30 Data & Analytics Terms Every Marketer Should Know

30 Data & Analytics Terms Every Marketer Should Know

Whether you’re new to the game or a seasoned pro, the amount of terms and acronyms in the marketing industry can be overwhelming. A rapidly evolving landscape with technology advancements, emerging channels and new regulations have only amplified this challenge. Curious marketers trying to better understand platforms or tech might be found muttering  “WTF is a vMVPD?”

Don’t get too caught up in the acronym du jour, instead focus on mastering key data and analytics terms that power modern marketing solutions. We’ve compiled a list of definitions to help you go beyond top-level terms like “machine learning” to develop a deeper understanding and maybe impress a colleague or two.

Without further ado, here’s our list of 30 data and analytics terms every marketer should know.

  1. 1st Party Data: The crown jewel of data – information the marketer has collected directly about their current and prospective customers. This can be as basic as an email, all the way to robust unified customer profiles of demographics, lifestyle and detailed purchase histories.
  2. 2nd Party Data: Data that is shared in a dedicated environment with a clearly defined set of permissions and rights set between each of the parties. As marketing evolves with the deprecation of 3rd party cookies and IDFA, collaborative data partnerships resulting in 2nd party data are becoming more prevalent.

     

  3. 3rd Party Data (3PD): Data that is primarily from businesses that do not have a direct relationship with consumers. Sources typically include a range of publicly available sources (e.g. census data), websites and mobile devices, or through the licensing of 1st and 2nd party assets.
  4. Algorithm: Advanced processes or sets of rules to be followed in calculations that can be used for all different types of use cases. In marketing, data scientists turn to algorithms like random forest, support vector machine, neural network and more to predict consumer behavior and how groups of people may respond to a brand’s message.
  5. API: Stands for application programming interface, a common method of exchanging data. With tons of data being generated in real-time, marketers have to respond quickly or risk missing opportunities to connect with their customers. API’s allow data to flow efficiently between the different tools and platforms they use.
  6. Bias: Describes how well a model matches the development sample. Low bias = highly matches.
  7. Binary Model: A model that predicts the probability of a single action (e.g. how likely the consumer is to respond to an offer). The answer is always on a scale of No to Yes, 0 to 1. Binary models are proven performers for predicting response, product propensity and price sensitivity in marketing.
  8. Cluster Analysis: A technique to group similar observations into a number of clusters based on the observed values of several variables. The records within each cluster are most similar to each other, whereas the records between clusters are most dissimilar. Clustering is often a technique used to develop audience “personas”.
  9. Custom Model: A solution that is built specifically for a marketer. Custom models often incorporate the marketer’s own data along with other supporting data sources to achieve a specific KPI.
  10. Data Dictionary: Provides detailed information about data that is available for analytics, such as standard definitions of data elements, their meanings, and allowable values. Dictionaries are typically designed for more technical teams and partnership discussions, but are very important for keeping data organized accessible throughout an organization.
  11. Data Enrichment: The process of appending supplemental, predictive data attributes to complete a customer profile. Providing flexibility and customization, data enrichment impacts all stages of the customer journey.
  12. Data Hygiene: The process of ensuring all data is accurate and up-to-date and that all unused data is migrated into the appropriate lifecycle stage for storage, archival or destruction on an ongoing basis. Strong hygiene practices are paramount for accurate audience targeting and meeting compliance regulations.
  13. Deterministic: Data attributes that are known or observed, and not derived from modeling or inferences. Deterministic data is the ideal but achieving enough scale for marketing campaigns is often a challenge, even for the world’s largest brands. Marketers typically use this data set in modeling to find larger audiences that look similar.
  14. Development Sample: A fully mature set of data (such as from a campaign) that includes historical performance information. The data samples is used to build a model and to verify the estimated model performance.
  15. Ensemble Methods: The process of combining multiple machine-learning models for improved marketing performance. Ensembles add more flexibility in custom solutions as each model can identify the best audiences in different ways.
  16. Gains Chart: A type of report that is generated from a custom model build and demonstrates the strength of the model by score groups. This table-based report is often paired with other graphical visualizations for analysis.
  17. Look-alike Model: Evaluates the traits and behaviors common to the “best customers” group, resulting in a large, qualified group of prospects.
  18. Match Key: A unique identifier that is used to join two or more data sets.
  19. Machine Learning: We all hear it, probably daily for a lot of you, but we’ve included it just in case. Algorithms or statistical models that find patterns or make predictions from data without explicit human instructions.
  20. Multi-behavioral Model: A complex model that simultaneously predicts the probability of many actions. They can incorporate profit values for each possible outcome to predict the estimated profit value of an audience.
  21. On-Demand Model: A model that has been previously built but can be used to score other data sets without any customization.
  22. Pre-selection: The process of defining a narrower data set for scoring (e.g. a specific gender, age, income, etc.)
  23. Probabilistic: Based on or adapted to a theory of probability; subject to or involving chance variation. i.e. ‘a likely match’.
  24. Propensity Model: Similar to a look-alike, but is more deliberate in looking specifically at the variables that drive consumers to take certain actions or hold specific opinions.
  25. QA: Stands for “quality assurance” and represents procedures to check for completeness and accuracy of data both before and after a model build. Models don’t always do what you expect and having a human element in the process can ensure decisions are being made with the best data available.
  26. Random Allocation: A process that allows marketers to fairly evaluate results when a record scores equally well across two or more models by randomly assigning the records to one of the models.
  27. Scoring: Using a model to calculate the estimated probability of a desired outcome.
  28. Seed: A set of data that describes a desired audience and is used to generate a look-alike or propensity model (e.g. social followers of a brand).
  29. Suppressions: Records excluded from development samples as to not skew results (e.g. recent buyers).
  30. Variable: A derived data element (e.g. purchased 3 pairs of shoes) that is used for data enrichment or as a predictor in model development.

