Podcasts

Learn how we used predictive analysis to forecast the 2020 Oscars and how the same analysis can be used to predict your business results this year.

Our analytics team discusses a predictive model for determining the college football playoffs.

Learn the differences between data-driven and data-inspired marketing and why you should push for more data-inspired plans.

Black Friday is no longer the biggest sale of the year. Consumers have less time to spend. Return rates are high. Learn what strategies you, as a CMO or retailer, should put in place to have a successful holiday shopping season, every year.

Understand the factors behind out of stock products and why you should incorporate inventory optimization into your marketing plans.

The latest insights from Oculus Connect and what brands should know about the future of marketing through virtual reality.

In this podcast, we discuss the customer journey – what they consist of today, their challenges and three ways to make them a more relevant tool.

What’s really behind a football fan’s decision to go to a game? It’s more than a team’s win/loss record. Callahan’s senior business analyst explains his data-driven model.

Learn how to solve current retail challenges by creating demand for your product, bringing insights to the retailer and building relationships with buyers.

A case study on how breaking down analytics on a store by store basis can deliver different insights than analyzing aggregate data.

An agency may be the most qualified entity to manage marketing analytics, pending these four deal-breakers.

Arm your marketing team with a dedicated data analyst who is trained to anticipate questions and provide the information necessary to achieve company goals.

Ben Smith, Callahan’s Director of Social and Emerging Media, shares his insight and perspective on the overall health of Facebook, its coverage in news, and what emerging trends could shape future marketing tactics.

Inspired by Maslow’s Hierarchy of Needs, we’re taking our own marketing-specific angle to his pyramid. In this third episode, we detail Level 5: Prediction – and how you can leverage the insights established within the previous layers to predict your marketing activity.

Inspired by Maslow’s Hierarchy of Needs, we’re taking our own marketing-specific angle to his pyramid. In this second episode, we detail Level 3: Internal factors, and Level 4: External factors – and how you can establish and define both within your existing business plans.

Inspired by Maslow’s Hierarchy of Needs, we’re taking our own marketing-specific angle to his pyramid. In this first episode, we detail Level 1: Delivery of activity, and Level 2: Efficiency and optimization – and how you can establish and define both within your existing business plans.

Callahan’s senior business analyst re-constructed a study from 1998 that looked at different metrics produced by professional golfers, and used that data to create a model that could predict golfers’ career earnings.  Analytics and data modeling like this can allow a marketing team – or a golfer – to understand the problems that are worth…

Marketing automation systems can accomplish a lot of complex tasks for a company. However, there’s often a limit to the insight their data dashboards can provide.

Callahan’s senior business analyst used his spare time once again (football season is over) to construct an algorithm to predict which movies have the highest chance of winning “Best Picture” at the 2019 Oscars. To do this, James used a predictive model that looked for trends and key indicators that in the end, created a…

Effective implementation of data forecasting as part of your analytics and reporting can establish benchmarks that empower a company to make category-leader-caliber decisions based on sales and data.

As a CMO, predictive analytics will provide you with the confidence to make big marketing decisions, but it should be viewed as a marathon, not a sprint.

Our senior business analyst built a statistical model to investigate the college football ranking system. Learn how his approach can be applied to your own marketing and analytics efforts.

The data you have at your fingertips may seem uninteresting at first glance, but can be extremely valuable. And combining different types of data sets can result in impactful results for your bottom line.

Are you the debating the advantages of Amazon Redshift vs. Google BigQuery? At Callahan, we’re making the move from Redshift to BigQuery as we release our Intelligence Platform 2.0. Stick with us as we explain how this shift will greatly impact future marketing insights. We’re taking advantage of new technology that will allow us to…

Facebook and Oculus Connect conferences reveal new ways consumers are communicating and the marketing technology changes that have real implications for growing brands.

We’ve busted five common analytics myths that every marketing leader should be aware of.

Facebook data is often underutilized but can drive tangible business results for both small businesses and big brands.

Where others see challenges surrounding the use of data on Facebook, Callahan’s Ben Smith sees opportunity for marketers.

Learn the ways front-end analytics can empower you to build a smarter, more effective media planning strategy.

To make decisions with data, you need an analytics team to manage and analyze it all. But what kind of people – and positions – do you need?

In marketing, we have a real problem with messy data. There are thousands of systems, and nothing talks to each other. We explain how to deal with it.

We’ve talked previously about tech stacks that can assist in analytics. This episode, we’re talking about if you’re building your own, what types of things you would need to build, and why technology alone is not the key to success.

In marketing, we have a real problem with messy data. There are thousands of systems, and nothing talks to each other. We explain how to deal with it.

If you’ve ever worked with an attribution model, or are considering investing in an attribution model, we have some bold opinions and practical advice you should hear.

It seems like data privacy has replaced data security as the big topic around data. Maybe it’s just Facebook getting in the news more often these days?

What marketing data matters, what doesn’t, and how to know the difference. Hint: it’s about business goals, not typical marketing KPIs.