The definition ofdigital transformation is contentious different commentators and theorists have differing views, and this can lead to some confusion. The confusion comes from opposing worldviews about what transforming means in the context of digital. Peoples interpretation depends on how they view the significance of an intervention in an organisation. For example, some practitioners might regard the (simple) implementation of a website for an organisation as digital transformation.
DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT Sprint Planning Meeting Backlog Daily Scrum Refinement Meeting Sprint Review Meeting Sprint Retrospective Meeting Figure 10.5 Scrum meeting Sprint planning To select what work needs to be done, prepare the sprint backlog with the team (and how much time it will take to the work) and work to a four-hour time limit for a two-week planning sprint During the first half of the sprint, the team agree what product backlog items need to be considered and during the second half the development team establish the tasks required to deliver the backlog items (called a sprint backlog) Daily Scrum Every day during a sprint, the team hold a stand-up meeting of no more than 15 minutes The meeting should happen at the same time, in the same location, every day; team members come prepared and each person answers three questions: left in chapter 78% DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT • What did I yesterday that helped the development team meet the sprint goal? • What will I today to help the development team meet the sprint goal? • Do I see any impediment that prevents me or the development team from meeting the sprint goal? Box 10.7 Using Scrum in marketing teams If you are going to be implementing agile methods into a marketing department, these are some of the key things to consider: • Review meetings can take too long for teams of eight or more people • A month is probably too long for a marketing sprint because of the unpredictability of schedule demands • Task estimating takes time to learn Marketing projects should be executed in 'mini development cycles', each lasting no more than a month Any impediments identified in the Daily Scrum are recorded by the Scrum Master and dis played on the team's Scrum board, with someone designated for working toward a resolution (outside of the Daily Scrum) Detailed discussions should not happen during the Daily Scrum Sprint review and retrospective During the sprint review, the team reviews the work that was completed and the planned work that wasn't completed At the sprint retrospective, the team answers two questions: What went well during the sprint? What could be improved in the next sprint? They then identify and agree continuous process improvement actions (see Box 10.7) Developing agile marketing campaigns Programmatic marketing Automated bidding on advertising inventory in real time, for the opportunity to show an ad to a specific customer in a specific context A good 'rule of thumb' is to use a marketing 70:20:10 rule: 70% ofyour marketing should be planned activity; • 20% of your marketing should be automated marketing that responds to various actions ofthe user (such as programmatic marketing); left in chapter 78% DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT • 10% of your marketing should be entirely agile - reacting to news and events as and when they happen In order to achieve this, the right resources are needed - a creative team who can generate sharable content quickly - the correct tools in place to listen to social media feeds/provide alerts to relevant topics and a culture that is open to ideas and experimentation (i.e one that is not risk-averse) Social media is a perfect medium for agile marketing because of its real-time responsiveness Figure 10.6 shows a Specsavers advert, with text added to reflect the Brexit Referendum re sult using Specsavers 'should have gone to Specsavers' slogan 42102 We look You listen Our hearing tests now include video technology LEAVE I Stay Specsavers Audiologists Should havegone to Specsavers JCDecaux Figure 10.6 Specsavers Advert Source: Joe Doylem/Alamy Stock Photo The growth hacking process There are five key pillars to achieving growth hacking success: Product/market fit (create an MVP - minimum viable product) User data analysis Conversion rate optimisation Viral growth left in chapter 78% DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT Retention and scalable growth Product/market fit (create an MVP - Minimum Viable Product) Product/market fit is a product or service offering that perfectly satisfies the need of a particu lar user segment, which creates a loyal and passionate user base Traditionally, marketers are given a finished product and it's their job to generate sales In the world of growth hacking, a different approach is taken instead, the product build phase should be entered as quickly as possible with a minimum viable product (MVP) This is a basic (or beta) product without any 'bells and whistles' Marketers are enlisted during this initial product development phrase to help put the MVP in front of potential customers to gain feed back This is done by running surveys, testing and