Here is a glossary of 100 common terms used in Marketing Mix Modelling, Customer Analytics and Marketing Science:

  1. Acquisition – using your marketing and media budget to win new customers into your business.
  2. Activation – setting a campaign to go live and start delivering results.
  3. Addressable media – media that goes to an individual
  4. Ad Server – a server which hosts your digital ads so that they can be served onto the sites where you book media
  5. AdStock – the amount of time a consumer theoretically remembers an ad for after seeing it
  6. Artificial Intelligence (AI)  – AI is a form of computer technology in which machines are trained to behave and learn like humans
  7. Attention – a metric that seeks to capture the amount of attention a consumer pays to an ad, rather than simply seeing it. Dwell time is often used.
  8. Attribution – the task of understanding and explaining which marketing and media channels drove which business results.
  9. Awareness – the percentage of your target audience tat is aware of your product or service
  10. BARB – Broadcast Audience Research Board, the body that manages the measurement and reporting of TV audience data
  11. Base – sometimes known as the intercept, or ‘constant’ this is the level of sales your business or brand generates without media or advertising spend intervention. Sometimes called Beta0.
  12. Broad Audience – a wide demographic target audience definition such as ‘Adults’, ‘Men’, ‘Women’
  13. Broadcast – channels that reach mass audience quickly through mass communication such as TV or radio.
  14. Categorical variables – are descriptive rather than numeric, for example gender, country or product type
  15. Churn – losing customers
  16. Churn Rate – the rate at which you lose customers, usually measured per week, month or year
  17. Coefficient – the “weighting factor” that the model estimates for each variable. So we might say that for every £1 invested, 0.01 (our coefficient) sales are generated i.e. a £100 cost per sale.
  18. Confidence Interval – The expected range in which you would expect a value to fall if that value was sampled multiple times
  19. Consideration – the proportion  of your target audience that would consider using your product or service. Identified through tracking studies like YouGov or bespoke research.
  20. Conversion – the rate at which visitors move to te next action in the path to purchase
  21. Conversion Funnel – a unified view of the journey consumers take from becoming aware of your product to purchasing and sometimes including post-purchase behaviour.
  22. CPA – Cost per Action, or sometimes cost per acquisition
  23. CPM – Cost per Thousand (the M stands for “mille”)
  24. CPT – Cost per Thousand
  25. CRM – Customer Relationship Management
  26. Cross sectional data – data collected at a specific place or point in time, like a research survey
  27. D2C – Direct to Consumer – selling products or services directly to consumers
  28. Data Science – using data to establish cause and effect using the scientific method
  29. Decay – related to AdStock – the theoretical rate at which the memory of an ad decays
  30. Decomposition – identifying the component parts of a time series data set. For example, identifying underlying seasonality and trend components.
  31. Demo – Demographic – usually applied to a demographic group
  32. Dependent variable  -the variable we are seeking to explain. The behaviour of the dependent variable is explained by the independent, or explanatory variables, sometimes also called the x variables.
  33. Dimensionality – in data science, this refers to the number of x variables in relation to the number of y variables.
  34. Direct Marketing – Marketing activity that seeks a measurable response at the individual consumer level
  35. Distribution – the element of the Marketing Mix that concerns the amount of retail coverage a business has. There can also be an online manifestation of this in the form of delivery times, returns policy etc.
  36. eCommerce – online websites that sell products and service and allow consumers to buy direct (see D2C)
  37. Econometrics – the practice of analysing economic data with statistics. Derived from the words “economy” and “metrics”
  38. Error term – e – the part of the model equation that captures the data that can’t be explained in the model. Sometimes also called the remainder term.
  39. eSOV – excess Share of Voice – the excess value when your share of voice is divided by your share of market – higher eSOV has been shown to assist brand sales or share growth
  40. Exploratory Data Analysis – the process of analysing a dataset to ensure that it is suitable for modelling
  41. Effective Frequency  – usually used in brand campaigns or launch campaigns. This is the number of times consumers might need to see an ad for it to register.
  42. Feature engineering – adjusting variables such as creating interaction variables to test an effect in a model
  43. Frequency – Sometimes called OTS (see OTS)
  44. ggplot2 – one of the leading graphics packages in the R modelling suite.
