Podcast: A People-First Agency Transformation Strategy

Transforming your agency from a long format agency to one focused on data analytics is a long, involved process.

It takes about 15 years, to be exact.

In this episode, I interview Jessica Hawthorne-Castro, CEO of Hawthorne, a national agency based in Los Angeles.

What we talked about:

– Your employees are your customers, too

– Why agency transformation takes 15 years

– Challenges to transformation

– How to be strategic when implementing change

Tips to Help You Understand and Action Your Advertising Data

Data gives advertisers the power to fine-tune their campaigns and deliver strong results. Industry experts shared their best advice about advertising data, including methods for collecting it, analyzing it, and putting it to use.

From Karla Crawford-Kerr, VP of Marketing, Hawthorne Advertising.

Don’t just collect data, analyze it continuously.

In advertising data science, it’s not enough to report on the past and present. Data without context only states the obvious—what has already happened. Businesses have spent the past few years accumulating massive amounts of data, with 60 percent-73 percent of that data to go unused, according to Forrester. Big and small data collection is clearly not enough. Data-driven decisions require analysis and insight to deliver value and lead to meaningful change.

To get the most out of advertising data science, marketers need to look ahead and focus on how data can help them make better decisions. Marketers must be open-minded and process-oriented in their Advertising Approach Analysis. This means testing, collecting data, evaluating, testing again, and analyzing to really learn from the data and make tweaks and game changing decisions. This process is a continuous circle in advertising. It never stops. To drive results, it’s important to focus on data from start to finish.

Here are seven steps that advertisers can take to optimize their use of data science.

  • Understand Audience and Audience Targeting. This means looking at how and where the target audience is consuming media. It’s critical to be medium agnostic. Do not overlook options like CTV based on perceived cost, as it may be an effective extension of linear or digital.
  • A/B Test. Test everything—messaging, content, and creative.
  • Test the results. Look at the results of campaigns for attribution and impact. See what worked and what didn’t.
  • Make data-driven decisions. Look at the short and long tail of conversions (first touch vs. last touch).
  • Trust your instincts. As a marketer it is important to know when to trust instinct vs. getting bogged down with the data. This could mean calling out false positives or evaluation of results that focus too narrowly on the short term.
  • More testing. Hone your message and creative.
  • Optimize spend and investment.
  • Start again. Understand your audience and audience targeting.


Mitch Larson

Director of Marketing Technology, Titan 47

There are answers for your cross-system attribution questions.

Today’s marketers have a gold mine of data sitting right in front of them that can be turned into insights with even the most basic of technical skills. A mix of Google Tag Manager, marketing channel APIs, and an object-oriented programming language, like Python, can allow you to accomplish even the most complex of tasks—including cross-systems attribution modeling.

Google Analytics has strong attribution capabilities out of the box, however, what if your main KPIs are simple transactions held in Google Analytics? This is a cross-systems attribution problem. You can use Google Tag Manager to create custom dimensions for the Google Analytics client id as well as your product or lead user ids (if applicable). Using Python and the Google Analytics API, you can extract multiple segments at scale into a data warehouse, such as Google BigQuery. From there, data integration can be performed across your Google Analytics data and your database or CRM. Now, you’ll have pre-conversion user data combined with post-conversion customer data for attribution modeling.

Languages like Python are also great for deploying different statistical-based attribution models once your data is integrated. The sky’s the limit when marketing is combined with technical skills and today’s wealth of knowledge online helps marketers jump right in.

Andrew Van Benschoten

Senior Manager, Data Science, Ovative Group

You can base most of your business decisions on simple models.

Start simple.

The data science field has many flashy technologies such as neural networks and reinforcement learning, but the majority of your business questions can be answered with more straightforward approaches.

Linear regression can get you ~70% of the output from a more complex model, and in 1/10th of the time. And in many cases, the added accuracy won’t change your business decision. I’m going to invest in the marketing channel with the highest-ranked ROAS regardless of whether that number is 5 or 5.1. These simpler approaches also have the added benefit of easier interpretability. Your clients may not understand pooling layers or stacking vs. ensembling, but they can understand that a one-dollar change in channel spend leads to a three dollar change in overall revenue.