iterating to improve the product Sustainable growth is only possible if a large group of people consider the product or service a 'must have' This is difficult to achieve without user feedback and product improvement be fore officially launching the product to upscale the business The idea of 'fail fast, fail cheap' and improvement via constant experimentation can be seen in the Instagram mini case study PayPal co-founder and technology start-up investor Peter Thiel believes that: If a product requires advertising or salespeople to sell it, it's not good enough Most tech start-ups take the 'traditional' approach of building a product and then seek funding to assist with bringing in new users via sales and marketing However, technology start-up investors require 'proof-of-concept' before releasing funding, so that key marketing metrics such as cost of acquisition and month-over-month growth can be provided to prove sustainability The model in Figure 10.7 shows each stage of the start-up process and how customer feed back is an important part of finding a business model that works The perfect target market for a start-up is a small target audience served by few or no com petitors because trying to enter a large market already served by competing companies will erode profits One of the main aspects of the products we use on a regular basis is that we're hooked on them How often you use platforms such as Facebook and Twitter and/or products like your iPhone or iPad? Eyal (2014) is an expert on applied consumer psychology; he has developed a model that helps people build better products and achieve product/market fit The 'Hook Can vas' can be seen in Figure 10.8 Designing a habit-forming product has four parts: trigger, ac tion, rewards and investment We will look at each of these in more detail next Trigger Triggers come in two types - external and internal External triggers are embedded within information and tell the user what to next (e.g click a link in an email) Internal triggers are when a product becomes aligned with a thought, emotion or pre-existing routine - i.e cap turing moments with friends/family via photos and sharing them on Facebook Once internal triggers become part of people's routine behaviour, the habit is formed left in chapter 78% DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT Listen to customers During customer development, a start-up searches for a business model that works If customer feedback reveals that its business hypotheses are wrong, it either revises from or "pivots" to new hypotheses Once a model is proven, the start-up starts executi building a formal organization Each stage of customer development is iterative: A start-up will probably fail several times before finding the right approach SEARCH EXECUTION 2 CUSTOMER CUSTOMER CUSTOMER COMPANY DISCOVERY VALIDATION CREATION BUILDING PIVOT Founders translate Start-up continues The product is company ideas to test all other refined enough from start-up mode, into business hypotheses and to sell Using with a customer model hypotheses, test assumptions tries to validate its proven development team customers' interest hypotheses, the searching for about customers start-up builds demand by rap answer, to functional create a "minimum" through early orders or product usage If there's no its model viable product" interest, the start idly ramping up marketing and to try out their up can "pivot" by sales spending proposed solution changing one or and scales up on customers more hypotheses the business needs, and then Business transitions departments executing Figure 10.7 Lean start-up Source: https://hbr.org/2013/05/why-the-lean-start-up-changes-everything left in chapter 78% DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT Action After a trigger comes the intended action There are two pulleys of human behaviour -moti vation and ability Although motivation is a widely contested topic in psychology, this model is based on the fact there are three core motivators driving our desire to act: seek pleasure and avoid pain; seek hope and avoid fear; and seek social acceptance and avoid rejection It is worth considering that negative emotions, such as fear, can be powerful motivators The other 'pulley' links to usability, i.e the ability of the user to take action easily Companies such as Pinterest, Instagram and Snapchat have simplified online content creation and sharing, they have used modern technology to take out steps Fogg (n.d.) describes six elements of simplic ity: time, money, physical effort, brain cycles (level of mental effort/focus needed to take an action), social deviance and non-routine (how much it matches or disrupts existing routine) To put this in context, Google has reduced the amount of time and cognitive effort required to find information The HOOK Canvas REWARD TRIGGER What internal trigger is Is the reward the product addressing? What external trigger fulfilling, yet leaves the user wanting more? getsthe userto the product? What "bit of work" is done What is the simplest to increase the likelihood of behavior in anticipation returning? of reward? INVESTMENT ACTION Figure 10.8 The Hook model Source: www.slideshare.net/nireyal/hooked-model/135 Rewards