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Marketing Analytics

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Published in: Business | Business Strategy | Business Studies
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The practice of measuring, managing and analyzing the marketing performance of a firm so that the ROI can be optimized and increased is called Marketing Analytics.

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  1. Module 4 - Part 1 JMARKETING ANALYTICS
  2. Course Plan Basics of marketing analytics, marketing decisions models, characteristics, types and benefits of marketing decisions models, Segmentation using factors analysis and cluster analysis, regression and choice based segmentation, positioning - perceptual maps: developing perceptual map, multi dimensional scaling
  3. Marketing Analytics The practice of measuring, managing and analyzing the marketing performance of a firm so that the ROI can be optimized and increased is called Marketing Analytics. • Marketing analytics portrays the customer insights and trends. The following reasons induce the use of marketing analytics: Getting information related with new marketing trends Identifying successful programs and evaluating their reasons of success. Analyzing trends over time Completely analyzing the ROI of each program Forecasting the outcomes
  4. Components of Marketing analytics People Components Input of Marketing Output analytics Tools & Technology Steps
  5. 3-Step Methodology for Marketing analytics UNDERSTAND MONITOR EXECUTE
  6. 1. 2. 3. 4. 5. Importance of Marketing Analytics Gaining a full view of customers across channels Becoming more effective and proactive Personalizing customer and market engagements Visualizing success across enterprise Trending data as a strategic asset
  7. Guidelines for Marketing Analytics 1. Use a balanced assortment of analytic techniques. 2. Assess your analytic capabilities, and fill in the gaps. 3. Act on what you learn
  8. Guidelines for Marketing Analytics 1. Use a balanced assortment of analytic techniques: teo On The Past Analyzirg The Present The Future
  9. Guidelines for Marketing Analytics 2. Assess your analytic capabilities, and fill in the gaps. Assessing your current analytic capabilities is a good next step. After all, it's important to know where you stand along the analytic spectrum, so you can identify where the gaps are and start developing a strategy for filling them in. For example, a marketing organization may already be collecting data • from online and POS transactions, but what about all the unstructured information from social media sources or call-center logs? Such sources are a gold mine of information, and the technology for • converting unstructured data into actual insights that marketers can use exists today. As such, a marketing organization may choose to plan and budget for adding analytic capabilities that can fill that particular gap. Of course, if you're not quite sure where to start, well, that's easy. Start where your needs are greatest, and fill in the gaps over time as new needs arise.
  10. Guidelines for Marketing Analytics 3. Act on what you learn There is absolutely no real value in all the information marketing analytics can give you — unless you act on it. In a constant process of testing and learning, marketing analytics • enables you to improve your overall marketing program performance by, for example: • Identifying channel deficiencies. Adjusting strategies and tactics as needed. Optimizing processes. Gaining customer insight. Without the ability to test and evaluate the success of your marketing programs, you would have no idea what was working and what wasn't, when or if things needed to change, or how. 10
  11. Marketing Decision Models A marketing decision model provides a way to visualize the sequence of events that can occur following alternative decisions in a logical framework, as well as the outcomes associated with each possible pathway. Decision models can incorporate the probabilities of the underlying states of nature in determining the distribution of possible outcomes associated with a particular decision. These probabilities are known to the decision-maker, but are critically important.
  12. Characteristics of Marketing Decision Models 1. 2. 3. 4. Generalized set of processes Purpose Assumptions Relationship between variables 12
  13. 1. Types of Marketing Decision Models On the basis of structural characteristics: Verbal Models: In verbal models, the variables and their relationships are stated in prose form. Such models may be mere restatements of the main tenets of a theory. Graphical Models: Graphical models are visual. They are used to isolate variables and to suggest directions of relationships but are not designed to provide numerical results. Mathematical Models: Mathematical models explicitly specify the relationships among variables, usually in equation form. Where y = degree of preference izl ao = model parameters to be estimated statistically
  14. Types of Marketing Decision Models 2. On the basis of Managerial Questions: Descriptive Decision Models: These models address the question, "what will happen if you Normative Decision Models: These models address the question, "What is out best course of action in a given situation?"