Throughout this process you might discover that you need a more complex approach to accomplish your business objectives, and that’s ok! Starting simple provides a perfect framework for conducting auxiliary tasks such as exploring the nuances of your data, thinking through appropriate KPIs, and the like. These critical components are often overlooked if you have to spend your time setting up your GPU configuration or debugging a beta-version ML library. At the end of the day, your goal is not to use the latest data science buzzword, but rather to uncover new insights that drive your client to act.

Brandon Turner

Owner, Reedy River Marketing, LLC

Data science can’t account for all the intangibles, so figure out the story behind it.

I think the biggest misconceptions about advertising data science are that the datasets have to be enormous, the algorithms have to be complex, and an advertiser’s background has to be extremely technical to make it work. The truth of the matter is that ADS scales based on the data you have to work with, and most importantly, the client’s objective. A lot of the platforms commonly used for digital marketing for a smaller and medium-sized business are already incorporating the machine-learning aspect on the backend. Smart-bidding capabilities, automated rules, and a variety of different conversion techniques are already taking advantage of the machine-learning aspect concerning user behavior and desired outcome.

The next step is to decide the best way to utilize these types of tests in conjunction with more manual data science techniques. There are only so many inputs we typically give to a platform to execute our campaigns. We simply can’t completely relay the intangibles and the human experience fully into the machine. Even if we could, it wouldn’t think, analyze, or optimize exactly as we do. That is why we need to find the best convergence of both, tested together, to ultimately achieve the client’s goals with all aspects of ADS working together as seamlessly as possible.

Finally, it is also extremely important not to get lost within the data. We have more data than we could ever fully digest in most cases. It is too easy to get caught up in treating the analysis like starting a 5,000 piece puzzle versus a beautiful painting that is just missing the last few strokes to complete it. The data is telling us an amazing story, but far too often we simply don’t listen. At least not actively.

Amandine Dovelos

Search Manager, GroupM Ireland

Capture the data you need to understand the full ecosystem.

This is one of the biggest challenges and is often overlooked: measuring the level of contribution of a channel in the mix to achieve the right trade-offs by efficiently connecting multiple data points. Let’s see how to bring the pieces of the puzzle together and unlock opportunities.

Data Visualization

Data visualization tools are the way to go, as they allow real-time reporting reflecting the maturity of tracking an entire KPI in real-time with a complete vision: based on data-driven models, algorithms and machine learning.

Attribution & Contribution

There is a real awareness on how to use attribution and contribution tools; it is now the norm to value the upstream of the funnel and not only value last-click models. It is demonstrating the necessity of synergies in the wider marketing ecosystems.

Bring That Puzzle Together

Between paid search and TV, the connection is intuitive: the behavior of a TV viewer is predictable and measurable, and the channel cross-synchronization will maximize the impact of TV. But how to prove the efficiencies driven on foot traffic, this is the holy grail that agencies can offer thanks to operational maturity and omnichannel approaches.

Data Limbo

Start using the data that the consumer actively shares with your business—think questionnaires, polls to maximize data capturing capabilities instead of fighting the growing chimeras of a zero-cookie internet.

The Way Through The Forest

Clustering and trainable algorithms can assist to find the true way in your forest of data. This can unlock unimaginable patterns and campaign automation with unprecedented efficiencies.

Reach For The Stars

To conclude, don’t forget to feed the top of the funnel to fill the bottom and start maximizing on the cross-channel synergies by taking advantage of symbiotic ecosystems.

Time to Stop Talking Around Big Data, Embrace It and Drive Your Business Forward

Author: Jessica Hawthorne-Castro, CEO
Original Link: Digitizing Polaris
Date Published: July 21, 2017

One of the most pressing challenges facing modern businesses today is shifting culture towards a data scientist-driven approach. To complete this shift, a company doesn’t just need a big data strategy, it needs a business strategy that incorporates big data in a way that empowers all employees to think like data scientists.

Developing the Data-Driven Business

For businesses, “big data” means taking information from various disconnected sources and using analytics tools to combine it and find new insights. It’s about correlation, discovering connections between events or information that were previously hidden

For the modern company, data is the most important asset, one that is dynamically changing. Valuable data sets come into play every day from mobile-based customer interactions to the billions of internet-connected “things” coming online. The trick for companies is to develop smart strategies to leverage this flood of data and to monetize data while improving customer loyalty and overall experience.

