This article was originally written for Authority Magazine’s “The CMO” publication.

Revenue Growth Analytics expert Armin Kakas has a few tips for managing your marketing budget, and they center around data warehouses, omni-channel analytics, and marketing mix modeling.

With the growing complexity of marketing channels and the need for businesses to constantly adapt their strategies, managing marketing budgets effectively has become more critical than ever. In this interview series, we speak with CMOs and other marketing leaders who have significant experience in budget management and optimization, to share their “5 Ways To Manage Your Marketing Budget For Improved ROI.” As a part of this series, we had the pleasure of interviewing Armin Kakas.

Thank you so much for doing this with us! To begin, can you share a bit of your backstory and how you got started in your career? 

I've been in Commercial Analytics for over 15 years, having built and led Revenue Growth Management and Advanced Analytics teams for mid- and large-cap companies across Retail, Distribution, and CPG. 

Originally from Eastern Europe, I completed my undergraduate and graduate degrees in the US, with several years of postgraduate studies in AI and Machine Learning.

After several years as an analytics executive, I started a specialized advisory firm helping companies build internal capabilities in the three areas of Revenue Growth Analytics: Margin Analytics & Optimization, Sales & Customer Growth Analytics, and Promotion Effectiveness & Optimization.

What are three strengths, skills, or characteristics that helped you to reach this place in your career? How can others actively build these areas within themselves? 

The three things that I've actively nurtured in my career were functional expertise, intellectual curiosity, and collaboration. 

In business school, I fell in love with quant courses (called "Decision Analysis" at the time) that were most akin to today's Business Stats or foundational Data Science classes. Since graduating with my MBA, I've spent much time augmenting my skills and bridging the gap between business and AI / Machine Learning. 

I recognized early on that if I can develop expertise in an analytical domain I have a knack for and that's in high demand, I'll always be successful in my career. And to be successful in analytics, you must have the intellectual curiosity to understand the problem statement, develop stakeholder empathy, and build solutions in a highly collaborative manner. 

Analytics is one of those constantly evolving functions, with tools and techniques that previously cost millions to deploy and can now be stood up in a fraction of the cost and time. So aspiring and current leaders in this space need to emphasize continuous learning, especially around building pragmatic, easy-to-use solutions that align closely with the maturity and operating rhythm of the company.

What factors do you consider when allocating your marketing budget across different channels and tactics?

Given my heavy quant background, I approach the marketing budget optimization problem as an analytical exercise. 

Most businesses should evaluate the impact of their marketing spend via Marketing Mix Modeling (MMM) and, if possible, complement it with Multi-Touch Attribution (MTA).

The primary factors I'm looking for when determining budget allocation are:

1. Historical ROIs (Incremental Revenue or Gross Profit $, divided by Marketing spend). I want to look at Historical ROIs by Channel (e.g., Google), Sub-channel (e.g., Youtube), and occasionally by a major campaign. MMM and MTA are viable paths to measuring ROIs, with the former a better option for longer-term impacts and the latter a more precise option for impact measurements. 

2. Estimated channel saturation rates. When combined with marketing ROIs, saturation helps me identify channels we need to pull back (low ROI, over-saturated) vs. accelerate investment (high ROI, under-saturated).

3. For marketing-driven membership, subscriber, or nonprofit businesses, we also want to look at things like CLV:CAC ratios (Customer Lifetime Value vs. Customer Acquisition Costs) and Marketing Break-Even figures (i.e., how long does it take, typically in months or years, to recoup my marketing investment).

4. Other things like cohort analyses by channel of acquisition and demographic / market analyses also play a crucial part in determining how to reallocate budgets. For example, we may see that retention rates for younger customer cohorts over time are substantially worse for one channel vs. another. Suppose that industry experts expect these younger cohorts to lead a bulk of my industry's expansion over the next 5 years - I should prioritize the channel that's better at acquiring and keeping that cohort.