The variable reward phase is when users are rewarded by solving a problem, thus reinforcing their motivation for taking the action in the first place Variable schedules of reward are a pow erful way to hook users Research has shown that levels of dopamine surge when the brain is expecting a reward and introducing variability multiplies the effect, activating the parts ofthe brain associated with wanting and desire Lotteries and slot machines work on this premise There are three ways a product can heighten a user's search for variable rewards: left in chapter 78% DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT • rewards of the tribe - gratification from others; • rewards of the hunt - material goods, money or information; • rewards of the self-mastery, completion, competency or consistency Investment This is the last phase of the Hook Canvas: before users create mental associations that activate automatic behaviours, they need to first invest in the product This links to a psychological phenomenon called the escalation of commitment - the more users invest time and effort into a product or service, the more they value it Therefore, this stage is about asking users to a bit of work-investment is generally in the form of asking the user to give some combination of time, data, effort, social capital or money Company's such as Giffgaff have utilised this phase well, by asking for user-generated content for its knowledge base (see the section on 'Artificial virality', p 588) More information about user behaviour and what drives customer engagement can be found on Eyal's website, www.nirandfar.com One product that has hooked millions of people is Instagram The photo- and video-sharing social network has a team that are conversant in psychology as much as technology Many people have made using the app a part of their daily routines - forming a connection between the need to capture images of things around them and using the app on an ever-present mo bile device For many people, Instagram started off as a brief distraction (i.e to relieve boredom), only to become part of a regular routine The fear of losing a special moment instigates a pang of stress, which triggers Instagram users to open the app and alleviate the pain by capturing a photo Also, because the app is a social network, it dispels boredom by connecting users with others, sharing photos and swapping banter It also lessens or stops the pain of 'fear of missing out' Instagram's journey to product/market fit is an interesting one, as you can read in Mini case study 10.8 Mini case study 10.8 Instagram Instagram Figure 10.9 left in chapter 78% DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT Instagram Source: tanuha2001/Shutterstock.com Instagram started life called Brbn, named after a whisky Originally launched as a location-based iPhone app, it allowed users to check-in at particular locations, make fu ture check-in plans, earn points for hanging out with friends and post pictures of their meet-ups It wasn't very successful All of its features confused users To tweak the app, they looked at the user analytics and found that most people were using it to share photos Based on the usage data, they scaled down the product and focused on its photo sharing infrastructure They also looked at the competition - Hipstamatic had great fil ters but photo-sharing was difficult, and Face-book was great for social networking but not photo-sharing They decided to build something in between After months of experimentation and prototyping, they released a simple photo sharing app called Instagram User data analysis The Instagram mini case study highlights the importance of user data analysis One of the key aspects of growth hacking is to find user patterns and test/optimise activities that are linked to growth However, one of the key challenges faced by start-ups is that there is so much data available it is becoming increasingly difficult to understand how the data can be used to create actionable insights User data analysis should be a mix of quantitative and qualitative research and a business should develop a systematic method to feed into business insights Main areas of user testing The five main areas of user testing are: Technology analysis, such as conversion rate per browser Heuristic analysis, such as relevancy, distraction and online value proposition Web analytics, such as flow reports Qualitative surveys, such as exit surveys Usability testing, such as user session videos The information gained from this type of analysis can then be used to test hypotheses relating to user growth and to validate ideas This process is essential to finding 'non-norm' so lutions to achieve growth in a short amount of time Another important tool (available in Google Analytics) is cohort analysis Instead of looking at cumulative totals or gross numbers, data are broken down into the performance of each group of customers (a cohort) that comes into contact with the product independently This method helps companies understand customer flows, which provides more predictive power than traditional gross metrics However, one of the main challenges facing a start-up during the launch stage is having enough customers