Are there any definitions that you think we missed? Feel free to reach out to Team Alliant with any questions or leave a comment to help us expand the list!

ABOUT THE AUTHOR

Natalie Carnazza, Marketing Coordinator

Natalie manages Alliant’s event sponsorships and social media. She is a graduate of Western Connecticut State University, where she earned a Bachelor’s of Business Administration in Digital & Interactive Marketing. Natalie interned on Alliant’s Product Marketing team throughout her senior year of college and was brought on full-time shortly after. When Natalie is not at work, you can find her writing pretty letters for her calligraphy side hustle or talking about her two cats.

Alliant Team Spotlight: Digital Platform & Agency Team

Alliant Team Spotlight: Digital Platform & Agency Team

Each quarter, InsightHub highlights a different Alliant team that executes important behind-the-scenes functions for DataHub Members. This edition visits friendly faces Matt Frattaroli and Chris Morse, as well as introduces Henry Cuero and David Bear, who all together manage the partnerships across the digital ad space, from platforms to agencies.

Meet the team:

Matt, VP of Digital Platform & Agency Partnerships, has led the digital team for three years. With his team located across the Northeast, Matt was – and will be again one day – always on the go. He’s been to 47 states with only Arkansas, North Dakota and Iowa left!

Chris, Director of Digital Partnerships, has been a key player in Alliants digital solutions for nine years. Today Chris platform partnerships, making Alliant audiences available across the ecosystem. A seasoned traveler, Chris has been to 14 different countries and looks forward to hoping on a plane again soon.

David, Senior Sales Director – Digital Audiences, joined the team in early 2020. He leads the team in building strategic relationships with holding companies and agencies.  When not working, you can find David eating his favorite food, thin crust pizza, with his wife.

Henry, Director of Digital Data Sales, has been driving key agency and media buyer relationships for two years. As an NYC resident, Henry loves Broadway shows – he’s seen Phantom of the Opera 5 times!

What does a day on the Digital Team look like?

Chris: Every day presents a unique and interesting challenge – that’s my favorite part about working at Alliant! Platforms are integral to how brands and agencies leverage Alliant’s audiences. A day can hold onboarding new audiences, educating platform sales teams, or providing on demand custom audience support through our Audience HelpDesk.

Henry: While Chris is busy on the platform side, David and I are responsible for agency relationships. When I’m not developing relationships with media buyers, I serve as a data consultant to agencies and their brand clients. This is where I provide new, strategic approaches to their digital efforts by leveraging our syndicated and custom segments.

Does data security and compliance play a role in your day to day work at Alliant?

Henry & David: Alliant is an industry leader in both data security and compliance, allowing us to “tout our prowess” with confidence and focus on building the best data solution for advertisers.

Chris: Platforms see us as an industry leader as well, which means data security and compliance is a key differentiator in growing our relationships and establishing new ones.