In your opinion, what are some common mistakes that marketers make when managing their budgets? How can they be avoided?

In my experience, primarily three things are the antidotes to effective, data-driven marketing management:

1. Politics: too often, we see marketing organizations operating in silos, each with its agenda and interpretation of the impact of their marketing efforts. Most companies would be better served if their marketing groups were organized around their customer cohorts or segments, with each cohort benefitting from integrated, holistic marketing efforts.

2. Sales & Marketing folklore: this is not a unique marketing problem - corporate lore is plaguing many functions. But I often see executives anchored to non-empirical beliefs that are either false or no longer true in a new environment. Not basing decisions on accurate and comprehensive data can lead to misallocation of resources and missed sales opportunities. 

3. Lack of internal analytics capabilities: in a recent survey we've commissioned with over 100 commercial leaders, 67% did not know the return on their marketing investments. This is especially true in B2B settings and especially for the mid-market space ($10MM-1B in revenues). Even companies with a good grasp of their Marketing ROI majority have an over-reliance on specialized vendors and marketing agencies for their fundamental analytics. I cannot underscore the importance of a lean but highly effective internal marketing analytics group comprising data engineering, analytics, and data scientist resources. We live in a world of democratized analytics, and most marketing analytics capabilities (from descriptive to predictive) should be developed and maintained in-house.

When allocating your budget, how do you balance short-term marketing goals with long-term brand building initiatives?

This is an ongoing challenge with most companies I have experience with (mostly B2B, some B2C). Companies must understand the impact of their marketing efforts (i.e., performance to budget - both revenue and spending) within the fiscal year (down to the week/month/quarter). But also have a data-driven intuition about the medium to the long-term impact of their marketing spend. 

Most B2C companies have a decent Attribution method - primarily through vendors and advertising agencies (B2B struggles in this space). What many companies lack, however, is an understanding of the long-term brand-building impact of their marketing investments. This is where techniques like Marketing Mix Modeling (MMM) come into play. 

MMM has been enjoying a renaissance, thanks to the democratization of analytics and the slow demise of 3rd party cookies that are making attribution much harder. 

With MMM, in addition to understanding the ROI of each marketing channel and optimizing our budget allocation for future periods, we can also evaluate the impact of marketing spend on things like NPS or customer loyalty scores.

Rapid Fire Question Round

What’s the biggest waste of money in marketing? Misallocation of resources due to channel silos and lack of data-driven decisions.

If you had to put all your money into one tactic, what would it be? I would invest in a strong marketing analytics team for informed strategy formation.

What are you reading right now? Sapiens: A Brief History of Humankind

What product, tool, or service do you wish existed? An affordable, universally compatible platform linking all offline and digital marketing data sources for seamless insights and audience optimization.

For start-ups and those with limited budgets, what tactics would you recommend to receive the highest return and fastest growth?

Focus on understanding your customer base, including demographic profiles, retention trends, profitability, and lifetime values, if you have them. Understand your ideal customer profiles, and hone in on that segment. Determine which channels and mediums provide the best scale and marketing ROI balance and accelerate investments there. 

Secondly, invest in a strong marketing data scientist - a hybrid data engineer, data visualization, and predictive modeling expert with solid sales and marketing domain knowledge. If you have a limited budget, having one exceptionally strong analytical resource will pay dividends.

How do you collaborate with other departments within your organization, such as sales or finance, to ensure alignment and maximize ROI from your marketing spend?

Cross-departmental collaboration is critical for effective Revenue Growth Management, and marketing is no exception. Regular communication, shared performance metrics (i.e., Revenue, Gross Profit $ and NPS goals), and highly collaborative planning sessions should all be part of the winning formula. 

Too often, Finance complains that Sales and Marketing can't quantify the return of their Promotional and Marketing spend. And similarly, Sales and Marketing complain that Finance is significantly curtailing their ability to increase market share or augment brand position. 