to provide meaningful data This is why product/market fit is so important - so that the product initially 'sells itself' left in chapter 78% DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT Conversion rate optimisation User data analysis is not an isolated approach to growth hacking - it links to every stage in the cycle, from product/market fit to retention The information collected from data can then be used for conversion rate optimisation (CRO), to help build an effective growth engine (as shown in Figure 10.10) This approach is basically using structured testing to improve website effectiveness Growth optimisation is moving from data, to insight and then to money User data analysis is needed throughout this CRO process, so that activity can be prioritised Generally, a company should use a minimum sample size of 250 to test changes for CRO They also need to think about business cycles - for example, if your weekend traffic is very different, ending a test by excluding that segment would make your sample unrepresentative Key CRO elements There are three main conversion rate optimisation elements: Tools - insights, creating pages, personalisation, campaign and automation People - insight, management, creative execution, test set-up, implementation, out source Process - planning and creating new ads and content, optimising old ads and content According to Eisenberg et al (2011), there are 30 key optimisation factors to consider (see Table 10.2) Box 10.8 Heuristic analysis According to Phillips (2016), a heuristic-based analysis approach for e-commerce companies would include the following: Determining conversion rate for different device types A helpful place to start is to determine whether the conversion rate for orders is different by device Segmenting critical conversion rates by key dimensions to understand differences, such as by marketing channel You may segment conversion rate by paid search on the mobile versus paid search on the desktop Identifying the bounce rate on your landing pages Product pages, category pages, brand pages and the home page can be landing pages for email, search and display advertising campaigns Ensuring that these]pages perform as effectively as possible by testing the creative is important left in chapter 78% DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT Identifying the exit rate on important pages The exit rate is the percentage of people who leave the site on that page If you notice large exit rates on certain pages, such as login pages, purchasing pages, ship ping pages and order summary pages, then you can consider testing them Source: Extract from Ecommerce Analytics by J Phillips (2016) Understand Prioritize Test & Visitors Planning Analyze Figure 10.10 The conversion optimisation loop Source: www.slideshare.net/seanellis/cro-preso-for-growth-hackers-conf-nov-2013 ellis-updated-28050686/7 Steps_to_Better ConversionsTwitter_SeanElliswwwGrowthHackerscom Table 10.2 30 Key optimisation factors Element Details Planning • WIIFM: What's in it for me? left in chapter 78% DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT Unique value proposition/campaign proposition The buying decision Categorisation Usability Look and feel • Searchability • Layout, visual clarity and eye tracking Structure • Purchasing • Tools • Error prevention • Browser compatibility • Product presentation • Load time • AIDAS (scent) Trust and credibility • Navigation/user oflinks Momentum • Product selection/categorisation Up-sell/cross-sell Calls to action/forms • Point of action Security and privacy • Persuasive copywriting Content • Headlines • Readability Communication • Use of colour and images • Terminology/jargon 'We-We' Test (customer-focused lan guage) Features like reviews Source: https://www.slideshare.net/Emerce/emerce-performance-bryan-eisenber There are seven main areas that can help improve website conversion and sales: A/B testing and multivariate testing Having a structured approach Customer journey analysis (covered in Chapter 8) Copy optimisation Online surveys/customer feedback Cart abandonment analysis Segmentation (covered in Chapters and 8) left in chapter 78% DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT Parizek (2013) has produced a conversion rate optimisation maturity model (see Figure 10.11), which is based on seven key pillars: • People - the quality and quantity of a team is essential • Knowledge - this is aligned with people CRO starts with online marketing basics, having an overview of what e-commerce is, how traffic generation works, what web analytics is and how to read reports and take actions The next stage is to add online testing knowl edge, principles of user experience, web analytics knowledge and copywriting skills It is impossible for a single person to be an expert in all those areas, so a well-acting team is needed • Activities - there are various quantitative and qualitative activities that can be run to understand customers better The higher the quality and frequency, the better the out come • Tests of strategy and frequency - one of the main CRO activities is A/B and multivariate testing (discussed next in this chapter) The maturity of a company's testing processes