  45. GRP – Gross Rating Point  -the percentage of your audience that sees an ad
  46. Heteroscedasticity – a certain type of pattern (too much variance) sometimes seen in residuals – it can suggest that the model is not properly capturing all the information it has available to it
  47. Homoscedasticity  – another residuals patter where the error is constant, indicating the model is good
  48. Impact – the actual number of consumers who see your ad
  49. Incrementality – business outcomes that are incremental to your static state
  50. Inference – drawing conclusions about a population based on the selection and statistical analysis of a sample from that population
  51. Interaction variable – a variable that is created when you combine two variables to text their combined effect e.g. an increase in adspend and a change in price
  52. Intercept – also the base and technically the point at which the models predicted line crossed the y axis
  53. Lag – any delay between seeing an ad and acting on it
  54. Lifetime Value (LTV) – the amount of revenue a customer or subscriber generates over the estimated average customer lifetime. Usually between 1 and 5 years depending on the product and category.
  55. Linear response – a non-decaying response function e.g. for every cup of coffee consumed the satisfaction score remained unchanged.
  56. Machine Learning (ML) – is a subset of AI in which machines (computers) use data to make predictions
  57. Marketing Science – applying the scientific method to marketing data
  58. MMM – Marketing Mix Modelling – the technique f using statistical models to examine which variables drive business outcomes
  59. Model – a reproducible mathematical or statistical representation of something that occurs in life
  60. MOSAIC – A customer classification system similar to ACORN above
  61. MRR – Monthly Recurring Revenue – the amount of money a subscriber pays per month whilst in contract or signed up to a specific service.
  62. Noise – random changes in variables from which no or very little value can be extracted
  63. Non-linear response – a decaying response function e.g. the more coffee a person consumes, the less satisfied the are with each additional cup, as opposed to linear response in which case each coffee would just be as satisfying as the last
  64. OLS – Ordinary Least Squares is a regression technique that seeks to minimise the difference between actual and predicted values – related to the term ‘best fit’
  65. OTS – Opportunities to See, a measure of frequency
  66. Optimise – the act of reorganizing a fixed resource to produce more yield from
  67. Optimser – a tool that can run optimisations automatically
  68. Opt-In – a privacy state in which a consumer can decide whether or not to receive communications from a business.
  69. Outlier – a value in data that is outside the upper and lower quartiles of a dataset
  70. Panel model – a modelling technique which allows us to build models using the same variables from different data sets e.g. countries or cities
  71. PPC – Pay Per Click – a form of search marketing
  72. Prediction  – applying your model to data to predict the value of y at x
  73. Programmatic – a system that bids for online display space automatically subject to a set of bid rules
  74. Prospect – a consumer who could buy bit currently does not
  75. Python – a programming language conceptually similar to R used to build models
  76. R – a programming language (code) used to run statistical models
  77. Reach – the percentage of your target audience that see one of your ads at least once
  78. Recency Frequency Value (RFV) – a method for scoring and classifying customers using transactional data
  79. Regression – a statistical technique used to find the ‘line of best fit’ relationship between two sets of variables, x and y.
  80. Residuals – The difference between the values predicted by a model and the observed values in the data
  81. Response Curve – The consumer response function to your media investments
  82. Ridge Regression (sometimes called Regularised Regression) is a form of regression that scales down the influence of less important variables in a regression model
  83. RMSE – Root Mean Squared Error  – measures the average difference between values predicted by a model and the actual values in the data. A key metric in model assessment
  84. R-Squared – Residuals squared – the total value of the residuals in a model, squared.
  85. R Studio – (Posit) – an integrated development environment, or IDE, like a GUI to write and edit R code
  86. Sales Funnel – a start to finish representation of your prospects’ path to purchase
  87. Seasonality – this is not about the seasons, but about identifying repeating cycles in a time series sample – these could be other days, weeks, months or years
  88. Share of Market (SOM) – your market share i.e. your sales as a percentage of total category sales
  89. Share of Voice (SOV) – your advertising spend as a share of your category’s ad spend
  90. Spot Match – a technique for matching TV spots to web traffic using a time window, usually of a few minutes
  91. Standard deviation – a measure of the distance of each value in a dataset from the mean if that dataset
  92. Standard error – the standard deviation of the mean of multiple samples, say we tale 10 samples, get a mean of each, we have 10 means. they have different values. we take the mean of these means and calculate the standard deviation of that mean
  93. Static State – your BAU business metrics – the natural level of business you generate without advertising spend input
  94. Sub Group – any target audience that is smaller than the full adult population e.g. Men aged 16-34
  95. TGI – Target Group Index – a major consumer behaviour survey
  96. Time series data – a sample of data that is spread over time, such as sales data by week over three years
  97. Vector – a vector is a string of data that has magnitude and direction e.g. 1,3,5,7,9,11,13 can be called a vector
  98. x variables  – our explanatory variables (x1, x2, x3 etc) – these are the variables that we are testing for an effect on our dependent or Y variable
  99. y variable – the variable we want to explain with our explanatory variables (x1, x2, x3 etc)
  100. YouGov – a major consumer survey of behaviour and attitudes, similar to TGI