In reality, these three teams *should* all be vying for the same common goals: increased revenues and profitability and happier customers who stay longer with us.

What tips do you have to get buy-in from the CEO and others in the C-Suite when requesting additional budget for new projects or tactics?

My advice is simple: demonstrate the ROI of the additional marketing investment, and tie it to the company's strategic objectives. 

Also, crucial that you depict the opportunity cost of not doing these incremental projects. Most CEOs I've interacted with are highly quantitative, care deeply about quarterly and strategic goals, and are willing to open up the corporate wallet if the investment ask is based on real data blessed by cross-functional teams, including Finance.

We'd love to know, which marketing software in your tech stack do you feel is most worth the investment?

1. Depending on where you are in your analytical and marketing maturity, there are a few key things I recommend in order of spending/effort (low to high):

2. Hire strong marketing analytics resources, including data engineering, and get your data in order. This means creating a purpose-built marketing data warehouse on the cloud (AWS, GCP, Azure).

3. Build or outsource Marketing Mix Modeling capabilities. I advocate for doing this in-house, as nowadays, several open-source resources can do a "good enough" job at MMM to help you evaluate the return on your marketing investment. 

4. Build or outsource Multi-Touch Attribution (MTA) capabilities. A robust MTA capability is ideal for optimizing your marketing spend, especially if you do omnichannel marketing (digital + offline). I always advise companies to tackle building this in-house (with expert consultant/contractor help) and hire the right resource(s) to maintain it. However, several competent vendors can handle MTA for companies.

5. Plan the seeds for a multi-year roadmap that includes a Customer Data Platform. This is especially crucial for B2C companies that are marketing-driven (e.g., nonprofits, SaaS, DTC). A good CDP will help streamline most aspects of customer and marketing analytics, deploy real-time personalization, help you evaluate and optimize customer journeys, and deploy new audiences. Implementing a CDP takes a heavy budget and time commitment, so I recommend it for companies over ~ $100MM in revenues or SaaS / DTC startups with healthy funding levels whose digital roadmap necessitates it.

Based on your experience and success, what are the five things marketing leaders should do to improve the ROI of their marketing efforts?

1. Establish a Purpose-Built Marketing Data Warehouse: Create a dedicated data warehouse tailored to marketing needs for optimized data management and insights.

2. Build an Internal Analytics Team: Develop a dedicated in-house team for omni-channel marketing analytics.

3. Use Comprehensive ROI Techniques: Leverage Marketing Mix Modeling and Multi-Touch Attribution for effective long-term and immediate impact evaluation.

4. Understand Your Customers: Deeply profile customers to better target and invest in profitable, growing segments.

5. Promote Collaboration: Ensure alignment of key cross-functional teams and eliminate marketing silos.

Lastly, if you could inspire a movement that would bring a great amount of good to the most people, what would that be? 

If I were to inspire a movement, it would revolve around the concept of "Outcome Based Analytics" (OBA). This approach can transform not just businesses but society as a whole.

We've been living in an era of increasing data ubiquity and are often caught in the sophistication of algorithms, complex models, and the next shiny big thing in technology. While these trends are undoubtedly significant, I want to refocus our attention on driving measurable, tangible outcomes from our analytics efforts and investments. 

The OBA movement emphasizes that solutions don't need to be perfect; they need to be "good enough" (80-90% of total value realization), actionable, and deployed in a way that aligns with the operating rhythm of the teams. This will democratize analytics and lead to cheaper, better, and faster decisions, making businesses more agile and adaptable.

How can our readers best continue to follow your work online?

I have a bi-weekly Revenue Growth Analytics newsletter accessible here, plus monthly or bi-monthly webinars on Commercial Analytics topics (our most recent one was doing Marketing Mix Modeling in-house). My work is also often published on LinkedIn (search for the hashtag #outcome_based_analytics or #revenue_growth_analytics).