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Marketing analytics

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Marketing analytics

This slide introduces the topic of Marketing Analytics, impact of marketing analytics, Dashboard, ePOS, Assortment Planning, and Market Basket Analysis.
Youtube links in the slides:
MARKETING ANALYTICS:
https://www.youtube.com/watch?v=-oHGG5jJPHQ

DYNAMIC PRICING:
https://www.youtube.com/watch?v=cKPaIsOQslo

INTRO TO ePOS:
https://www.youtube.com/watch?v=7VVKtPge-zw
https://www.youtube.com/watch?v=XWuwlChR8c4

MARKET BASKET ANALYSIS
https://www.youtube.com/watch?v=sJWXgcZ-E2g

This slide introduces the topic of Marketing Analytics, impact of marketing analytics, Dashboard, ePOS, Assortment Planning, and Market Basket Analysis.
Youtube links in the slides:
MARKETING ANALYTICS:
https://www.youtube.com/watch?v=-oHGG5jJPHQ

DYNAMIC PRICING:
https://www.youtube.com/watch?v=cKPaIsOQslo

INTRO TO ePOS:
https://www.youtube.com/watch?v=7VVKtPge-zw
https://www.youtube.com/watch?v=XWuwlChR8c4

MARKET BASKET ANALYSIS
https://www.youtube.com/watch?v=sJWXgcZ-E2g

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Marketing analytics

  1. 1. MARKETING ANALYTICS Presented by: Deep J. Gurung Assistant Professor Department of Commerce CHRIST (Deemed to be University) Main Campus, Bengaluru (India)
  2. 2. Defined • Marketing analytics is the process of identifying metrics that are valid indicators of marketing’s performance in pursuit of its objectives, tracking those metrics over time, and using the results to improve how marketing does it work. ▫ Analytics are both the process and the collective output of that process—performance information with the ideal use as a management tool. ▫ Metrics are the “atomic unit” of analytics. The marketing analytics process consists of creating a series of metrics or measurements in specific areas.
  3. 3. Components from definition… • Valid indicators: ▫ There are many things about marketing’s work and results that are measurable. ▫ The analytics process must determine which metrics have meaning and best represent the value that marketing creates for the organization.
  4. 4. • Pursuit of objectives: ▫ The analytics process is ideally built to measure progress toward a set of objectives. ▫ The objectives come first, followed by an identification of the relevant performance metrics.
  5. 5. • Tracking metrics over time: ▫ The analytics process isn’t about taking a random, one-time snapshot of a performance measurement, but tracking measurements over time to monitor trends and direction of performance.
  6. 6. • Improve how marketing works: ▫ There are several reasons a marketing organization might implement an analytics process, such as accountability or justification of resources, but the noblest and ultimately most valuable reason is to improve its performance.
  7. 7. Key Elements of Marketing Analytics • People: The marketing analytics process is created, executed, and managed by people who own it. In most marketing organizations, the process owner is the chief marketing officer (CMO) or the marketing director. • Steps: The marketing analytics process consists of a sequence of steps.
  8. 8. • Tools and technology: While the marketing analytics process isn’t necessarily complex, tools and technology help marketing organizations deliver greater value faster than they ordinarily might. • Input and output: Data feeds the process, with insights and decisions as the output of the process.
  9. 9. Marketing Analytics Capabilities • Understanding marketing’s performance. For internal use, the metrics an analytics process tell marketing how it is performing. • Reporting marketing’s performance. For external use, the output of the analytics process, or at least part of it, is useful to show the rest of the organization that it is getting a return on its investment in marketing.
  10. 10. Impact of Marketing Analytics • Brand recognition: Understand the mindshare your brand enjoys and the sentiments customers have toward it. • Content: Know with certainty which of your marketing content is most widely consumed, shared, and produces the best conversion. • Channel optimization: Compare performance of various marketing channels, such as email or pay-per-click, to improve their performance or invest only those that perform the best.
  11. 11. • Customer understanding: Gain a deeper understanding of customer behavior to better understand their needs and preferences. • Predictive intelligence: Accurately predict early in the buying cycle which customers will buy and when.