is extremely important: tests can be executed on an ad hoc basis; or a more mature ap proach will plan and execute in a testing roadmap Or, better still, tests are run in an iter ative manner • Processes - the overall CRO processes in a company are another important asset Do key departments cooperate smoothly? How about communication and politics within the company? Are deliverables such as testing roadmaps, testing summaries and learn ing overviews recorded and used? All of these are variables that influence CRO results significantly Sponsor - this is usually a high-ranking employee who is an advocate of CRO, trusts the team and fights for budget They support CRO efforts and share the plans and results with senior management, if the team are unable to • Tools - tools need to be in place to conduct analyses and tests There are many different tools available to this - such as web analytics, heatmaps, surveys, feedbacks, targeting and testing tools In general, the more mature a company's CRO efforts are, the more so phisticated the tools A/B and multivariate testing Often site owners and marketers reviewing the effectiveness of a site will disagree and the only method to be certain of the best-performing design or creative alternatives is through design ing and running experiments to evaluate the best to use Matt Round, then director of person alisation at Amazon, speaking at the E-metrics summit in 2004, said the Amazon philosophy, described further in Case study 10.2, is: Data trumps intuition A/B testing and multivariate testing are two measurement techniques that can be used to review design effectiveness to improve results A/B testing In its simplest form, A/B or AB testing refers to testing two different versions of a page or a page element such as a heading, image or button Some members of the site are served alternately, with the visitors to the page randomly split between the two pages Hence it is sometimes called 'live split testing' The goal is to increase page or site effectiveness against key performance indicators including clickthrough rates, conversion rates and revenue per visit left in chapter 79% DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT A/B or AB testing Refers to testing two different versions of a page or a page element, such as a heading, image or button, for effectiveness Level5 Level4 Data-driven Level3 Optimization as a Level2 Level1 Optimization using Starting with Regular Online Testing and Optimization with key online clear plans marketing asset as-hoc Online Optimization Testing PEOPLE | Online marketing generalist | | Part-time conversion II Level Level basics II copywriting Level1 Advanced traffic and | | conversion report analysis | || |Online testing & FREQUENCY | 1-2 tests per quarter PROCESSES | None TOOLS | Web analytics tools 11 Level team || Level | 1-2 tests permonth || Random/ad-hoc || Level1 11 II segmentation Customer analytics 11.Businessanalytics 11 MBA JL 11 Level || Multichannel analysis and Basic segmentation and Advanced segmentation and T targeting 11 Extensive UX research |• Personalization 11 optimization 1:1 Personalization 11 Data mining || 360° business analysis IIIterative testing II Disciplined testing 2-3 testsper month 11.3+tests permonth 11 6+ tests per month || Regular and standardized || •Optimized || Super-optimized Il Level 11 Level | Customer survey and || || Customer experience feedback Heatmaps and screen management tools Personalization tools T recording tools TIME || Head ofOnline || Managementskills 11 Advanced analytics | tools Advanced analytics including 11 Level survey analysis II Regular and planned testing I Ad-hoc testing I Targeting knowledge • Excellence in UCD/UX 11 Customer feedback and targeting IlCompetitor analysis | | Online testing and targeting SPONSOR None team 11 Level I UX principles and testing TESTING STRATEGY 1- No testing strategy Il | ACTIVITIES I Basic traffic and conversion! | Sales report monitoring II.Large conversion optimization Il • Deeperknowledge about CRO, UX and analytics Content management and || Conversion optimization report analysis || Smallconversion optimization | KNOWLEDGE Basics of online marketing | || Full-time conversion II optimization specialist optimization specialist CRO is in your company's DNA Il Director level r 11.VP level Level || Personalization automation tools II Multichannel analytics and II Optimization tools 10 || Entire organization Figure 10.11 Conversion rate optimisation maturity model Source: http://online-behavior.com/analytics/conversion-optimization-model When completing A/B testing it is important to identify a realistic baseline or control page (or audience sample) to compare against This will typically be an existing landing page Two new alternatives can be compared to previous control, which is known as an ABC test Different variables are then applied, as in Table 10.3 An example of the power of A/B testing is an experiment Skype performed on its main top bar navigation, where it found that changing the main menu option 'Call Phones', to 'Skype Credit' and 'Shop' to 'Accessories' gave an increase of 18.75% revenue per visit (Skype were speaking at the 2007 E-metrics summit) That's significant when