What has helped your team become more efficient reaching your goals in the past year?

Matt: Our newly developed internal reporting tool, known as DSR (Digital Sales Report). DSR consolidates Alliant audience usage reporting from 14 adtech platforms into a unified view. It’s been fun and rewarding collaborating with internal teams to build this custom tool that sales, marketing, analytics and product teams benefit from.

David: In fact, I recently used the DSR to show a client our growth via testing at the account level – nice to see all of the hard work paying off!

What are the biggest opportunities for digital marketing in 2021?

Chris: Shifting away from the cookie. Having a stable, privacy compliant identifier and working closely with industry colleagues is an exciting opportunity to tackle a post-cookie world.

Matt: Either the cookie or addressable TV – wherever the consumer eye goes, so do the dollars.

David: Don’t forget about the focus on 2nd party data!

Can you share a challenge you faced on a recent project that resulted in a surprising outcome?

Chris: Building FLA compliant audiences was a challenging project to align across Legal and Data Science, but resulted in differentiated solutions in a tightly regulated market.

Henry: The FLA process has also helped us become more thorough and thoughtful consultants in our space. Challenging yet rewarding.

What is your favorite thing about working at Alliant?

Matt: We are well positioned at the crossroads of the rapidly evolving consumer marketing space. With our expertise in handling PII, compliance, understanding transactional data and digital distribution – we have the foundation every cross-channel marketer needs to thrive in 2021.

David: The people I work with every day and our position in the market, they’re unmatched!

Henry: The people are great, but I’d have to say how Alliant is always two steps ahead of the data trends makes the job exciting and engaging.

ABOUT THE AUTHOR

Natalie Carnazza, Marketing Coordinator

Natalie manages Alliant’s event sponsorships and social media. She is a graduate of Western Connecticut State University, where she earned a Bachelor’s of Business Administration in Digital & Interactive Marketing. Natalie interned on Alliant’s Product Marketing team throughout her senior year of college and was brought on full-time shortly after. When Natalie is not at work, you can find her writing pretty letters for her calligraphy side hustle or talking about her two cats.

Alliant Partner Spotlight: LiveRamp

Alliant Partner Spotlight: LiveRamp

Each quarter, InsightHub highlights a different Alliant team, however, this quarter, in honor of supporting your 2021 multichannel planning we’re highlighting one of our partners that makes that possible: LiveRamp. Alliant’s VP of Digital Platform & Agency Partnerships, Matt Frattaroli, was recently featured in a LiveRamp partner video. Check it out here!

LiveRamp is a leading data connectivity platform that’s powered by core identity capabilities and an unparalleled network. LiveRamp enables companies and their partners to better connect, control, and activate data to transform customer experiences and generate more valuable business outcomes.

The Alliant and LiveRamp partnership has grown over the years from data onboarding and digital audience distribution to include Liveramp’s IdentityLink. This allows for people-based marketing initiatives across digital channels and addressable TV segment distribution to cable and satellite providers for audience targeting on linear TV.

LiveRamp helps Alliant onboard, pseudonymize, and distribute highly predictive audiences. Given Alliant’s analytic approach and an ethos grounded in privacy by design, LiveRamp was a natural choice for an onboarding partner. LiveRamp serves Alliant by delivering at-scale products and services that can be activated virtually anywhere across the data marketplace.

“In order to ensure our audiences are available across the digital world, we partner with best-of-breed platforms like LiveRamp,” says Matt Frattaroli, Alliant VP of Digital Platform & Agency Partnerships. “We work across their product and strategy teams to ensure that we are always on the forefront of identity and audience delivery to DSPs like MediaMath to TV platforms like Hulu and Xfinity to the latest platforms like Amazon DSP and Snapchat. LiveRamp helps future-proof our identity and provides a virtual guarantee that Alliant can be everywhere our clients need us.”

“Alliant is known as ‘The Audience Company’ for good reason: with over 2,200 segments based on transactional and behavioral data, making them a leader for retail, D2C and finance audiences,” says Kathleen Matawaran, Customer Success Manager, Data Partnerships at LiveRamp.

Want to learn more about our partnership with LiveRamp? Reach out to Matt and the Digital Team to brainstorm how your 2021 programmatic and TV campaigns can benefit.