  12. 12. Getting Started with Analytics • Assessing Organizational Readiness • Reviewing Objectives • Establishing Metrics ▫ Website: traffic sources, most visited pages, search ranking, visits or unique visits, time of site or page, bounce rate, and exit pages are all potentially important for measuring website performance. ▫ Social media: social network reach (followers, fans, subscribers, or contacts), shares/retweets, engagement, posts, and referral traffic. Your social media metrics should include your blog.
  13. 13. ▫ Email marketing: database or list size, number of sends, open rate, click-through rate, and bounces are all standard metrics for email marketing. ▫ Digital advertising: this includes pay-per-click and other forms of paid, digital media. Key metrics could include impressions, click-through, inquiries, landing page views, conversion rate, opportunities identified, revenue generated, and program ROI.
  14. 14. Summary • https://www.youtube.com/watch?v=- oHGG5jJPHQ
  15. 15. Marketing Analytics Process • Step 1: Identify Metrics ▫ Social media gives us likes, shares, posts, tweets, and other metrics. ▫ Email marketing generates opens, bounces, click- through, subscribes, and more. ▫ Websites let us track visits, unique visits, referral sources, search terms, and much more. ▫ There are conversion rates, content downloads, views, and a seemingly endless stream of metrics at the disposal of the modern marketing team. ▫ Identifying meaningful metrics is very difficult if there is no marketing strategy or set of related objectives.
  16. 16. ▫ Effectiveness metrics would include conversion rates, a behavioral indicator that means a prospect voluntarily took the desired action that moves them further down the sales funnel. ▫ Effectiveness metrics help marketing measure its impact.
  17. 17. Step 2: Data Collection • DATA TYPES ▫ Contact information (name, email, mobile) ▫ Demographic indicators such as age, gender, address, marital status. ▫ Behavioral indicators such as purchase preferences, preferred medium of communication & brand engagement, etc. •
  18. 18. • DATA SOURCE ▫ Website ▫ Social media ▫ Landing pages ▫ In-store tablets ▫ Marketing tools capturing user behavior
  19. 19. Step 3: DATA PREPARATION • DATA CLEANING ▫ Remove duplicate data ▫ Ensure consistency in formatting of the data  Age is defined in same units – years/months  Gender is Male/Female across the file ▫ Update missing data ▫ Contact customer and get missing information ▫ Find similar profiles in your database and estimate ▫ Analyze outlier data separately
  20. 20. • PRECAUTIONS ▫ Do not use average/median values to fill empty spaces ▫ Personal Biases to fill missing data can result in significant errors ▫ Do not run math operations on abstract data  Abstract data such as City names (Mumbai, Hyderabad, Bangalore) are assigned numbers 1,2,3 and then averaging may reveal 2 as the most common city. (Huge mistake in analysis)
  21. 21. • Taking the data and, through inspection and analysis, turning it into actionable information. • When analyzing marketing metrics, what marketers should do is understand the current state, compare it to the ideal state, and then do root-cause determination to explain any differences.
  22. 22. Step 4: Analyze the Metrics • ANALYTICAL TECHNIQUES • RFM (Recency, Frequency, Monetary) ▫ Will help you identify your best customers • LTVC (Life Time Value of a Customer) ▫ Will help you evaluate customer cost of acquisition • Segmentation ▫ Will help you run targeted marketing campaigns
  23. 23. RFM (Recency, Frequency, Monetary) RFM analysis is a marketing technique used to determine your best customers quantitatively by using information about: ▫ Recency - How recent was the purchase ▫ Frequency - How often does the customer purchase ▫ Monetary - How much has the customer spent
  24. 24. • RFM method: Ranking • BENEFITS OF RFM: ▫ Reach out to your best customers and make ▫ them feel special ▫ Make them your brand ambassadors ▫ Align your marketing expenses better
  25. 25. Life Time Value of a Customer • LTVC (Lifetime value of your customer) is a great way to identify how much value your customer will bring to you over his/her lifetime.
  26. 26. Benefits of LTVC • Determining the right amount of money to invest in acquiring a customer • Analyze customer acquisition strategy and solidify your marketing budget
  27. 27. Segmentation • Customer Segmentation is the subdivision of a market into discrete customer groups that share similar characteristics
  28. 28. • RESULTS • Identifiability ▫ Are you able to easily differentiate between segments • Substantiality ▫ Are your clusters big enough • Accessibility ▫ Are you able to reach your customers • Stability ▫ Will these clusters remain stable with time • Actionable ▫ Are the segments helping with marketing direction