you have hundreds of mil lions of visitors! It also shows the importance of being direct with navigation and simply de scribing the offer available rather than the activity Table 10.3 left in chapter 79% DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT A/B test example Test A (Control) Test Original page B (Test page) New headline, existing button, existing body copy Existing headline, new Original page Test button, existing body copy Existing headline, Original page Test existing button, new body copy Control page The page against which subsequent optimisation will be assessed Typically a current landing page Mini case study 10.9 Multivariate testing at National Express Group increases conver sion rate The National Express Group is the leading provider of travel solutions in the UK Around billion journeys a year are made worldwide on National Express Group's bus, train, light rail and express coach and airport operations A significant proportion of ticket bookings are made online through the company's website at www.nationalexpress.com The company used multivariate testing provider Oracle Maxymiser to run an exper iment to improve conversion rate of a fare selection page that was the penultimate step in booking The analysis team identified a number of subtle alterations to content and calls to action on the page with the aim of stimulating visitor engagement and driving a higher percentage of visitors through to successful conversion without changing the structure of the page or National Express brand identity In order to aid more effective up-sell to insurance add-ons, changes to this call to action were also proposed It was decided that a multivariate test would be the most effective approach to de termine the best-performing combination of content The variants jointly developed by Oracle Maxymiser and the client were tested with all live site visitors and the conversion rate of each combination monitored They tried 3,500 possible page combinations and during the live test the underperforming combinations were taken out to maximise version rates at every stage At the end of the testing period, after reaching statistical validity, results showed that the best combination of elements showed a 14.11% increase in conversion rates for the page, i.e 14.11% more visitors were sent through to the fourth and final step in the left in chapter 79% DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT registration process, immediately hitting bottom-line revenue for National Express (Fig ure 10.12) Content Maxybox A combination Maxybox B Maxybox C Maxybox D Maxybox E Lift on control Variant Variant Variant Variant Variant 14.11% Variant Variant Variant Default Default 14.09% Variant Variant Variant Default Default 11.15% Variant Variant Variant Default Variant 10.57% Default content Variant Variant Default Default Default 0.00% Conversion rate uplift by page combination: Page 14.11% 14.09% 11.15% combination 10.57% Default 0% 0% 2% 4% 6% 8% 10% 12% 14% 16% Figure 10.12 Results of multivariate testing for National Express Multivariate testing Multivariate testing is a more sophisticated form of A/B testing that enables simultaneous testing of pages for different combinations of page elements that are being tested This enables selection of the most effective combination of design elements to achieve the desired goal An example of a multivariate test is shown in Mini case study 10.9 In order to achieve significantly higher increases in sales growth, companies need to com plete six to seven A/B tests or multivariant tests a month An example of how powerful this technique can be is demonstrated in Mini case study 10.10 left in chapter 79% DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT Forward path analysis Reviews the combinations of clicks that occur from a page This form of analysis is most beneficial for important pages such as the home page, product and directory pages Use this technique to identify messaging/navigation combinations that work best to yield the most clicks from a page Clickstream analysis and visitor segmentation Clickstream analysis refers to detailed analysis of visitor behaviour in order to diagnose prob lems and opportunities Table 10.4 gives an indication of the type of questions asked by author Dave Chaffey when reviewing clients' sites Path analysis Aggregate clickstreams are usually known within web analytics software as 'forward' or 're verse' paths This is a fairly advanced form of analysis, but the principle is straightforward you seek to learn from the most popular paths Viewed at an aggregate level across the site through 'top paths' type reports, this doesn't appear particularly useful as the top paths are often: • Home page: Exit • Home page: Contact Us: Exit News page: Exit Mini case study 10.10 How Obama raised $60 million by running an experiment To demonstrate the power of conversion rate optimisation across all types of campaigns, a simple experiment in December 2007 has actually changed the course of history Dan Siroker was the Director of Analytics for the Obama 2008 campaign; his job was to use data to help make better decisions when running the campaign He did this by running an experiment to test