  15. 1. 2. 3. 4. 5. 6. Benefits of Marketing Decision Models Improved consistency in decisions More decision options Assessing the impact of variables Facilitating group decision making Updates mental models Improves allocation of resources
  16. Target Marketing A target market is a group of consumers or organizations most likely to buy a company's products or services. Because those buyers are likely to want or need a company's offerings, it makes the most sense for the company to focus its marketing efforts on reaching them. • Marketing to these buyers is the most effective and efficient approach. The alternative - marketing to everyone - is inefficient and expensive. 16
  17. Steps in Target Marketing 1. Determine demand patterns 2. Establish possible bases of segmentation 3. Identi otential market segments 4. Choose a target market approach 6. Position the company's offering in 7. Outline the appropriate marketing mix(es) Analyze Consumer Demand Target the Market Develop the Marketing Strategy 17
  18. Market Segmentation • Facilitates Right Choice of Target Market • Facilitates Effective Tapping of the Chosen Market Makes the Marketing Effort More Efficient and Economic Helps Identify Less Satisfied Segments and Concentrate on Them 18
  19. Bases for Market Segmentation Geographic Segmentation • Location • Size • Population density Climate Demographic Segmentation • Age & life cycle changes • Gender • Marital status • Income • Social class • Family size • Occupation • Educational level • religion Psychographic Segmentation • Lifestyles • Personality Values • beliefs Behavioral Segmentation • Occasions Regular or Special • Benefits User Status interested, not potential first time , regular , ex- user Quantity consumed light, medium, heavy • Buyer readiness stage • Loyalty status hard core, soft core, switchers • Attitude split, 19
  20. 1. Segmentation Methods Factor Analysis: Factor analysis is a marketing research technique that analyses a large number of variables and reduces them to a smaller number of key factors to better explain a given marketing situation. Factor analysis is useful in benefit and psychographic research segmentation. The central aim of factor analysis is orderly simplification of several inter-related measures using quite sophisticated mathematics to test for or confirm generalizations. the most important statistic for these procedures is the correlation coefficient. 20
  21. i. Il. Segmentation Methods There are two major types of factor analysis used in market segmentation. They are: R Factor Analysis: It reduces the amount of data by finding similarities in response to particular variables. Q Factor Analysis: It finds grouping of people that respond similarly to research issues.
  22. Segmentation Methods 2. Cluster Analysis: Cluster analysis is especially useful for market segmentation. • Segmenting a market means dividing its potential consumers into separate sub-sets where Consumers in the same group are similar with respect to a given set of characteristics Consumers belonging to different groups are dissimilar with respect to the same set of characteristics This allows one to calibrate the marketing mix differently according to the target consumer group. Help marketers discover distinct groups in their customer bases, and then use this knowledge to develop targeted marketing programs 22
  23. Segmentation Methods The underlying definition of cluster analysis procedures mimic the goals of market segmentation: - to identify groups of respondents that minimizes differences among members of the same group - highly internally homogeneous groups while maximizing differences between different groups highly externally heterogeneous groups • Market Segmentation solution depends on variables used to segment the market method used to arrive at a certain segmentation 23
  24. l. 2. 3. 4. 5. 6. 7. 8. 9. 10. ll. 12. 13. 14. Importance of Market Segmentation Adjustment of product and market appeals Better position to spot marketing opportunities Allocation of marketing budget Understanding and meeting the needs of consumers Stronger positioning Enhanced efficiency Competitive advantages Targeted media Market expansion Better communication Increases profitability Identifies new market Reduces cost Reduces credit risks
  25. Positioning • Market Positioning refers to the ability to influence consumer perception regarding a brand or product relative to competitors. The objective of market positioning is to establish the image or identity of a brand or product so that consumers perceive it in a certain way. • For example: A handbag maker may position itself as a luxury status symbol A TV maker may position its TV as the most innovative and cutting-edge A fast-food restaurant chain may position itself as the provider of cheap meals 25
  26. Tasks involved in Positioning Competitor's Identification Determmmg ow ompetltor s are perceive an evaluated Determining Competitor's position Analyzing customer's preferences Making the positioning decision Monitoring the position 26
  27. 1. 2. 3. 4. 5. 6. 7. 8. 9. 1(). 11. 12. Positioning Strategies Attribute Positioning Price/Quality Positioning Use or Application Positioning Product user Positioning Usage and use time Positioning Product class Positioning Category Positioning Benefit Positioning Price-quality Positioning Competitive Positioning Corporate identity Positioning Brand endorsement Positioning 27