  29. 29. Step 5: Take Improvement Actions • OBVIOUS: The improvement actions are obvious • COMPLICATED- Example • The analysis shows that the same strategy was in use for all PPC campaigns: a unique landing web page was set up for those who clicked on the ad. The offer or “call to action” conversion on the landing page was similar to previous campaigns that did perform well.
  30. 30. A/B testing • two versions of a landing page to visitors. • The A version is the control page and the B version has some variation. • The variations might include different call-to-action text, graphics, a different page layout, color scheme, or any conceivable change. • The goal is to find what variations perform the best. Both pages are presented to visitors, and the analytics are monitored closely. • The visitors themselves determine the winning page.
  31. 31. • Repetition of the analytics process is necessary to produce consistent and sustained improvement.
  32. 32. Marketing Dashboards
  33. 33. Definition “A dashboard is a visual display of the most important information needed to achieve one or more objectives, consolidated and arranged on a single screen so the information can be monitored at a glance.” -Stephen Few
  34. 34. • Why You Need a Dashboard ▫ Marketing dashboards are important because they render the large volume of data a marketing analytics process can produce into a meaningful, understandable and actionable summary.
  35. 35. • Dashboard distills the most important results of the marketing analytics process into an easily digested visual summary.
  36. 36. Keys to Success with Dashboards • If the dashboard(s) that marketing publishes become the primary influencer of the opinion of marketing, the marketing team cannot afford to get this wrong. • Ideal selection of metrics • Accurate Data: The collection of marketing analytics data often requires tuning, tweaking, and testing methods across several measurement cycles to get it right and gain confidence in the data.
  37. 37. • Sharing and collaboration
  38. 38. Executive level marketing dashboards • Marketing program metrics: overall performance Indicators such as revenue generated and current budget status. • Customer program metrics: customer acquisition costs, customer retention rate, customer lifetime value, Net Promoter Score, and so on. • Lead generation metrics: new leads by channel, cost per lead, conversion rates, opportunities created, and so on. • Website metrics: traffic sources, top pages, unique visitors, bounce rate, time on site, and so on.
  39. 39. Supporting marketing dashboards • Demand generation metrics: a dashboard for reporting on the performance of campaigns and metrics associated with the flow of leads from capture to qualification and conversion. • Content marketing metrics: a dashboard for tracking the content creation, publication, and consumption process. The content marketing dashboard should include metrics for volume as well as engagement, such as sharing.
  40. 40. • Social media metrics: a dashboard for tracking the activity, engagement, and reach of social media marketing efforts across all social media networks in use. • Public relations metrics: a dashboard for tracking media mentions by frequency, source, topic, type, tone, impressions, and impact (response). • Events marketing metrics: a dashboard for tracking events by type, cost, participation, leads, and revenue generated.
  41. 41. • Digital marketing metrics: a dashboard for tracking the results of digital marketing efforts such as impressions, landing page views, conversion rates, leads captured, and revenue generated. • Web marketing metrics: a dashboard for tracking website effectiveness through traffic sources, page views, unique visits, referrals, keywords, search rank, and other key metrics.
  42. 42. Tools and Technologies • Marketing Analytics Technology • Demand Metric defines marketing automation as the strategies, processes, and software technology that enable marketing departments to automate, measure, and improve the performance of strategies, activities, and workflows.
  43. 43. Marketing Analytics Tools • Strategic: tools and systems for business intelligence, customer intelligence, understanding buying behavior, advanced attribution, and predictive analytics. Strategic marketing analytics are those that help provide direction to the marketing function.
  44. 44. • Operations and logistics: tools and systems for managing, testing, and optimizing a web presence, mobile, multichannel campaign performance, demand, and geo-modeling.