two pages of the campaign splash page - the media sec tion and the call-to-action button Four buttons and six different media types were tested (three images and three videos); the metric to measure success was sign-up rate (number of people who signed up divided by number of people who saw a particular variation) The test was run using Google Website Optimizer and was a multivariate test (i.e they tested all of the combinations of buttons and media against each other at the same time) Staff believed that 'Sam's video' would be the best media However, all of the videos did worse than all of the images This page had a sign-up rate of 11.6%, against the original sign-up rate of 8.26% (40.6% improvement) This equated to 2.8 million email addresses and an additional $60 million in donations Key lessons learned: Every website visitor is an opportunity - take advantage of this through website optimisation and A/B or multivariate testing left in chapter 79% DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT • Always question assumptions - videos were the most popular media but they didn't perform • Experiment early and often - small incremental changes can generate aggregated marginal gains Source: http://blog.optimizely.com/2010/11/29/how-obama-raised-60-million-by-running-a simple-experiment/ Table 10.4 A summary of how an analyst will interpret web analytics data GA is terminology for Google Analytics (www.google.com/analytics), one of the most widely used tools Analystquestion Typical web analytics re port terminology How successfulis the siteat Conversion goals (GA) Diagnosis ofanalyst used to improve performance Is engagement and conversion consistent with other achievingengagement and sites in the sector? Bounce rates (GA) Pages/ outcomes? visit (GA) Whereare visitors entering Top entry pages the site? Top landing pages (GA) What are maximum engagement and conversion rates from different referrers? • How important is the home page compared to other page categories and landing pages? Does page popu larity reflect product popularity? Check that messaging and calls to action are effective on these pages • Assess source of traffic, in particular keywords from search engines, andapply elsewhere Arethefull range of digital media channels relevant for a company represented? What are sources of visitors (referrers)? Referrers Traffic sources • Is the level of search engine traffic consistent with Filters set upto segment the brand reputation? visitors What are the main link partners driving free traffic (potential for more)? • Is page popularity as expected? Are there problems with findability caused by navigation labelling? What is the most popular content? Which content is most likely to influence visitors to Top content (GA) outcome? Which content is most popular with returningvisi tors segment? • How popular are different forms of navigation, e.g top menu, sidebar menus? Which are the most popular findability methods? • What are the most popular searches? Where Site search (GA) searches tend to start? Are they successfully finding content or converting to sale? Are these as expected (home page, About Us page, transaction completion)? Where visitors leave the site? Top exit pages (GA) Arethere error pages (e.g 404 not found) that cause visitors to leave? How can attrition in conversion funnels be im proved? Which clickstreams are taken? Path analysis Top paths (GA) What does forward path analysis show are the most effective calls to action? • What does reverse path analysis indicate about the pages thatinfluence sale? Clickstream analysis becomes more actionable when the analyst reviews clickstreams in the context of a single page - this is forward path analysis or reverse path analysis left in chapter 79% DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT On-site search effectiveness On-site search is another crucial part of clickstream analysis since it is a key way of finding content, so a detailed search analysis will pay dividends Key search metrics to consider are: • number of searches; average number of searches per visitor or searcher; % of searches returning zero results; • % of site exits from search results; • % of returned searches clicked; • % of returned searches resulting in conversion to sale or other outcome; • most popular search terms - individual keyword and key phrases Reverse path analysis Indicates the most popular combination of pages and/or calls to action that lead to a page This is particularly useful for transactional pages such as the first checkout page on a consumer site; a lead-generation or 'contact us' page on a business-to-business site; an email subscription page or a call-me-back option Visitor segmentation Segmentation is a fundamental marketing approach, but it is often difficult within web an alytics to relate customer segments to web behaviour because the web analytics data aren't integrated with customer or purchase data, although this is possible in the most advanced sys tems such as Adobe Analytics, Sitecore and Mixpanel However, all analytics systems have a capability for some segmentation and it is possible to create specific filters or profiles to help understand one type of site visitor behaviour