  28. Positioning using Multi-Dimensional Scaling Multidimensional Scaling (MDS) is a class of procedures for representing perceptions and preferences of respondents spatially by means of visual display. • Perceived psychological relationships among stimuli are represented as geometric relationships among points in multidimensional space. These geometric representations are often called spacial maps. Multidimensional scaling are use for: To determine the number and nature of dimensions consumers use to perceive different brands. To position brands on these dimensions. To identify the position of consumer's ideal brand. 28
  29. 1. 2. 3. 4. Objectives of MDS Develop techniques of full text analysis Provides visual presentation of similarities Help explain observed similarities Applied to any kind of distances 29
  30. Applications of MDS in Marketing Brand image measurement • Market segmentation New product development Assessing advertising activities and its effectiveness Distribution channel decisions 30
  31. Perceptual Mapping/ Positioning Map Firms use perceptual or positioning maps to help them develop a market positioning strategy for their product or service. As the maps are based on the perception of the buyer they are sometimes called perceptual maps. • Positioning maps show where existing products and services are positioned in the market so that the firm can decide where they would like to place (position) their product. Firms have two options they can either position their product so that it fills a gap in the market or if they would like to compete against their competitors they can position it where existing products have placed their product.
  32. Perceptual Mapping/ Positioning Map The diagram below is a Perceptual Map of UK chocolate confectionery Brands High Low Q uality . Milk Tray 32
  33. Perceptual Mapping/ Positioning Map • Perceptual maps can help identify where (in the market) an organization could position a new brand. • In our example this could be at the medium price and medium quality position, as there is a gap there. There is also a gap in high price low quality but consumers will not want to pay a lot of money for a low quality product. Similarly the low price high quality box is empty because manufacturers would find it difficult to make a high quality chocolate for a cheap price or make a profit from selling a high quality product at a low price.
  34. 1. 2. 3. 4. 5. Developing Perceptual Maps Selecting determinant attributes Listing the competitors Market survey Plotting the results Checking for errors
  35. 1. 2. 3. 4. 5. 6. 7. Benefits of Perceptual Maps Checks Reality Impact of campaigns Monitors new products Monitors competition Look for gaps Understands segments Track preference changes 35
  36. Importance of Positioning Placing the product in customers mind • Connects product offerings with target market Product cannot be everything to everyone Creates a locus in customers mind Providing competitive advantages Better serving and covering the market 36
  37. Module 4 — Part 2 WEB ANALYTICS
  38. Course Plan Click stream analytics, engagement quantification frameworks, anonymous vs. registered users analysis, Social Media Analytics - User generated content - Sentiment Analysis- Analytics in digital decoding consumer intent, decoding customer sentiments from comments, Text mining from opinion platforms 38
  39. Web Analytics Web analytics is the measurement, collection, analysis and reporting of web data for purposes of understanding and optimizing web usage. Web analytics is not just a process for measuring web traffic but can be used as a tool for business and market research, and to assess and improve the effectiveness of a website. Web analytics applications can also help companies measure the results of traditional print or broadcast advertising campaigns. It helps one to estimate how traffic to a website changes after the launch of a new advertising campaign. Web analytics provides information about the number of visitors to a website and the number of page views. It helps gauge traffic and popularity trends which is useful for market research. 39
  40. Web Analytics Process Collection of data Typically, counts. Basically, data collection Examples: • Time stamp • Referral URL Query terms Drives Drives Processing of data into information Typically, ratios. Data becomes metrics. Drives Drives Developing Drives key performance Drives indicators Counts and ratios infused with business strategy. Examples: • Time on page • Bounce rate Unique visitors Examples: Conversion rate • Average order value Task completion rate Formulating online strategy Online goals, objectives, or standards for organization. Examples: Save money Make money Marketshare 40
  41. Web Analytics Terminology 1. Hit: A hit is a request to a web server for a file. There may be many hits per page view since an HTML page can contain multiple files, such as images. 2. Pageview: A request for a file, or sometimes an event such as a mouse click, that is defined as a page in the setup of the web analytics tool. 3. Event: A discrete action or class of actions that occurs on a website. 4. Vist/session: A visit or session is defined as a series of page requests or, in the case of tags, image requests from the same uniquely identified client.