Three Tactics to Become a Data Driven Company

Think Differently

To think differently, you must create a business strategy that focuses on data, one driven by the leaders of the organization. Adopting a big data approach requires a cultural shift within all levels of the organization. For example, training is crucial for the adoption of big data-focused analytics tools. Staff members must understand how to look at large data sets and pull insights, whether they are based on customer behaviors, problems in the supply chain, or adjustments that could be made to operational processes.

The training needs to include actual sessions with the technology to showcase the ease of use and how it relates to the specific individual’s role within the company. It also needs to be backed by management and include a shift to a more of a real-time and cross-departmental structure. This requires management to loosen some of the constraints of the traditional business hierarchy, where previously insights had to go up a lengthy chain of approval or review. There are still checks and balances, but there is a shorter cultural pathway from finding out something is wrong to implementing a data-driven fix.

Another cultural change involves ceasing to make decisions and taking action based solely on “gut feelings.” This doesn’t limit intuition or innovation, but it mitigates risks and uses hard data as a way to augment creativity and decision making. It’s a model with built-in accountability as staff members need to justify their recommended actions with data.

Make Real World Changes through Data

Companies need to make dramatic data-centric shifts because of the real, impactful changes it can produce. Big data will drive change and induce innovation in your company as well as spur growth, improve agility and foster communication. And data is the most important asset to monetize drive loyalty and safeguard privacy and security.

Consider these examples of the direct impacts of a big data approach:

  • Review financial data to uncover incidences of fraud or potential risk
  • Refine a product’s attributes based on customer-derived data and sales numbers. For example, analyze how customers run on an IoT-enabled treadmill and adjust its construction accordingly.
  • Analyze target markets to see if demographic parameters are off-base and make adjustments
  • Enhance the customer experience by offering deals and retention strategies that are backed by data insights
  • Develop surprising new marketing initiatives and targets through review of sales data combined with customer service data, and other sources

For an industry-specific example, the advertising industry is rapidly changing due to new streams of data. Advertisers and broadcasters crave detailed metrics on viewer behaviors, they are now receiving new details via “second screen” behaviors. A significant number of consumers utilize their phones or tablets while watching television and their actions during this time provide advertisers with rich data about likes and dislikes, which can then be used for more targeted advertising campaigns.

Build a Big Data Strategy

In order to reach the goal of a data-driven company, every firm needs a big data strategy that breaks from conventional thinking. This is a whole business initiative, not an IT initiative; however, IT should enable your result. IT should be at the lead of the implementation as they control the information, but multi-departmental cooperation is a must. Remember the business goals always comes first. Develop a sound business strategy that incorporates data as a core element, with a focus on how staff members make decisions that ultimately impact the business. Augment these decisions with rich and multi-sourced data, and you’ll create a company that can move with agility beyond its competitors.

Here are some of the key steps for creating a data-driven company:

  • Assess pressing business needs. What are the unknowns about products, customers, and internal processes?
  • Map out your data architecture and identify all data sources. Can you pull together customer service chat transcripts along with website visitation metrics? You need disconnected data sets.
  • Determine how to consolidate and relate all of the data sets.
  • Develop user-friendly solutions to analyze the data to help you find surprising insights. Data should drive discovery. Let users play with the information in new ways. Rigid Excel reports and big data analytics do not mix.
  • Turn those insights into actions.
  • Consistently look to bring in new data sets.

There are some of the caveats of the big data approach that companies should be cognizant of so that the can make informed strategic moves. An over- reliance on big data shouldn’t remove personal communication between the company and consumer. And, while there are many correlations that can be uncovered through big data analysis, there’s still a human element needed to confirm that the correlations make contextual sense to the business. It’s still an “art and science” approach, ideally one where the data makes decisions better and faster.

And don’t worry if you are just entering the big data game. The truth is that cost effective data management wasn’t available until recently, so it is not unusual to have a ton of data in many, many different places. As you begin, make a goal of centralizing your data so that scalability to meet the needs of your business is less complicated down the road. The amount of data gathered increases exponentially; so, you’ll be glad you started now.


Jessica Hawthorne-Castro is the CEO of Hawthorne Direct, a technology-based agency specializing in analytics and accountable brand advertising. She has strategically positioned the agency to be at the forefront of the marketing paradigm where art meets science. Throughout her tenure with Hawthorne Direct, she has fostered long-standing relationships with the company’s expansive base of diversified clients resulting from an unwavering commitment to unparalleled service. Her role principally involves fostering long-standing client relationships to develop highly strategic and measurable advertising campaigns, designed to ignite immediate consumer response.