Examples include: First-time visitors or returning visitors • Visitors from different referrer types including: - Google organic - Google paid - Strategic search keyphrases, brand keyphrases, etc - Display advertising Converters against non-converters • Geographic segmentation by country or region (based on IP addresses) • Type of content accessed, e.g are some segments more likely to convert? For example, speaking at Ad Tech London '06, MyTravel reported that it segments visitors into: - Site flirt (two pages or less) - Site browse (two pages or more) - Saw search results - Saw quote - Saw payment details - Saw booking confirmation details Budgeting To estimate profitability and return on investment of e-channels as part of budgeting, compa left in chapter 79% DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT nies need to consider both tangible and intangible costs and benefits A similar approach can be used to calculating the ROI of enhancements to an e-commerce site Hanson (2000) suggests an approach to this that requires identification of revenue from the site, costs from site and costs from supporting it via a call centre These are related to profit as follows: Operatingprofit = Net income from sales - E-commerce site costs-Call-centre costs Net income from sales = (Product price - Unit cost) x Sales - Fixed product costs E-commerce site costs = Site fixed costs + ((% site support contacts) x Cost site support contact x Sales) Call-centre (CC) costs = CC fixed costs + ((% CC support contacts) x Cost CC support tact x Sales) Different approaches for estimating costs are recommended by Bayne (1997): Last year's Internet marketing budget This is assuming the site has been up and run ning for some time • Percentage of company sales It is again difficult to establish this for the first iteration of a site Percentage of total marketing budget This is a common approach Typically, the per centage will start small (less than 5%, or even 1%), but will rise as the impact of the In ternet increases • Reallocation of marketing dollars The money for digital marketing will often be taken by cutting back other marketing activities • What other companies in your industry are spending? This is definitely necessary in order to assess and meet competitive threats, but competitors may be over-investing Creating an effective online presence In this model of 'paying whatever it takes', a company spends sufficient money to create a website that is intended to achieve their objectives This may be a costly option, but for industries in which the Internet is having a significant impact, it may be the wise option A larger-than-normal marketing budget will be necessary to achieve this • A graduated plan tied into measurable results This implies an ongoing programme in which investment each year is tied into achieving the results established in a measure ment programme • A combination of approaches Since the first budget will be based on many intangibles, it is best to use several methods and present high-, medium- and low-expenditure options for executives with expected results related to costs As a summary to this section, complete Activity 10.2 Activity 10.2 Creating a measurement plan for a B2C company Purpose To develop skills in selecting appropriate techniques for measuring digital business effectiveness Activity left in chapter 79% DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT This activity acts as a summary to this section on digital business measurement Review Table 10.5 and assess the frequency with which metrics in each of the following categories should be reported and acted upon for a sell-side e-commerce site For each column, place an R in the row for the frequency with which you think the data should be recorded Table 10.5 Alternative timescales for reporting e-commerce site performance Promotion Behaviour Satisfaction Outcomes Profitability Hourly Daily Weekly Monthly Quarterly Re-launch In Chapter 1, we started this text with a case study of the world's largest digital business, which has transformed the taxi industry In this last chapter we offer the case of the world's second-largest online retailer, showing how the growth hacking culture of test, learn, refine is key to its success Case Study 10.2 Learning from Amazon's culture of metrics Context Why a case study on Amazon? Surely everyone knows about who Amazon is and what it does? Yes, well, that may be true, but this case goes beyond the surface to review innovations in Amazon's business and revenue model based on a historical review from its published annual reports (United States SEC filings) Like eBay, Amazon.com was born in 1995 The name reflected the vision of Jeff Bezos to produce a large-scale phenomenon like the River Amazon This ambition has proved justified since, just eight years later, Amazon passed the $5 billion sales mark - it took Wal-Mart 20 years to achieve this Vision and strategy Amazon's mission statement centres around its customers, providing them with a place where they can search and discover anything they may wish to buy online This is a fairly generic statement, but previous statements (i.e from the SEC filings in 2008) are more specific left in chapter 79%