  42. Web Analytics Terminology 5. Bounce Rate: The percentage of visits that are single page visits and without any other interactions (clicks) on that page. 6. Click Path: the chronological sequence of page views within a visit or session. 7. Unique user: The uniquely identified client that is generating page views or hits within a defined time period (e.g. day, week or month). 8. Active Time / Engagement Time: Average amount of time that visitors spend actually interacting with content on a web page, based on mouse moves, clicks, hovers and scrolls.
  43. Web Analytics Terminology 9. Average Page Depth / Page Views per Average Session: Page Depth is the approximate "size" of an average visit, calculated by dividing total number of page views by total number of visits. 10. Average Page View Duration: Average amount of time that visitors spend on an average page of the site. 11. Exit Rate / % Exit: A statistic applied to an individual page, not a web site. 12. First Visit / First Session: A visit from a uniquely identified client that has theoretically not made any previous visits. 43
  44. Web Analytics Terminology 13. Frequency I Session per Unique: Frequency measures how often visitors come to a website in a given time period. 14. Impression: an instance of an advertisement appearing on a viewed page. 15. New Visitor: A visitor that has not made any previous visits. 16. Page Time Viewed / Page Visibility Time I Page View Duration: The time a single page is on the screen, measured as the calculated difference between the time of the request for that page and the time of the next recorded request. If there is no next recorded request, then the viewing time of that instance of that page is not included in reports.
  45. Web Analytics Terminology 17. Repeat Visitor: A visitor that has made at least one previous visit. 18. Return Visitor: A Unique visitor with activity consisting of a visit to a site during a reporting period and where the Unique visitor visited the site prior to the reporting period. 19. Session Duration / Visit Duration: Average amount of time that visitors spend on the site each time they visit. It is calculated as the sum total of the duration of all the sessions divided by the total number of sessions. 45
  46. Web Analytics Terminology 20. Single Page Visit / Singleton: A visit in which only a single page is viewed (this is not a 'bounce'). 21. Site Overlay: a report technique in which statistics (clicks) or hot spots are superimposed, by physical location, on a visual snapshot of the web page. 46
  47. Data Collection Methods There are two major methods for collecting data for web analytics: 1. Log Files Analysis: The older of the two methods, simply counts the hits made in the web server logs and stores the data in an easily-readable, easily-managable format. This method is based on server-side data collection; there is nothing stored on the visitor's computer, nothing that runs in their browser. 2. Page Tagging: Concerns about the accuracy of log file analysis in the presence of caching, and the desire to be able to perform web analytics as an outsourced service, led to the second data collection method, page tagging.
  48. Data Collection Methods Lo#iles Urchin 5 (core) Page Tags Google Anatytics. Eloqua. Urchin 5 (sq)plemental) Request Request Logrd Server Delivered Internet Page Requested Web Logged Server Page Tag Server Deliv I og File Processe I Web Data I og File rocessed I Web Data JS Page Tag Requ•sts Image Intemet Pago Requested 48
  49. Data Collection Methods — Logfile Analysis • Does not require changes to the website or extra hardware installation. . Doesn't require extra bandwidth. Freedom to change tools with a relatively small amount of hassle. • Logs both page request successes and failures. • • Can only record interactions with the web server. Server must be configured to assign cookies to visitors. Only available to companies who run their own web servers. Cannot log physical locations.
