personas: Dulux example

Figure 2.13 Internet use by businesses in European countries Source: European Commission, 2009

Dulux persona example Aims: The aim was to position Dulux.co.uk (Figure 2.13) as “the online destination for colour scheming and visualisation to help you achieve your individual style from the comfort of your home”. Specific outcomes on the site are to browse colours, add colours to a personal scrapbook, use the paint calculator and find a stockist. Further aims were to ‘win the war before the store’i.e. to provide colour help tools that can help develop a preference for Dulux before consumers are in-store and to prompt other ideas to sell more than one colour at a time. Specific SMART objectives were to increase the number of Unique Visitors from 1M p.a. in 2003 to 3.5M p.a. in 2006 and To drive 12% of visitors to a desired outcome (e.g. ordering swatches).

Figure 2.14 Dulux site (www.dulux.co.uk)

Target audience variation Based on research, it was found that the main audience for the site was female with these typical demographics and psychographics: Would be adventurous 25-44 women, online Lack of confidence with previous site: - Gap between inspiration (TV, magazines, advertising) and lived experience (Large DIY sheds,nervous discomfort) - No guidance or reassurance previously available currently on their journey Colours and colour combining is key Online is a well-used channel for help and guidance on other topics 12 month decorating cycle Propensity to socialise Quality, technical innovation and scientific proficiency of Dulux is a given

Example personas First time buyer. Part time Mum. Single Mum. Penny Edwards, Age: 27, Partner: Ben, Location: North London, Occupation: Sales Assistant Part time Mum. Jane Lawrence, Age: 37, Husband: Joe, Location: Manchester, Occupation: Part time PR consultant Single Mum. Rachel Wilson, Age: 40, Location: Reading, Occupation: Business Analyst

Business consumer characteristics Variation in organisation characteristics size of company (employees or turnover) industry sector and products organisation type (private, public, government, not-for-profit) application of service (which business activities do purchased products and services support?) country and region. Individual role role and responsibility from job title, function or number of staff managed role in buying decision (purchasing influence) department product interest demographics: age, sex and possibly social group.

Consumer behaviour models Information / experience seeking behaviour models Hierarchy of response buying process models Multi-channel buying models Trust-based models Community participation models

1. Information / experience seeking behaviour models Directed information-seekers. These users will be looking for product, market or leisure information such as details of their football club’s fixtures. They are not typically planning to buy online. Undirected information-seekers. These are the users, usually referred to as ‘surfers’, who like to browse and change sites by following hyperlinks. Members of this group tend to be novice users (but not exclusively so) and they may be more likely to click on banner advertisements. Directed buyers. These buyers are online to purchase specific products online. For such users, brokers or cybermediaries that compare product features and prices will be important locations to visit. Bargain hunters. These users (sometimes known as ‘compers’) want to find the offers available from sales promotions such as free samples or competitions. For example, the MyOffers site (www.myoffers.co.uk) is used by many brands to generate awareness and interest from consumers. Entertainment seekers. These are users looking to interact with the Web for enjoyment through entering contests such as quizzes, puzzles or interactive multi-player games.

2. Hierarchy of response buying process models Figure 2.15 A summary of how the Internet can impact on the buying process for a new purchaser

Figure 2.16 Initial product search showing e-retailers available Source: Google, 2011

Figure 2.17 Currys’ product category page (www.currys.co.uk)

3 Multichannel buying models Table 2.5 The impact of channel experience on customer relationship

4 Trust-based models Figure 2.19 Segmentation based on information need and trust Source: Adapted from Forrester (2006) Teleconference Driving Sales With SegmentationAnalyst: Benjamin Ensor of Forrester Research, 27 February 2006

Competitor benchmarking Activity – which aspects of a competitors online marketing activity should be reviewed?

Suggested benchmarking activities Benchmarking tools: See: http://www.smartinsights.com/marketplace-analysis/competitor-analysis/online-competitor-benchmarking-tools/

Figure 2.20 Benchmark comparison of corporate websites Source: Bowen Craggs & Co (www.bowencraggs.com)

Figure 2.21 Taobao (www.allthingsgreen.net)

Types of portal Table 2.8 Portal characteristics

and (c) disintermediation omitting both wholesaler and retailer Figure 2.22 Disintermediation of a consumer distribution channel showing: (a) the original situation, (b) disintermediation omitting the wholesaler, and (c) disintermediation omitting both wholesaler and retailer

(c) reintermediation or countermediation Figure 2.23 From (a) original situation to (b) disintermediation or (c) reintermediation or countermediation

estate agents to sell their property Figure 2.24 Example of a channel chain map for consumers selecting an estate agents to sell their property

Porter’s five forces

Impact of the Internet on the five competitive forces Table 2.7 Impact of the Internet on the five competitive forces

Figure 2.25 Different types of online trading location

Figure 2.26 Alternative perspectives on business models

Publisher revenue models Revenue from subscription access to content. Revenue from Pay Per View access to documents. Revenue from CPM display advertising on site (e.g. banners ads, skyscrapers or rich media). CPM stands for ‘cost per thousand’ where M denotes ‘Mille’. Revenue from CPC advertising on site (pay per click text ads) CPC stands for ‘Cost Per Click’. Advertisers are charged not simply for the number of times their ads are displayed, but according to the number of times they are clicked. Revenue from Sponsorship of site sections or content types (typically fixed fee for a period) – Affiliate revenue (typically CPA, but could be CPC) Affiliate revenue is commission based, for example if you display links to Amazon books on your site, you can receive around 5% of the cover price as a fee from Amazon. Such an arrangement is sometimes known as Cost Per Acquisition (CPA). Amazon, and others offer a tiered scheme where the affiliate is incentivised to gain more revenue, the more they sell. Hence this is often called a pay-per-performance ad deal.

Figure 2.27 Revenue model spreadsheet Source: SmartInsights.com