ABOUT THE AUTHOR

Natalie Carnazza, Marketing Coordinator

Natalie manages Alliant’s event sponsorships and social media. She is a graduate of Western Connecticut State University, where she earned a Bachelor’s of Business Administration in Digital & Interactive Marketing. Natalie interned on Alliant’s Product Marketing team throughout her senior year of college and was brought on full-time shortly after. When Natalie is not at work, you can find her writing pretty letters for her calligraphy side hustle or talking about her two cats.

Alliant Team Spotlight: ETL Team

Alliant Team Spotlight: ETL Team

Each quarter, InsightHub highlights a different Alliant team that executes important behind-the-scenes functions for DataHub Members. This edition introduces Lou Ferreira, Ping Ye, Kierstin Kohn-Sandoval, and David Larsen, the developers who build and maintain Alliant’s Extract – Transform – Load (ETL) processes. These are the complex routines that manage all data flows into the DataHub.

The ETL team specializes in analyzing complex CRM files from multiple sources and designing the code that will apply  customized business rules that transform the data into Alliant-compatible formats. The resulting normalized data sets ensure that all transactional data is uniform, detailed and ensures the security of Member business information.

Lou, Director of Data Integration, has been with Alliant for 13 years. He manages the team, creating accurate and consistent business rules for ingesting incoming data. He is an avid pasta eater and an expert speller (he even won his 8th grade spelling bee!)

Ping joined Alliant in 2016 as a Senior ETL Developer. Ping is committed to fitness, often seen around the office doing jumping jacks and taking frequent walks.

Kierstin joined Alliant in 2014 as a Production Specialist. After several years assembling and shipping custom Audiences for Alliant Members, she moved over to the ETL team. Kierstin enjoys hiking with her dog, bike rides and gardening. She is looking forward to going to concerts again!

Dave has been an ETL Developer at Alliant for two years, with a special focus on creating and maintaining variables.  When not in the office, you can find him eating Hawaiian pizza with his wife (and college sweetheart!)

What does a day on the ETL team look like?

Lou: We work across many teams to make ETL happen — Alliant has many new Members coming onboard and each new dataset requires a lot of preparation. Collaborating with Data Governance and Analytics, we do a rigorous audit of the Member’s data, conduct an internal data review, build a custom data mart, map source to target data, and then we write the ETL scripts. It really is an art!

How would you describe the value of ETL to the Alliant DataHub?

Dave: Critical. It is essential to have accurate data which both maximizes and optimizes the DataHub for our Members.

Ping: Plus, when data is in a standard format and decoded, that’s when it works best for developing analytic variables and models — and allowing accurate scoring.

Does data security play a role in your day to day work at Alliant?

Lou: Data security has always been a core value at Alliant. We separate consumer PII (personally identifiable information) data from transactional data as the first step when receiving the information. When we ship an audience, we use a “link-key’” to join the consumer to their data — prioritizing privacy at all times.

What has helped your team become more efficient reaching your goals in the past year?

Lou: Collaboration. The team works together closely on every data integration assignment. Each team member attends the data audit reviews — so we learn together how to handle ingesting and arranging different data.

Kierstin: Collaboration is foundational. We also added a member of the analytics team to our data auditing meetings which has helped us become more efficient in our ETL creation process.

Dave: I would attribute our efficiency to weekly status meetings and good communications with colleagues.

Can you share a challenge you faced on a recent project that resulted in a surprising outcome?

Lou: A client had asked Alliant to append specific attributes to enrich to their customer file. The problem was they had multiple IDs for many customers, which created challenges in customer account tracking, maintenance and order processing. Thanks to some creative thinking and hard work, Alliant was able to link all of the client’s customers and orders to a single, unique ID.

What is your favorite thing about working at Alliant?

Dave: Although the work is challenging, I really enjoy the people that I work with — it’s a great team to be a part of!

Kierstin: I’ve been at Alliant for six years now and my favorite things about working here are the company culture and my awesome coworkers.

Lou: I really do love my job. It’s a constant challenge to work with millions of consumer records and billions of transactions to make sure that all of the data is reliable and accurate.

What is the best and most challenging part about working from home?

Dave: I like that I can put in extra hours to catch up on projects, but my dog barking and family can be a distraction.

Kierstin: The best part is that I gain almost seven hours a week back in commuting time, but it is challenging having to provide my own snacks.

If you were stranded on a desert island, what is the one food you couldn’t live without?

Ping: Honestly, I couldn’t live without vegetables! As someone who loves to cook, fresh food is essential to me.