  50. Data Collection Methods — Page Tagging Near real-time reporting. Easier to record additional information. Able to capture visitor interactions within flash animations. Requires extra code added to the website. Uses extra bandwidth each time the page loads. Can only record successful page loads, not failures. Hard to switch analytic tools. 50
  51. Traffic • Page Views Advertising Optimize Website E-Marketing Plans Web Analytics Time Consuming Problems due to certain types of visitors. 51
  52. Clickstream Analytics • On a Web site, clickstream analysis (also called clickstream analytics) is the process of collecting, analyzing and reporting aggregate data about which pages a website visitor visits and in what order. The path the visitor takes though a website is called the clickstream. There are two levels of clickstream analysis, traffic analytics and e-commerce analtyics. Tramc analytics operates at the server level and tracks how many pages are served to the user, how long it takes each page to load, how often the user hits the browser's back or stop button and how much data is transmitted before the user moves on. 52
  53. Clickstream Analytics E-commerce-based analysis uses clickstream data to determine the effectiveness of the site as a channel-to-market. It's concerned with what pages the shopper lingers on, what the shopper puts in or takes out of a shopping cart, what items the shopper purchases, whether or not the shopper belongs to a loyalty program and uses a coupon code and the shopper's preferred method of payment. Because an extremely large volume of data can be gathered through clickstream analysis, many e-businesses rely on big data analytics and related tools such as Hadoop to help interpret the data and generate reports for specific areas of interest. Clickstream analysis is considered to be most effective when used in conjunction with other, more traditional, market evaluation resources. 53
  54. Engagement Quantification Frameworks • Engagement is the process by which government, organizations, communities and individuals connect in the development and implementation of decisions that affect them. It is used as a tool to achieve outcomes, develop understanding, educate and agree to solutions on issues of concern. The level of engagement appropriate for each situation can range from a one-way transfer of information through to consulting and even actively involving or empowering stakeholders in the decision making process.
  55. Effective Engagement Clear, Relevant and Timely ommunicatio Transparent Decision Making Inclusiveness 55
  56. Engagement through Product Life Cycle Concept Planning • • Options Analysis tage Preliminary Design • Detailed Design • Construction • tage 56
  57. Benefits of Engagement Project and problem definition • Solution testing and value management Risk quantification Risk mitigation Credibility and reputation 57
  58. Anonymous Vs Registered User Analysis Anonymous User User of a website, program or other systems who has previously registered. Users have an account on intranet and have logged in. Identified by username The average of reputation is 59% 49.4% of the contributed content by registered users have been deleted over time Only registered can reply on an issue Users in the registered group will automatically inherit the permissions one assigned to the anonymous group Registered users can participate in a poll Registered User Citizens that visit the portal without having registered. Users are not known to intranet. Tracked by their IP addresses The average of reputation is 49% 85.2% of the contributed content by anonymous users have been deleted over time Allowing anonymous users to do this is inviting trouble It is not possible for anonymous users They don't have such permissions 58
  59. Social Media Analytics It is the systematic and scientific ways to consume the vast amount of content created by Web-based social media outlets, tools, and techniques for the betterment of an organization's competitiveness • Fastest growing movement in analytics Social Media Tweeter Facebook Linlkedln Insights Solutions Course of Actions 59
  60. Social Media Analytics HBR Analytic Services survey (HBR, 2010) 75% of the companies did not know where their customers are talking about them 31% do not measure effectiveness of social media only 23% are using social media analytics tools 7% are able to integrate social media into marketing Measuring the Social Media Impact Descriptive analytics — simple counts/statistics Social network analysis Advanced analytics — predictive analytics, text mining 60
  61. l. 2. 3. 4. 5. 6. 7. 8. Steps in Social Media Analytics Think of measurement as a guidance system, not a rating system Track the elusive sentiment Continuously improve the accuracy of text analysis Look at the ripple effect Look beyond the brand Identify your most powerful influencers Look closely at the accuracy of your analytic tool Incorporate social media intelligence into planning