  45. 45. The Definitive Guide to Predictive Analytics for Retail Marketers • Behavioral clustering: helps better understand how customers behave while purchasing. • Product/category-based clustering: segments customers into groups based on which products they purchase. This insight lets marketers make more intelligent choices about which offers to extend to customers.
  46. 46. • Brand-based clustering: identifies brand affinity groupings that customers have. For example, customers who prefer brand A also prefer brand C, but not brand B. • Predictive lifetime value: predicts future lifetime values of customers. Useful for setting spending parameters on costs of new customer acquisition.
  47. 47. • Propensity to engage: predicts how likely it is for a customer to take certain actions, such as clicking on a link in a promotional email message. • Propensity to convert: predicts the likelihood that a customer will respond to call-to-action offers extended to them via email, direct mail, or other means. • Propensity to buy: identifies customers who are ready to make a purchase, allowing marketers to trigger those purchases with a special offer, or market more aggressively to those who aren’t ready to buy.
  48. 48. • Upsell recommendations: helps increase the average size of order by predicting premium products or greater quantities in which a customer might have interest. • Cross-sell recommendations: at the time of purchase suggests other products that are frequently purchased together. • Next sell recommendations: after a customer has already purchased a product, suggests products that are likely next purchases.
  49. 49. TRADITIONAL PRICING • Marketers develop prices based: • Cost to produce the product, • Standard margins, • Prices for similar products, • Volume discounts • Universal 10 percent price hike on everything.
  50. 50. Marketing Analytics & Pricing 3 Pricing strategies • Cost Based Pricing • Demand Based Pricing • Value Based Pricing
  51. 51. Pricing Analytics • Pricing analytics are the metrics and associated tools used to understand how pricing activities affect the overall business, analyze the profitability of specific price points, and optimize a business’s pricing strategy for maximum revenue.
  52. 52. Turn Data into Profit • Listen to the data • Automate • Build skills and confidence • Actively manage performance
  53. 53. Four Metrices • Willingness to pay (WTP) : sometimes called price sensitivity, is the maximum amount a customer is prepared to pay for your product or service.
  54. 54. • Feature value: relative preference analysis, measures which features are more or less important to customers relative to other features
  55. 55. • Average revenue per user (ARPU): measure of the revenue generated each month from each user. • ARPU = Total MRR ÷ Total number of Customers • Monthly Recurring Revenue (MRR): all of your recurring revenue normalized into a monthly amount.
  56. 56. • Customer acquisition cost (CAC) and lifetime value (LTV) : spend the right amount to drive new customers to your service without jeopardizing the revenue from that customer. This is known as the LTV/CAC ratio
  57. 57. DYNAMIC PRICING • Video • https://www.youtube.com/watch?v=cKPaIsOQs lo
  58. 58. Point of Sale (POS) data • INTRO TO ePOS: • https://www.youtube.com/watch?v=7VVKtPge- zw • Video • https://www.youtube.com/watch?v=XWuwlCh R8c4 • Video
  59. 59. • POS data collection is passive data collection and used to help “predict user preferences based on an historic profile of interactions with a company or site.” • Every time you use your POS, you are accessing and creating multitudes of data points about your customer, about buying habits, about the market that you serve and about ecommerce capabilities and potential growth areas for your business.
  60. 60. Micro scale vs Macro Scale POS data collection • Point of sale data is data collected by a business when a transaction happens. • On a micro scale this includes any checkout at a retail store, handheld POS hardware and even QR or barcode scanners from apps. • On a macro scale data is collected from groups of retailers, like all ecommerce stores in a specific niche, shopping mall data, or even city-wide data.
  61. 61. POS solutions • Terminal POS: Hardware and software solutions that may include barcode scanners, cash registers and app scanners. • Cloud-Based POS: online POS systems, often used in conjunction with existing hardware like tablets or computers. Heavily utilized by online stores and ecommerce websites. • Mobile POS: normally used as payment processing systems, usually adopted by small business owners
  62. 62. Using/Leveraging POS data • Optimize your inventory and stock levels ▫ Inventory counts ▫ Check stock levels at different stores ▫ Transfer stock ▫ Manage returns, etc. ▫ Automated reorder points • Make staffing decisions