  62. Sentiment Analysis The practice of applying Natural Language Processing and Text Analysis techniques to identify and extract subjective information from a piece of text. The process of analyzing unstructured text to extract relevant information and transforming it into useful business intelligence It determines if an expression is positive, negative, or neutral, and to what degree It is an emerging field that attempts to analyze and measure human emotions and convert it into hard facts
  63. Sentiment Analysis Also called opinion mining since it includes identifying attitudes, emotions, and opinions of a company's product, brand, or service It is text analytics that looks at the face value of the words to give them meaning - Gives insight into the emotion behind the words Helps businesses monitor news articles, online forums and social networking sites for trends in opinions about their products and services • Businesses have realized that not all opinions are equally important- Some opinions carry more weight than others A negative tweet by Lady Gaga will have a much greater impact than a tweet by an ordinary person It is a tool to allow users to generate 'influence scores' to identify people, blogs, forums etc. that are important
  64. Types of Sentiment Analysis anua eyboard rocessi Natural Language Processing
  65. Levels of Sentiment Analysis Docunent level sentiment analysis Sentence level sentiment analysis Sentiment Analysis Comparative Aspect sentiment Based analysis sentiment analysis Sentiment Lexicon Acquisition 65
  66. 1. Methods for Sentiment Analysis The Naive Bayes classifier: The Naive Bayes classifier uses the Bayes Theorem, that for our problem says that the probability of the label (positive or negative) for the given text is equal to the probability of we find this text given the label, times the probability a label occurs, everything divided by the probability of we find this text: P(tectllabel) * P(label) P(labelltect) = P(tect) Since the text is composed of words, we can say: P(wordl, word2, word3.„ label) * P(label) P(label wordl, word2, word3.„) word3„.) 66
  67. Methods for Sentiment Analysis We want to compare the probabilities of the labels and choose the one with higher probability. Since the term P(wordl, word2, word3.. .) is equal for everything, we can remove it. Assuming that there is no dependence between words in the text (which can cause some errors, because some words only "work" together with others), we have: P '(labellwordl, word2...) = P(label) * P(wordlllabel) * P(word211abel)... With a training set we can find every term of the equation, for example: P(label=positive) is the fraction of the training set that is a positive text; P(wordl/label=negative) is the number of times the wordl appears in a negative text divided by the number of times the wordl appears in every text.
  68. Methods for Sentiment Analysis 2. The svwr Classifier: This classifier works trying to create a line that divides the dataset leaving the larger margin as possible between points called support vectors. As per the figure below, the line A has a larger margin than the line B, so the points divided by the line A have to travel much more to cross the division, than if the data was divided by B, so in this case we would choose the line A. 68
  69. Methods for Sentiment Analysis 3. Multi-Layer Perceptron: Input ike it Hidden Output 69
  70. Methods for Sentiment Analysis 4. Clustering Classifier: 70
  71. Text Mining Text Mining is also known as Text Data Mining. The purpose is too unstructured information, extract meaningful numeric indices from the text. Thus, make the information contained in the text accessible to the various algorithms. Information can extract to derive summaries contained in the documents. Hence, you can analyze words, clusters of words used in documents. • In the most general terms, text mining will "turn text into numbers". • Such as predictive data mining projects, the application of unsupervised learning methods.
  72. Areas of Text Mining Natural Language Processing Information Extraction (NLP) Data Mining Information Retrieval (IR) 72
  73. Text Mining Process Text ansformatio Feature 3 Selection Data Mining Text Pre-processing 2 4 Process of Text Mining 6 5 Applications Evaluate 73
  74. Text Mining Applications Analyzing open-ended survey responses Automatic processing of messages, emails, etc Analyzing warranty or insurance claims, diagnostic interviews, etc Investigating competitors by crawling their web sites
  75. Business Value Tangible Elements Monetary assets Stockholder equity Fixtures Utility Business Value Intangible Elements Brand Recognition Good will Public benefit Trademarks
  76. Steps to deliver Business Value Foster a team environment to effectively de [ver value, Understand the Vision Be c ear about the business value of the project Evangelize the vision and business value to the project team Measure the realization of the business val ue. 76

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