  63. 63. • Gain product and customer insights ▫ Product affinity (which items are frequently bought together): Use this information when bundling products and recommending related products at checkout. ▫ Order history: Learn what specific customers like, then make smart recommendations for future purchases and retargeting campaigns. ▫ Sales by product: Identify sales trends by product (or category) and dig deeper into those trends to find out the WHY. ▫ Refunds, returns and exchanges: Find out which items are being returned, what customers are buying instead, and even who’s a serial returner.
  64. 64. ASSORTMENT PANNING Assortment planning in retail is when a store optimizes: ▫ Visual merchandising, ▫ Store layout, and ▫ Product placement for the most conversions. • Product assortment planning happens by period, whether daily, weekly, monthly, quarterly, or some other cadence.
  65. 65. Importance of assortment • Price point • Shelf life • Category proportions • The width is the number or variety of different product categories. • The depth is the amount of product and brand variation within an individual category
  66. 66. • Visual merchandising • Store layout • Promotions • Reorder points • Lead time
  67. 67. Assortment Planning • Setting goals • Historical data • Product hierarchy • Cross merchandise • Capitalize on impulse buys • Use of Planogram
  68. 68. PLANOGRAM • A planogram is a diagram that shows how and where specific retail products should be placed on retail shelves or displays in order to increase customer purchases. • Planogramming is a skill used in merchandising and retail space planning. • A person with this skill is be referred to as a planogrammer.
  69. 69. Analytics in Assortment Planning • AirtableAnalyse2 • Aptos • DotActiv • First Insight • Intelligence Node • JDA Assortment • Mi9 Retail
  70. 70. ASSOCIATION RULES • An association rule implies that a particular item is likely to occur given the presence of some itemset.
  71. 71. Market Basket Analysis • https://www.youtube.com/watch?v=sJWXgcZ- E2g • MBA
  72. 72. • Allows retailers to identify the relationship between items which are more frequently bought together ▫ Itemset: Groups of items are called itemsets. ▫ Confidence : It is the measure of uncertainty or trust worthiness associated with each discovered pattern. ▫ Support : It is the measure of how often the collection of items in an association occur together as percentage of all transactions. The number of baskets that an itemset appears
  73. 73. EXAMPLE • Customer 1: Bread, egg, papaya and oat packet • Customer 2: Papaya, bread, oat packet and milk • Customer 3: Egg, bread, and butter • Customer 4: Oat packet, egg, and milk • Customer 5: Milk, bread, and butter • Customer 6: Papaya and milk
  74. 74. • Customer 7: Butter, papaya, and bread • Customer 8: Egg and bread • Customer 9: Papaya and oat packet • Customer 10: Milk, papaya, and bread • Customer 11: Egg and milk
  75. 75. Support • Support: Percentage of orders that contain the item set. In the example above, there are 11 orders in total, and {bread, butter} occurs in 3 of them. • Support = Freq(X,Y)/N • Support = 3/11 = 0.27
  76. 76. Confidence • Confidence: Given two items, X and Y, confidence measures the percentage of times that item Y is purchased, given that item X was purchased. • Confidence = Freq(X,Y)/Freq(X)
  77. 77. • Percentage of times that butter(X) is purchased, given that bread(Y) was bought: • Confidence (butter -> bread) = 3/3 = 1 • Percentage of times that bread is purchased, given that item butter was purchased: • Confidence (bread->butter) = 3/7 = 0.428
  78. 78. • Values range from 0 to 1, ▫ where 0 indicates that Y is never purchased when X is purchased, ▫ and 1 indicates that Y is always purchased whenever X is purchased.
  79. 79. LIFT • Lift{X,Y} = lift{Y,X} = support{X,Y} / (support{X} * support{Y}) • lift{butter, bread} = lift{bread, butter} = support{butter, bread} / (support{butter} * support{bread}) • lift{butter, bread} = lift{bread, butter} =(3/11)/((3/11)*(7/11)) • lift{butter, bread} = lift{bread, butter} =1.571
  80. 80. Lift Value Interpretation • Lift = 1; implies no relationship between X and Y (i.e., X and Y occur together only by chance) • Lift > 1; implies that there is a positive relationship between X and Y (i.e., X and Y occur together more often than random) • Lift < 1; implies that there is a negative relationship between X and Y (i.e., X and Y occur together less often than random)
  81. 81. Reference: Marketing Analytics Roadmap- Methods, Metrics, and Tools 1st ed. edition By Jerry Rackley Appress • ISBN-10: 1484202600 • ISBN-13: 978-1484202609

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